esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Postural Performance Data
#### 3.3.2 Weight Distribution Index (WDI)  
In contrast to the stability indicator, the weight distribution index during the more demanding positions on the foam pads (PO, PC; Figure 3C,D) showed a clear difference  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Effects/Variance Analysis (p/\(\eta_{\text{p}}\)\({}^{2}\))**} \\ \cline{3-5}
**Parameters/Test Positions** & & \multicolumn{3}{c}{**Covariates**} \\ \cline{3-5}  & & **Sports** & \multicolumn{3}{c}{**Age**} & **Height** & **Weight** \\ \hline \multirow{5}{*}{ST} & NO & 0.285/0.092 & 0.212/0.030 & 0.843/0.001 & 0.281/0.023 \\  & NC & 0.637/0.048 & **0.016/0.109** & 0.800/0.001 & 0.166/0.037 \\  & PO & 0.543/0.058 & 0.123/0.046 & 0.260/0.025 & 0.034/0.085 \\  & PC & 0.859/0.025 & 0.855/0.001 & 0.653/0.004 & 0.063/0.066 \\ \hline \multirow{5}{*}{WDI} & NO & **0.025/0.193** & 0.849/0.001 & 0.938/0.000 & **0.017/0.107** \\  & NC & **0.117/0.133** & 0.522/0.008 & 0.954/0.000 & 0.073/0.062 \\  & PO & **0.018/0.205** & **0.007/0.134** & 0.065/0.065 & 0.454/0.011 \\  & PC & **0.206/0.108** & 0.167/0.037 & 0.074/0.061 & 0.402/0.014 \\ \hline \multirow{5}{*}{HEEL} & NO & 0.283/0.093 & 0.488/0.009 & 0.801/0.001 & 0.077/0.060 \\  & NC & 0.477/0.065 & 0.364/0.016 & 0.907/0.000 & 0.259/0.025 \\  & PO & **0.016/0.208** & 0.120/0.047 & **0.020/0.101** & 0.041/0.079 \\  & PC & **0.015/0.211** & 0.662/0.004 & **0.016/0.109** & **0.016/0.109** \\ \hline \multirow{5}{*}{LEFT} & NO & 0.789/0.032 & 0.631/0.005 & 0.713/0.003 & 0.533/0.008 \\  & NC & 0.861/0.025 & 0.780/0.002 & 0.326/0.019 & 0.136/0.043 \\ \cline{1-1}  & PO & 0.594/0.052 & 0.321/0.019 & 0.475/0.010 & 0.547/0.007 \\ \cline{1-1}  & PC & 0.976/0.009 & 0.827/0.001 & 0.903/0.000 & 0.655/0.004 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis of variance (covariates: age, weight, and height) between groups by test positions (eyes open, looking forward, and without foam pads (NO), eyes closed, looking forward, and without foam pads (NC), eyes open, head straight, and without foam pads (PO), and eyes closed, head straight, and foam pads (PC)) and parameters (stability indicator (ST), weight distribution index (WDI), heel, left). Meaningful differences (\(\eta_{\text{p}}\)\({}^{2}\) > 0.10) between sports marked in bold.  
Figure 2: Parameter stability indicator (ST) for different sports in positions NO (**A**), NC (**B**), PO (**C**), and PC (**D**). CON— recreational athletes, BAS—Basketball, SKI—Ski jumping, DIV—Diving, GYM—Gymnastics. Values are given as mean and standard deviation.  
between the technical sports (gymnastics, diving, and ski jumping) and the team sports (basketball). A significant difference (\(p\) = 0.025) was observed for the position NO between the gymnasts (6.09 \(\pm\) 2.85) and the basketball players (4.75 \(\pm\) 2.49; Figure 3A).
## 1 Introduction
That is, a good method should be able to solve the real power, the reactive power, mutual coupling terms, the loss, and the counter flows altogether, so the inter-relationship can remain intact without variations. The basic circuit theory and power balance theory could be satisfied over the whole network. In this study, a method is developed to solve the above problem. The method is based on a converged AC load flow with several matrix formulations, where all electrical components can be solved and decomposed, including the flow, the loss, the mutual couplings, and the counter flows, and the flow can be traced from any direction of this network. Furthermore, to make the derivation more complete, the transmission line charging was also considered in this study. Mathematical models were derived without using any optimization, any graphic reasoning, or heuristics. Test systems were presented to verify the proposed approach, and the results show satisfaction of all theories and inter-relationships. All power sources can be analyzed including distributed small generators or green power. Besides, a line usage concept is proposed to allocate the loss where counter flow may be sometimes encouraged or rewarded in reducing the transmission loss, i.e., to avoid overload. The method can be integrated with the existent tariffs in a deregulated market.
## 5 Results and Discussions
### System 3: The IEEE 118-Bus
#### 5.3.1 Case 1: APL Minimization  
The weighting factors in Equation (18) are taken as \(\omega_{1}=1,\omega_{2}=\omega_{3}=0\); in this case as only the APL minimization (OF\({}_{1}\)) is considered.  
The QODELFA is implemented for unity, 0.866 lag, and 0.82 lag power factors. The results are listed in Table 9. It may be clearly observed that the APL is decreased from the base-case value (1297.95 kW) to 518.653 kW with unity power factor DGs (Case 1.1) by using the proposed algorithm. From the comparison outlined in Table 9, the APL obtained by applying QODELFA is better than that of 576.182 kW from QOTLBO [29], 574.710 kW from KHA [31], and 525.277 kW from SFSA [32].  
The APL is more reduced in Case 1.2 (when the power factor is set to 0.866 lag) by implementing the proposed QODELFA, where it reaches 148.931 kW. This value is lower than that of 312.661 kW from KHA [31], and 155.159 kW from SFSA [32].  
The most significant LR value is reached when setting the power factor to 0.82 lag (Case 1.3), where it is increased to 89.77% by using the proposed algorithm. Table 9 and Figure 15 respectively depict the small values of SD and the fast convergence to the optimal solutions for all cases, which indicates the robustness and effectiveness of the QODELFA.
## 1 Introduction
Long codes cannot be captured once interfered with, which means they cannot meet the anti-interference requirements of the transparent forwarding satellite communication system in this paper. In addition, refs. [21; 22; 23; 24] adopt the direct acquisition method of XFAST and use overlapping codes to perform correlation operations on the received signals. However, the folding process of local pseudo-codes will increase the mutual interference between pseudo-codes and reduce the sensitivity. Therefore, this method is more suitable for acquisition under the condition of high SNR, but not suitable for the condition of low SNR in this paper. None of the existing acquisition methods can meet the requirements of acquisition success rate and hardware resource occupation at low SNR. Therefore, a matching filter acquisition method based on high-power clock multiplexing is proposed, and the adaptive decision threshold design method based on auxiliary channels is used to achieve the reliable acquisition of a spread spectrum code under the condition of low SNR. Based on the above ideas, the main contributions of this paper are as follows:  
* The synchronization head overhead is removed to improve transmission efficiency--a burst spread spectrum signal design method using an aperiodic long code spread spectrum is proposed, and a signal model without physical layer synchronization overhead is constructed;
* Reliable acquisition is ensured while eliminating synchronization overhead--the acquisition strategy of the aperiodic long code spread spectrum signal is given, and the factors affecting the acquisition performance are analyzed in detail;
* The hardware resource complexity problem caused by aperiodic long code acquisition is effectively solved--the matching filter length design method is analyzed, and a matching filter implementation method based on high-power clock multiplexing is proposed. This method can meet the requirements of resource-limited engineering applications and high security, and has wide application value;
* The reliable acquisition of spread spectrum code under the condition of low SNR is realized--an adaptive decision threshold design method based on auxiliary channels is proposed. The rest of this paper is organized as follows. Section 2 introduces the design of the structure of a burst spread spectrum signal based on an aperiodic long code. In Section 3, the acquisition performance is analyzed, and the acquisition method of the aperiodic length code without physical layer synchronization overhead is designed. In Section 4, the design and implementation of matching filter length based on high-power clock multiplexing are introduced. In Section 5, an adaptive decision threshold design method based on auxiliary channels is presented. In Section 6, simulation and experimental tests are carried out, and the results are given and discussed. A summarization of this paper is given in Section 7.
# Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation
## 3 Methodology
### Complexity Analyses of SSTGL  
We show the algorithm in Algorithm 1. Since SSTGL introduces no trainable parameters, its and space complexity remain the same as the GNN backbone. In addition, without any change to the neural network structure, its inference time complexity also remains the same as the GNN backbone. Therefore, the main extra time complexity comes from computing the self-supervised loss. Let \(|E|\) and \(|V|\) be the number of edges and nodes in the user-item graph, respectively, \(s\) denote the number of epochs, \(B\) denote the batch size, and \(d\) denote the embedding size. The time complexity of the whole training phase is \(O(|E|d(2+|V|s))\), which is the same as an existing self-supervised GNN model [12].  
```
0: Given the original user-POI graph \(\mathcal{G}=(\mathcal{V},\mathcal{E})\), the layer \(L\) of GNN model.
0: The similarity scores \(\hat{Y}_{UP}\).
1: Initialize all node embeddings \(\mathbf{Z}^{(0)}\) and compute similarity scores by Equation (7);
2:while not converge do
3:for each layer \(l\) in \([0,\dots,L-1]\)do
4: Aggregate neighbor information to update node embeddings \(\mathbf{Z}^{(l+1)}\) by Equation (3);
5:endfor
6: Compute final node embeddings \(\mathbf{Z}\) using readout strategy by Equation (4);
7: Compute similarity scores \(\hat{Y}_{UP}\) by Equation (5);
8: Compute recommendation loss \(\mathcal{L}_{main}\) by Equation (6);
9: Compute self-supervised loss \(\mathcal{L}_{ssl}\) by Equation (15);
10: Backpropagate based on overall loss \(\mathcal{L}\) computed by Equation (16).
11:endwhile
```  
**Algorithm 1** The framework of SSTGL
## 3 Results
### Single-Orbit Analysis
Figure 7: Same format as Figure 4, but for the synchronous abnormal perturbations of \(Te,Ne,No^{+},V_{z},Ex,\) and \(Ez\) of orbit 3806_1 on 10 October 2018, 1 day before the Ms 7.1 Papua New Guinea EQ influential area. quartiles (Q1) of these revisiting orbits, respectively. The pre-defined allowed ranges were calculated by adding Q3 and Q1 and subtracting multiple IQR values to extract the anomaly for \(No^{+}\) and \(V_{z}\). Normally, it is best to choose the multiple as 1.5 or 2.0, but no more than 2.0. Otherwise, some samples would not be classified as anomalous, although they are [25]. Figures 8-11 separately show the \(No^{+}\) and \(V_{z}\) results for anomalous orbits 2939_1, 3775_1, 3458_1, and 3806_1, represented by red curves. The median values of the background trends are plotted by blue lines, the upper/lower bounds by different color blocks, and the blue dotted lines denote the locations of the epicentral latitudes. The observations of the current orbit that significantly exceeded the given thresholds were judged as anomalous parts, denoted by arrow indications. It is noted that the absolute values of \(No^{+}\) in orbit 3775_1 and orbit 3806_1 (shown in Figures 10 and 11) are far beyond the upper boundaries, and the latitude ranges affected are up to about 40\({}^{\circ}\), while in orbit 2939_1 and orbit 3458_1 (shown in Figures 8 and 9), the \(No^{+}\) data did not exceed the upper boundary in such a large range except for the candidate seismo-ionospheric anomaly area. Meanwhile, the \(V_{z}\) values in these orbits are stable, rarely exceeding the threshold ranges of 2.0 times IQR, except for the sudden disturbances in the EQ abnormal areas. Figure 8: The variation of the (**a**) \(No^{+}\) and (**b**) \(V_{z}\) detected by orbit 2939_1 with its revisiting orbits from 4 August to 8 September, which are impacted by the Ms 7.1 loyalty Islands EQ, instead of the geomagnetic storm. Figure 10: Same format as Figure 8, but for the variation of the (**a**) _No_\({}^{+}\) and (**b**) _V_\({}_{2}\) detected by orbit 3775_1 with its revisiting orbits from 28 September to 23 October, which were jointly affected by strong geomagnetic storms and the Ms 6.9 Southeast of Loyalty Islands EQ. Figure 9: Same format as Figure 8, but for the variation of the (**a**) _No_\({}^{+}\) and (**b**) _V_\({}_{2}\) detected by orbit 3458_1 with its revisiting orbits from 2 September to 27 September, which are impacted by the Ms 7.4 Indonesia EQ, instead of the geomagnetic storm.
## 4 Application to Data  
The methodology just described was applied to JET experimental data. Specifically, a step-by-step application has been followed on a test pulse, the 92238 one, and described in Section 4.1. Then, the same method has been applied to other discharges characterized by either standard operational plasma parameters or unusually higher ones, such as very high plasma current and high external additional heating power and the results summarized in Table 1.
# How Time Window Influences Biometrics Performance: An EEG-Based Fingerprint Connectivity Study
## 2 Materials and Methods  
### The Datasets  
In this study, we used two different freely available EEG datasets: the EEG Motor Movement/Imagery Dataset [21] and the DREAMER dataset [22]. The EEG Motor Movement/Imagery Dataset is a high-density EEG dataset (consisting of 64 channels) that is publicly and freely accessible and includes recordings from 109 healthy subjects. The raw data are available on the PhysioNet website ([[http://physionet.org/pn4/eegmmidb/](http://physionet.org/pn4/eegmmidb/)]([http://physionet.org/pn4/eegmmidb/](http://physionet.org/pn4/eegmmidb/)), accessed on 28 May 2024). This dataset has been previously employed for brain-computer interfaces and biometric applications. The EEG signals were acquired with a sampling rate of 160 Hz, referenced to the average of the ear-lobe electrodes, and organized into several runs involving resting state, motor movement, and imaginary tasks. For the aim of the present study and analysis, we selected the two resting state runs (eyes open and eyes closed), each lasting 1 min. The DREAMER database is a multi-modal dataset that includes electroencephalogram (EEG) data from a 14-channel system and electrocardiogram (ECG) signals, collected during emotional stimulation through audio-visual content. Data was gathered from 23 participants, who also provided self-assessments of their emotional states after each stimulus, rating valence, arousal, and dominance. The signals were recordedusing portable, wearable, wireless, and low-cost off-the-shelf equipment, demonstrating the potential for integrating affective computing techniques into everyday applications. The subjects under examination were shown 18 film clips that aimed to trigger different emotional states and a 'neutral' clip to help the subject return to a neutral emotional state. For the intent of this study, we used the EEG signals acquired during the first minute of each of the 18 acquisitions made while watching the 'neutral' clip.
## 3 Design and Analysis of the Sliding-Mode Controller
### Closed-Loop Dynamics  
The next step in the design of the adaptive SMC is calculating the switching function parameter \(k_{v}\). Such a design is performed considering a stable operation of the SMC (25); thus, \(\Psi=0\) implies \(k_{c}\cdot i_{L2}=i_{L1}\) and \(i_{L2}=i_{R}+k_{v}\cdot\left(v_{R}-v_{C2}\right)\), where \(k_{c}=\frac{d}{dt}\). Since the SMC generates the control signal \(u\), the \(C_{2}\) voltage can be described using the averaged model given in Equation (17), which results in the following Equation:  
\[\frac{dv_{C2}}{dt}=-\frac{k_{c}}{C_{2}}\cdot i_{L2} \tag{38}\]  
Taking into account that \(i_{R}\) is the output of a high-pass filter acting on the load current, the load transients always end with \(i_{R}=0\). Therefore, the dynamic behavior of the \(C_{2}\) voltage is described by the following Equation since \(i_{L2}=k_{v}\cdot\left(v_{R}-v_{C2}\right)\) for \(i_{R}=0\):  
\[\frac{dv_{C2}}{dt}=-\frac{k_{c}}{C_{2}}\cdot k_{v}\cdot\left(v_{R}-v_{C2}\right) \tag{39}\]  
Transforming the previous expression to the Laplace domain leads to Equation (40), which describes the closed-loop dynamic behavior of the power system. Since the closed loop exhibits a first-order dynamic, the settling time of \(C_{2}\) voltage is given by \(t_{s,C2}=3.9\cdot\tau_{CL}\) as reported in [35]. Therefore, the \(k_{v}\) parameter is calculated in terms of the desired settling time using Equation (41), which shows two characteristics; \(k_{v}\) is an adaptive value with negative sign, which is important information to finish both the transversality and reachability analyses.  
\[\frac{v_{C2}}{v_{R}}=\frac{1}{\tau_{CL}\cdot s+1}\quad\text{where} \quad\tau_{CL}=-\frac{C_{2}}{k_{c}\cdot k_{v}} \tag{40}\] \[k_{v}=-\frac{3.9\cdot C_{2}}{k_{c}\cdot t_{s,C2}} \tag{41}\]
# Performance Analysis of DF Relay-Assisted D2D Communication in a 5G mmWave Network
## 1 Introduction
### Motivation  
The research works discussed in the above section portray various techniques involving DF relay-aided D2D communication. The optimal management of relays enhances the spectral efficiency (SE), as well as the energy efficiency (EE) of the overall network. Some papers [6; 7; 9; 10; 14] focused on extending the coverage area of D2D communication. Relay selection is also an important area that needs attention. Many researches in the literature have focused on the issues of coverage probability, EE [5; 6], throughput [7; 9; 10], power optimization, etc. The urban mmWave network consists of a large number of users and is susceptible co-channel interference. Furthermore, the distance between the users in a mmWave network is much less in comparison to a cellular network, and with the increase in the distance, the SINR becomes degraded, thereby decreasing the data rate. To alleviate this shortcoming, we propose a scheme through which the interference can be minimized, thereby enhancing the SINR and the data rate of the users. The different modes in the proposed scheme operating at different frequencies help reduce the co-channel interference and increase the SINR, thereby enhancing the throughput of the users. Furthermore, the introduction of the existing DF relay from the literature survey in our model helps enhance the throughput and extend the coverage area of D2D communication in a mmWave network. Thus, this work focused on enhancing the coverage probability of the D2D users, as well as finding an optimal method of relay selection in a mmWave network.
# The Thermal Analysis and Heat Dissipation Structure Optimization of a Propeller Driver System
## 1 Introduction
[24] designed a finned water-cooled heat sink and studied the influence of cooling water inlet and outlet positions, fin height, thickness, and spacing on the thermal resistance and flow resistance performance of the radiator. The study found that the heat dissipation performance of the central arrangement of the cooling water inlet and outlet is better than that of the diagonal arrangement, and the optimal fin coverage area of the radiator is almost equal to the chip area. [PERSON] et al. [25] studied the heat dissipation performance of a heat sink with perforated fins on a cylindrical base of a light-emitting diode (LED), and they studied the influence of the size of each fin of the heat sink and the number of perforations on the heat dissipation performance. Studies have found that a large number of small-sized perforations are beneficial to improve heat dissipation performance. [PERSON] et al. [26] used the finite volume method for numerical analysis of three-dimensional microchannels. The optimal geometric characteristics obtained numerically (the aspect ratio and the optimal channel shape (hydraulic diameter). [PERSON] et al. [27] used experimental methods to study the heat dissipation performance of finned heat pipes, finless heat pipes, and grooved heat pipes. [PERSON] et al. [28] studied the effect of heat sinks of different geometries on the heat dissipation of photovoltaic panels. [PERSON] [29] proposed a new method based on entropy generation minimization to optimize the plate fin heat sink. This equation can be used for both natural and forced convection. However, the heat dissipation of propeller-drive devices has not been extensively studied until now. In this paper, we investigate the thermal performance of a specific propeller driver using both numerical simulations and experiments. To improve the working stability and reduce the temperature of electronic equipment, we design a new cylindrical heat sink for the driver. We use the orthogonal experimental method to analyze and discuss the effects of three structural parameters, namely base thickness, fin length, and fin number, on the heat dissipation performance of the heat sink.
# FAIDM for Medical Privacy Protection in 5G Telemedicine Systems
## 4 Security Analysis
### Formal Verification Using BAN Logic
#### 4.2.6 Unforgeability  
If the adversary wants to forge validated anonymous identity, adversary has to acquire gateway node \(GW_{i}\)'s secret \(s_{i}\) and private key \(S_{i}\). The adversary has to solve CMDLP if adversary wants to compute gateway node \(GW_{i}\)'s secret \(s_{i}\) and private key \(S_{i}\) from public parameter \(Q_{i}\).
# Inspection Robot and Wall Surface Detection Method for Coal Mine Wind Shaft
## 2 Challenges and Solutions of Wind Shaft Wall Inspection
### The Solution of the Wind Shaft Wall Inspection System
#### 2.2.1 Mapping Analysis of Water Vapor Dust Concentration and Image Definition in Wind Shaft  
According to the analysis of the working conditions of the wind shaft, it can be seen that the relative humidity, particulate matter and dust concentration in the shaft are relatively high, and the premise of shaft wall condition detection is that the inspection system can overcome the problem of low visibility in the wind shaft. In order to ensure the feasibility of image acquisition, a wind shaft simulation experiment platform is also built. Figure 2 is a schematic diagram of the test scene of this simulation experiment. The experimental equipment includes a dust concentration measuring instrument, humidifier, temperature and humidity meter, etc.  
Due to the long longitudinal distance of the shaft, the dust concentration and relative humidity are different at different depths. The humidity in coal mine shafts is different at different heights, higher near the ground and lower at the bottom of the shaft, and that does affect image recognition. Therefore, this experiment mainly tests the shooting performance of an intrinsically safe camera under the conditions of dust concentration and humidity change. The specific scheme includes: changing the concentration of dust in the experimental environment under the condition of constant illumination and humidity, and  
Figure 2: Schematic diagram of test scenario.  
detecting the shooting situation of the KBA12H camera. Table 1 shows some shooting effects of the intrinsically safe camera. Due to the airtightness of the experimental environment, the actual water vapor concentration can only reach 97.3%.  
According to the experimental results in Table 1, from the analysis of the above experimental results, it can be seen that with the increase in water vapor concentration and dust concentration, the picture definition taken by the camera becomes worse; especially when the humidity exceeds 90%, the image of the target can hardly be obtained. It should be noted that with the increase in the brightness of external light sources and the decrease in shooting distance, the image clarity was obviously improved.