esa-sceva/satcom-chunk-collection huggingface dataset:
# A Telepresence Wheelchair with 360-Degree Vision Using WebRTC
## 2 Methods
### Remote Control Telepresence Wheelchair via WebRTC  
In the preceding sections, we have presented a unique telepresence wheelchair system with 360-vision video communication. To establish a real-time 360-degree vision wheelchair interaction, both the remote client and the wheelchair must retrieve a WebRTC-enabled web browser from a web application server. However, WebRTC only supports video communication and provides the capability for modifying various aspects of the application program interface (API) for developers to build independent applications. For instance, the users can make a modification related to speakers, microphones, video cameras, and input data. WebRTC does not include a mechanism for exchanging the control signal. Therefore, to control the wheelchair over WebRTC, a controller API must be developed to transfer the control signals from the remote user to the wheelchair.  
In this work, the remotely controlling architecture via WebRTC for a telepresence wheelchair is depicted in Figure 3. As developed herein, a WebRTC media channel refers to a connection between two WebRTC clients for exchanging real-time media, while a WebRTC data channel relates to a connection for transferring data in one or more arbitrary formats. WebRTC data channel uses stream control transmission protocols (SCTP), which allows it to deliver and retransmit data. Moreover, WebRTC data channel API supports a flexible set of data types, such as strings and binary types. These types of data can be helpful when working with the transferring of a control signal from a remote user to the wheelchair. By using WebRTC data channel API, the control signal can be sent from a remote user to the wheelchair at the same time as the video communication. It is worth noting that a media channel and a data channel will be multiplexed and demultiplexed into a single peer connection between the WebRTC clients with the same user datagram protocol (UDP).  
Figure 3: Remote control architecture of a telepresence wheelchair based on WebRTC.  
To transmit control signals, we have developed an independent user-friendly interface with control buttons. The user interface permits a remote user to observe the surrounding environment of the wheelchair with audio and video communication and navigate the wheelchair by using the control buttons at the same time and in real-time. The control signal originating from the remote user will be sent via the WebRTC data channel. At the same time, responsive to receiving the control signal from the remote user via the WebRTC data channel, the controller API at the wheelchair browser will modify functionality associated with the wheelchair computer and transfer the signals to hardware. In other words, the inputs of the controller API would be the data obtained from the remote user via the WebRTC data channel, and the outputs of the controller API would be the voltage values to control the wheel motors. By doing so, the framework will exchange information between hardware and software during the navigation process.
# Fast Segmentation Algorithm for Cytoid Macular Edema Based on Omnidirectional Wave Operator
## 4 Conclusions  
This paper proposes a method of automatically segmenting CME in fundus OCT images, which can achieve rapid segmentation while maintaining good segmentation accuracy. It also has good applicability and robustness. This algorithm compares image pixels with fluid particles. By considering the fluid potential energy equation in fluid mechanics, we propose a potential energy equation suitable for images and design an orientation adjustment function, based on the omnidirectional wave operator, to realize the segmentation of CME. The algorithm is insensitive to randomly distribute speckle noise through tracking the gradual gray value characteristics of the region boundary. Through ROI extraction, the interference of sub-choroidal angiography can be filtered, while accelerating the calculation process. Compared with the manual segmentation results of experts, the Dice index, accuracy, recall, and F1-score of the segmentation results of this algorithm are 81.1%, 88.8%, 75.0%, and 81.3%, respectively, which have a good consistency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Dice Index** & **Precision** & **Recall** & **F1-Score** \\ \hline
4 directions & 0.811 & 0.888 & 0.750 & 0.813 \\ \hline
6 directions & 0.787 & 0.903 & 0.683 & 0.777 \\ \hline
8 directions & 0.816 & 0.955 & 0.716 & 0.818 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the operator with a different number of directions.  
Figure 7: Segmentation results of the operator in different directions, (**a**) the result of 4-direction operator; (**b**) the result of 6-direction operator; (**c**) the result of 8-direction operator.  
The average operation time is 1.2 s, which can provide an important basis for the automatic detection of clinical ophthalmic diagnosis and treatment instruments.  
Conceptualization, J.L. and S.L.; methodology, J.L. and S.L.; software, J.L.; validation, J.L. and S.L.; formal analysis, H.C.; investigation, J.L. and S.L.; resources, J.L., S.L., X.C. and Y.W.; data curation, J.L.; writing--original draft preparation, J.L., S.L., X.C., H.C. and Y.W.; writing--review and editing, J.L., X.C. and Y.W.; supervision, X.C. and Y.W.; project administration, J.L., S.L. and Y.W.; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2017 YFC0109901.  
Data used in this paper is available at www.duke.edu/~sf59/Chiu_BOE_2014_dataset.htm. Data is also contained within the article [14].  
The authors would like to thank [PERSON] for the open-access data set they provided and thank [PERSON] for the manual marking of the data set in Tianjin Eye Hospital, China.  
The authors declare no conflict of interest.
# Identification of Deformation Stage and Crack Initiation in TC11 Alloys Using Acoustic Emission
###### Abstract  
The Acoustic Emission (AE) is a widely used real-time monitoring technique for the deformation damage and crack initiation of aero-engine blades. In this work, a tensile test for TC11 titanium alloy, one of the main materials of aero-engine, was performed. The AE signals from different stages of this test were collected. Then, the AE signals were decomposed by the Variational Mode Decomposition (VMD) method, in which the signals were divided into two different frequency bands. We calculated the energy ratio by dividing the two different frequency bands to characterize TC11's degree of deformation. The results showed that when the energy ratio was \(-0.5\) dB, four stages of deformation damage of the TC11 titanium alloy could be clearly identified. We further combined the calculated Partial Energy Ratio (PER) and Weighted Peak Frequency (WPF) to identify the crack initiation of the TC11 titanium alloy. The results showed that the identification accuracy was 96.33%.  
acoustic emission (AE); aero-engine blade; deformation damage; crack initiation +
Footnote †: journal: Applied Physics Letters
## 5 Experiments  
### Experimental Settings  
In order to train the proposed response weight and bitext classifier, the pairs of a query and a response are needed. Dailydialog [30] is used for this purpose, as this data set contains open-domain multi-turn dialogue pairs. Since RGRW does not target multi-turn dialogues, the dialogue pairs in Dailydialog are converted into single-turn dialogue pairs by regarding the odd-numbered utterances and the even-numbered utterances as queries and responses, respectively.  
The performance of the response weight is verified through _keyword detection_. For this task, the MAUI Twitter data set [31] is used, since the tweets are relatively short and the keywords are labeled at the tweets of this set by crowd-sourcing. The average tweet length is 78.55, and the average number of keywords per tweet is 3.92. Even if this data set has its own training and validation sets, only the test set is used in the experiments since the proposed response weight and bitext classifier are trained with Dailydialog.  
The Reddit data set [13] is adopted for the experiments of the response generator. The data set also consists of open-domain dialogues, but the dialogues are single-turn pairs. The queries and the responses that are shorter than four words or longer than 20 words are excluded from the data set following the study of [PERSON] et al. [6]. Table 2 summarizes the statistics on the data sets after pre-processing.  
The hyper-parameters for the transformer encoder of the response weight are equivalent to those of the transformer base model. The dimension of embedding vector is 512, and that of the inner-layers of feed-forward networks is 2048. The number of heads in multi-head attention is eight, and the number of transformer encoder layers is six. The batch size of training and validation sets is 32, and the learning rate is 0.0001. In addition, label smoothing [32] of \(\epsilon_{ls}\) = 0.1 is used.  
The encoder of the response generator is the pre-trained BERT base model, and the transformer decoder setting for response generation is similar to GPT-2. The embedding vector dimension is 768, and the inner-layers of feed-forward networks have 3072 dimensions. The number of heads in the multi-head attention is 12, and the number of transformer encoder layers is six. The batch size of training and validation sets is 32, and the learning rate is \(6.25\times 10^{-5}\).
## 3 Simulations  
Simulations are performed for a row of turbines as well as for two idealized wind farms. The overall setup is similar to a study performed previously on the wake behind the Horns Rev wind farm [8], where \(U_{0}\) = 8 m/s, with the same wind turbines (a downscaled version of the NREL 5 MW turbine corresponding to a Vestas V80 with rated power of 2 MW), with a hub height of 70 m and a \(R\) of 40 m. The simulations have a resolution (\(\Delta\)x) of 0.1 \(R\) (4 m) which has shown to be sufficient in earlier studies [10; 23]. A non dimensionalized (with \(U_{0}\) and \(R\)) time step (\(\Delta\)t) of 0.05 is used. In earlier studies using the FBL technique [8; 10; 11; 23] a time step ranging between 0.025 and 0.05 have been used with stable results. All these time steps fulfil the CFL condition (which for \(\Delta\)x = 0.1 and u = 1 is fulfilled for \(\Delta\)t < 0.1). The turbulence box has a lower resolution 1.67 \(\Delta\)x and a larger time step of 0.12, therefore a spatial and temporal interpolation is employed. The total boundary layer height is 25 \(R\) (1000 m) and the grid has an equidistant area of 8 \(R\) in the vertical direction. The simulations ran first for 12,000 \(\Delta\)t to allow for the turbulence to advect through and fill the domain. All the turbines also need to adjust to the flow. The simulations thereafter continued to ran and the studied parameters are averaged for 12,000 \(\Delta\)t corresponding to 50 min in physical time. The boundary layers used are described below, followed by a further description of the used wind farm configurations and cases.
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 4 Proposed Sub-Band BadNets Backdoor Attack in Remote Sensing Imagery
### Sub-Band Attack Example  
We present an example where we planted the backdoor trigger with a trigger size of \(9\times 9\) using both All-bands BadNets and a sub-band-based attack, as shown in Figure 3. We then applied the Neural Cleanse approach to generate the trigger by reverse engineering. We observe that the reverse-engineered trigger from the sub-band-based attack (Figure 3b) is significantly larger compared to the traditional BadNets attack (Figure 3c). We also reverse-engineered the trigger for the same target class with a clean DCNN model, as shown in Figure 3d. Additionally, we found that the reverse-engineered trigger from the sub-band-based attack appears similar to that from the clean model and does not exceed the anomaly index threshold of 2. This indicates that Neural Cleanse faces a much greater challenge in detecting sub-band-based attack scenarios.
###### Abstract  
In 2022, a record-breaking extreme compound heatwave-drought (CHD) event occurred in China's Yangtze River Basin (YRB), which significantly reduced terrestrial ecosystem Gross Primary Production (GPP), as many previous studies have shown. However, it remains uncertain how GPP responds to extreme CHD events of varying intensities, as well as the differences in GPP responses among different vegetation types in the YRB. This study used two independent GPP products (GPP\({}_{\text{GOSIF}}\) and GPP\({}_{\text{FluxSat}}\)) and Enhanced Vegetation Index (EVIMODIS) data to investigate these uncertainties during three identified CHD years: 2003, 2013, and 2022. In 2022, when the CHD event intensity was the strongest, the YRB experienced the most significant GPP reductions, with amplitudes of \(-0.37\) gC m\({}^{-2}\) day\({}^{-1}\) (\(-3.05\) standard deviation, hereafter STD) and \(-0.57\) gC m\({}^{-2}\) day\({}^{-1}\) (\(-5.97\) STD) for GPP\({}_{\text{GOSIF}}\) and GPP\({}_{\text{FluxSat}}\), respectively. In the less intense years, the year 2003 had less than one-third of GPP reductions in 2022, while the year 2013 even showed an overall slight GPP increase. Moreover, the year 2022 also showed the widest extent with significant GPP reductions exceeding one STD, which was more than twice the grid points in 2003 and 2013. This study also revealed significant differences in GPP responses across different vegetation types. In 2022, GPP reductions were the most pronounced in shrub-dominated areas, followed by evergreen forests, while deciduous forests and mixed forests experienced relatively smaller decreases. However, there were no significant differences among vegetation types in 2003 and 2013. These findings enhance our understanding of the variability in the GPP responses to extreme CHD events of varying intensities in the YRB terrestrial ecosystems, and this enhancement provides an important scientific basis for optimizing related vegetation and carbon cycle models.  
extreme compound climate event; terrestrial GPP; the Yangtze River Basin 2022  
2
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 5 Performance Analysis
### Comparison between Simulations and Measurements  
In this section, we examine the validity of our proposed physical layer. In [13], we performed simulations, and in this paper, we have evaluated the performances empirically in a controlled laboratory. To validate or reject the results, we first set the difference between the simulation results and the empirical ones. Since in our experiments, we have evaluated two types of distances and four voltages, we consider the BER for LOS as the mean of all configurations, and the same procedure is applied for the NLOS situation. The results are presented in Table 7. After that, a Kolmogorov-Smirnov (K-S) test is done using the kstest2 function of MATLAB. The two-sample K-S test is a non-parametric hypothesis test that evaluates the difference between the distributions of the two sample data vectors over the range of \(x\) in each dataset. Mathematically, it can be written as follows:  
\[T_{K-S}=\sup_{x}(F_{1}(x)-F_{2}(x)) \tag{8}\]  
where \(F_{1}(x)\) and \(F_{2}(x)\) correspond to the proportion of \(x_{1}\) and \(x_{2}\) values less than or equal to \(x\), respectively.  
In Table 7, we can notice that the difference between the simulation results and the experimental results for a target BER of \(10^{-4}\) is quite small for the LOS and NLOS cases. We have a difference of 1.1 dB for the LOS situation, whereas for NLOS, it is 0.7 dB. These results validate the implementation in GNU Radio and confirm that our proposed approach is very efficient in mitigating the bursty nature of impulsive noise while having quite a low level of complexity. The K-S test is also another confirmation of this assertion. For LOS and NLOS, the return value of zero indicates that the test does not reject the null hypothesis at a 5% significance level. In other words, the results of the K-S test validate that the measurement results and the simulation results come from the same distribution at a confidence interval of 95%.  
Figure 11: BER performance for different voltages in the NLOS situation.
# Generic Component-Based Mission-Centric Energy Model for Micro-Scale Unmanned Aerial Vehicles
## 3 Model
### An Energy Model for the FINken Quadcopters
\tag{11}\]  
Combining Equation (11) with a _Constant Power Function_ for the tracking LEDs yields the final LED power function:  
\[P_{LED}(id)=k_{tracking}+P_{id}(id) \tag{12}\]  
802.15.4 Transceiveris currently not using any power saving mechanisms, which enables us to model it using a _Constant Power Function_:  
\[P_{tx}=k_{tx} \tag{13}\]  
Remote Control Receiveris constantly receiving information from the remote control to be able to emergency shut off the copter and is modelled through a _Constant Power Function_:  
\[P_{rc}=k_{rc} \tag{14}\]  
GPS-Receiver draws constant power independent of GPS reception. It is modelled through a _Constant Power Function_:  
\[P_{gps}=k_{gps} \tag{15}\]  
It is important to model the last four components independently, because they can easily be attached and removed from each copter, which changes the copter's energy consumption. The **Resulting Parameter Space** is the Cartesian product of the parameter spaces of all components, because no two components use the same parameters: \(\mathcal{P}_{FINken}=\{0,1\}\times\mathbb{R}^{+}\times\mathbb{R}^{3}\times\{0,1,2\}\). With the corresponding parameters \(\vec{p}=(a,z,\vec{x},\vec{y},\vec{z},l)\). The activation parameters for motors and the led configuration are directly defined in the mission and can be extracted from the mission program. The acceleration parameters can be acquired through various sources. The copter contains an on-board inertial measurement unit (IMU), which output turn rates and accelerations in all three axes. Another means of acquiring the values of the parameters is the usage of an external position tracking system and the computation of numerical derivatives. In case of a fully equipped copter the final energy model is constructed from the power functions of the individual components, see Equations (8)-(15), through the application of Equation (2). The resulting model is  
\[P_{FINken}(\vec{p})=P_{motor}(\vec{p})+P_{driver}(\vec{p})+P_{ap}(\vec{p})+P_{ LED}(\vec{p})+P_{tx}(\vec{p})+P_{cgps}(\vec{p}) \tag{16}\]If components are removed from the copter, the energy model can easily be adapted because the respective parts of the energy model can be removed easily. A basic FINken3, for example, only consists of _Motor_, _Motor Driver_ and _Autopilot_. The energy model for such a copter set-up is  
\[P_{FINken_{min}}(\vec{p})=P_{motor}(\vec{p})+P_{driver}(\vec{p})+P_{ap}(\vec{p}) \tag{17}\]  
The ability to adapt the energy model if components are attached or detached is very important for such a research platform, because the platform is frequently modified for different experiments. A second aspect of adaptability are the coefficients of the component energy models. These can be derived through measurement or in the case of the motor component it needs to be learned. Consequently, a modification of the copter, which incorporates large changes in weight or body shape needs an additional training phase for the motor energy model to adapt this model to the changes of the hardware.
## 4 Empirical Study
### Evaluation Metrics  
The evaluation metrics are the mean absolute error (MAE), the root mean square error (RMSE), the bias and the symmetric mean absolute percentage error (SMAPE). These metrics provide different insights into the performance of models at the individual level. Bias determines the percentage of the difference between the total estimations and actuals. It is defined as  
\[\text{Bias}=\frac{\sum_{t=1}^{n}\sum_{i=1}^{k}(\widehat{y}_{i,t}-y_{i,t})}{ \sum_{t=1}^{n}\sum_{i=1}^{k}y_{i,t}}\times 100 \tag{4}\]  
where \(\widehat{y}_{i}\) is the predicted value and \(y_{i}\) is the actual value for individual \(i\) at point \(t\).  
Since we have many 0 values, it is not possible to use MAPE. Thus, we used SMAPE; and its definition is given by Equation (5) :  
\[\text{SMAPE}=\frac{1}{n}\sum_{t=1}^{n}\frac{\sum_{t=1}^{n}|\widehat{y}_{i}-y_{ t}|}{\sum_{t=1}^{n}\frac{|\widehat{y}_{i}|+|y_{i}|}{2}}\times 100 \tag{5}\]  
where \(\widehat{y}_{i}\) represents the predicted value and \(y_{i}\) denotes the actual value at point \(t\).  
We also calculate MAE and RMSE at individual level. We determined them as Equations (6) and (7):  
\[\text{MSE}=\frac{1}{n}\sum_{t=1}^{n}\sum_{i=1}^{k}(y_{i,t}-\hat{y}_{i,t})^{2} \tag{6}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{t=1}^{n}\sum_{i=1}^{n}(y_{i,t}-\hat{y}_{i, t})^{2}} \tag{7}\]  
where \(\widehat{y}_{i}\) is the estimated value and \(y_{i}\) is the actual value for individual \(i\) at point \(t\).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dataset** & **Fintech Mobile Application** & **Online Retail II** \\ \hline Cohort Size & 53,576 & 4993 \\ Calibration Period Length (Weeks) & 49 & 80 \\ Holdout Period Length (Weeks) & 25 & 25 \\ Minimum Date & 4 March 2021 & 2 December 2009 \\ Training End Date & 10 February 2022 & 16 June 2011 \\ Maximum Date & 3 August 2022 & 8 December 2011 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of the used data (These statistics were found after some preprocess).
# Three-Dimensional Multi-Agent Foraging Strategy Based on Local Interaction
## 3 Preliminary Information
### Problem Statement and Foraging Strategy Introduction  
Our paper handles multi-agent foraging, which involves multiple agents collecting randomly distributed pucks and carrying them back to a base. To make the problem more interesting, in this article we solve the problem of 3D multi-agent foraging in a GPS-denied bounded workspace with many unknown obstacles. The goal of our multi-agent foraging problem is as follows. _Considering an unknown 3D cluttered bounded workspace, multiple agents begin moving from the base to search for all of the randomly distributed pucks. Each agent is controlled such that they carry the sensed pucks back to the base. All agents must move based only on local interaction with their neighbors, without relying on global localization._  
The proposed foraging strategy has the following two steps. The first step is to detect every puck in the 3D cluttered bounded workspace in a provably complete manner. Initially, all agents are located at the base. As an exceptional situation, we further handle the case where a number of agents are not initially located at the base (Section 5.1.3).  
The search process is performed by moving agents one by one to expand the sensing coverage until no coverage holes remain. The 3D cluttered workspace is considered to have been completely covered when every puck has been sensed by at least one agent. This provably complete puck search is presented in Section 5.1.  
The second step is to return every puck to the base. In the collection step, no global localization or global communication is utilized. This collection step based on local interaction is presented in Section 5.2.
# Analysis of Multi-Antenna GNSS Receiver Performance under Jamming Attacks
## 2 Methodology
### Multi-Antenna Signal Simulations and Processing  
The multi-antenna GNSS signal simulator developed in [25] was used to generate multi-antenna signals with different types of interference. For the simulations, it was assumed that antenna elements are identical and there is no mutual coupling. A static user scenario was considered and the interference source was also assumed to be static. The simulated signals are processed using a modified version of the GSNRxTM software receiver [26] to generate code and carrier phase measurements. The software receiver was modified to incorporate multi-antenna processing with Power Minimization (PM) and MPDR processing techniques.