text:
A PLACE THAT IS WORLD HERITAGE
Sóller is a beautiful town on the North East coast of Mallorca. It is 3km inland of its port, Puerto de Sóller, surrounded by mountains, in a fertile valley which it shares with the village of Fornalutx and the hamlet of Biniaraix.
Sóller valley and port are about half an hour from Palma de Mallorca airport and have their own railway, tram and motorcoach services. The valley and the surrounding mountains, "the Tramuntana Sierra" are the favourite resort of people from all over the world, especially those who enjoy nature walks, culture, water sports and good food, or who simply seek a peaceful place away from the noisy tourism of possibly better known venues.
With its 13,000 inhabitants, Sóller is the commercial and cultural centre of the Tramuntana Sierra and the starting point of many itineraries following mountain and coastal paths and about the "valley of oranges". The town square, with its imposing church, trees, open air cafés and mountain backdrop, is one of the most beautiful in the island. Surrounded by narrow streets with many shops, it is the popular meeting place for residents and tourists alike, especially on market days. From here one can take the antique Tram to Puerto Sóller to bathe or take a boat trip to the Torrente de Pareis.
Suggested visits
Port of Sóller
Over the years, the port of Sóller has become one of the fashionable holiday resorts of the island. The almost circular bay, with two beautiful beaches, an attractive shopping area, the pedestrian sea front and many excursion routes, offers a unique experience to the visitor.
The beautiful setting is best appreciated from the lighthouse or the Mirador de Sta. Catalina, high on the coast. At sunset there are no more romantic spots. From either viewpoint one has a splendid view of the port and the pleasure boats which sail several times a day to the Torrente de Pareis.
It is easy to see how access to the valley was difficult in days gone by and how sea trade with France was important for the survival of the town.
The antique Tram links the port to the town of Sóller, a few Km inland, so that visitors may combine sun and sand with culture, shopping, a visit to the weekly market, a trip to Palma on the train, or simply enjoy the peace of this unique setting.
Biniaraix & Fornalutx
Biniaraix is a small town, located 2 km from Sóller, with its square and the church of the "Immaculate Conception", built in the sixteenth and seventeenth centuries. At the end of Sant Josep street, from the square, there are the washings that the women of the town used at that time to wash their clothes. From there, the path called "Barranco de Biniaraix" begins, probably from the Middle Ages, which meanders among the stone stairs of the terraced fields populated by millenary olive trees from where a stream flows. This is one of the ways to go from Soller to Lluc on foot.
Fornalutx is located in the center of the Sierra de Tramuntana, which crosses the island of Mallorca and draws a deep valley that goes from Puig Mayor to Sóller.
The origin of Fornalutx has its beginning at the same moment of the Conquest, being in ancient times a Moorish village, a fact that can be deduced by the layout of its primitive streets. Together with the neighboring city of Sóller, the two formed a single municipality until 1813 and in 1837 their constitutional rights were recognized as an independent municipality. Fornalutx is considered one of the most beautiful villages in Spain.
Llucalcari
Llucalcari, also known as Es Carrer, is a small hamlet some 3km from Deyà towards Sóller. Built on the steep side of a mountain facing the sea it consists of some twenty houses and is one of the most idyllic scenes in Mallorca. It has a small, simple chapel dating from 1600.
Some of the larger houses have defence towers. Descending to the sea by a stone stairway between pine trees we reach a small cove called 'es Canyaret', with a fresh water spring where some people take mud baths, believing there to be theraputic properties.
Deia is a small village located in the Serra de Tramuntana mountain range, and boasts surroundings of unparalleled natural beauty which offer visitors a wide range of recreational opportunities. Visitors can enjoy strolling through its streets, and admire the traditional stone-built houses typical of Mallorcan architecture, hike one of the many mountain trails (the Ruta de pedra en sec long-distance GR-221 footpath, or Dry Stone Route), and explore the area by bike. The village's quiet charm has long attracted a multitude of romantics, bohemians and artists, from the Archduke Luis Salvador of Austria, the composer Manuel de Falla, the painters Leman, Junyer and Russinyol, the poets Robert Graves and Laura Riding, and archaeologists such as William Waldren, who all settled here after having found the inspiration they were looking for.
It has its origin in the Muslim farmhouse of Oloron. The documented history begins in the thirteenth century, from the conquest of Mallorca, and revolves around its castle. During the Catalan occupation of the Kingdom of Mallorca, they resisted at the Castillo de Alaró.
The scenic charm of Alaró invites to hiking. They emphasize by their tradition, the excursions of the Castle of Alaró, to which it is possible to be acceded on foot from the same town, and to Orient (that belongs to Bunyola).
Alaró was the first municipality to have electricity. The power station was inaugurated on August 15, 1901 and the promoters were the brothers Gaspar and Josep Perelló Pol. Of the electrical factory that constitutes the only civil monument of Alaró, there were some vestiges. A monument has now been restored and the inauguration was on August 15, 2001.
Puigpunyent is a small village in the south-western area of the Serra de Tramuntana on the island of Mallorca. It currently has around 2,000 inhabitants divided between three population centers: Puigpunyent, Galilea and Son Serralta.
As a mountain municipality, it offers some of the most impressive landscapes that can be found in Mallorca. Vineyard, orange, olive and almond trees cover the valleys of the municipality. You can also see an important set of terraces full of millenary olive trees. Mountains and valleys are connected by a considerable number of old public roads that constitute an important patrimonial and ethnological value; they are bridleways, some of which still retain their original cobblestones.


Located in a 200-year old Georgian Town House, Janet Poole House is situated in the heart of the literary Bloomsbury district - within walking distance of many famous London attractions. Flat Studios London quaint Bloomsbury district with in walking distance of many famous London attractions. Having recently undergone extensive refurbishment, the apartments now offer the ambiance and character necessary to create a home away from home, whether you are travelling on business or for pleasure for a long or short term stay in London.


text:
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Home Mercedes-Benz Mercedes-Benz B-Class Johannesburg: Radar-based collision warning system
Mercedes-Benz Technology
Mercedes-Benz B-Class Johannesburg: Radar-based collision warning system
Gerald Ferreira
Radar-based collision warning system
Rear-end collisions account for around 22 percent of all accidents involving casualties or fatalities in Germany today. The corresponding figure for the USA is as high as 31 percent.
Detailed analysis by Mercedes-Benz's in-house accident research department indicates that almost half of these rear-end collisions could be avoided or the severity of impact could be reduced with the aid of radar-based safety systems such as COLLISION PREVENTION ASSIST and adaptive Brake Assist. This analysis drew on the experience acquired with the Mercedes-Benz innovations DISTRONIC PLUS and BAS PLUS.
The next milestone has been reached in the democratisation of automotive safety technology: the new B-Class generation from Mercedes-Benz will be the first car worldwide in the compact segment to offer radar-based collision warning with adaptive Brake Assist as standard. The company expects the introduction of this technology to have a significant positive effect on accident statistics. This expectation is backed up by test results: during tests with 110 car drivers in the driving simulator, the accident rate in three typical situations fell from 44 to eleven percent, thanks to a combination of COLLISION PREVENTION ASSIST and adaptive Brake Assist.
The new assistance system fits the bill precisely here: it is able to provide a driver who may be distracted with visual and acoustic warnings and to prepare Brake Assist for precision braking. Braking is initiated as soon as the driver steps emphatically on the brake pedal.
Radar-based COLLISION PREVENTION ASSIST with adaptive Brake Assist
is able to recognise an inadequate distance from a vehicle ahead in the speed range between 30 and 250 km/h and to warn the driver
recognises when the gap is decreasing. In case of a detected imminent danger of collision, the driver is provided with a visual and acoustic warning
is able to recognise stationary objects ahead of the vehicle at a speed
of up to approx. 70 km/h and output corresponding warnings
is able to adapt the activation threshold for the warning and adaptive Brake Assist to given driving situations, e.g. bumper-to-bumper traffic
calculates the precise braking force ideally needed to avoid an accident when a danger of a collision is detected and makes best possible use of any distance remaining. This means that the driver behind also has better chances of preventing a rear-end collision
The brake pressure is adjusted if the situation changes – if the vehicle ahead accelerates, it is decreased to the level the driver requires; if the distance to moving and stopping vehicles decreases, the brake pressure is increased further. When PRE-SAFE® is on board, COLLISION PREVENTION ASSIST provides one of the activation parameters for the preventive safety systems (e.g. belt tensioner).
The new system meets the key "Forward Collision Warning" requirements of the NHTSA, the American road and vehicle safety authority.
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redpajama_set_name: RedPajamaCommonCrawl


Platform trucks, as the name suggests are those kind moving trucks which are used to carry goods between different platforms. They are made in such a way that large and heavy items can easily be moved from one place to another with the use or minimum human power.
The platform trucks are used at large places where items need to be moved from one place to another and moving the item to next platform is not possible by hand due to large size and weight of the item. The platform trucks are automatic with motors or manual which can be pulled by humans to move items from one place to another. The most commonly places where the trucks for platforms are used include airports, railways stations, factories and ware houses.
The platform trucks are made in such a way that they are portable and can be moved to any location and their sizes are compact. The trucks must also be able to handle heavy weight and must be able to move efficiently in remote and small places.
We specialize in the platform truck supply where the trucks for different purposes and places are available from us. You can choose your desired kind of platform trucks at reasonable rates where we also allow our customers to get the trucks shipped to their desired location in the United Kingdom.


The podcast for fathers and expectant fathers of twins. Learn all about having and raising twins from a fathers perspective.
If you enjoy this podcast, please take a few seconds and leave an honest review on iTunes by clicking here. It will help other parents of twins find the show.
How early can I know the gender of twins?
Where are the pictures of just me?


text:
The Miss Hall's School athletic department, in conjunction with the Athletic Association, has developed the Athletic Hall of Fame which recognizes the student-athletes, teams, coaches, and administrators that have helped develop a strong athletic culture.
Nominees are awarded every other year. Nominations are not currently being accepted.
Nominate Alumnae for Other Awards
MHS Athletics Hall of Fame — Inaugural Class of 2013
Kim Boland '94
A former MHS athlete and current coach, Ms. Boland played four years of varsity basketball, three years of varsity softball, and a year of varsity soccer as a student at MHS. She returned to the School in 1999 and coached basketball for ten years. She currently coaches volleyball and softball, volunteers with the Special Olympics, and has coached many Catholic Youth Center teams.
Madge Buerger
Mrs. Buerger dedicated forty-two years to MHS athletics, serving seven years as a physical education instructor and twenty-three years as Director of Athletics, coaching nearly every sport. Mrs. Buerger remains a favorite of Miss Hall's alumnae around the world, and the Madge Buerger Blue/Gold Cup is named in her honor.
Anne Pyle Dennis '34
A multisport athlete at MHS, Mrs. Dennis also enjoyed horseback riding, skating, and skiing. She later broke the west coast record for the 100-meter freestyle swim and remained athletic throughout life, joining the Adirondack Forty-Sixers in her seventies, skiing until age eighty, and riding horses into her nineties.
2003-04 MHS Varsity Volleyball Team
The 2003-04 volleyball team won the WNEPSWVA league championship and advanced to the New England Championships, the first MHS team to do so. The roster featured seniors Beatriz Ceballos, Sachiko Nagase, Cecily Platt, Alison Uttermann, Carrie Voight, and Dani Yarwood; juniors Valentina Delfino and Alex Gheblikian; sophomores Sam Rudd and Sophea Yath; and freshwoman Sophina Verjee. Ariel Sims '05 managed the team, which was coached by Dave Peirce.


text:
Q: How to pass a custom variable to styles file through 'withStyles'? I'm trying to create a custom marquee component with a custom animation time prop, but i need to pass this prop to my styles file through withStyles function and i dont know how do it
This is how i did the component and how i used the styles
Marquee Component:
import React from 'react';
import { withStyles } from '@material-ui/core';
import styles from './MarqueeStyles';

const Marquee = ({ classes, children, animationTime }) => {
  return (

{children}

{children}

{children}

{children}

  );
};

export default withStyles(styles)(Marquee);

Styles file:
import { createStyles } from '@material-ui/core';

const styles = (theme) => {
  return createStyles({
    marquee: {
      ...someStyles

      '& .scroll': {
        animation: `$marqueeAnimation 30s linear infinite`,
      },
    },

    '@keyframes marqueeAnimation': {
      from: {
        transform: 'translateX(0)',
      },
      to: {
        transform: 'translateX(calc(-100% - 1rem))',
      },
    },
  });
};

export default styles;

i want to do something like:
<Marquee animationTime="30s" />

import { createStyles } from '@material-ui/core';

const styles = ({theme, animationTime) => {
  return createStyles({
    marquee: {

      ...someStyles

      '& .scroll': {
        animation: `$marqueeAnimation ${animationTime} linear infinite`,
      },
    },

    '@keyframes marqueeAnimation': {
      from: {
        transform: 'translateX(0)',
      },
      to: {
        transform: 'translateX(calc(-100% - 1rem))',
      },
    },
  });
};

export default styles;

redpajama_set_name: RedPajamaStackExchange


I am developing an app to give automatic weather updates for my country (Grenada), especially when there is a hurricane warning. But if I can get updates for the Carribbean or anywhere in the world, I would like to know that too. Please tell me if the above scenarios are possible.


How to Order: Online ordering on Barrington Sports is simple. Just follow the easy steps below.
Barrington Sports is not responsible for any import taxes due on deliveries where payable in certain countries where this applies.


Last week I had an "ear" catching conversation with one of my patients. She has been suffering from chronic neck pain for many years. Finally, her family physician recommended a steroid epidural injection for pain relief. Epidural means getting an injection into the spinal cord. This patient is a registered nurse and she is well aware of the complications and side effects epidural injections can have.
She decided to get acupuncture treatments first to exhaust all resources before she would put herself at risk with and epidural injection. So far Acupuncture has worked very well for her. A few weeks after she started treatments with her Practitioner of Chinese Medicine (aka Acupuncturist) she feels great and does not even consider other pain relieving medications. During her last visit she said "Igor, I could have gotten one of those contaminated methylprednisolone injections. Thanks to your treatments with Acupuncture I did not have to expose myself to that risk." This might sound a bit dramatic but the less procedures are performed on your body, the lesser the risk of negative side effects.
In our Acupuncture Clinic in Denver we see many patients with carpal tunnel syndrome, neck, low back, shoulder and knee pain, who are able to avoid surgery because Acupuncture works for them. I like to point out that especially with pain issues it is crucial to find a Practitioner of Chinese Medicine to perform Acupuncture on you. Other health care professionals including MD's, Chiropractors and Physical Therapists have very minimal requirements to get a "license to needle". They might be great professionals in their own discipline but they do not even get close to the skills of an Acupuncturist when it comes to treating a patient with needles.


text:
CMS: Global 0.5-deg Wetland Methane Emissions and Uncertainty (WetCHARTs v1.3.1)
Dataset Version: 1.3.1.1
This dataset provides global monthly wetland methane (CH4) emissions estimates at 0.5 by 0.5-degree resolution for the period 2001-2019 that were derived from an ensemble of multiple terrestrial biosphere models, wetland extent scenarios, and CH4:C temperature dependencies that encompass the main sources of uncertainty in wetland CH4 emissions. There are 18 model configurations. WetCHARTs v1.3.1 is an updated product of WetCHARTs v1.0 Extended Ensemble. Three new features in the updated version include (1) the model output data is updated from 2001-2015 to 2001-2019, (2) the model drivers are replaced from using ERA-interim to ERA5 reanalysis data, and (3) the Global Lakes and Wetlands Database (GLWD) wetland extent definitions have been adjusted for the 50-100% Wetland, 25-50% Wetland, and Wetland Complex (0-25% Wetland) categories. The intended use of this product is as a process-informed wetland CH4 emission data set for atmospheric chemistry and transport modeling. Users can compare estimates by model configuration to explore variability and sensitivity with respect to ensemble members
There are 19 data files,18 models per year per file for 19 years, in netCDF (*.nc) format included in this dataset.
Figure 1. Estimated daily emissions of methane from wetlands in North American for August 2019. Estimates were produced using the WetCHARTs v1.3.1 model using ensemble member 1931, a global scale factor of 124.5 Tg CH4/yr, the CARDAMOM heterotrophic respiration model, a temperature-dependent CH4:C q10=3, and extent parameterization based on the Global Lakes and Wetlands Database. Source: WetCHARTs_v1_3_1_2019.nc
Bloom, A.A., K.W. Bowman, M. Lee, A.J. Turner, R. Schroeder, J.R. Worden, R.J. Weidner, K.C. McDonald, and D.J. Jacob. 2021. CMS: Global 0.5-deg Wetland Methane Emissions and Uncertainty (WetCHARTs v1.3.1). ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1915
Dataset Overview
Dataset Revisions
This dataset provides global monthly wetland methane (CH4) emissions estimates at 0.5 by 0.5-degree resolution for the period 2001-2019 that were derived from an ensemble of multiple terrestrial biosphere models, wetland extent scenarios, and CH4:C temperature dependencies that encompass the main sources of uncertainty in wetland CH4 emissions. There are 18 model configurations. WetCHARTs v1.3.1 is an updated product of WetCHARTs v1.0 Extended Ensemble. Three new features in the updated version include (1) the model output data is updated from 2001-2015 to 2001-2019, (2) the model drivers are replaced from using ERA-interim to ERA5 reanalysis data, and (3) the Global Lakes and Wetlands Database (GLWD) wetland extent definitions have been adjusted for the 50-100% Wetland, 25-50% Wetland, and Wetland Complex (0-25% Wetland) categories. The intended use of this product is as a process-informed wetland CH4 emission data set for atmospheric chemistry and transport modeling. Users can compare estimates by model configuration to explore variability and sensitivity with respect to ensemble members.
Project: Carbon Monitoring System
The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data, and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.
Bloom, A.A., K.W. Bowman, M. Lee, A.J. Turner, R. Schroeder, J.R. Worden, R. Weidner, K.C. McDonald, and D.J. Jacob. 2017. A global wetland methane emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs version 1.0). Geoscientific Model Development 10:2141–2156. https://doi.org/10.5194/gmd-10-2141-2017
Ma, S., J.R Worden, A. Anthony Bloom, Y. Zhang, B. Poulter, D.H. Cusworth, Y. Yin, S. Pandey, J.D. Maasakkers, X. Lu, L. Shen, J. Sheng, C. Frankenberg, C.E. Miller, and D.J. Jacob, 2021. Satellite methane observations constrain the latitudinal distribution of wetland emissions and their temperature sensitivity. AGU Advances. https://doi.org/10.1029/2021AV000408
Related Dataset
Bloom, A.A., K. Bowman, M. Lee, A.J. Turner, R. Schroeder, J.R. Worden, R.J. Weidner, K.C. McDonald, and D.J. Jacob. 2017. CMS: Global 0.5-deg Wetland Methane Emissions and Uncertainty (WetCHARTs v1.0). ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1502
Spatial Coverage: Global
Spatial Resolution: 0.5-degree resolution
Temporal Resolution: Monthly
Temporal Coverage: 2001-01-01 to 2019-12-31
Site Boundaries: Latitude and longitude are given in decimal degrees.
Global -179.75 179.75 89.75 -89.75
Data File Information
There are 19 data files,18 models per year per file for 19 years, in netCDF (*.nc) format included in this dataset. The files are named WetCHARTs_v1_3_1_YYYY.nc, where YYYY is a year from 2001–2019.
Data File Details
Spatial reference system: WGS 84 (EPSG:4326)
Unit: degree
No data value: -9999
Table 1. Variables in the data files.
wetland_CH4_emissions mg m-2 d-1 Daily emissions of methane from wetlands
latitude degrees_north Latitude coordinate
longitude degrees_east Longitude coordinate
Four-digit code specifying the 18 model configuration (e.g., 1931; Table 3), where:
first position = global_scale_factors
second position = heterotrophic_respiration_model
third position = temperature_dependence
fourth position = extent_parameterization
time days since 2001-01-01 00:00:00 Middle of each month
crs Details of the coordinate reference system
time_bnds days since 2001-01-01 00:00:00 Start and end time for each time stamp
Table 3. Model configuration codes. The ensemble model member names follow a 4-digit format (e.g., 1234).
1 global_scale_factors 1 = 124.5 Tg CH4/yr
2 = 166 Tg CH4/yr
3 = 207.5 Tg CH4/yr
2 heterotrophic_respiration_model 1–8 = MsTMIP models
9 = CARDAMOM
3 temperature_dependence 1 = CH4:C q10 is 1
2 = CH4:C q10 is 2
4 extent_parameterization 1 = SWAMPS & GLWD
2 = SWAMPS & GLOBCOVER
3 = PREC & GLWD
4 = PREC & GLOBCOVER
Bloom et al. (2017) have constructed a global wetland CH4 emission model ensemble for use in atmospheric chemical transport models (WetCHARTs v1.0).
Ma et al. (2021) have tested and refined a range of bottom-up models including the WetCHARTs v1.3.1 with satellite-based top-down estimates, adding new constraints on the latitudinal distributions of CH4 and its temperature sensitivity.
The mean extended ensemble wetland emissions data were compared against a range of independent wetland CH4 regional emission estimates (Bloom et al., 2017).
This process-informed wetland CH4 emission dataset for atmospheric chemistry and transport modeling (WetCHARTs Extended Ensemble, 18 simulations) is derived by combining a range of CH4:C temperature sensitivities, global wetland emissions scaling factors, and dynamic wetland extent maps to represent the uncertainty. The wetland CH4 emissions statistics within each 0.5° x 0.5° grid cell are derived based on precipitation and the data-constrained terrestrial carbon cycle analysis derived for the period 2001–2019. The mean of the extended model ensembles was incorporated into GEOS-Chem and the model outputs have been compared against a suite of regional flux estimates. The wetland CH4 emission ensembles robustly represent the global magnitude and uncertainty of wetland CH4 missions. The ensemble configurations of inundation extent, carbon decomposition, and temperature dependence have together provided a characterization of the dominant source of uncertainty in global wetland CH4 estimates and can then be used to quantify (a) the probability of individual ensemble members; and (b) the combined probability distribution of carbon models, CH4:C temperature dependencies and wetland extent scenarios.
WetCHARTs v1.3.1 is an updated product of WetCHARTs v1.0 Extended Ensemble. Three new features in the updated version include:
The model output data have been updated to span 2001–2019,
The model meteorological drivers are replaced from using ERA-interim to ERA5 reanalysis data, and
The GLWD (Global Lakes and Wetlands Database) wetland extent definitions within WetCHARTs have been adjusted for the 50-100% Wetland, 25-50% Wetland, and Wetland Complex (0-25% Wetland) categories (that span 75%, 37.5% and 17.5% of land area respectively).
For details see Bloom et al. (2017).
These data are available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).
Bontemps, S., P. Defourny, E. Van Bogaert, O. Arino, V. Kalogirou and J.R. Perez.2011. GLOBCOVER 2009 Products description and validation report. Université catholique de Louvain & European Space Agency. https://doi.pangaea.de/10013/epic.39884.d016
Lehner, B., and P. Döll. 2004. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296:1–22. https://doi.org/10.1016/j.jhydrol.2004.03.028
Ma, S., J.R Worden, A. Anthony Bloom, Y. Zhang, B. Poulter, D.H. Cusworth, Y. Yin, S. Pandey, J.D. Maasakkers, X. Lu, L. Shen, J. Sheng, C. Frankenberg, C.E. Miller, and D.J. Jacob, 2021, Satellite methane observations constrain latitudinal distribution of wetland emissions and their climate sensitivity. Accepted by AGU Advances.
1.3.1.1 2022-05-05
Latitude, longitude variable names changed in the data files to improve compatibility with GIS software
https://doi.org/10.3334/ORNLDAAC/1915
1.3.1 2021-09-23 WetCHARTs v1.3.1 is an updated product of WetCHARTs v1.0 Extended Ensemble (Bloom et al., 2017). The new features are described in Section 5. https://doi.org/10.3334/ORNLDAAC/1915
1 2017-06-05 Initial release. https://doi.org/10.3334/ORNLDAAC/1502


text:
package me.grada.ui.activity;

import android.app.Activity;
import android.content.Intent;
import android.graphics.Bitmap;
import android.location.Location;
import android.net.Uri;
import android.os.Build;
import android.os.Bundle;
import android.provider.MediaStore;
import android.support.annotation.Nullable;
import android.support.design.widget.FloatingActionButton;
import android.support.design.widget.Snackbar;
import android.view.MenuItem;
import android.view.View;
import android.view.ViewGroup;
import android.widget.ArrayAdapter;
import android.widget.EditText;
import android.widget.ImageView;
import android.widget.Spinner;

import com.google.android.gms.maps.GoogleMap;
import com.google.android.gms.maps.MapView;
import com.google.android.gms.maps.MapsInitializer;
import com.google.android.gms.maps.OnMapReadyCallback;
import com.google.android.gms.maps.model.LatLng;
import com.squareup.otto.Bus;
import com.squareup.otto.Subscribe;
import com.squareup.picasso.Picasso;

import java.io.IOException;

import javax.inject.Inject;

import butterknife.Bind;
import butterknife.ButterKnife;
import butterknife.OnClick;
import io.github.yavski.fabspeeddial.FabSpeedDial;
import io.github.yavski.fabspeeddial.SimpleMenuListenerAdapter;
import me.grada.R;
import me.grada.di.Injector;
import me.grada.io.event.LocationUpdateEvent;
import me.grada.io.event.ReverseGeocodingEvent;
import me.grada.io.event.ShowLocationRationaleEvent;
import me.grada.io.task.ReverseGeocodeTask;
import me.grada.ui.fragment.LocationFragment;
import me.grada.utils.AttachmentHelper;
import me.grada.utils.MapViewInteractor;
import me.grada.utils.ViewUtils;

/**
 * Created by yavorivanov on 05/01/2016.
 */
public class AddSignalActivity extends BaseActivity implements OnMapReadyCallback {

    public static final String ATTACHMENT_URI = "attachment_uri";

    @Inject
    Picasso picasso;

    @Inject
    Bus bus;

    @Bind(R.id.top_view_group)
    ViewGroup topViewGroup;

    @Bind(R.id.bottom_view_group)
    ViewGroup bottomViewGroup;

    @Bind(R.id.map_view)
    MapView mapView;

    @Bind(R.id.close_map_fab_view)
    FloatingActionButton closeMapFabView;

    @Bind(R.id.edit_location_fab)
    FloatingActionButton editLocationFabView;

    @Bind(R.id.marker_view)
    View markerView;

    @Bind(R.id.map_view_overlay)
    View mapViewOverlay;

    @Bind(R.id.attachment_fab_view)
    FabSpeedDial attachementFabView;

    @Bind(R.id.category_view)
    Spinner categoryView;

    @Bind(R.id.edit_address_view)
    EditText editAddressView;

    @Bind(R.id.address_view)
    EditText addressView;

    @Bind(R.id.attachment)
    ImageView attachmentView;

    private Snackbar snackbar;
    private LocationFragment locationFragment;
    private Location location;

    private MapViewInteractor mapViewInteractor;
    private GoogleMap googleMap;

    @Override
    protected void onCreate(@Nullable Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_add_signal);

        Injector.INSTANCE.getImageFetcherComponent().inject(this);
        ButterKnife.bind(this);

        // Gets the MapView from the XML layout and creates it
        mapView.onCreate(savedInstanceState);
        // Needs to call MapsInitializer before doing any CameraUpdateFactory calls
        MapsInitializer.initialize(this);
        mapView.getMapAsync(this);

        getSupportActionBar().setDisplayHomeAsUpEnabled(true);

        ArrayAdapter<CharSequence> categoryAdapter = ArrayAdapter.createFromResource(this,
                R.array.category_array, android.R.layout.simple_spinner_dropdown_item);
        categoryView.setAdapter(categoryAdapter);

        if (getIntent().hasExtra(ATTACHMENT_URI)) {
            showAttachment((Uri) getIntent().getParcelableExtra(ATTACHMENT_URI));
        }

        attachementFabView.setMenuListener(new SimpleMenuListenerAdapter() {
            @Override
            public boolean onMenuItemSelected(MenuItem menuItem) {
                if (menuItem.getItemId() == R.id.action_file) {
                    Intent intent = AttachmentHelper.getFileChooserIntent();
                    startActivityForResult(Intent.createChooser(intent, "Select a file"),
                            AttachmentHelper.TYPE_FILE_CHOOSER);
                    return true;
                }
                return super.onMenuItemSelected(menuItem);
            }
        });

        locationFragment = LocationFragment.newInstance();
        getSupportFragmentManager().beginTransaction()
                .add(locationFragment, LocationFragment.TAG)
                .commitAllowingStateLoss();
    }

    @Override
    protected void onStart() {
        super.onStart();
        bus.register(this);

        if (mapViewInteractor != null) {
            mapViewInteractor.onStart();
        }

        if (Build.VERSION.SDK_INT > Build.VERSION_CODES.LOLLIPOP_MR1) {
            locationFragment.getPermission();
        } else {
            locationFragment.connectToLocationProvider();
        }
    }

    @Override
    public void onResume() {
        super.onResume();
        mapView.onResume();
    }

    @Override
    protected void onStop() {
        locationFragment.disconnectLocationProvider();
        bus.unregister(this);

        if (mapViewInteractor != null) {
            mapViewInteractor.onStop();
        }

        super.onStop();
    }

    @Override
    public boolean onOptionsItemSelected(MenuItem item) {
        if (item.getItemId() == android.R.id.home && mapViewInteractor.onBackPressed()) {
            return true;
        }
        return super.onOptionsItemSelected(item);
    }

    @Override
    public void onMapReady(final GoogleMap googleMap) {
        this.googleMap = googleMap;
        this.googleMap.getUiSettings().setMyLocationButtonEnabled(true);

        final View contentView = findViewById(android.R.id.content);
        ViewUtils.addOnGlobalLayoutListener(contentView, new Runnable() {
            @Override
            public void run() {
                mapViewInteractor = new MapViewInteractor.Builder()
                        .topView(topViewGroup)
                        .bottomView(bottomViewGroup)
                        .overlayView(mapViewOverlay)
                        .closeFabView(closeMapFabView)
                        .editModeViews(editLocationFabView, editAddressView, markerView)
                        .googleMap(googleMap)
                        .build();

                handleLocation();
            }
        });
    }

    @Override
    public void onLowMemory() {
        super.onLowMemory();
        mapView.onLowMemory();
    }

    @Override
    public void onDestroy() {
        super.onDestroy();
        mapView.onDestroy();
    }

    @Override
    public void onBackPressed() {
        if (!mapViewInteractor.onBackPressed()) {
            super.onBackPressed();
        }
    }

    @Override
    protected void onActivityResult(int requestCode, int resultCode, Intent data) {
        if (requestCode == AttachmentHelper.TYPE_FILE_CHOOSER && resultCode == Activity.RESULT_OK) {
            showAttachment(data.getData());
        }
        super.onActivityResult(requestCode, resultCode, data);
    }

    @OnClick(R.id.submit_view)
    public void onSubmit(View view) {
        mapViewInteractor.animateTo(new LatLng(51.4572006, 0.0306386));
    }

    private void showAttachment(Uri uri) {
        try {
            Bitmap bitmap = MediaStore.Images.Media.getBitmap(getContentResolver(), uri);
            attachmentView.setImageBitmap(bitmap);
        } catch (IOException e) {
            e.printStackTrace();
        }
    }

    @Subscribe
    public void onShowLocationRationaleEvent(ShowLocationRationaleEvent event) {
        snackbar = Snackbar.make(findViewById(android.R.id.content),
                getString(R.string.nearby_location_permission_rationale),
                Snackbar.LENGTH_INDEFINITE)
                .setAction(getString(R.string.enable_location), new View.OnClickListener() {
                    @Override
                    public void onClick(View v) {
                        locationFragment.promptLocationPermission();
                    }
                });
        snackbar.show();
    }

    @Subscribe
    public void onReverseGeocodingEvent(ReverseGeocodingEvent event) {
        addressView.setText(event.getAddress());
    }

    @Subscribe
    public void onLocationUpdateEvent(LocationUpdateEvent event) {
        location = event.getLocation();
        handleLocation();
    }

    private void handleLocation() {
        if (mapViewInteractor == null ||location == null) {
            return;
        }

        LatLng latLng = new LatLng(location.getLatitude(), location.getLongitude());
        mapViewInteractor.animateTo(latLng);
        new ReverseGeocodeTask().execute(latLng);
    }
}

redpajama_set_name: RedPajamaGithub


text:
\section{Introduction}

To date, there are more than 4000 exo-planets discovered using various techniques, such as radial velocity, transit surveys, and
a recently developed method based on disk kinematics \cite{Mayor1995,Basri2005,Ricker2015,Pinte2019}. How exactly these planets
form remains one of the major questions in modern astrophysics. One direct way to solve the problem is to image the (proto)planets
that are still in the making within their natal disks, as they can provide clear insights on where, when, and how planets are born.
Unfortunately, such observations are very challenging because the embedded planets are much fainter in the optical/infrared regime
compared to the host central star, and even to the emission knots, if present, from the disk material in the vicinity of the planet.
Consequently, only a few young stars have been claimed to harbour (proto)planets so far \cite{Keppler2018,Muller2018}, and most of
them still require further confirmation \cite{Quanz2013,Currie2019,Wagner2019}.

Nowadays, infrared and millimeter images of disks, taken by the Very Large Telescope Spectro-Polarimetric High-contrast
Exoplanet REsearch (VLT/SPHERE) and Atacama Large Millimeter/submillimeter Array (ALMA), are able to
achieve angular resolutions on the order of ${\sim}\,0.05^{\prime\prime}$ \cite{Beuzit2008}, corresponding to spatial scales
of ${\sim}\,7\,\rm{AU}$ at the distance of nearby star formation regions (${\sim}\,140\,\rm{pc}$, for instance Taurus).
The accumulating SPHERE and ALMA surveys have revealed a variety of disk substructures, such as rings \cite{Avenhaus2018,Long2018,Andrews2018},
gaps \cite{ALMA2015,Boekel2017,Andrews2020}, spirals \cite{Benisty2015,Perez2016,Benisty2017,Huang2018a}, and arcs \cite{vandermarel2013,Dong2018,Cazzoletti2018,Perez2018}.
The interaction between the disk and embedded planets is one of the proposed explanations for the formation of these features \cite{Kley2012}.
The properties of substructures together with the disk morphology can be used to constrain the parameters of the embedded planets \cite{Fung2015,Lodato2019}.
Alternative mechanisms, which can produce substructures without planets, include zonal flows generated by the magnetorotational
instability turbulence \cite{Bai2014,Cui2021}, fast pebble growth near condensation fronts of volatiles \cite{Zhang2015},
operation of a secular gravitational instability \cite{Takahashi2016}, and the pile-up of drifting pebbles \cite{Drazkowska2016, Jiang2021}.
In these scenarios, disk substructures may play a role of dust trapping, which on the one hand prevents dust particles from
rapid inwards drift \cite{Weidenschilling1977}, and on the other hand promotes grain growth and concentration to form planetesimals \cite{Pinilla2015}.

It is of particular interest to investigate the properties and stability of disk substructures, because such efforts
help to understand how the disk evolves and for what timescales the substructures will impact the process of planet formation.
Arcs, or their analogs, e.g., crescents and horseshoes, are often interpreted as vortices arising from the Rossby wave
instability (RWI) at the edge of a gap created by a young planet \cite{vandermarel2013,Zhu2014}. Some numerical simulations
show that, in weakly turbulent disks, vortex-driven arcs and rings might survive for more than
${\sim}10^3$ planetary orbits, and even for disk evolutionary timescales (${\sim}\,\rm{Myr}$) \cite{Fu2014,Hammer2021,Jiang2021}.
However, observational constraints on the strength of turbulence exist for only a few disks \cite{Teague2016,Flaherty2017}.

Rings and arcs in some cases appear extraordinarily far from the central star \cite{Dong2018,Benisty2018}. In such cold
and dense regions, gravitational instability may start to operate, which in turn complicates the determination for
the fate of substructures. HD\,143006 stands out from this disk category because of its complex geometry and very
bright arc outside of the millimeter dust disk \cite{Benisty2018,Perez2018}. This is a G7 young star located
in the Upper Scorpius association at a distance of 166\,pc \cite{Luhman2012,GAIA2018}. Its effective temperature
and luminosity are ${\sim}\,5623\,\rm{K}$ and ${\sim}\,3.8\,L_{\odot}$, respectively \cite{Barenfeld2016,Andrews2018}.
Comparing the location in the Hertzsprung-Russell diagram with models of pre-main-sequence evolutionary tracks returns
a stellar mass of ${\sim}\,1.77\,M_{\odot}$, and an age of $4\,{-}\,12\,\rm{Myr}$ \cite{Andrews2018}.
In this work, we focus on this intriguing object, and conduct three-dimensional radiative transfer modeling of
the spectral energy distribution (SED), and high resolution infrared and millimeter images, with the goal of
constraining the dust density and temperature distributions. On the basis of the modeling result, we investigate
the properties and gravitational stability of the substructures.

\section{Observational data}
\label{sec:obs}

The SED has been widely used to constrain the grain size distribution and overall structure of the disk \cite{Robitaille2007}.
We compiled the photometry of HD\,143006 from the GAIA \cite{GAIA2018}, 2MASS \cite{Cutri2003}, WISE \cite{Cutri2013}, and IRAS
catalogs \cite{Neugebauer1984}. In addition, individual flux measurements from optical to millimeter regimes reported
in the literature are also collected \cite{Grankin2007,Carpenter2008,Sylvester1996,Natta2004,Barenfeld2016,Andrews2018}.
As shown in \cref{fig:sed}, the SED shows a clear dip at $\lambda\,{\sim}\,10\,\mu{\rm m}$, suggesting that substructures
(e.g., gaps) exist in the distribution of micron-sized dust grains. A linear fit to the data points longer than $0.8\,\rm{mm}$ results
in a millimeter spectral index of $\alpha\,{\sim}\,2.9$, which yields a dust opacity slope of 0.9 under an assumption
of optically thin emission in the Rayleigh-Jeans limit \cite{Miyake1993,Andrews2007}. This value is lower than that of
the interstellar medium dust (i.e., ${\sim}\,1.7$), indicating that dust grains in the disk
have already grown to millimeter sizes \cite{Ricci2010,Testi2014}.

\begin{figure}[H]
 \centering
 \includegraphics[scale=0.43]{hd143006_sed_maintext.eps}
 \caption{(Color online) SED of the HD\,143006 disk. The red dots refer to photometric data points at different wavelengths, whereas the red solid
 line shows the Spitzer/IRS spectrum. The black solid line stands for the best-fit model with its parameter set given in \cref{tab:paras}.
 The dashed line represents the input photospheric model from the Kurucz database.}
 \label{fig:sed}
\end{figure}

\begin{figure}[H]
 \centering
 \includegraphics[scale=0.43]{hd143006_pintensity_xR2.eps}
 \caption{(Color online) J-band polarized intensity from SPHERE observations. The quantity shown here is actually the intensity
  scaled by $r^2$, where $r$ is the distance to the central star. The region masked by the coronagraph (with a diameter of 145 mas) is
  indicated by the gray circle. The magenta dashed curves depict the position and extent of two rings. The arrows point to two
  shadows that are located at $\rm{PA}\,{\sim}\,190^{\circ}$ and $340^{\circ}$. The nomenclature of these substructures is directly
  taken from Benisty et al. \cite{Benisty2018}.}
 \label{fig:sphere}
\end{figure}

\begin{figure*}[t]
 \centering
 \includegraphics[scale=0.6]{hd143006_mmimage.eps}
 \caption{(Color online) {\it Left panel:} fiducial image of the Band 6 ALMA observation generated by the DSHARP team \cite{Andrews2018}.
 The region enclosed with white dashed lines defines the arc structure in this work. The boundaries are described as $59.7\,{\leq}\,R\,{\leq}\,83\,\rm{AU}$
 and $118^{\circ}\,{\leq}\,{\rm PA}\,{\leq}\,165^{\circ}$. The magenta dashed curves depict the position and extent of two rings identified from the
 SPHERE observation, see \cref{fig:sphere}. {\it Middle panel:} best-fit model image. The green and red dotted lines
 depict two cut directions ($\rm{PA}\,{=}\,141.5^{\circ}$ and $51.5^{\circ}$), along which the observational and model surface brightnesses
 are extracted and compared in the {\it right panel}. The thickness of the grey lines represents the uncertainties of the observation.
 Both the model and observed surface brightness of the cut 2 are scaled down by a factor of two to have a better visualization.}
 \label{fig:mmimage}
\end{figure*}

Using the VLT/SPHERE instrument, Benisty et al. \cite{Benisty2018} observed HD\,143006 at an angular resolution of ${\sim}\,0.037^{\prime\prime}$.
\cref{fig:sphere} shows the J-band ($\lambda_{0}\,{\sim}\,1.25\,\mu{\rm m}$) polarized intensity, which traces the scattered
light from micron-sized dust grains populating the disk's surface layer \cite{Benisty2018}. The disk reveals
a strong east/west brightness asymmetries, and two rings. The outer Ring {\#}1 extends from $R\,{\sim}\,0.3^{\prime\prime}$
to $0.5^{\prime\prime}$, and appears obscure in the western side. The inner Ring {\#}2 has a central position
of ${\sim}\,0.15^{\prime\prime}$. Along the azimuthal direction, its brightness is not continuously distributed, but shows
two shadows at $\rm{PA}\,{\sim}\,190^{\circ}$ and $340^{\circ}$. These prominent features are indicative of a complex disk
configuration with a misaligned inner disk \cite{Min2017}. The clear signature of flux depletion between rings (i.e., in the gap)
and also in the central cavity is consistent with the dip shown in the mid-infrared SED.

HD\,143006 was selected as one of the 20 disks observed in the Disk Substructures at High Angular Resolution Project (DSHARP) \cite{Andrews2018}.
With an angular resolution of ${\sim}\,0.05^{\prime\prime}$, the 1.25\,mm continuum image obtained with ALMA is resolved into three
rings (B8, B40, and B64) at radial locations of $R\,{\sim}\,0.05^{\prime\prime}$, $0.24^{\prime\prime}$ and $0.39^{\prime\prime}$
respectively, see the left panel of \cref{fig:mmimage}. Note that the nomenclature of these rings is directly taken
from P\'erez et al. \cite{Perez2018}. Unlike the J-band scattered light, millimeter dust rings are broadly symmetric,
except that the innermost B8 ring features two peaks along a PA of ${\sim}\,165^{\circ}$.  Ring {\#}2 identified in the SPHERE image lies
closer to the central star than the B40 ring, whereas the position of the SPHERE Ring {\#}1 matches well with the B64 ring.
The most distinct feature in the ALMA image is the bright arc in the southeast, located just exterior to the B64 ring, and
extends from ${\sim}\,118^{\circ}$ to ${\sim}\,165^{\circ}$ in the azimuthal direction. This arc has a counterpart in the SPHERE
image, but it shows a broader radial and azimuthal extent.

\section{Radiative transfer modeling}
\label{sec:rtmodel}

The origin of the rich substructures of HD\,143006 is yet not fully understood. Hydrodynamic simulations demonstrate that
an inclined equal mass binary causes disc breaking into two distinct annuli, with the resulting inner disk misaligned with
the outer region \cite{Facchini2018,Benisty2018}. This scenario can produce azimuthal asymmetries shown in the scattered light.
Ballabio et al. \cite{Ballabio2021} found that besides an inclined binary, a planetary companion located
at $R\,{=}\,32\,\rm{AU}$ is required to simultaneously explain the morphological features in the scattered light and
the millimeter dust rings. We do not attempt to re-investigate the same story. Instead, we are interested to constrain
the temperature and density distribution of the disk with detailed radiative transfer modeling, and to infer the
properties of the substructures.

\subsection{Disk configuration}

\cref{fig:sketch} shows the configuration of our model. The inner and outer disks do not share the same geometry. They
are misaligned with an angle of $\varpi\,{=}\,30^{\circ}$, which can be roughly constrained by the location
of the shadows \cite{Min2017}. The near side of the outer disk is in the east, whereas the western part of the inner disk
is closer to us. The inclination and position angle of the outer disk are $18.6^{\circ}$ and $169^{\circ}$, respectively \cite{Huang2018b},
while for the inner disk we adopt an inclination of $11.4^{\circ}$, and PA of $0^{\circ}$. The border between the inner
and outer disks is fixed to $R_{\rm b}\,{=}\,18\,\rm{AU}$, which is not well constrained. \cref{tab:paras} summarizes
the values of these geometric parameters that are all kept constant in the fitting process for simplicity.

\begin{figure}[H]
 \centering
 \includegraphics[scale=0.4]{hd143006_disk_sketch.eps}
 \caption{(Color online) None-scaled cartoon of the disk configuration. The inner (red color) and outer (grey color) disks are misaligned with
 an angle of $\varpi$, see \cref{tab:paras}. The east part of the outer disk is the near side to us, whereas the near side of the inner
 disk is in the west. The blue dotted lines mark the major and minor axis of the outer disk. Prominent substructures discussed
 in \cref{sec:obs} are labeled out. The B8 ring exhibits two peaks along a PA of ${\sim}\,165^{\circ}$, which are indicated with
 the two dark red regions.}
 \label{fig:sketch}
\end{figure}

\subsection{Dust density distribution}

To account for the complex geometry of the HD\,143006 disk, we introduce three-dimensional radiative transfer models that include
two distinct dust grain populations, i.e., a small grain population (SGP) and a large grain population (LGP). The model is established
under the spherical coordinates. The resolution in the radial ($R$), poloidal ($\theta$), and azimuthal directions ($\phi$)
are 256, 1024, and 181 cells, respectively. Grid refinement is considered in the vicinity of the border ($R_{\rm b}$) between
the inner and outer disks as well as the location of the rings. This is done to ensure a smooth transition from optically thin to
thick cells, meaning that these disk regions are well resolved by the grid.

The inner and outer radii, and total dust mass of the disk are denoted as $R_{\rm in}$, $R_{\rm out}$
and $M_{\rm dust}$, respectively. The SGP occupies $f_{\rm SGP}\,{=}\,15\%$ of the total dust mass, and
its scale height follows a power-law profile:
\begin{equation}
h(R) = h_{100}\times\left(\frac{R}{100\,\rm{AU}}\right)^\beta.
\label{eqn:heightgas}
\end{equation}
The parameter $\beta$ quantifies the disk flaring, and $h_{100}$ stands for the scale height at $R\,{=}\,100\,\rm{AU}$.
Conversely, the LGP dominates the dust mass, i.e., $0.85M_{\rm dust}$, and is concentrated close to
the midplane with a scale height of $\Lambda\,h$, where the settling parameter $\Lambda\,{=}\,0.2$. Such a vertical
stratification of grain size distributions has been commonly used to mimic the effect of dust settling
in protoplanetary disks \cite{Dullemond2004,Dalessio2006,Liu2019}. Observationally, it is difficult to constrain the
total mass of small grains characterized by $f_{\rm SGP}$, because infrared observations only probe a portion of small grains
in the surface layer of the disk. The parameter $\Lambda$ is highly related to the strength of turbulence that is poorly constrained
from observations \cite{Teague2016}. In our models, $h_{100}$, $\beta$ and $\Lambda$ work together to define the scale height of
millimeter dust grains that basically determines the characteristic temperature of millimeter continuum emission. As $h_{100}$ and
$\beta$ are treated as free parameters (see \cref{sec:betarange}), the effect of a mild change to $\Lambda$ on the millimeter continuum
can be roughly compensated by according variations in $h_{100}$ and/or $\beta$ that result in a similar millimeter dust scale height.
Therefore, to reduce the total number of free parameters, $\Lambda$ was fixed during the fitting procedure. We directly took the values
for $f_{\rm SGP}$ and $\Lambda$ that were adopted in the literature modeling of protoplanetary disks \cite{Andrews2011,Fedele2018,Liu2019}.

The volume density of the dust grains is parameterized as
\begin{equation}
\rho_{\rm{SGP}}(R,z,\phi)\,{=}\,\frac{f_{\rm SGP}\,{\times}\,\Sigma_{\rm dust}(R,\phi)}{\sqrt{2\pi}\,h}\,\exp\left[-\frac{1}{2}\left(\frac{z}{h}\right)^2\right], \\
\label{eqn:sgp}
\end{equation}
\begin{equation}
\rho_{\rm{LGP}}(R,z,\phi)\,{=}\,\frac{(1-f_{\rm SGP})\,{\times}\,\Sigma_{\rm dust}(R,\phi)}{\sqrt{2\pi}\,{\times}\,\Lambda\,h}\,\exp\left[-\frac{1}{2}\left(\frac{z}{\Lambda\,h}\right)^2\right], \\
\label{eqn:lgp}
\end{equation}
where $\Sigma_{\rm dust}(R,\phi)$ is the dust surface density profile, and varies both in the radial and azimuthal axis.
Previous studies on axisymmetric disks usually parameterize $\Sigma_{\rm dust}$ with an analytic formula, for example
a power law or a power law plus an exponential taper \cite{Lynden-Bell1974,Andrews2011}. However, in radiative transfer
models, linking such smooth (or clean) surface densities to high resolution ALMA images is impractical
because the wealth of fine-scale features and asymmetries are extremely hard to be captured. Instead, to construct the
surface density profile particularly for HD\,143006, we develop an iterative fitting loop (see \cref{sec:surdens}), which
is improved upon the algorithm that has been successfully applied to a few other targets \cite{Liu2019}.

According to \cref{eqn:sgp,eqn:lgp}, SGP and LGP follow a same surface density pattern. In principle, the distributions of
small and large grains are different, because large grains gradually decouple from the gas as the disk evolves, while small grains
are expected to be well-mixed with the gas. Generally, one can scale down the gas distribution to represent the surface density profile of SGP.
However, constraining the gas surface density requires high resolution and high sensitivity observations
of multiple gas lines \cite{Isella2016}, which are currently not available for HD\,143006. Moreover, although the quality of the SPHERE
data is high, translating polarized scattered light map to surface densities is not straightforward, because the morphology and intensity of infrared
polarization are highly dependent on the polarization efficiency, e.g., grain composition, shape and size distribution. Unfortunately, until now, we
have poor knowledge on these dust properties in protoplanetary disks. Since our work focuses more on the ALMA data that probe large dust grains,
for simplicity, we assumed that the surface densities of small and large grains follow the same profile. Such an assumption is not expected
to have a significant impact on the properties (e.g., mass, millimeter optical depth, and Toomre Q parameter) of the disk substructures
that will be derived and discussed in \cref{sec:resdis}.

\begin{table}[H]
\footnotesize
\begin{threeparttable}
\caption{Overview of the model parameters.}
\label{tab:paras}
\doublerulesep 0.1pt \tabcolsep 5pt
\begin{tabular}{lccc}
\toprule
 Parameter      & Explored values    &  Best fit         &  Comment     \\
 \hline
 $T_{\rm eff}$\,[K]          & 5623  &  5623             &   fixed  \\
 $L_{\star}\,[L_{\odot}]$    & 3.8   &  3.8              &   fixed  \\
 $D$\,[pc]                   & 166   &  166              &   fixed  \\
 $A_{\rm V}$\,[mag]          & 0.4   &  0.4              &   fixed  \\
 $R_{\rm in}$\,[AU]          & 0.2   &  0.2              &   fixed  \\
 $R_{\rm out}$\,[AU]         & 100   &  100              &   fixed  \\
 $R_{\rm b}$\,[AU]           & 18    &  18               &   fixed  \\
 $\Lambda$                   & 0.2   &  0.2              &   fixed  \\
 $f_{\rm SGP}$               & 0.15  &  0.15             &   fixed  \\
 $\Sigma_{\rm dust}\,[\rm{g/cm^2}]$     &  see \cref{sec:surdens}   &  see \cref{fig:surdens}  &   free   \\
 $\beta$              & 1.05, 1.1, 1.15, 1.2, 1.25 &  $1.1_{-0.05}^{+0.1}$  &   free  \\
 $H_{100}$\,[AU]      & 4, 6, 8, 10, 12       &  $10_{-2}^{+0}$           &   free  \\
 $M_{\rm dust}\,[10^{-5}\,M_{\odot}]$ & $-$   &  $9.7_{-0.3}^{+1.3}$ \tnote{(a)} & $-$   \\
 $a_{\rm max.SGP}$\,[$\mu{\rm m}$] &  2  &    2              &  fixed \\
 $a_{\rm max.LGP}$\,[mm]           &  3  &    3              &  fixed \\
 $i_{\rm inner}\,[^{\circ}]$     & 11.4  &   11.4           &  fixed \\
 ${\rm PA_{inner}\,[^{\circ}]}$  & 0     &   0              &  fixed \\
 $i_{\rm outer}\,[^{\circ}]$     & 18.6  &   18.6           &  fixed \\
 ${\rm PA_{outer}\,[^{\circ}]}$  & 169   &   169            &  fixed \\
 $\varpi\,[^{\circ}]$            & 30    &   30             &  fixed \\
\bottomrule
\end{tabular}
\begin{tablenotes}
\item[(a)] The total dust mass $M_{\rm dust}$ is obtained by integrating the surface density $\Sigma_{\rm dust}$ that
is constructed in the fitting procedure. Hence, $M_{\rm dust}$ is not a direct fitting parameter.
\end{tablenotes}
\end{threeparttable}
\end{table}

\subsection{Dust properties}
\label{sec:dustmodel}
Birnstiel et al. \cite{Birnstiel2018} proposed a dust model to interpret the ALMA data of the DSHARP disks. We directly
take their model that consists of water ice, astronomical silicates, troilite, and refractory organic material, with
volume fractions being 36\%, 17\%, 3\% and 44\%, respectively. The bulk density of the dust mixture
is $\rho_{\rm grain}={\rm 1.675\,g\,cm^{-3}}$. We use the Mie theory to calculate the mass absorption/scattering coefficients.
The grain size distribution follows the power law ${\rm d}n(a)\propto{a^{-3.5}} {\rm d}a$ with a
minimum grain size fixed to $a_{\rm{min}}=0.01\,\mu{\rm m}$. The maximum grain size is set to $a_{\rm{max}}\,{=}\,2\,\mu\rm{m}$
for the SGP. To account for the effect of grain growth implied by the shallow millimeter spectral index (see \cref{sec:obs}), we
adopt $a_{\rm{max}}\,{=}\,3\,\rm{mm}$ for the LGP.

Zhu et al. \cite{Zhu2019} showed that taking dust scattering into account is important to estimate the total amount of
dust mass in protoplanetary disks. Full scattering matrices are included in our radiative transfer models to treat
dust scattering in a more realistic way.

\subsection{Stellar heating}
\label{sec:heating}
For simplicity, only stellar irradiation is considered as the heating source of the disk. The stellar parameters are listed
in \cref{tab:paras}. The photospheric spectrum is taken from the Kurucz database \cite{Kurucz1994}, assuming ${\rm log}\,g\,{=}\,3.5$
and solar metallicity. We found that a mild extinction of $A_{\rm V}\,{=}\,0.4\,\rm{mag}$ is needed to match the
optical part of the SED. The extinction law is taken from Cardelli et al. \cite{Cardelli1989}.

\subsection{Fitting procedure}

\subsubsection{Explored ranges for $\beta$ and $H_{100}$}
\label{sec:betarange}

There are three free parameters/quantity in the model: $\beta$, $H_{100}$, and $\Sigma_{\rm dust}$.
The disk flaring $\beta$ and scale height $H_{100}$ define the vertical structure of the disk. The location of
shadows seen in the SPHERE observation is sensitive to the height of the scattering surface $z_{\rm scat}$ as
a function of $R$. Using the equation developed in Min et al. \cite{Min2017}, Benisty et al. \cite{Benisty2018}
estimated $z_{\rm scat}/R\,{=}\,0.12$ at $R_{\rm b}\,{=}\,18\,\rm{AU}$. Assuming that the scattering surface is
higher than the scale height $h$ by a factor of ${\sim}\,2–4$, the disk aspect ratio $h/R\,{\sim}\,0.03\,{-}\,0.06$.
We note that such a calculation is highly dependent on the adopted geometric parameters and dust temperature
as well, and therefore the result can only be treated as a guide.

In the fitting process, we explored a grid of values for $\beta$ and $H_{100}$ that are within the ranges of
[1.05, 1.25] and [4\,AU, 12\,AU], respectively. Using \cref{eqn:heightgas}, the deduced aspect ratio $h/R$
spans a relatively broader range of 0.03$\,-\,$0.1.

\subsubsection{Building the two-dimensional surface density}
\label{sec:surdens}
The most difficult part of the modeling is to construct the surface density profile $\Sigma_{\rm dust}(R,\phi)$. This
is due to three reasons. First, it is naturally understood that for HD\,143006, $\Sigma_{\rm dust}$ varies not
only in the radial direction, but also with azimuthal angle. Second, what the ALMA image (see the
left panel of \cref{fig:mmimage}) shows is the millimeter continuum emission from a three dimensional disk
projected onto the plane of sky. The projection parameters (i.e., inclination and position angle) are different
between the inner and outer disks. Third, there is a misalignment complicating the geometry further.

To deal with the difficulty, we devised an iterative fitting loop. An initial surface density profile $\Sigma_{\rm ini}(R,\phi)$ should be given.
In practice, it can be an arbitrary form, for instance a power law. The surface density is closely linked to the ALMA observation
due to the fact that the optical depth at millimeter wavelengths is low. Therefore, we extract the surface brightness on the ALMA image
along 181 azimuthal angles ($\phi$) distributed from 0 to $360^{\circ}$. The observed two-dimensional surface brightness
distribution ${\rm SB}_{\rm obs}(R,\phi)$ was first deprojected using the geometric parameters given in \cref{tab:paras}, and then
imported into the radiative transfer model as the initial surface density $\Sigma_{\rm ini}(R,\phi)$.
After that, the iteration starts, and is followed by the steps itemized below.

\begin{itemize}
\item[(a)] We perform the radiative transfer simulation using the well-tested code \texttt{RADMC-3D}\footnote{http://www.ita.uni-heidelberg.de/~dullemond/software/radmc-3d/.} \cite{radmc3d2012}. The raytraced model image at 1.25\,mm is obtained.
\item[(b)] To simulate the ALMA observation, we compute the model visibilities at the same uv coordinates of the observation.
           Using the Common Astronomy Software Applications (CASA) package \cite{McMullin2007}, the resulting model visibilities was CLEANed
		   with the same parameters used for HD\,143006 in the DSHARP project \footnote{https://almascience.org/alma-data/lp/DSHARP}.
\item[(c)] On the CLEANed image, we extracted the surface brightness ${\rm SB}_{\rm mod}(R,\phi)$ at the same
           locations as what we did on the observed ALMA map.
\item[(d)] A ratio $\xi(R,\phi)$ as a function of radius and azimuthal angle is calculated through dividing the observed brightness
           by the model brightness: $\xi(R,\phi)\,{=}\,{\rm SB}_{\rm obs}(R,\phi)/{\rm SB}_{\rm mod}(R,\phi)$. We deprojected $\xi(R,\phi)$
		   using the disk geometric parameters to get a scaling factor $\xi^{\rm D}(R,\phi)$ for the next iteration.
\item[(e)] The surface density profile $\Sigma_{\rm dust}(R,\phi)$ used as the input for the model is scaled by the point-by-point ratio $\xi^{\rm D}(R,\phi)$.
           The fitting loop goes back to step (a).
\end{itemize}

The iteration continues	until the changes in the model surface brightness are less than 5\% at all locations. This is typically
satisfied after ${\sim}\,20$ steps. Once the convergence is achieved, we also simulated the SED, and polarized intensity map
at $1.25\,\mu{\rm m}$, in addition to the millimeter image.

\begin{figure*}[t]
 \centering
 \includegraphics[scale=0.55]{surface_density_2d.eps}
 \caption{(Color online) {\it Left panel:} two-dimensional surface density profile of the best-fit model. {\it Middle panel:} two-dimensional temperature distribution
 of the best-fit model. The green dashed circles mark the border ($R_{\rm b}\,{=}\,18\,\rm{AU}$) between the inner and outer disk. {\it Right panel:} optical depth
 at 1.25\,mm ($\tau_{\rm 1.25\,mm}$, black line) and mass-averaged mean dust temperature ($T_{\rm dust}$, blue line) as a function of
 radius. To highlight the properties towards the arc, we actually show an azimuthal average on $\tau_{\rm 1.25\,mm}$ and $T_{\rm dust}$ within
 $\rm{PA}\,{=}\,118^{\circ}$ and $165^{\circ}$, see the white curved arrow in the left panel.}
 \label{fig:surdens}
\end{figure*}

\subsubsection{Evaluating the quality of fit}
\label{sec:fitquality}
It can be seen from \cref{tab:paras} that there are 25 different combinations of $\{\beta,H_{100}\}$. For each of
these possibilities, we separately ran the above-mentioned iteration from scratch until they converge. In the end,
we obtained 25 models, and their final converged surface densities $\Sigma_{\rm dust}(R,\phi)$ (therefore, the total
dust mass in the disk $M_{\rm dust}$) are different from each other.

Near-infrared scattered light is produced by small dust grains in the surface layer of the disk. We first compare the
morphological features in the polarized intensity map between the model and SPHERE observation. \cref{fig:irmodimg} shows
four models as an illustration. The effect of shadowing cast by the misalignment is very sensitive to the aspect ratio.
We found that models with aspect ratios within $0.07\,{\lesssim}\,h/R\,{\lesssim}\,0.10$ are able to reproduce the overall
morphology of the SPHERE image. \cref{tab:fitres} summarizes the fitting results. There are 9 models with $h/R$ in the satisfied
range.

Then, we evaluate the quality of fit to the SED and ALMA data for the 9 models. The goodness of fit is given by
\begin{equation}
\chi^2\,{=}\,\sum_{i=1}^{N}\left(\frac{\mu_{i}-\omega_{i}}{\sigma_{i}}\right).
\end{equation}
For the SED, $N$ is the number of observed wavelengths, $\omega_{i}$ are the
photometric measurements, and $\mu_{i}$ are the predicted flux densities at
these wavelengths. The observational flux uncertainties are denoted as $\sigma_{i}$.
For the ALMA image, $N$ is the number of pixels taken into account, $\omega_{i}$ are the
observed surface brightness in each pixel, and $\mu_{i}$ are the model surface brightness.
The quantity $\sigma_{i}$ now is the rms noise of the ALMA image, which we fixed to
$0.015\,\rm{mJy/beam}$ for every pixel \cite{Andrews2018}. The total goodness of fit
is evaluated via
\begin{equation}
\chi^2_{\rm total}\,{=}\,g\cdot\chi^2_{\rm SED}+(1-g)\cdot\chi^2_{\rm ALMA},
\end{equation}
where $g$ is a weighting factor that is adjustable to make a compromise for the quality of fit between the SED and
ALMA data. There is not an universal value for $g$. The $\chi^2_{\rm SED}$ and $\chi^2_{\rm ALMA}$ values for each model
are listed in \cref{tab:fitres}. As can be seen, $\chi^2_{\rm SED}$ is roughly an order of magnitude lower
than $\chi^2_{\rm ALMA}$. To balance the weighting between these two independent assessments, $g$ was taken to be 0.9.
However, we emphasize that our choice is only applicable for this particular study. \cref{fig:sedmodels} shows how
the SED varies with the geometric parameters of the disk. The best fit is identified as the one with the minimum
$\chi^2_{\rm total}$. The uncertainties of the best-fit parameter set are deduced from the models
with $\chi^2_{\rm total}$ lower than 1.1 times the minimum $\chi^2_{\rm total}$.

\section{Results and discussion}
\label{sec:resdis}
The fitting results are presented in \cref{fig:sed,fig:mmimage,fig:irmodimg}, whereas \cref{tab:paras}
summarizes the corresponding parameter set. Our model well matches the ALMA data and the
morphology of the infrared polarized intensity map, see for instance how high the quality
of fit is for the arc and rings in the right panel of \cref{fig:mmimage}.

The model SED is broadly consistent with the observation, except for the data points
at ${\sim}\,3\,\mu{\rm m}$ and ${\sim}\,40\,\mu{\rm m}$. The geometry of the inner disk
for HD\,143006 might be more complicated than what we assumed (see \cref{fig:sketch}),
for instance there might be an puffed-up inner rim \cite{Dullemond2001}.
Moreover, a variability in accretion or an inclined companion can lead to changes
in the structure of the inner disk \cite{Flaherty2012}. These factors can explain the
discrepancies in the near-infrared flux. For simplicity, we assume that the flaring
index $\beta$ and scale height $H_{100}$ are the same in the inner and outer disk.
The mild overestimation of far-infrared flux can be reconciled by reducing $H_{100}$ (or $\beta$,
or both) for the outer disk while maintaining the vertical structure of the inner disk.
Nevertheless, such finely refinements are not expected to have a significant impact on
the overall properties (i.e., temperature, surface density, and Toomre parameter) of
the disk.

\begin{figure*}[t]
 \centering
 \includegraphics[scale=0.6]{toomreq_2d.eps}
 \caption{(Color online) {\it Left panel:} two-dimensional distribution of the Toomre parameter $Q$, assuming a constant gas-to-dust mass ratio of 30 throughout the disk.
 The green curve indicates the contour level of $Q\,{=}\,1$. The black and blue curved arrows mark two azimuthal ranges (i.e., $118\,{\leq}\,{\rm PA}\,{\leq}\,165^{\circ}$ and $-195\,{\leq}\,{\rm PA}\,{\leq}\,118^{\circ}$, respectively), within which an average on $Q$ is calculated separately, and then compared in the {\it right panel}.}
 \label{fig:toomre}
\end{figure*}

The left panel of \cref{fig:surdens} shows the surface densities projected onto the plane of sky.
A projection is performed to have a one-to-one comparison with the observation. The reconstructed surface densities show
obvious fluctuations along both the radial and azimuthal directions. We checked the quality of the ALMA
data, and found that in most disk regions (excepting for the gap around $R\,{\sim}\,22\,\rm{AU}$), the
signal-to-noise ratio is higher than 3. In the arc and rings where we are most interested, the ratio is higher than ${\sim}\,10$.
These mean that the derived surface densities for the arc and rings are reliable since our iterative fitting is driven by the data.
Nevertheless, surface density fluctuations in the gap around $R\,{\sim}\,22\,\rm{AU}$ may reflect the noise of the ALMA observation.
The right panel presents the optical depth at 1.25\,mm and the mass-averaged temperature, derived from an azimuthal average
between ${\rm PA}\,{=}\,118^{\circ}$ and $165^{\circ}$. As can be seen, all the three ALMA rings (B8, B40, B64) are marginally
optically thick: $\tau_{1.25\,\rm{mm}}\,{\sim}\,1$. This is different from the result presented by
Huang et al. \cite{Huang2018b}, who obtained an optical depth of $\tau_{1.25\,\rm{mm}}\,{\lesssim}\,0.2$.
When calculating $\tau_{1.25\,\rm{mm}}$, they assumed a temperature profile being the simplified expression for a passively
heated, flared disk: $T\,{\propto}\,R^{-0.5}$ \cite{Chiang1997,Dullemond2001}.
It is clear that such an approximation is not sufficient for HD\,143006, see the blue solid
line in the figure. Instead, due to the misalignment, the temperature exhibits a discontinuity
at the boundary ($R_{\rm b}\,{=}\,18\,\rm{AU}$) between the inner and outer disk. Along the radial
direction towards the arc (${\rm PA}\,{=}\,141.5^{\circ}$), stellar photons can directly impinge upon
the inner rim of the outer disk, resulting in a sharp increase in temperature. Beyond that
radius, the temperature decreases by roughly following a power law with $T\,{\sim}\,20\,\rm{K}$
at the arc location. Moreover, in the analysis of Huang et al. \cite{Huang2018b}, dust scattering
is not taken into account. The difference in optical depth between our work and theirs can be understood
in terms of these two reasons. The bright arc is optically thick with $\tau_{1.25\,\rm{mm}}\,{\sim}\,2.7$.

\begin{table}[H]
\footnotesize
\begin{threeparttable}
\caption{Dust mass and mean temperature in the substructures}
\label{tab:massres}
\doublerulesep 0.1pt \tabcolsep 8pt
\begin{tabular}{lccc}
\toprule
 Substructure & Region & Dust mass  &  Mean $T_{\rm dust}$     \\
              & [AU or $^{\circ}$]  & [$M_{\oplus}$] & [K]     \\
 \hline
 B8  & $4.6\,{\leq}\,R\,{\leq}\,10.7$  &  $0.6_{-0.1}^{+0.0}$  & $74.1_{-6.1}^{+0.5}$  \\
 B40 & $35\,{\leq}\,R\,{\leq}\,44.8$   &  $3.9_{-0.0}^{+0.4}$  & $48.0_{-3.0}^{+0.0}$  \\
 B64 & $52.5\,{\leq}\,R\,{\leq}\,74.6$ &  $15.5_{-0.3}^{+1.8}$ & $28.7_{-2.4}^{+0.6}$  \\
 \multirow{2}{*}{Arc} & $59.7\,{\leq}\,R\,{\leq}\,83$  &  \multirow{2}{*}{$6.3_{-0.2}^{+0.9}$} & \multirow{2}{*}{$23.1_{-2.0}^{+0.5}$} \\
                      & $118\,{\leq}\,{\rm PA}\,{\leq}\,165$ &  &  \\
\bottomrule
\end{tabular}
\end{threeparttable}
\end{table}

The misalignment leads to a highly asymmetric temperature distribution, see the middle panel of \cref{fig:surdens}.
The eastern/western sides of the outer disk's rim (located at $R_{\rm b}\,{=}\,18\,\rm{AU}$, the green dashed circle in the plot)
are directly exposed to the central star. The typical temperature there reaches $110\,\rm{K}$. Conversely, the
northern/southern parts of the rim are cool with $T_{\rm dust}\,{\sim}\,60\,\rm{K}$. \cref{tab:massres} gives the
mean temperature of each substructure. We defined the arc region as the area enclosed with the
white dashed lines in the left panel of \cref{fig:mmimage}. The radial range of the arc overlaps with
that of the B64 ring, because we considered that both features are tightly connected. Adopting the
condensation temperature reported in the literature \cite{MartinDomenech2014,Luna2014,Zhang2015}, the B8, B40,
and B64 rings are associated with the ice lines of ${\rm NH_3}$, ${\rm H_2S}$, and ${\rm CH_4}$, respectively. By integrating the surface
density, we obtained the dust mass in the substructures. The total dust mass of the best model
is $M_{\rm dust}\,{=}\,9.7\times10^{-5}\,M_{\odot}$ (or $32\,M_{\oplus}$). The arc contains
about 20\% of the dust mass. This number should be regarded as a lower limit
due to the optical thickness $\tau_{1.25\,\rm{mm}}\,{\sim}\,2.7$. The mass fraction is lower
than that of the arc in HD\,135344B (i.e., ${\sim}\,50\%$), which shares a similar disk morphology \cite{Cazzoletti2018}.
The ring masses range from 0.6 to $16\,M_{\oplus}$. They are systematically lower
than those derived in the younger (${\sim}\,1\,\rm{Myr}$) HL\,Tau disk. In HL\,Tau, the dust masses
in the rings are from 16 to ${\sim}\,120\,M_{\oplus}$ \cite{Liu2017}. If the rings are created
by planets, this may suggest that much of the dusty material in the HD\,143006 disk have already been
converted to the mass of planets (or planetary cores).

As can be seen from \cref{tab:massres}, the rings, particularly the B64 ring, and the arc occupy a large amount of dust mass.
In addition, the arc is located so distant from the central star that the temperature is as low as $23\,\rm{K}$. Gravitational
instability may be in action at these cold and dense regions. We checked this possibility using the Toomre criterion \cite{Toomre1964}.
The Toomre $Q$ parameter is defined as
\begin{equation}
Q=\frac{c_{s}\Omega_{k}}{\pi G \Sigma_{\rm gas}},
\end{equation}
where $c_s$ is the local sound speed, $\Omega_{K}(R)$ is the angular frequency, and $\Sigma_{\rm gas}$ stands for the gas surface density.
In terms of $Q$, a disk/substructure is unstable to its own self-gravity if $Q\,{<}\,1$, and stable if $Q\,{>}\,1$. As there is no
constraints on $\Sigma_{\rm gas}$, for simplicity, we scaled up the constructed $\Sigma_{\rm dust}$ by a constant
gas-to-dust mass ratio of 30 that is found to be representative via millimeter dust and gas observations of a sample of protoplanetary
disks \cite{Williams2014,Ansdell2016,Ansdell2017}. The two-dimensional distribution of $Q$ is shown in the left panel of \cref{fig:toomre}.
The value is below unity at the center of the arc, as indicated by the green contour. The right panel of \cref{fig:toomre} displays the azimuthally
averaged $Q$ as a function of radius. Two ranges of azimuthal angles were considered in order to highlight the difference
at various locations. While most regions including the three rings are gravitationally stable, $Q$ approaches ${\sim}1.3$ at
the radial position of the arc. Pohl et al. \cite{Pohl2015} showed that an embedded massive planet (planet-to-star mass
ratio of 0.01) can induce gravitational collapses in massive disks when $Q$ is around unity. Nevertheless, analyzing
infrared interferometric data excludes the presence of a companion with mass ratios of ${\gtrsim}\,0.1$ \cite{Benisty2018}.
Future searches for less massive (proto)planets with high-contrast imaging are desired to test the hypothesis.

The gas-to-dust mass ratio reflects how quickly the dust and gas components dissipate, in particular relative to each other.
The inherited interstellar medium gas-to-dust mass ratio is 100. ALMA observations of dust and gas lines reveal that
the ratio can drop to 10 in some disks \cite{Ansdell2016,Soon2019}. Arcs are often interpreted as a result of dust trapping into
vortices that preferentially concentrate dust grains. Numerical simulations of vortex formation and dust trapping have shown that the
gas-to-dust mass ratio in the center of vortices can decrease down to 10 and even ${\sim}\,1$ \cite{Meheut2012,Sierra2017}.
In such a situation, $Q$ will be above unity, and gravitational instability is suppressed accordingly. Future high resolution
and high sensitivity observations of gas lines are required to constrain the gas surface density, and therefore the
gas-to-dust mass ratio, particularly in the local arc region. Furthermore, hydrodynamic simulations dedicated on
HD\,143006, with the inclusion of the feedback of dust onto gas \cite{Crnkovic2015}, will help to understand the
fate of the arc.

Vortices are regions acting as local pressure maxima in the disk, and they can form at the edge of a planet-induced gap
due to the excitation of RWI. An important prediction of dust trapping toward vortices is that larger dust grains are expected
to be concentrated into a narrower region \cite{Birnstiel2013,Lyra2013}, which has been observationally identified in several
disks, e.g., Oph IRS 48 \cite{vandermarel2015}, HD\,142527 \cite{Casassus2015}, MWC\,758 \cite{Marino2015}, and
HD135344B \cite{Cazzoletti2018}. Investigating the spatial distribution of dust grains with different sizes requires high
resolution observations at multi-wavelengths. Due to the lack of such datasets, we distributed the SGP and LGP
into the same azimuthal extent in the radiative transfer models. Future high resolution observations at different wavelengths,
and follow-up search for (proto)planets are indispensable to clarify the nature of the arc in the HD\,143006 disk.

\section{Summary}

We performed three dimensional radiative transfer modeling of the HD\,143006 disk that shows three dust rings and a bright arc
in the outermost region on the DSHARP ALMA map. The goal of our work is to constrain the density and temperature distributions,
and to investigate the possibility of triggering a gravitational instability in the substructures. In order to account for the
ALMA and SPHERE observations, our model features a complex geometry with a misalignment between the inner and outer disk.

All of the three rings (B8, B40, B64) are marginally optically thick ($\tau_{1.25\,\rm{mm}}\,{\sim}\,1$) at the observed ALMA wavelength,
while the optical thickness of the arc is $\tau_{1.25\,\rm{mm}}\,{\sim}\,2.7$. Our self-consistent modeling gives higher optical depth
than those derived from a simple analytic method in the literature. We found that the temperature as a function of radius resembles
two connecting power laws with a sharp jump at the boundary of the misalignment. The temperature in the rings are from 74 to
29\,K, and it drops down to 23\,K in the arc. The total dust mass in the disk is about $32\,M_{\oplus}$, with the arc
sharing about 20\% of the amount. The dust masses in the rings range from $0.6\,M_{\oplus}$ to $16\,M_{\oplus}$, which are systematically
lower than those found in the younger HL\,Tau disk.

While all the rings are gravitationally stable, the Toomre parameter $Q$ in the arc is around unity when assuming a gas-to-dust mass ratio
of 30 that is constant in the entire disk. The arc is close to the threshold of gravitational collapse in this case. If gravitational instability is
in action and the cooling is efficient enough, giant planets may quickly form \cite{Mayer2002}. However, if the arc indeed traces a vortex,
millimeter dust grains preferentially concentrate in the arc, which will decrease the local gas-to-dust mass ratio. Given that the gas-to-dust mass
ratio is an unknown assumption, adopting a lower value would increase our estimate of $Q$. In this situation, gravitational instability will
not take place. Future gas and dust observations at high resolutions are required to better constrain the gas-to-dust mass ratio, and to
clarify the origin of the arc in the HD\,143006 disk.

\Acknowledgements{We thank the anonymous referees for their constructive comments that highly improved the manuscript.
We sincerely thank Myriam Benisty for sharing the reduced SPHERE data of HD\,143006. Y.L. acknowledges the financial support
by the Natural Science Foundation of China (Grant No. 11973090). M.F. acknowledge funding from the European Research Council (ERC) under the
European Union's Horizon 2020 research and innovation program (grant agreement No. 757957). We acknowledge the science research grants from
the China Manned Space Project with NO. CMS-CSST-2021-B06. {\it Herschel} is an ESA space observatory with science instruments provided by
European-led Principal Investigator consortia and with important participation from NASA. ALMA is a partnership of ESO (representing its
member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation
with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. This work has made use of data from the
European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the
institutions participating in the Gaia Multilateral Agreement.}

\InterestConflict{The authors declare that they have no conflict of interest.}

\bibliographystyle{scichina}

redpajama_set_name: RedPajamaArXiv


Spending a week in DR traveling to and fro, one thing becomes clear- fruit, fruit, fruit…is everywhere. No matter where you are it seems if a bounty of fruit is just an arms reach (or a tree climb) away. While continuing our search for housing, we visited a home for rent that sat on a large piece of land with many fruit trees. As we toured the house I was laughing as Renzo's mom Taty started picking fruit off their trees, gathering goodies to take home. It seemed as though everywhere we went (family & friend's houses, etc) Taty would make sure to grab some fruit off the trees before we left.
When we went to visit Renzo's cousins, it was the same story. Head to the backyard and see what you can find. He and his cousin Robert started throwing rocks and bottles up into the trees to try and knock some fruit down.
Here is a picture of Renzo & cousin Robert. Notice the red bag in Renzo's hand. Lol, can you guess what it is filled with?
We got a bunch of mangoes there, but no avocados yet. Seeing how keen Taty was to get some avocados, her cousin Robert told us he knew "just where to go". Turns out "just where to go" was his co-worker's house so since we were all in the car already we just stopped by. His co-worker wasn't home but his wife and children were, so Robert drove around a bit until we found him hanging out at a friend's house. Robert told him we were looking to get some avocados. Of course this was no problem at all, so his friend jumped in the car with us and took us to his home, where he loaded us up with enough avocados from his yard to feed an army (apparently he had 17 different types of avocados growing on his land!).
At one point we even came across a young boy walking on the side of the road with a large basket of fruit, with one gigantic soursop fruit sitting on top. Taty told her cousin Robert to pull over to inquire about buying it. When we pulled over, Taty asked the boy how much he wanted for the soursop. "Gratis" he said. Turns out his grandmother had given it to him from her yard, and here he was gifting it to a perfect stranger simply out of the kindness of his own heart! Amazing!
I asked Renzo if this gathering/giving of fruit is typical in DR, or if I was just witnessing Taty doing her thing. According to him and his mom, apparently it is considered perfectly acceptable to pick some fruit for yourself wherever you are. Wow! I was really blown away by the spirit of all these people sharing mother nature's bounty.
I will leave you with a photo of Taty's kitchen counter. It was too unbelievable not to take a picture. All of this fruit was gathered in the one week we were there, and all of it was "gratis".
Amen sister. Humanity-WE in action. Now time for the juices and guac!
Quality goods here on dominicanroots.com, man.
gather a lot from you. A useful blog without a doubt.


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a half day component that is instructed in compliance with STCW Reg V1/6 and Part A of part A-VI/6 paragraph 4 and comes with a half day within the class room addressing subjects such as for instance maritime security key threats, recognition and awareness of threats, programs and processes for combatting threats, exercises, communications and reporting.
This component is needed from January 1, 2014 as part of the revision to the STCW Code mandated because of the 2010 Manila Amendments, by all personnel utilized or involved on-board ships that the ISPS Code applies.
One and a half day personal survival training course module, also referred to as Basic water Survival, which can be instructed in compliance to STCW Code A – VI/1-1 and is composed of every day inside class addressing topics particularly disaster situations, evacuation, survival art and relief ships, individual lifesaving appliances, survival at water, crisis radio gear and helicopter assistance. The idea component is accompanied by a written exam. The rest regarding the course is invested in a swimming pool doing the simulation of abandon ship procedures. The practical workouts are subject to ongoing assessment by the teacher.
This can be a one day training course which will be instructed to STCW Code A – VI/1-4 as revised because of the 2010 Manila Amendment. This course covers working relationships on board, health insurance and hygiene, drugs and alcohol, shipboard management framework and duties, problems and safe working practices, with enhanced coverage of communications, control of weakness, teamwork and marine ecological awareness issues. The program is followed by a written exam.
So how exactly does the LYG system work?
Luxury Yacht Group introduces businesses to crew through a balance of technology and personal contact. Our web site is database driven, therefore it is in your most useful interest to help keep your internet based subscription updated. Additionally, our staff coordinators have the capacity to familiarizes you with numerous employers so approach all interacting with each other with increased standard of decorum.
Login to our site frequently and check your My Jobs web page. Our database will alert one to available tasks for which you could be an appropriate applicant.


A write-up about 50th mens birthday cakes.
I've tried milk, buttermilk, a combination of unique things and I really like using sour cream. Additional ingredients like chopped walnuts or oats could possibly be included with give you the base a crunchy taste. That's every time an alternative will be able to assist you.


The Reunion will take place in an air conditioned pavilion. We ask attendees to bring a covered dish for lunch. Meat and beverages are provided.
There will be a brief business meeting, genealogy discussion & bingo. Small bingo prizes are accepted (value $3-5). It is a great opportunity to meet extended family.
Maps will be provided from the picnic to Ft Zeller in Newmanstown. Learn more about Fort Zeller, built by Heinrich Zeller in 1945.
Please reply to Hoffman1bb@comcast.net or 717-222-1705 by July 3.


There has been a change to the event schedule for Riverbender Cyclcross Saturday, Oct 12, 2013. While this doesn't affect many of you, please be sure to read the revised schedule and know your start time.
Awards asap following each category!
Call-ups will be done 5 min before start.
Results will continue to have Novice and Sport separated for points & prizing. Tech Guide is available here.
Please email or pm me if you want the original digital file to print. It's free! Buy me a beer if you like it, or if underage, get a chocolate bar.