text:
\section{INTRODUCTION}

Localization is a crucial step to achieving autonomous movement. The literature shows different techniques, such as the Ultra-Wideband (UWB) localization approaches. For example, the authors in \cite{lidar_nlos} proposed a UWB NLOS identification approach using the LiDAR point cloud. With the LiDAR point cloud map, NLOS ranging measurements can be identified by judging whether obstacle occlusions exist between the mobile tag and anchor. Authors in \cite{monocular} proposed an enhanced tightly coupled sensor fusion scheme using a monocular camera and UWB ranging to perform Simultaneous Localization And Mapping (SLAM). UWB is a robust solution in Global Navigation Satellite System (GNSS) denied environments. It can provide accuracy up to a centimetre level, making it ideal for replacing complex and expensive motion-capture approaches such as with the LiDAR or monocular camera \cite{UWB_multiuav}.

Research work to utilize UWB in various localization applications is shown in the literature. Localization can be achieved with UWB modules installed on quadcopters to actively record ranging information, which can be exchanged between different quadcopters to determine the position of its neighbouring Unmanned Aerial Vehicle (UAV) relative to itself. To account for the lack of information from distance-only measurements for the relative position estimate, a combination of nonlinear and linear trajectory \cite{Quadcopter_uwb} was utilized.

A similar approach proposes using multiple ground robots and achieves relative localization. By determining the position of the other robots relative to itself, the approach then

\begin{figure}[H]
\centering
\includegraphics[width=8.7cm, height=5.0cm]{images/stfig1}
\caption{Overview of the problem where UWB tag on robot may receive longer ranging measurements due to NLOS conditions. This will affect localization accuracy if not mitigated.}
    \label{fig:mesh1}
\end{figure}

\noindent fuses the optimized pose with odometry information \cite{zhiqiang}. Authors in  \cite{Graph_optimization_UWB} used UWB ranging measurements in a graph optimization approach with an outlier rejection algorithm to mitigate the effects of NLOS signals. However, the outlier rejection was achieved by setting fixed criteria in an inequality equation, which may not generalize well for different NLOS scenarios. While both these works produce significant improvements for relative localization, not dealing with Non-Line-Of-Sight (NLOS) situations effectively may cause the results to degrade significantly.

Recent research shows the popularity of UWB for positioning, especially within the robotics community, due to its low cost, low power consumption, and small size. Robust localization can be achieved through the use of UWB ranging measurements fused with data from onboard sensors such as the Inertial Measurement Unit (IMU) through an Extended Kalman Filter (EKF) and utilizing UWB based communication to transfer the quadcopter's orientation as proposed in \cite{RL_UWB_EKF_IMU}. UWB ranging measurements are also fused with visual odometry to remove visual drift and improve robustness through a pose graph optimization from visual tracking constraints and the proposed smoothness and range constraints \cite{UWB_fastloc_mapping}. Although UWB can provide up to centimetre-level accuracy and a maximum reading range of up to 100 metres, NLOS measurements could significantly degrade localization results. Figure \ref{fig:mesh1} shows a scenario where the robot will receive measurements from UWB anchors which are blocked by obstacles. UWB measurements are based on the signal Time Of Arrival (TOA) or Time Difference Of Arrival (TDOA) \cite{NLOS_lowcostuwb}. Obstacles and occlusions in the environment would cause the TOA to be much larger than it is, leading to a longer ranging measurement. On the other hand, the TDOA could be smaller. This error in ranging measurement due to NLOS conditions causes sub-optimal localization results. Removing these measurements would not be ideal as it may cause significant errors since not enough measurements were considered for localization, making it essential to identify these NLOS measurements and mitigate their effect on localization.

The effort to mitigate NLOS range measurements to improve localization accuracy can be seen in the literature. For example, \cite{Graph_optimization_UWB} proposes an outlier rejection algorithm for NLOS scenarios. This was done by setting criteria in an inequality equation to determine if the range measurement satisfies this equation. The range constraint is rejected and not included in the cost function if the inequality is satisfied. However, excluding all measurements deemed NLOS may result in worse results because there may not be enough range measurements to get an accurate relative estimation of the robot position. Instead, the ranging measurements deemed NLOS should be mitigated and included in localization.

Therefore, this paper proposes an approach to identify NLOS ranging measurements by giving a probability to each ranging measurement used for localization through Weighted-Least-Square (WLS). We first collected data between a UWB tag carried on a moving robot and fixed UWB anchors in the environment in NLOS conditions, followed by Line-Of-Sight (LOS) conditions. This data is used to train a NN model capable of computing the probability that a ranging measurement is LOS. To verify the localization accuracy, the model trained previously is then used in a series of experiments on the data collected in different environments by using the probability of ranging measurement being LOS computed by the NN model in a WLS localization and comparing the results to the Non-Weighted Least-Square (NWLS) localization. The effects of having more NLOS mitigated are also evaluated in the corridor environment. The contributions of this paper are summarized as follows:
\begin{itemize}
\item We built a NN model trained using the data we collected and labelled according to its LOS and NLOS conditions, which returns a probability of the ranging measurement being LOS based on the UWB data provided.
\item We performed WLS localization using the probabilities generated for each ranging measurement through our NN model and compared the results with NWLS localization.
\item We have conducted experiments (in office, lobby and corridor environments) to evaluate the accuracy of the proposed approach and improve the localization accuracy by 16.93\% on the lobby testing data and 27.97\% on the corridor testing data using the NN model trained with all extracted inputs from the office training data.
\end{itemize}
The remainder of this paper is as follows: Section II introduces the related work. Section III introduces the method and design of our approach. Extensive experiments were carried out in Section IV to validate the effectiveness of the proposed approach. Finally, we conclude in Section V.

\section{Related Work}
Research and literature in the robotics field show a growing interest in navigation and path planning localization. The use of the Global Positioning System (GPS) cannot be applied indoors as the signals from satellites are efficiently diffracted by surrounding buildings and obstructions \cite{NLOS_WLS_CS}. This leads to a growing interest in research focused on localization in GPS-denied environments using UWB devices with different methods of mitigating the NLOS errors. For example, authors in \cite{Range_beacon} proposed a robust range-only beacon localization through trilateration and implementing a voting scheme where each consistent measurement pair "votes" for its solution. The most significant number of votes will be taken as the location estimate. However, the method is not robust to NLOS scenarios which could degrade localization results.

Authors in \cite{NLOS_WLS_CS} proposed to model and characterize amplitude and delay statistics of UWB channels and identify NLOS signals using the model. WLS localization is then performed to evaluate their performances. Authors in \cite{NLOS_DL} proposed to employ a method based on Convolutional Neural Network (CNN) to extract non-temporal features from raw channel impulse response (CIR) signals from the UWB and Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) to classify LOS/NLOS signals. Finally, authors in \cite{UWB_Trilateration} proposed a robust trilateration method by using a confidence-based trilateration method using data from the Multipath Fading Channel (MPF). However, data from channel statistics may not be available in many scenarios in low-cost UWB devices.

Authors in \cite{NLOS_lowcostuwb} proposed an NLOS identification and mitigation technique for low-cost UWB devices using different machine learning algorithms on data that were collected specifically for two situations when in NLOS. Authors in \cite{NLOS_IMU_UWB} proposed an NLOS error compensator using the principle of inertia through a "virtual inertial point" built from the IMU and the motion trend of the UWB. Authors in \cite{NLOS_Sensornode_loc} proposed a New Hypothesis Test method for NLOS identification and mitigation by comparing the Mean Square Error (MSE) of the range estimates with the variance of the LOS range estimates. However, these approaches can replace their NLOS mitigation techniques with a NN model to classify signals better without complex architectures.

\section{Proposed NLOS Identification Method \& Design}
An overview of the approach is shown in Figure \ref{fig:mesh2}. In this section, we will explain the input data extracted from the UWB tag carried by the moving robot, which consists of the distance measurement, the Residual Received Signal Strength Indicator (RxRssi), the First Path Received Signal Strength Indicator (FpRssi), the difference between Received Signal Strength (RSSD), and the Standard Deviation (STD) of ranging measurements made between a 0.5-second window, between UWB tag carried by the moving robot and fixed UWB anchors in the environment. These input data will be fed

\begin{figure}[H]
\centering
\includegraphics[width=8.8cm, height=5cm]{images/ovd}
\caption{Overview of NLOS mitigation localization process.}
    \label{fig:mesh2}
\end{figure}
\noindent into our trained NN model to generate the probability of the ranging measurement being LOS, which refers to a number between 0 to 1, used as the weight for the ranging measurement in the WLS localization.

\subsection{System Overview}
UWB plays an essential role in range-only localization in GPS-denied areas. However, even though UWB is robust to NLOS scenarios due to its ability to penetrate different materials such as walls, metals, and liquids \cite{UWB_adapted_error_map}, NLOS measurements will return a larger ranging measurement, which will affect localization accuracy. Therefore, NLOS measurements need to be identified and mitigated.

Low-cost UWB devices do not provide data from channel statistics used in other NLOS ranging mitigation approaches. Therefore, the NN model in this work can be applied in different environments to mitigate the effects of NLOS ranging measurements, using only data available from low-cost UWB devices. The model returns a probability of the ranging measurement being LOS, based on the UWB data extracted from the UWB node carried by the robot and fed into the NN model. The probability, distance measurement and known UWB anchor locations are then used in a WLS localization process to estimate the robot's position.

\subsection{Neural Network Model}
The NN model used in this paper was built using Tensor Flow, an open-source deep-learning library for building \cite{TensorFlow}.
UWB data collected from the UWB tag carried by the moving robot in different environments is used as the model's input vector. The input data then goes through the fully connected layers, and finally, the probability of ranging measurement being LOS is received at the output layer.
\subsubsection{Input Features}
The input layer takes in data extracted from the UWB node attached to the robot as it traverses the environment. As shown in Figure \ref{fig:mesh2}, the feature extraction process refers to the use of the RxRssi, which refers to the relative power present in the received signal, and FpRssi, which refers to the power present in this first signal detected, at each timestamp to calculate the RSSD, and using the timestamp and ranging measurement to calculate the STD within a 0.5-second window. Experiments were carried out with the
\begin{figure}[H]
\centering
\includegraphics[width=9.0cm, height=7.0cm,left]{images/Inputheatmap}
\caption{Correlation of input data vs ranging error}
    \label{fig:idvre}
\end{figure}

\noindent NN models trained with different combinations of input data. Input data includes the RxRssi and FpRssi extracted from the UWB node at every timestamp, the RSSD, which is also a good indicator for LOS/NLOS conditions, and lastly, the STD of ranging measurements made within a 0.5-second window between the UWB node attached to the robot and each anchor as a higher STD will indicate that there is an NLOS ranging measurement \cite{Survey_NLOS_TOA}. The relationship between the inputs and the ranging error is represented as Spearman's coefficient and expressed in a heatmap in Figure \ref{fig:idvre}. It assesses how well two variables correlate to each other with a monotonic function \cite{corelation}. Spearman's coefficient ranges from -1 to +1, where -1 indicates a perfect negative relationship, 0 indicates no correlation and +1 indicates a perfect positive relationship. A positive relationship indicates that the other variable increases as one variable increases. A negative relationship indicates that the other variable decreases as one variable increases. For example, Figure \ref{fig:idvre} shows that FpRssi and RxRssi have a negative monotonic relation with ranging error.
In contrast, RSSD and STD of ranging have a positive monotonic relationship with ranging error. The combination of inputs chosen through experimentation will go through the Fully Connected layer (FCN), which uses the Rectified Linear Units (ReLU) activation function. Finally, the output layer uses the Sigmoid activation function, which outputs a value between zero to one. This is mainly used in models to predict the probability as an output, which can be used as weights for the localization algorithm.

\subsubsection{Model Architecture}
Different hyperparameters were experimented with during training to select the proper hyperparameters for the NN model. In particular, the number of hidden layers, neurons per hidden layer, learning rate and the number of epochs trained. The results were evaluated based on the model's accuracy and precision on test data. The classification results with different hyperparameters are listed in Table \ref{modelarchitecture} below.

\begin{table}[H]
\caption{Evaluation of hyperparameters for NN model.}
\begin{center}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Hidden Layers} & \textbf{Neurons}& \textbf{Epochs}& \textbf{Accuracy}& \textbf{Precision}
\\
\hline
 3 & 100 & 100 & 79.96 & 0.7886 \\
\hline
 3 & 300 & 50 & 83.91 & 0.8542\\
\hline
 15 & 100 & 100 & 81.75 & 0.8265\\
\hline
 10 & 300 & 50 & 82.64 & 0.8288\\
\hline
 10 & 300 & 300 & 93.29 & 0.9321\\
\hline

\end{tabular}
\label{modelarchitecture}
\end{center}
\end{table}

Using the best hyperparameters, which produce the best accuracy and precision, as shown in Table \ref{modelarchitecture}, four different models which take different combinations of inputs were trained for NLOS mitigation experimentation.

\subsection{Weighted Least-Square Localization}
The NLOS identification results from the NN model was used to improve the localization results. In this section, we present the WLS localization technique to mitigate the effects of NLOS ranging measurements. By considering the known UWB anchor positions, we can express the WLS estimate of the robot's position as follows \cite{NLOS_WLS_CS}:

\begin{equation}
\hat{x}= \operatorname*{argmin}_{\mathbf{\emph{x}}=(x,y)} \{ \sum_{i=1}^{N} (\beta_i\hat{d_i} -\parallel \mathbf{\emph{x}}-\mathbf{X}_i\parallel)^2 \} \label{eq}
\end{equation}

where \textbeta\textsubscript{\emph{i}},  is the weight for the ranging measurement derived from the probability of a ranging measurement being LOS computed by the NN model, \^{d}\textsubscript{\emph{i}} refers to the ranging measurement to the \emph{i}\textsuperscript{th} anchor, \emph{x} refers to the robot position to be minimized, X\textsubscript{\emph{i}} refers to the location of the \emph{i}\textsuperscript{th} anchor, and \emph{N} is is the number of anchors in the environment. Minimizing \eqref{eq} is done through numerical search methods like Levenberg-Marquardt or Newton's method. In this experiment, Trust Region Reflective (TRF) algorithm was used as it is a generally robust method. To avoid convergence to local minima of the loss function, the constraints and initial guess must be close to the solution \cite{NLOS_WLS_CS}. Therefore, for evaluation purposes, the known starting position of the robot is used for localization, and every estimate for the previous position is used as the initial position for the next estimate.

\section{Experimental Results}

Experimental results presented here are performed on different environments from where the NN model was trained to demonstrate the effectiveness of mitigating NLOS ranging measurements for localization. In addition, results were compared to ground truth obtained with the use of RPLiDAR A3 to perform Adaptive Monte Carlo Localization (AMCL) \cite{amcl} given a map created through GMapping \cite{gmapping}.

Experiments were carried out using corridor test data with a different number of UWB anchors to test the effects of mitigating more NLOS ranging measurements and including them in localization.

\subsection{Measurement Campaign}
\subsubsection{Training Data -- Office and Lobby}
In this work, we carried out a measurement campaign for training using low-cost UWB devices in the office and lobby

\begin{figure}[H]
\centering
\includegraphics[width=7cm, height=6cm]{images/setup_exp}
\caption{Overview of data collection. LOS data was collected along this path, while NLOS data was collected in a path obstructed by the wall.}
    \label{fig:mesh3}
\end{figure}

\noindent of Building 3 at the Singapore University of Technological Design (SUTD), Singapore.

The training data was collected using LinkTrack UWB devices, a low-cost UWB that uses TDOA protocol that provides IMU information and measures up to 100 metres with a 50Hz sampling rate. As Channel Impulse Response (CIR) must be extracted from the chip, which adds complexity to the system \cite{NLOS_lowcostuwb}, our approach focuses mainly on data readily available from the UWB. Therefore, we only use data recorded by the UWB devices: the timestamp, RxRssi, FpRssi, and distance measurement. Data collected is separated between NLOS and LOS scenarios. For LOS measurements, the robot carrying a UWB node moves along a path with no obstructions collecting UWB data from UWB anchors within LOS as shown in Figure \ref{fig:mesh3}. For NLOS measurements, the UWB anchors were obstructed from the robot during data collection. Data collected were then labelled accordingly at each timestamp, with LOS measurements labelled as 1 and NLOS measurements labelled as 0.
Each ranging data recorded consists of a timestamp, the distance measurement, RxRssi, FpRssi, the RSSD values, the STD of ranging measurements made within a 0.5-second window, and the LOS/NLOS indicator.

Different inputs were used to train the NN model to determine the inputs that produce the best results.

\subsubsection{Testing Data -- Office, Lobby and Corridor}
Testing data was collected in the office, lobby, and corridor environments. Data collected in this section was used to test the NN model after being trained in a different environment. The robot traversing around the environment carries a single LinkTrack UWB node. Fixed UWB anchors are placed around the environment with known locations. The robot collects ranging measurements, RxRssi, FpRssi and timestamp data from detected UWB anchors as it travels around different environments. At the same time, AMCL poses data is also collected with the help of a map of the environment built through GMapping to be used for ground truth and evaluation purposes.

Unlike training data collection, testing data was not separated between LOS or NLOS measurements and therefore contains both LOS and NLOS measurements.

\subsection{Lobby Test Data Results Using NN Model Trained Using Office Training Data}

\begin{figure}[H]
\centering
\includegraphics[width=4cm, height=5cm]{images/lobby}
\caption{Map of lobby environment using GMapping algorithm with 2D-LiDAR.}
    \label{fig:mesh6}
\end{figure}

The NN model in this section was trained using the office training data mentioned in Section IV.A1 and tested using the lobby testing data mentioned in Section IV.A2. The lobby environment, as shown in Figure \ref{fig:mesh6} is wider as compared to the office environment where the model was trained.

There is a large wall in the lobby environment where the robot traverses. The wall causes much more ranging measurements to be NLOS due to the signals from the UWB anchors being reflected off the walls to reach the UWB node carried by the robot or the fixed UWB anchors placed around the lobby environment.

Different combinations of inputs from the office training data were used to train the NN model to determine which inputs produced the best results. The best results according to Table \ref{tab2} and Figure \ref{fig:cdf2} for NWLS vs WLS localization is as shown in Figure \ref{fig:lobby1}. As seen from Table \ref{tab2}, all WLS approaches using different inputs to train the NN model produced improvements in the localization results.

There is a 16.93\% improvement in the estimated position error in terms of metres for WLS localization using the NN
\begin{table}[H]
\caption{Localization results of WLS localization using NN model trained with different combinations of inputs from office training data vs NWLS localization tested on lobby testing data with 5 UWB anchors.}
\begin{center}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Approach}&\multicolumn{4}{|c|}{\textbf{Error in metres}} \\
\cline{2-5}
\textbf{Used} & \textbf{\textit{Mean}}& \textbf{\textit{Median}}& \textbf{\textit{SD}}& \textbf{\textit{Improvement}}
\\
\hline
NWLS& 2.79& 2.65& 1.43& Nil	 \\
\hline
WLS All inputs& 2.32& 2.01& 2.03& 16.93\% \\
\hline
WLS No FpRssi& 2.35& 2.03& 2.05& 16.06\% \\
\hline
WLS No RxRssi& 2.33& 2.04& 2.00& 16.65\% \\
\hline
WLS No RSSD& 2.37& 2.07& 1.92& 15.34\% \\
\hline
WLS No STD& 2.44& 2.06& 2.16& 12.75\% \\
\hline

\end{tabular}
\label{tab2}
\end{center}
\end{table}

\begin{figure}
  \centering
  \subfigure[WLS using NN model trained using office training data with all inputs vs NWLS localization tested on lobby testing data using 5 UWB anchors.]
  {\includegraphics[width=8.0cm]{images/2}
  \label{fig:lobby1}
  }
  \subfigure[CDF for NN models trained with different combinations of input tested on lobby testing data with 5 UWB anchors.]{\includegraphics[width=8.0cm]{images/CDF5UWBLobbyResults} \label{fig:cdf2}}
  \caption{Lobby localization results with 5 UWB anchors} \label{fig:lobbyplots}
\end{figure}

\noindent model trained with all inputs compared to NWLS localization using AMCL pose as ground truth. Figure \ref{fig:cdf2} shows the Cumulative Distribution Function (CDF) for WLS and NWLS localization approaches using different models that indicate there is a higher probability of having a minor error with WLS localization as compared to NWLS. The results show that the NN model can mitigate the effects of NLOS ranging measurements and improve localization results for WLS localization compared to NWLS localization.
\subsection{Corridor Test Results Using NN Model Trained Using Office Training Data}
The NN model in this section was trained using the office training data mentioned in Section IV.A1 and tested using the corridor testing data mentioned in Section IV.A2. The approach was experimented on in a larger map as shown in Figure \ref{fig:mesh8}, which will cause fewer anchors to be detected for localization and more NLOS ranging measurements recorded at each timestamp. Experiments were carried out in this environment using 5, 8 and 9 UWB anchors placed
\begin{figure}[H]
\centering
\includegraphics[width=4cm, height=5cm]{images/corridor}
\caption{Map of corridor environment using GMapping algorithm with 2D-LiDAR.}
\label{fig:mesh8}
\vspace{-0.3cm}
\end{figure}

\begin{figure}[H]
  \centering
  \subfigure[WLS using NN model trained using office training data with all inputs vs NWLS localization tested on corridor testing data using 5 UWB anchors.]{\includegraphics[width=8.0cm, height=6.0cm]{images/3}
  \label{fig:corridor1}} \\
      \subfigure[CDF for NN models trained with different combinations of input tested on corridor testing data with 5 UWB anchors.]{\includegraphics[width=8.0cm, height=6.0cm]{images/CDF5UWBCorridorResults} \label{fig:cdf3}}\\
  \caption{Corridor localization results with 5 UWB anchors} \label{fig:corridorplots1}
\end{figure}

\noindent in the environment. For experiments carried out with 5 UWB anchors placed in the environment, as seen from Table \ref{tab3}, all WLS approaches using different combinations of input extracted from the office testing data to train the NN model produced improvements in the localization results. Using the

\begin{table}[H]
\caption{Localization results of WLS localization using NN model trained with different combinations of inputs from office training data vs NWLS localization tested on corridor testing data with 5 UWB anchors.}
\begin{center}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Approach}&\multicolumn{4}{|c|}{\textbf{Error in metres}} \\
\cline{2-5}
\textbf{Used} & \textbf{\textit{Mean}}& \textbf{\textit{Median}}& \textbf{\textit{SD}}& \textbf{\textit{Improvement}}
\\
\hline
NWLS& 3.41& 2.88& 2.47& Nil	 \\
\hline
WLS All inputs& 2.45& 2.36& 1.39& 27.97\% \\
\hline
WLS No FpRssi& 2.65& 2.43& 1.64& 22.09\% \\
\hline
WLS No RxRssi& 2.35& 2.26& 1.23& 31.10\% \\
\hline
WLS No RSSD& 2.36& 2.28& 1.38& 30.57\% \\
\hline
WLS No STD& 2.52& 2.45& 1.36& 25.11\% \\
\hline
\end{tabular}
\label{tab3}
\end{center}
\end{table}

\begin{table}[H]

\caption{Localization results of WLS localization using NN model trained with different combinations of inputs from office training data vs NWLS localization tested on corridor testing data with 8 UWB anchors.}
\begin{center}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Approach}&\multicolumn{4}{|c|}{\textbf{Error in metres}} \\
\cline{2-5}
\textbf{Used} & \textbf{\textit{Mean}}& \textbf{\textit{Median}}& \textbf{\textit{SD}}& \textbf{\textit{Improvement}}
\\
\hline
NWLS& 2.97& 2.64& 1.96& Nil	 \\
\hline
WLS All inputs& 1.72& 1.23& 1.53& 41.98\% \\
\hline
WLS No FpRssi& 1.75& 1.25& 1.45& 41.14\% \\
\hline
WLS No RxRssi& 1.68& 1.21& 1.46& 43.49\% \\
\hline
WLS No RSSD& 1.66& 1.20& 1.43& 44.05\% \\
\hline
WLS No STD& 1.70& 1.24& 1.36& 42.81\% \\
\hline
\end{tabular}
\label{tab4}
\end{center}
\end{table}

\begin{table}[H]
\caption{Localization results of WLS localization using NN model trained with different combinations of inputs from office training data vs NWLS localization tested on corridor testing data with 9 UWB anchors.}
\begin{center}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Approach}&\multicolumn{4}{|c|}{\textbf{Error in metres}} \\
\cline{2-5}
\textbf{Used} & \textbf{\textit{Mean}}& \textbf{\textit{Median}}& \textbf{\textit{SD}}& \textbf{\textit{Improvement}}
\\
\hline
NWLS& 3.39& 3.29& 1.75& Nil\\
\hline
WLS All inputs& 1.42& 1.18& 0.91& 58.02\%\\
\hline
WLS No FpRssi& 1.53& 1.45& 0.92& 54.79\%\\
\hline
WLS No RxRssi& 1.50& 1.41& 0.91& 55.72\%\\
\hline
WLS No RSSD& 1.50& 1.39& 0.92& 55.62\%\\
\hline
WLS No STD& 1.55& 1.45& 0.91& 54.15\%\\
\hline
\end{tabular}
\label{tab5}
\end{center}
\end{table}

\noindent NN model trained with all inputs, a 27.97\% improvement in the estimated position error in terms of metres was achieved for WLS localization compared to NWLS localization.
The results of the experiment using 5 UWB anchors in the environment according to Table \ref{tab3} is as shown in Figure \ref{fig:cdf3}. Figure \ref{fig:cdf3} shows that the probability of minor error in localization for WLS approaches is higher than that of NWLS localization. Figure \ref{fig:corridor1} shows the localization results using the NN model trained with all inputs compared to NWLS localization. This shows that our approach can improve localization results by using the weights computed by the NN model. Furthermore, our approach includes all NLOS ranging measurements, which was mitigated through a WLS localization as excluding NLOS ranging measurements from localization could produce worse results. For experiments conducted with 8 UWB anchors as seen in Table \ref{tab4} and 9 UWB anchors as seen in Table \ref{tab5}, all WLS approaches using different inputs to train the NN model produced improvements in the localization results. The results for the WLS localization using the model trained with all inputs improved by 41.98\% with 8 UWB anchors and improved by 58.02\% with 9 UWB anchors compared to NWLS localization.

\subsection{Office And Corridor Test Results Using NN Model Trained using Lobby Test Data}
The NN model in this section was trained using the lobby training data mentioned in Section IV.A1 and tested using the lobby and corridor testing data mentioned in Section IV.A2. A model was trained using LOS and NLOS data collected from lobby training data to prove that the approach can improve localization accuracy. The data was collected separately and labelled accordingly, as mentioned in Section IV-A1. The NN model trained with all inputs shown in Table \ref{tab:E2} shows localization accuracy improving by 43.64\% with WLS on the office testing data. There is also a significant improvement on the corridor testing data, as shown in Table \ref{tab:E3}.

\begin{table}[H]
        \caption{Localization results of WLS localization using NN model trained using lobby training data with all inputs vs NWLS localization tested on office testing data with 5 UWB anchors.}
        \centering
        \begin{tabular}{|c|c|c|c|c|}
        \hline
        \textbf{Approach}&\multicolumn{4}{|c|}{\textbf{Error in metres}} \\
        \cline{2-5}
        \textbf{Used} & \textbf{\textit{Mean}}& \textbf{\textit{Median}}& \textbf{\textit{SD}}& \textbf{\textit{Improvement \%}}
        \\
        \hline
        NWLS& 2.02& 1.98& 0.67& Nil\\
        \hline
        WLS & 1.14& 0.95& 0.65& 43.64\%\\
        \hline
        \end{tabular}
        \label{tab:E2}
        \hfill
        \end{table}

    \begin{table}[H]
        \caption{Localization results of WLS localization using NN model trained using lobby training data with all inputs vs NWLS localization tested on corridor testing data with different number of UWB anchors.}
        \centering
        \begin{tabular}{|c|c|c|c|c|}
        \hline
        \textbf{Approach}&\multicolumn{4}{|c|}{\textbf{Error in metres}} \\
        \cline{2-5}
        \textbf{Used} & \textbf{\textit{Mean}}& \textbf{\textit{Median}}& \textbf{\textit{SD}}& \textbf{\textit{Improvement}}
        \\
        \hline
        NWLS 5 UWB& 3.41& 2.88& 2.47& Nil\\
        \hline
        WLS 5 UWB& 2.99& 2.75& 2.09& 12.20\%\\
        \hline
        NWLS 8 UWB& 2.97& 2.64& 1.96& Nil\\
        \hline
        WLS 8 UWB& 2.09& 1.53& 1.99& 29.68\%\\
        \hline
        NWLS 9 UWB& 3.39& 3.29& 1.75& Nil\\
        \hline
        WLS 9 UWB& 2.04& 1.77& 1.49& 39.80\%\\
        \hline
        \end{tabular}
        \label{tab:E3}
     \end{table}

\section{Conclusion}
We proposed an approach to mitigate NLOS ranging measurements for UWB-only localization with a NN Model trained and experimented with in different environments. We showed the effectiveness of mitigating NLOS ranging measurements and including them in localization. Our NN models were trained using UWB data extracted from the office and lobby environments. Experiments with the NN model trained using office training data and tested with lobby testing data improved localization results by 16.93\% for the case of 5 UWB anchors. Experiments using corridor testing data showed an improvement of 27.97\% for the case of 5 UWB anchors, 41.98\% for the case of 8 UWB anchors and 58.02\% for the case of 9 UWB anchors. Experiments with the NN model trained using lobby training data and tested on the office and corridor testing data showed significant improvements in localization accuracy.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
Q: Use microsoft.applicationinsights.tracelistener for ASP.Net Core 3.1 application I have installed Microsoft.ApplicationInsights.AspNetCore in my ASP.Net Core 3.1 web application and did setup using next code:
public void ConfigureServices(IServiceCollection services)
{
    services.AddApplicationInsightsTelemetry("aiInstrumentationKey");
}

After tracing some output string into Trace.Information("Test trace string") found it outputs to console, but not into application insights traces. Found that it possible for microsoft.applicationinsights.tracelistener to fix this issue, but haven't found resources that explicitly saying how to configure this.

A: Solution is fairy simple: just add Trace.Listeners.Add(new ApplicationInsightsTraceListener("aiInstrumentationKey")); after application insights telemtry setup:
public void ConfigureServices(IServiceCollection services)
{
    services.AddApplicationInsightsTelemetry("aiInstrumentationKey");
    Trace.Listeners.Add(new ApplicationInsightsTraceListener("aiInstrumentationKey"));
}

redpajama_set_name: RedPajamaStackExchange


text:
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Jazz History Online
Sidetracks
Vocal Intimacy
CD Reviews	by Thomas Cunniffe March 5, 2019 April 20, 2019
KARRIN ALLYSON: "MANY A NEW DAY" (Motéma 183)
Continuing with the concept albums that have worked so well for her in the past, Karrin Allyson's new recording "Many a New Day" is a collection of songs from musicals by Richard Rodgers and Oscar Hammerstein. The R&H catalog has been ridiculed in recent years for being corny and old-fashioned, but Allyson, pianist Kenny Barron and bassist John Patitucci mine gold from this rich (and at its time, revolutionary) mother lode of songs. They generate a soulful groove on the opening "Oh, What a Beautiful Morning" and a happy, skipping swing on the title track (which leads to a very effective ritardando at the coda). Allyson adds a wordless intro and a lesser-known lyric to the lively "Happy Talk" and via a slow, sexy version of "I Cain't Say No", she adds psychological depth to the usually comic character of Ado Annie—a role which Allyson admits she'd love to play. Barron plays a gorgeous solo on "I Have Dreamed," which also features a warm vocal (and the rarely-heard verse). It's good to hear Patitucci on acoustic bass, where he displays a rich tone and solid support on solo turns during "Out of My Dreams" and "Bali Ha'I". The earnestness that Allyson brings to this repertoire makes Hammerstein's lyrics and Rodgers' music contemporary, especially when dealing with eternal topics like young love ("Hello, Young Lovers") or continuing social problems like forbidden love ("We Kiss in a Shadow") and racial prejudice ("You've Got to Be Carefully Taught"). Still, Allyson, Barron and Patitucci have a little fun with "The Surrey with the Fringe on Top" where each phrase modulates to a new key, and Allyson sings her only scat solo on the entire CD. Overall, "Many a New Day" is more restrained than Allyson's previous albums but the intimacy of the ensemble and Allyson's moving performances make this recording a welcome addition to her discography.
LAURIE ANTONIOLI & RICHIE BEIRACH: "VARUNA" (Origin 82698)
Varuna is a dwarf planet located near Pluto, and although it was only discovered about fifteen years ago, it has already become the subject of an original song by vocalist Laurie Antonioli and pianist Richie Beirach. The lyrics touch upon the mythological and astronomical meanings of both the planet and its namesa ke, and the song's mystical, out-of-time setting is a perfect opener for the second duet album by Antonioli and Beirach. "Varuna" (the album) displays a wide range of music styles from the straight-ahead swing of Harry Warren's "Summer Night" through the free-form "Resolution Suite". The suite (which also includes bassist Pepe Berns) is probably the most challenging piece on the album, but it is still accessible: "Revelation" includes an intensely whispered poem over instrumental improvisation; "Transformation" starts with a whisper but grows into full voice; "Exaltation" is a trance-like wordless improvisation which sounds like it might have part of a longer track. Another echo of the avant-garde appears on a further track: In a brief interpolation of Scriabin's "Prelude in E-Flat Minor", Antonioli borrows a technique from composer George Crumb, as she sings into the piano as Beirach plays and holds down the damper pedal, allowing the combined vibrations of voice and piano to accumulate. This episode leads into a stunning rendition of "My Funny Valentine" which shows that this old standard still has a lot of life within it. The final three tracks of this album are quite special. Antonioli brings a world-weariness to the standard "Haunted Heart". Beirach plays remarkable solos on this track and the following "Impressions", both standing as fine statements in their own right, but quite striking in their differences of style and execution. The album concludes with a unique version of "Over the Rainbow" which re-defines the nature of both the music and the lyric. It is a total reconstruction of the song, similar to the classic 1961 version of "Laura" created by Jeanne Lee and Ran Blake, where all previous renditions seem trivial and out-dated. "Varuna" is a challenging program, but it is also a fascinating disc that will reward repeated listening.
SINNE EEG & THOMAS FONNESBAEK: "EEG-FONNESBAEK" (Stunt 15082)
Danish vocalist Sinne Eeg (whose name is pronounced SEE-neh EE) has built a strong following across Europe and Asia, but she is virtually unknown to American audiences. Her new CD, "Eeg-Fonnesbaek" is the first of her albums to be issued in the US, and with any luck, it should stir greater interest in this superb musician. Eeg's soulful delivery, flawless pitch, exceptional diction, and impressive scat stand in strong relief to the sole accompaniment of Thomas Fonnesbaek's bass. While the promotional materials note that Eeg is also a fine songwriter, on this brief but satisfying album she focuses on standards and jazz originals. She truly understands the marriage of lyrics and melody, which is clearly evident in her stark rendition of "You Don't Know What Love Is". In that extraordinary track, every note seems to be perfectly placed with just the right inflections to bring out the hurt embedded in the lyric. Fonnesbaeck's open arpeggiated chords on electric bass are quite effective here, as is his overdubbed acoustic arco line in the introduction and coda. On the other hand, Eeg seems willing to take risks with the phrasing of songs like "Body and Soul" and "Come Rain or Come Shine", as she alternatively rushes and stretches the phrases of the lyrics to fit her conception of the melodies. This technique causes a few odd breaths within the phrases, but overall, these are still remarkable and fresh interpretations of these great standards. While there is no shortage of female jazz singers on the US scene, Sinne Eeg deserves to be heard by American audiences, and if she supports this new CD with a nationwide tour, the chances of greater recognition could easily become a reality.
ELISABETH LOHNINGER & WALTER FISCHBACHER: "BALLADS IN BLUE" (JazzSick 5085)
Procrastination is the enemy of art. There are numerous examples of projects never started because of schedule conflicts or misaligned priorities. Sometimes fate steps in and forces the action in spite of it all. Vocalist Elisabeth Lohninger and her husband, pianist Walter Fischbacher had talked about an all-ballads album for years, but for one reason or another (and Lohninger lists several of them in her liner notes) the project didn't become a reality until early this year, when the building which held their recording studio was sold, and the area had to be vacated. "Ballads in Blue" was the last recording made at Lofish studios, and it represents the end of a highly productive period for this couple. Whether or not they felt ready to tackle the challenge, Lohninger and Fischbacher have created a wonderful program covering songs from three generations of American Popular Song (plus one of Lohninger's originals, the lovely "I Remember Christmas"). Lohninger's previous recordings have been edgy and progressive, but here the innovations are turned inward as on "You Don't Know What Love Is" and "When I Fall in Love", where Lohninger creates subtle melodic variations over the tender sounds of Fischbacher's piano. Near the end of "So Many Stars", Lohninger gently enters with a wordless background to Fischbacher's solo. It sounds like something that might happen when the two were making music in their living room; it rarely occurs in the confines of the recording studio. And while the two have reharmonized songs like "All the Things You Are" and "Skylark", both Lohninger and Fischbacher embrace these new harmonies as readily as if the songs had been sung in this manner for decades. While there are no lyric changes to "I've Grown Accustomed to His Face", I wonder if this song is actually a farewell to the studio. Despite the unfortunate circumstances that caused this CD to be made, I'm glad that fate intervened. To my ears, it's the best album that Lohninger and Fischbacher have ever made.
NEW WEST GUITAR GROUP: "SEND ONE YOUR LOVE" (Summit 659)
Although the album "Send One Your Love" is credited to the New West Guitar Group (Jeff Stein, John Storie and Perry Smith), it actually spotlights the work of five singers: Gretchen Parlato, Sara Gazarek, Becca Stevens, Tierney Sutton and Peter Eldridge. Parlato leads off with the title track (composed by Stevie Wonder) and her delicate vocal is set off with a broken-time background which allows plenty of interplay between voice and instruments. Stevens sounds a little tentative on "Detour Ahead" but she expertly navigates the strong syncopations within the arrangement. Eldridge's version of Joni Mitchell's "Black Crow" builds in intensity with each succeeding verse, and Gazarek breaks your heart with her understated reading of "I Fall in Love Too Easily". The quick paced Sutton version of "You'd Be So Nice to Come Home To" features impressive unison lines by the guitars at the outset, joined by Sutton at the midpoint before she launches into a well-crafted scat solo. The guitars provide a gentle backdrop for Parlato's take on "Like Someone in Love" which features several intriguing note choices as she creates variations on the melody. Stevens sounds much more comfortable on Elliott Smith's "Waltz No. 1", but as with Parlato, she would have benefited from a little more presence in the overall mix. The biggest surprise on the album might be Eldridge's finger-snapping version of Kurt Weill's "My Ship", but the arrangement works due to the ingenious backbeat which sounds like water lapping on the side of a boat, and the rippling effects used in one of the bridge sequences. Sutton gets the penultimate spot with a touching version of Randy Newman's "When She Loved Me" and Gazarek's earnest version of James Taylor's "Secret o' Life" caps off the album on a philosophical note. My only criticism of this album falls with the packaging. While the arrangements are duly credited, there is no indication of the audio placement of the three guitars, nor any solo credits. Despite all of their guest stars, this is still the New West Guitar Group's album, and they deserve credit for their superb solo work.
Vocal Jazz Roundup
Vocal Jazz Roundup 3
Content copyright 2011-2021. JazzHistoryOnline.com. All rights reserved.

redpajama_set_name: RedPajamaCommonCrawl


The inhabitants in query will be as small as a handful of people or as massive as all the inhabitants of a number of continents (as an example, in the case of a pandemic ). Public health has many sub-fields, however usually consists of the interdisciplinary categories of epidemiology , biostatistics and health providers Environmental health , group health , behavioral health , and occupational health are additionally vital areas of public health.
The main focus of public health interventions is to forestall and handle illnesses, accidents and other health conditions through surveillance of cases and the promotion of healthy conduct , communities , and (in facets relevant to human health) environments Its intention is to stop health issues from occurring or re-occurring by implementing educational programs , creating insurance policies , administering providers and conducting analysis 53 In many instances, treating a illness or controlling a pathogen can be important to preventing it in others, reminiscent of throughout an outbreak Vaccination applications and distribution of condoms to forestall the unfold of communicable diseases are examples of common preventive public health measures, as are educational campaigns to advertise vaccination and the usage of condoms (together with overcoming resistance to such).
An inquiry into mental health companies in Tayside will go forward after Scottish ministers agreed to accept further safeguards to make sure its inquiry into psychological health services in Tayside will go forward after Scottish ministers agreed to just accept extra safeguards to make sure its inquiry into psychological health companies in Tayside will go forward after Scottish ministers agreed to accept additional safeguards to make sure its independence.


text:
Home Ben Dixon Show
CEOs Claim They Care About The Environment and Income Inequality
CEOs release a statement that suggests they are concerned with income inequality and that, perhaps, they will no longer place shareholder interests above the interests of the broader community. I'll believe it when I see it in action. Benjamin Dixon discusses.
http://thebenjamindixonshow.com/
The Benjamin Dixon Show is a progressive talk radio, YouTube, podcast. The inaugural episode aired February 9, 2015. Since that time, Benjamin has established his voice as significant contributor and thought leader in Progressivism. He has interviewed distinguished guests including Professor Eddie Glaude, Jr., Nina Turner, Shaun King, DeRay McKessen, Thomas Frank, Bakari Sellers and Adolph Reed. Benjamin also is the author of two books — God is Not a Republican & Lessons for My Son—and an eBook, Ten Life Changing Moments. Dixon is also the founder and editor of ProgressiveArmy.com. He holds a Bachelor's Degree in Economics, a Master's Degree in Political Science, and is approaching the end of his Doctoral program in International Development with a focus on International Security.


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Of the many active ingredients in marijuana, cannabinoids — the miracle molecules that deliver most of the plant's medical efficacy — are not the whole picture. Some cannabis consumers may be aware of terpenes, the cannabinoid-like chemicals that give herb such a pungent aroma.
What most do not know is that terpenes also deliver therapeutic relief, just like their cousins the cannabinoids.
Terpenes are produced in special secretory cells within the trichomes of the plant, the nearly microscopic resinous stalks that cover the flowers and sometimes leaves. This is also where all cannabinoids, like THC and CBD, are created. About 20,000 terpenes exist in nature; more than 200 have been identified in cannabis (compared to 111 cannabinoids).
Like amino acids, terpenes are powerful building blocks within the plant's physiology that aid in the production of vitamins, hormones, pigments, resins, and — yes, that most cherished part of the herb — cannabinoids. Cannabis plants release more terpenes when temperatures are higher.
Beyond odor, terpenes play several roles, including protecting the cannabis plant against predators like insects and animals. These special molecules constitute roughly 10 to 20 percent of the total pre-smoked resin in the trichome. It is estimated that 10 to 30 percent of smoke resin produced by marijuana comes from terpenes.
Limonene is a terpene that conveys a smell of citrus (orange, tangerine, lemon, lime, or grapefruit), juniper, rosemary, or peppermint. Lemons and other citrus fruits contain large quantities of limonene, especially in the rind. Thus, when one perceives an aroma of citrus in cannabis (or any other plant), it is literally the same chemical that produces this odor in fruits such as lemons, limes, and oranges.
While pleasant aromas are certainly desired on the part of recreational smokers, patients and the severely ill are more concerned with medicinal efficacy. Like myrcene, limonene has several medical benefits. It has been found to aid in digestion, to alleviate depression, and to contribute anti-bacterial and anti-fungal properties.
Limonene is also an anti-inflammatory, giving it efficacy for a number of conditions, and an anti-proliferative, meaning it is an effective anti-cancer medicine. The anti-cancer properties of limonene are what have generated the greatest media attention and are arguably the molecule's most promising trait.
This therapeutic terpene is known to help fight anxiety and even treats acid reflux (heartburn). Limonene also can enhance one's mood and has even been found to dissolve gallstones.
As with all areas of study of the medical qualities of cannabis, more research needs to be conducted to better understand the properties of terpenes like limonene. While human trials are in short supply, the fact that limonene does not appear on Schedule I of the Controlled Substances Act (unlike THC, CBN, and cannabis itself), it is easy to obtain and legal to use in studies.
A 2013 study conducted at the University of Arizona Cancer Center and published in the journal Cancer Prevention Research found that limonene was effective in preventing cancer cell proliferation and reducing tumor size. This human trial involved 43 women who had recently been diagnosed with breast cancer. A follow-on study in 2015 found similar results.
A study conducted in 2014 in France and published in the journal Anti-inflammatory and Anti-allergy Agents in Medicinal Chemistry reported that limonene has anti-inflammatory properties, making it valuable in the treatment of cancer (an inflammatory condition).
The study discovered that limonene possesses a "surprising" ability to prevent tumors from invading surrounding tissue by decreasing their ability to create new blood vessels. This powerful terpene was also found to play a role in the healing of skin and tissue regeneration.
OG Kush: A balanced hybrid strain that delivers total mind and body relaxation. It relieves pain and anxiety without causing drowsiness.
Lemon OG Kush: A fast-acting indica-dom that provides stress relief, including relaxation, appetite stimulation, and mood elevation (it fights depression).
Jack the Ripper: A potent hybrid that produces thick, harsh smoke and delivers intense euphoria, in addition to pain and stress relief.
Lemon Diesel: A balanced hybrid that first delivers a fast-acting sativa high, followed by indica-like body relaxation.
Lemon C4: A hybrid that delivers happiness and cerebral relaxation.
Sour Diesel: A potent sativa-dominant hybrid that provides relief for stress, anxiety, pain, and depression. It has long legs (is long lasting).
This post was originally published on July 23, 2015, it was updated on October 5, 2017.


Is Google Places an Effective Platform for Marketing a Local Business?
Google has dominated the global search engine market for years, but its progress in the local search arena has been less obvious. In 2010, it overcame an unsuccessful attempt to buy out Groupon, the local daily deal service, and went on to develop its own simple local business marketing platform that includes several separate features. Used alone, Google Places for Business, a local business directory service like an online phone book, is not a standout compared to its major competitors. So what makes it the most effective online option for putting your company's address and brand in front of local customers?
Google Places for Business is notably similar to its main competitor Bing Places for Business. It allows a business to register and appear in a local online search. Two key differences set them apart.
1. A Google Places listing will appear in the Google search engine, which holds 67 percent of the search market share. Bing Places appears in Bing. Combined with Yahoo, Bing Places reaches 33 percent. Since placing your businesses name in front of more clients is clearly the purpose behind marketing, if you were to select only one service, Google Places is the better option.
2. Google Places includes Google Maps, and shares a platform with Google AdWords and Offers. Together, these features elevate Google to the top position.
Ideally, after the registration process, your business details will appear on the page one results of a Google search for the category and location relevant to your business. In a search for a "wine shop in Columbus, Ohio," for example, the results will show "The Wine Bistro, 1750 Lane Ave" with a photo of the business and a customer review rating on a horizontal, scrolling display carousal at the top of the page, along with a dozen or so other listings.
The display carousal is a new feature that Google rolled out recently for particular business categories, but is expanding. Other categories would generally appear as a text and hyperlink list with a pinned Google Maps location. The display carousal, in addition to being more visually appealing and space efficient, allows the user to sort the listings by customer review ranking and the business's photo or logo appears with the address.
Registering for Google Places is free and easy. Aside from the mildly inconvenient step of waiting for a verification PIN, for about a week, to arrive in the mail to access the service's review, AdWords and Offers portals, the process is immediate.
The Bing Places for Business is, in all ways, similar to Google Places except that it doesn't include additional marketing services, such as Google AdWords and Google Maps, and its search engine experiences less traffic.
However, it is worth taking the time to register for the Bing Places service since there is no cost, and it reaches a different market. Yelp is another common local business directory. Yahoo's local business marketing service is also an option, but on Yahoo the premium services that Google and Bing Places offer, such as making photos available to users, requires paying a fee.
As with a website ranking in Google, there is no guarantee that registering with Google Places alone will result in your business appearing on page one of the search results.


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2020年海洋气象研究热点回眸
邱春华1,2, 王东晓1,2, 罗希1, 张婷1
1. 中山大学海洋科学学院, 珠海 519082;
2. 南方海洋科学与工程广东省实验室 (珠海), 珠海 519020
Review of hotspots of marine meterology in 2020
QIU Chunhua1,2, WANG Dongxiao1,2, LUO Xi1, ZHANG Ting1
1. School of Marine Science, Sun Yat-sen University, Zhuhai 519082;
2. Southern Marine Scienceand Engineering Guangdong Laboratory(Zhuhai), Zhuhai 519020
摘要 回顾了2020年影响东亚气候的梅雨锋、季风、台风、海气通量、风暴潮等天气过程与机制研究取得的重要进展,探讨了海洋气象学的未来发展方向。
邱春华
王东晓
罗希
张婷
关键词 : 海气相互作用, 气象灾害, 天气预报
Abstract:Marine meterology is important to global climate and meteorological disaster. We summarize the development in weather processes and mechanisms which influenced the climate of East Asia in 2020, including the Meiyu front, monsoon, tropical cyclone, air-sea fluxes, and storm surge. We also discuss the future hotspots in marine meterology.
Key words: air-sea interaction meterological disaster weather forecast
基金资助:南方海洋科学与工程广东省实验室(珠海)创新团队建设项目(311020004)
通讯作者: 王东晓(通信作者),教授,研究方向为海气相互作用及海洋环流,电子信箱:dxwang@mail.sysu.edu.cn E-mail: dxwang@mail.sysu.edu.cn
作者简介: 邱春华,副教授,研究方向为海洋环流,电子信箱:qiuchh3@mail.sysu.edu.cn
邱春华, 王东晓, 罗希, 张婷. 2020年海洋气象研究热点回眸[J]. 科技导报, 2021, 39(1): 115-125.
QIU Chunhua, WANG Dongxiao, LUO Xi, ZHANG Ting. Review of hotspots of marine meterology in 2020. Science & Technology Review, 2021, 39(1): 115-125.
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text:
Miss America turns 100 this week. Does anybody care?
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Can Disney Princess culture actually be positive for kids? A surprising new study says yes.
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A bronze statue to honor iconic suffragists Sojourner Truth, Susan B. Anthony and Elizabeth Cady Stanton will be unveiled in New York City's Central Park on August 26, 2020 to celebrate the centennial anniversary of the 19th Amendment ratification.
Amy Schumer on how she's raising son Gene to be a 'really strong feminist'
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ABSTRACT—The authors discuss certain limitations to the technique of consolidating porous paint in a vapor-saturated environment described by Hansen et al. in earlier articles. The treatment of a severely flaking gouache on paper by Karel Appel is described, including various tests carried out using the chosen consolidant, a solution of poly(vinyl acetate) AYAF in ethanol, both in and out of the vapor-saturated environment. The final tests failed to provide the desired effect, resulting in some discoloration and staining of the treated areas. The artist himself later was consulted and indicated that his technique had helped create the problems in the consolidation process. In conclusion, the authors point out that it is extremely important to test the consolidant carefully with increasing exposure to solvent vapor before using it on an actual object.


text:
Q: Make HTTP requests without opening a new connection If a browser opens a connection to a remote server, is it possible to access that same connection via Javascript?
I have a small Ethernet module on my network that I program sort of like this (pseudocode):
private var socket
while(true) {
    if(socket is disconnected) {
        open socket
        listen on socket (port 80)
    }
    if(connection interrupt) {
        connect socket
    }
    if(data receive interrupt) {
        serve
    }
    if(disconnection interrupt) {
        disconnect socket
    }
}

The point is that it listens on one socket for HTTP requests and serves them.
In my web browser, I can connect to the device, making an HTTP GET request for some HTML/JS that I've written, and it works. A connection is opened on the socket and the files come back as HTTP responses.
Now I want to click a button on the webpage and have the browser send an HTTP POST request over that same connection. In my Javascript, I have (edited and formatted for clarity):
// This function sends an HTTP request
function http(type, url, data, callbacks) {
    // make a new HTTP request
    var request = new XMLHttpRequest();

    // open a connection to the URL
    request.open(type, url + (data ? "?" + data : ""));

    // add headers
    if(type == "POST")
        request.setRequestHeader('Content-Type', 'application/x-www-form-urlencoded');

    // register callbacks for the request
    Object.keys(callbacks).forEach(function(callback) {
        request[callback] = function() {
            callbacks[callback](request.responseText);
        };
    });

    // send and return the request
    request.send();
    return request;
}

// Here is where I call the function
http("POST",                // use POST method
     "http://192.168.1.99", // IP address of the network device
     dataToSend,            // the data that needs to be sent
     {                      // callbacks
         onloadend: function(data) {
             console.log("success. got: " + data); // print 'success' when the request is done
         },
         onerror: function(data) {
             console.log("There was an error.");   // print 'error' when it fails
             console.log(data);
         }
     }
);

The issue here is that this opens a new connection to the device, but I want to use the same socket that the browser is already connected to. Is this possible, and if so, how?

A: There is no application control inside the browser to decide if a new connection is used for the next request or if an existing connection is used. In fact, it is perfectly normal that the browser will use multiple connections in parallel to the same server and your server has to be able to deal with this.
Since your server architecture seems to be only able to deal with one connection at a time you either would need to change the architecture to handle multiple parallel connections or to make sure that you only need to handle a single connection at a time. The latter could be achieved by not supporting HTTP keep-alive, i.e. by closing the connection immediately after each response. This way a new request will result in a new connection (which is not what you wanted according to your question) but your server will also be able to handle this new connection (which is what you likely ultimately need) since the previous one was closed.


Price discrimination is charging consumers with different prices for identical similar products, which are not related to costs of production. An important point to mention, Products that varies in prices due to cost variation and justification are not considered as price discrimination. For example, charging different prices for the same product for different geographical locations does not result into price discrimination, because of the transportation or delivering cost differential, which considered as a geographical cost justification. This report demonstrates the price discrimination in a monopolist market, the three conditions that should be satisfied in order for price discrimination to occur, and the degrees and types of price discrimination.
Furthermore, there are three substantial conditions that should be satisfied in order for price discrimination to take place. Firstly, market power; in order for a firm to price discriminate, the firm should have a market power. To illustrate, a market power exists when there are few firms that can control the price of its products. On the other hand, price discrimination in a perfectly competitive market cannot be seen. Since a perfectly competitive firm is a price taker, it has no power to control prices for different products and has to take the price decided by the market.


text:
Yong Saetae (; , Teochew: Dên Iong; ?–?) was a Chinese merchant and the father of King Taksin the Great. The Vietnamese called him Trịnh Yển (鄭偃).

His birth name was Dên Dag (). Born in Chenghai County, Chaoshan, China. It was said that he was a broad-minded and unconventional man. His fellow countrymen did not like him, and called him Pai Gian Dag ( or , lit. "Dag, the ruffian" in Teochew). he came to Siam to do business, and became rich, and changed his name to Yong Saetae. Later, he became a tax-collector, and married a wealthy royal woman Nok-lang (later Princess Phithak Thephamat), who later gave birth to Taksin.

References

Year of birth missing
Year of death missing
18th-century Thai people
Yong Saetae
People from Chenghai
Thonburi Kingdom
Qing dynasty emigrants

redpajama_set_name: RedPajamaWikipedia


text:
Voralpenland bezeichnet in der Regionalgeografie die Gebiete rund um die – kulturell, klimatisch oder wirtschaftlich – hochalpin geprägte Kernzone der Alpen. Es handelt sich überwiegend um Mittelgebirge, die geologisch noch zu den Alpen gehören, während das Alpenvorland außerhalb der Alpen liegt. Oft werden die beiden Begriffe jedoch fehlerhaft für Synonyme gehalten.

Der Begriff "Voralpenland" wird in der Fachliteratur nur vorsichtig verwendet, weil er sich nicht gegen einige spezifischere Begriffe absetzt:
 allgemein die Voralpen, veraltet Niederalpen als die Vorberge der Alpen, die subalpinen mittelgebirgsähnlichen Höhenzüge am Alpenrand, und die dazwischenliegenden Talebenen, gegenüber dem flachen bis sanftwelligen, teils von alten Moränen durchzogenen Alpenvorland, das sich bis an die Donau zieht
 die Schweizer Region Voralpen
 die nördlichen Voralpen als Teil der Nordalpen, also die Kalkvoralpen als gegensätzliche Unterteilung zu den Kalkhochalpen in den nördlichen Kalkalpen, zusammen mit der Flyschzone, und einigen anderen Gesteinszonen
 die bayerischen Voralpen als Gebirgsgruppe, gegenüber den allgemein zu den Nordalpen zählenden, den Kalkhochalpen vorgelagerten Berggebieten in Schwaben, Bayern und Salzburg
 die salzburgischen Voralpen, die man zu den Salzkammergutbergen rechnet, gegenüber dem voralpinen Landstrich des nördlichen Flachgaus, der auch die salzburgische Seenplatte miteinbezieht
 das oberösterreichische Voralpenland, das die Gebirgsgruppe der oberösterreichischen Voralpen umfasst, die Gebiete zwischen Kalkalpen und Hausruck, die nur teilweise innerhalb des nördlichen Salzkammerguts liegen, sowie einige tiefeingreifende Talöffnungen
 die niederösterreichischen Voralpen von der Eisenwurzen bis zum Wienerwald
 das südöstliche Alpenrandgebiet, namentlich östliches Randgebirge, Lavanttaler Alpen, Pohorje (Bachergebirge), geologisch Teile der Zentralen Ostalpen
 die südlichen oder italienischen Voralpen (ital. prealpi) als südlichste Gebirgszüge der Südalpen und der italienischen Westalpen
 die französischen Voralpen (fr. préalpes), die sich, bestehend aus den Savoyer Voralpen, den Dauphiné-Voralpen und den Provenzalischen Voralpen, vom Rhone-Tal bis zum Mittelmeer erstrecken.
 die voralpine Zone und die Randalpen-Zone als klimatologischer Begriff

Einzelnachweise

Geographie (Alpen)
Region in Europa