esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Model and Control
### Bionic Power Optimization Experiments  
As shown in Figure 14, the robot (LAWCDR) includes a Robomaster M2006 front wheel motor, four JuFeng 2814 kv700 motors, four 40A ESCs, four APC1055 propellers, a 60 mm double-layer nitrile rubber suction cup, and two Feetech SM45 BL RS485 servos. The flight controller is CUAV fmuv5 nano flight controller. The wheel diameter is 14 cm. The onboard computer is Nvidia Jetson Xavier NX, through the mavlink protocol and ROS for planning trajectory.  
Figure 12: Simulation of control quantity of robot pitch channel.  
Figure 13: Simulation on robot extended state observer.  
As shown in Figure 15, the robot position feedback indoors is output through the Optitrack motion capture system and sent to the robot through the Xbee communication module. The outdoor position feedback is output via differential GPS and the RTCM differential data from the base station is sent to the robot via Xbee as well. The robot performs position decomposition to obtain outdoor position feedback.  
To verify the power optimization effect of the robot, we tested the power and control amount of the robot in different operating states when the robot was hovering in the air. The angular velocity control output of the pitch channel in the preparation state and flight state is shown in Figure 16. To better illustrate the variation, mean filtering of \(n=200\) was performed (shown as the red line). The control of the pitch axis is normalized to [-1,1]. In order to balance the non-coincidence of the center of gravity and geometric center, the robot needs to reduce the control margin of the pitch axis. From the preparation state to the flight state, the amount of control is reduced from 0.38 to 0.15, and the control margin is increased from 0.62 to 0.85. This causes an increase of 37.1% in the control margin. Compared to the preparation state, the flight state increases the control margin and improves the control performance of the robot.  
Figure 14: Robot platform (LAWCDR).  
Figure 15: Setup of the robot in the experiments.  
The total robot power is shown in Figure 17. We also performed the mean filtering with \(n=200\) (as shown in the red line). The total robot hovering power is about 830 W when the robot is in the preparation state and about 780 W when the robot is in the flight state. The power difference is about 6%. If the robot works in the flight state, the working time of the robot can be extended. Taking a 6 s 5000 mah battery as an example, the robot working time can be extended from 481 s to 512 s.  
Figure 16: The output control quantity at preparation state and flight state.  
Figure 17: Total power at preparation state and flight state.
## 5 Experimental Results
### Case 1: Continuous Intense Fluctuations of Wind Speed  
For Area 1, the wind speed fluctuations are systematically modeled, as illustrated in Figure 6. A comparison of simulation results is conducted between the proposed coordinated control strategy and the scenario without additional frequency control. The system frequency, DC link active power outputs from G1 and G2, and DC voltage are observed to assess the system's frequency stability and power fluctuation regulation capability. The simulation results are presented in Figures 7-9 for comparative analysis.  
The comparison of system frequency changes shown in Figure 7 indicates that, with wind speed fluctuations, the frequency deviation exceeds +0.2 Hz approximately 2 s after initiation, continuously rising to 50.2 Hz. Within the 40 s simulation duration, the frequency exceeds the 50 \(\pm\) 0.3 Hz range for a total of 20 s. At the end of the simulation, the frequency remains unstable, showing a large fluctuation range and a slow recovery process. Using the auxiliary frequency control strategy of AFC and AGC ensures that the frequency consistently remains within the 50 \(\pm\) 0.2 Hz range throughout the simulation. Approximately 3 s following the cessation of wind speed fluctuations, the frequency promptly stabilizes at 50 Hz.  
The observed variations in DC link active power and generator output are illustrated in Figures 8 and 9. The results indicate that without auxiliary frequency control, the increment in wind power within Area 1 mainly relies on the isolated governor responses of generators G1 and G2, which adapt their outputs to assimilate this increase. However, this adjustment capability is notably constrained, and even after the cessation of wind speed fluctuations, the output from the generators continues to demonstrate minor yet persistent oscillations. When using the coordinated control strategy of AFC and AGC, the AGC unit G1 in Area 1 takes on the task of balancing most of the wind power increment. The auxiliary frequency controller promptly reacts to variations in wind power output that cause frequency fluctuations, issuing control commands that swiftly adjust the DC link transmission's active power. After the stabilization of wind speed, both the DC link active power and the generator output rapidly regain stability.  
Figure 6: Wind speed variations.
# Measurement of Dielectric Properties of Thin Materials for Radomes Using Waveguide Cavities
## 3 Results
### Simulated Results
We observed similar findings for the WR137 cavity. Table 7 shows that there was little error between the simulated and calculated permittivity values and loss tangents for a WR137 cavity with a shim iris of 7.2 mm diameter. The percentage error for each of these properties also supports this argument. We obtained a permittivity error of less than 2% and a loss tangent error of less than 27%. These results \begin{table}
\begin{tabular}{c c c c c c} \hline
**Simulated** & **Simulated** & **Calculated** & **Calculated** & & \\
**Permititivity** & **Loss Tangent** & **Permititivity** & **Loss Tangent** & **Permititivity** & **Loss Tangent** \\
**(\(\epsilon_{r-sim}\))** & **((tan \(\delta\))\({}_{sim}\))** & **(\(\epsilon_{r}\))** & **(tan \(\delta\))** & **Error (\%)** & **Error (\%)** \\ \hline \multirow{4}{*}{1.5} & 0.1 & 1.4964 & 0.1142 & 0.24 & 14.2 \\  & 0.05 & 1.4888 & 0.0538 & 0.75 & 7.6 \\  & 0.01 & 1.492 & 0.0106 & 0.53 & 6 \\  & 0.005 & 1.4929 & 0.0051 & 0.47 & 2 \\  & 0.001 & 1.4945 & 0.0011 & 0.37 & 10 \\ \hline \multirow{4}{*}{2.2} & 0.1 & 2.195 & 0.1144 & 0.23 & 14.4 \\  & 0.05 & 2.1944 & 0.0515 & 0.25 & 3 \\  & 0.01 & 2.195 & 0.0101 & 0.23 & 1 \\  & 0.005 & 2.1944 & 0.0061 & 0.25 & 22 \\  & 0.001 & 2.1966 & 0.0011 & 0.15 & 10 \\ \hline \multirow{4}{*}{2.9} & 0.1 & 2.8888 & 0.1232 & 0.39 & 23.2 \\  & 0.05 & 2.9084 & 0.0589 & 0.29 & 17.8 \\ \cline{1-1}  & 0.01 & 2.8982 & 0.0111 & 0.06 & 11 \\ \cline{1-1}  & 0.005 & 2.9035 & 0.0057 & 0.12 & 14 \\ \cline{1-1}  & 0.001 & 2.9027 & 0.0011 & 0.09 & 10 \\ \hline \end{tabular}
\end{table} 
Table 6: Results from simulated WR650 waveguide cavities in CST Studio Suite. It displays various simulated permittivity and loss tangents alongside the calculated permittivity and loss tangent values. It also displays the errors for each characteristic. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Simulated** & **Simulated** & **Calculated** & **Calculated** & & \\
**Permititivity** & **Loss Tangent** & **Permititivity** & **Loss Tangent** & **Permititivity** & **Loss Tangent** \\
**(\(\epsilon_{r-sim}\))** & **((tan \(\delta\))\({}_{sim}\))** & **(\(\epsilon_{r}\))** & **(tan \(\delta\))** & **Error (\%)** & **Error (\%)** \\ \hline \multirow{4}{*}{1.5} & 0.1 & 1.4768 & 0.1216 & 1.55 & 21.6 \\  & 0.05 & 1.4884 & 0.0549 & 0.77 & 9.8 \\  & 0.01 & 1.4942 & 0.0113 & 0.39 & 13 \\  & 0.005 & 1.4926 & 0.0054 & 0.49 & 8 \\  & 0.001 & 1.4926 & 0.0008 & 0.49 & 20 \\ \hline \multirowshow that Equations (3)-(5) were accurate and reliable and that the y-z orientation of a material is possible. Therefore, with the verification of the accuracy of these simplified equations, we can begin our measurement process for the chosen materials. We can then apply these formulas to calculate the unknown dielectric properties of our candidate materials. This will then allow us to identify the suitability of these non-radome fabrics for radome applications and even further testing and verification at different frequencies.
# Low-Latency, Three-Phase PMU Algorithms: Review and Performance Comparison
## 3 Simulation Results
### Tests under Steady-State Conditions  
These tests require applying three-phase waveforms whose parameters, which are the positive sequence phasor, the frequency and the ROCOF, have constant values (it implies \(\text{ROCOF}=0\)). The target is evaluating the capabilities of the PMU algorithms in returning accurate estimates (ideally constant and equal to those present in the test waveform) in the presence of different disturbing factors.
## 4 Results and Discussion
### Genetic Algorithm-Artificial Neural Network (GA-ANN)  
As explained earlier, the GA has a higher effect on the ANN performance [52]. The most effective GA parameters, which are used to build hybrid GA-ANN models, should be selected/determined. The probabilities of mutation and reproduction percentage were assigned at 25% and 9% respectively. A single point was used with 70 percent of probability for the crossover. The maximum number of generations (G\({}_{\text{max}}\)) should be identified and used in the next step. A parametric study was examined to determine the impact of G\({}_{\text{max}}\) on the network performance using different number of population sizes. As shown in Figure 9, 600 generations were considered as the stopping criterion, and RMSE results were obtained to determine the optimal number of generations. Based on Figure 9, RMSE values for all population sizes are constant after generation number of 500. Therefore, the optimal number of generations was considered to be 500 for designing GA-ANN models. Figures 10 and 11 show the R\({}^{2}\) and RMSE results of model constructed in Figure 9 for training and testing datasets, respectively. As the final step of GA-ANN modeling, a set of hybrid GA-ANN models were built to assess the best population size considering different sizes of 50
# Engineering Resource-Efficient Data Management for Smart Cities with Apache Kafka +
## 3 DMP Software Architecture Design
### StreamHandler  
StreamHandler resides at the fog and receives AV data streams from all the active AV sources (CCTV cameras, network-enabled microphones, and AV anonymisation instances) via RTSP. During initialisation, StreamHandler accesses the REST API of the AVRegistry to discover the active AV sources and their details. During operation, StreamHandler consumes the AV RTSP streams and segments them according to a pre-specified time intervals to generate binary documents, suitable for persistent storage. StreamHandler archives the generated AV data files and also exposes a REST API to accept requests from SmartViz about the transmission of AV data from specific AV sources (reference to AV Source id) and from specific points in time. Upon such requests, StreamHandler retrievesthe necessary binary files, compiles a unified/edited version of the stream that corresponds to the timeframe requested and generates a link to the said binary file that is to be consumed by SmartViz.
# Towards Automatic Depression Detection:
## 4 Dataset  
### Depression Datasets  
There exist several datasets designed for the depression detection task [1; 39; 40; 41; 42; 43; 44]. Among these datasets, the most popular ones are the DAIC-WoZ and Audio-Visual Depressive Language Corpus (AViD-Corpus) datasets, because they are the only two publicly available datasets.  
The DAIC-WoZ dataset contains clinical interviews designed to support the diagnosis of psychological distress conditions [39]. DAIC-WoZ involves recordings and transcriptions of 142 participants that went through clinical interviews with a computer agent. For each participant, DAIC-WoZ provides a Patient Health Questionnaire (PHQ-8) [45] score, which indicates the depression severity. In addition, a binary label of the PHQ-8 score is also provided to represent the presence of depression. A score greater than or equal to 10 indicates that the participant is suffering from depression. The DAIC-WoZ dataset is split into a training set (107 participants; 30 are depressed, and 77 are non-depressed), a development set (35 participants; 12 are depressed, and 23 are non-depressed), and a test set according to [39; 46]. Labels in the training set and the development set are publicly available, while those in the test set were not given by the authors.  
The second dataset is AViD-Corpus [40; 47]. In AViD-Corpus, eighty-four participants were required to answer a series of queries (free-form part) in a human-computer interaction. In addition, they were requested to recite an excerpt of a fable (north wind part). The training, validation, and test sets were split according to [47], and the corresponding labels were all available. For AViD-Corpus, the model was trained on the training set and evaluated on the test set. Participants were labeled with Beck Depression Index-II (BDI-II) [48] scores. BDI-II is a self-reported 21 multiple choice depression inventory, whose scores range from 0-63. If the BDI-II score of a participant is over 29, this indicates the presence of depression.
## 2 Related Work  
### Data Preprocessing  
In this paper, we will test two datasets, MPII [17] and FSD [18], as shown in Figure 2, which also shows the proportion of different categories of data in the FSD dataset. An up-sampling strategy is used in the data preprocessing stage to address the problem of unbalanced data categories in the FSD training set. It will randomly repeat a small number of sample categories so that the data in the processed training set have the same percentage in each category.  
For a sample \(x\) in the minority class, the distance from the sample to all samples in the minority class sample set is calculated using the Euclidean distance as the criterion to obtain its k-nearest neighbors. Then, a sampling ratio is set according to the sample imbalance ratio to determine the sampling multiplier \(N\) (\(N\) should be less than the number of minority samples \(x\) minus 1). For each minority class sample \(x\), some samples are randomly selected from its k-nearest neighbors. Assuming that the selected nearest neighbors are \(\tilde{x}\), each randomly selected nearest neighbor \(\tilde{x}\) can be constructed with samples \(x\) according to Equation (1) (\(x_{new}\)) :  
\[x_{new}=x+rand(0,1)*(\tilde{x}-x) \tag{1}\]  
The model's accuracy on the test set with or without the upsampling strategy is shown in Table 1, and it can be seen that the accuracy of the model on the test set is improved after processing with the upsampling strategy.  
Figure 1: Manuscript structure.  
In addition, since the critical point sequence length in the FSD training set data varies greatly, as shown in Figure 3, it is necessary to select a suitable sequence length to input into the model. However, the maximum value of sequence length in the training set data is 2037 frames, and the minimum value is 42 frames, in which fewer than 500 frames account for 27% of the samples. Therefore, the sample data can be divided into 350 intervals according to the effective frame length. A random frame is sampled from each interval to form a sample frame with a frame length of 350 during training, and it is used as the input of the model, which can effectively improve the model performance, as shown in Table 2.  
It is worth noting that since the original training data only contains human joint coordinates and the confidence degree, this paper draws on the angular feature extraction and human activity model design methods proposed by [PERSON] et al. [19] for AngNet regarding joint angle and [PERSON] et al. [20] for a human activity model based on the Cartesian coordinate system, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Up-Sampling Strategy** & **Test Set Accuracy** \\ \hline Yes & 98.35\% \\ No & 97.23\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effects of up-sampling on model accuracy.  
Figure 2: Percentage of different classes in FSD.
# Toward Personal Identification Using Multi-Angle-Captured Ear Images: A Feasibility Study
## 2 Materials and Methods
### Model Building Based on Three Different Camera Angles  
This study is primarily aimed at examining the feasibility of personal identification using multidirectional ear images. To perform the examination, we trained four different CNN models using distinct training datasets comprising ear images that were captured from specific angles:  
1. Model H: Trained using ear images captured from the High position;
2. Model E: Trained using ear images captured from the Ear position;
3. Model L: Trained using ear images captured from the Low position;
4. Model A: Trained using a combination of ear images from all three positions (High, Ear, and Low positions).  
Table 2 summarizes the number of training images employed for each model. Models H, E, and L utilized 1160 images each, whereas Model A was trained using an expanded dataset comprising 3480 images. Notably, these models were subsequently evaluated for their generalization across the test images captured from the same or different directions.
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 2 System Model
### Proposed Semantic Image-Adaptive Transmission Framework  
An overview of the proposed CCB architecture for image transmission is presented in Figure 2. The main parts of the architecture are as follows.
## 5 Experiment Analysis
### Evaluation Metrics and Fitness Function  
The fitness function used for training the DE-COMA algorithm remains consistent with the one mentioned earlier in the text. In the comparative experiments, we continue to use the average return obtained by our agents within \(n\) training steps, and the win rate recorded every \(evaluate_{per_{epoch}}\) training steps. The win rate calculation method, denoted as \(win_{rate}\), is as shown in Formula (5).  
\[win_{rate}=\frac{win_{flag}}{evaluate_{per_{epoch}}} \tag{7}\]  
where \(win_{flag}\) represents the number of victories within training steps. Specifically, when all agents achieve success in the decision-making process, the \(win_{rate}\) is set to 1. If they all fail, the \(win_{rate}\) is set to 0. In other situations, the \(win_{rate}\) is calculated as the ratio of \(win_{flag}\) to \(evaluate_{per_{epoch}}\). Overall, \(win_{rate}\) is a value between 0 and 1.
# Low-Power, High Data-Rate Digital Capsule Endoscopy Using Human Body Communication
## 5 Discussion  
The methods presented in this paper have the merit of implementing the capsule endoscope configured using only digital logic. The presented capsule endoscope was implemented using the FPGA, and has the properties that the I/O voltage was 3.3 V and core voltage was 1.5 V. Therefore, it is possible to fabricate low-power capsule endoscopes using an advanced process with an operating voltage rather lower than that of FPGA. This architecture has the additional advantage in that its processes are less affected because it is implemented as digital logic. As can be seen from comparison with other capsule endoscopes, the proposed method can support a relatively high data rate in spite of using its clock a few tens of MHz slower. In the future, it is predicted that image data with a higher data rate could be transmitted if the image compression method is used.  
Capsule endoscopy is currently used to diagnose the condition of the small intestine. To modern people, it is more important to judge the disease in digestive organs such as the large intestine or stomach, than to judge the disease in the small intestine. There is also a growing demand for next-generation capsule endoscopes to meet these needs. Because the large intestine or stomach has a larger space than the small intestine, this space becomes an environment in which the capsule endoscope moves rapidly and changes its position frequently [8]. As the movement time of the capsule is fast, the ability to transmit high resolution images at high speed is required. Due to a larger space than the capsule, the optimal reception channel selection and real-time acquisition function are required according to the capsule position change. The algorithm and architecture presented in this paper would be suitable for solving the requirements of next-generation capsule endoscopes.  
The use of the capsule endoscopy for treatment as well as diagnosis is being studied. The capsule endoscope performs phototherapeutic treatment that illuminates blue light corresponding to _Helicobacter_ bacteria according to pH sensing, and measures the state of the stomach [27]. These capsules still use the wireless communication system. The proposed HBC methods can be used to change this system to one that is smaller size and lower in power consumption. The technique presented in the paper is also applicable to the newly proposed medication management device [28]. We consider that it can be variously applied to new devices that transmit the bio-related information in the body in real time.
# Consensus-Based Distributed Target Tracking with False Data Injection Attacks over Radar Network
## 3 Distributed State Estimation Algorithm with False Data Injection Attacks
### Time Update for Each Group  
Before employing the consensus method, each group carries out the time update simultaneously. The time update consists of two steps, prediction and information calculation, which are given as follows.  
**Prediction**.: _Given the state \(\hat{\mathbf{x}}_{i}(k-1|k-1)\) and the covariance \(\mathbf{P}_{i}(k-1|k-1)\) of group \(i\) at time \(k-1\):_  
\[\hat{\mathbf{x}}_{i}(k|k-1)=\mathbf{F}(k-1)\hat{\mathbf{x}}_{i}(k-1|k-1) \tag{27}\]  
\[\mathbf{P}_{i}(k|k-1)=\mathbf{F}(k-1)\mathbf{P}_{i}(k-1|k-1)\mathbf{F}^{T}(k- 1)+\mathbf{Q}(k-1) \tag{28}\]  
**Information Calculation**: The information state \(\Omega_{i}(k|k-1)\) and information matrix \(\mathbf{Y}_{i}(k|k-1)\) of group \(i\) can be calculated as  
\[\Omega_{i}(k|k-1)=\mathbf{P}_{i}(k|k-1)^{-1}\hat{\mathbf{x}}_{i}(k|k-1) \tag{29}\]  
\[\mathbf{Y}_{i}(k|k-1)=\mathbf{P}_{i}(k|k-1)^{-1} \tag{30}\]