esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The contributions of this study to the literature and important highlights are as follows:  
* Unlike other studies, this study obtained loss samples from conventional vertical magnetic shunts optimized and manufactured by the transformer manufacturer. * Based on these loss samples, the appropriate location and size of the horizontal magnetic shunts were determined using the size-loss relationship via a parametric finite element analysis. * A parametric analysis was carried out separately for the long front walls and the short side wall of the tank. * In addition, this study determined the tank losses according to the size and position of the horizontal magnetic shunts using a parametric analysis and presented the transformer manufacturer's preferences. It can be observed that the reference loss examples presented in existing methodologies are all tank walls, and the number of magnetic shunts is represented in the form of multiple packages [3; 27; 32]. In existing methodologies, the number of parametric variables appears to be a variable such as \(\Delta\)h [27]. Furthermore, no determination based on a size-loss relationship is evident in the existing methods. The loss examples referenced in the proposed method pertain to short, long, and entire tank walls. In the proposed method, a reduced shunt package is employed. In the proposed method, the size-loss relationship is determined by defining five parametric variables, namely \(\Delta\)x, \(\Delta\)y, \(\Delta\)z, \(\Delta\)w, and \(\Delta\)d. To emphasize the innovative nature and contributions of our study, the method we propose and a comparison of existing methods are given in Table 1.  
The rest of this paper is structured as follows: Section 2 presents the theoretical background. Section 3 covers the materials and methods, including the transformer simulation model and the parametric analysis. Section 4 presents the results of the parametric and full-load analyses. Finally, Section 5 presents the conclusions.
# A Generalized Spatial Modulation System Using Massive MIMO Space Time Coding Antenna Grouping
## 4 Conclusions  
In this paper, a new technique is proposed to match 5G requirements. The proposed technique is based on using STC and antenna grouping. The use of STC and antenna grouping enhances the performance of the proposed technique in terms of data rate and BER. On the other hand, the conventional GSM technique is modified by changing the number of transmitting and receiving antennas, considering a selective fading channel and QAM modulation. The proposed technique and the modified GSM are evaluated, and the simulation results show that the GSM-STC outperforms the conventional and modified GSM. The simulation results reveal that the SNR for the proposed technique is enhanced by 11 dB and 4 dB compared to conventional and modified GSM, respectively, for 16-QAM. This means that GSM-STC is a good candidate for massive MIMO due to its advantage in achieving diversity gain and increased data rate with lower BER. Moreover, the simulation results also show that the modified GSM technique achieves a higher data rate compared to the conventional GSM technique.  
**Author Contributions:** Conceptualization, A.I.Z. and W.K.B.; Methodology, M.N., A.I.Z. and W.K.B.; Software, M.N., A.I.Z. and W.K.B.; Validation, A.I.Z., W.K.B. and M.H.A.; Formal Analysis, A.A.Z. and W.K.B.; Investigation, M.N.; Resources, M.N., A.I.Z. and W.K.B.; Data Curation, M.N.; Writing--Original Draft Preparation, M.N.; Writing--Review & Editing, A.I.Z., W.K.B. and M.H.A.; Visualization, M.N., A.I.Z. and W.K.B. Supervision, A.I.Z., W.K.B. and M.H.A.; Project Administration, M.N., A.I.Z. and W.K.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research has no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Sound Activity Monitor Circuit for Low Power Consumption of Always-On Microphone Applications
## 2 Proposed Approach and Detailed Circuit
### Approach 1: Connection Location of SAM Input  
Figure 3 shows two cases of connecting SF and SAM. Figure 3a illustrates a case in which SF and SAM receive MEMS signals in common. This architecture has the advantage of being able to completely power off the SF in the absence of sound input, as SAM can directly monitor sound signals from the MEMS device. However, there is a problem of lowering the sensitivity of MEMS devices. The sensitivity of a MEMS device can be expressed by the following equation:  
\[\textit{VIN}=\textit{VDR}\times\mathrm{C_{m}}/[\mathrm{C_{m}}+\mathrm{C_{p1}}+ \mathrm{C_{p2}}+\mathrm{C_{p3}}], \tag{1}\]  
in which \(\textit{VDR}\) is the driving voltage of the MEMS device, \(\mathrm{C_{m}}\) is the MEMS capacitance of the microphone, \(\mathrm{C_{p1}}\) is the parasitic capacitance of MEMS, \(\mathrm{C_{p2}}\) is the input capacitance of SF, and \(\mathrm{C_{p3}}\) is the input capacitance of SAM. As shown in Equation (1), the sensitivity of the MEMS device decreases due to the input parasitic capacitance \(\mathrm{C_{p3}}\) of SAM. Therefore, in order to improve the MEMS sensitivity, it is necessary to minimize the parasitic capacitance connected to the input terminal from the MEMS. Figure 3b shows the proposed SAM connection configuration. The SAM is not connected to the input port of the MEMS, but to the output of the SF. In the proposed SAM connection configuration, the MEMS sensitivity is improved according to the following equation  
\[\textit{VIN}=\textit{VDR}\times\mathrm{C_{m}}/[\mathrm{C_{m}}+\mathrm{C_{p1}}+ \mathrm{C_{p2}}]. \tag{2}\]  
Figure 3: Architecture approach 1: (**a**) Configuration when SF and SAM share MEMS input terminal; (**b**) Configuration when MEMS input connects only to SF.  
Figure 2: Top-level architecture of Sound Activity Monitor (SAM).  
When SF power is completely turned off in this configuration, the SAM cannot detect any sound signals. Therefore, SF should not be turned off completely even when there is no sound and should be operated to detect sound with minimal power consumption. For this reason, SF should be designed to have two operating modes: a normal power mode that can precisely measure sound and a low power mode that measures only the presence or absence of sound.
## 3 Proposed 2D DW-ZCC Code
Table 2 shows the 2D-spectral/spatial DW-ZCC code matrix for S\({}_{g,h}\) sensors obtained by combining the spectral code sequence X\({}_{g}\) and spatial code sequence Y\({}_{h}\) as \(DZ_{gh}=Y_{h}^{T}X_{g}\), where X\({}_{g}\) and Y\({}_{h}^{T}\) are the g\({}^{th}\) and h\({}^{th}\) code sequences of X and Y, respectively, with \(g=\left(1,2,3,\ldots\ldots K_{1}\right)\) and \(h=\left(1,2,3,\ldots\ldots K_{2}\right)\). Here, K\({}_{1}\) and K\({}_{2}\) respectively represent the number of codes corresponding to the number of sensors in both the spectral and spatial code sequences.
## 2 Material and Methods
### In Vitro Photodynamic Activity against Bacteria
#### 2.3.5 Synergy Test  
The bacteria were prepared in five sets of samples. Sample A (only RB at 1.0 \(\times\) 10\({}^{-6}\) M) was subjected to ultrasound only. Samples B (RB at 7.5 \(\times\) 10\({}^{-7}\) M and Ce6 at 2.5 \(\times\) 10\({}^{-7}\) M), C (RB at 5.0 \(\times\) 10\({}^{-7}\) M and Ce6 at 5.0 \(\times\) 10\({}^{-7}\) M), D (RB at 2.5 \(\times\) 10\({}^{-7}\) M and Ce6 at 7.5 \(\times\) 10\({}^{-7}\) M) were subjected to ultrasounds and further immediately to light at a wavelength of 660 nm. Sample E (Ce6 at 1.0 \(\times\) 10\({}^{-6}\) M) was subjected only to light irradiation. The control experiments were performed as follows: sample F (light control), sample G (ultrasounds control), sample H (ultrasounds + light control), and sample I (bacterial growth control). Light at a maximum of 660 nm and a light dose of 50 J/cm\({}^{2}\) was used. Ultrasound irradiation provided a dose of 512 J/cm\({}^{2}\) (1 MHz, 40% duty cycle). The samples were then plated on TSA and incubated for 24 h at 36 \(\pm\) 1 \({}^{\circ}\)C. After this time, the the number of bacteria was counted, and the log reduction was calculated. A dark toxicity control was also performed.
# Feasibility Study of Passive Bistatic Radar Based on Phased Array Radar Signals
## 2 Overview of Experimental PBR System
### Signal Processing Method  
The flow chart of the signal processing method is shown in Figure 4. Respectively, for the reference channel signal and surveillance channel signal, the brief process is described as follows.  
Firstly, for the reference channel signal, the CF, BW, PW, and TOA of the direct wave pulse are obtained by the parameter estimation method based on template matching, which is a key step in the signal processing flow, and we will discuss it in detail in Section 3. The TOA is used to distinguish different pulse signals, and then each pulse signal is down-converted to baseband according to the  
Figure 3: High performance sampling module and signal processing platform.  
Figure 2: Photographs of the reference antenna.  
estimated CF. The estimated PW and BW are used to reconstruct the baseband reference signal to perform matched filtering on the baseband scattered wave signal.  
As for the surveillance channel signal, the simultaneous multi-beam forming method is adopted to cover the surveillance airspace. In addition, since the CF of transmitted signal hops in a broad frequency range, wideband beamforming algorithm must be adopted. The PBR system forms seven frequency invariant beams to cover the surveillance airspace, as shown in Figure 5.  
After digital beamforming, the receiving data are transformed from array element space to beamspace. We use the estimated CF to down-convert the signal to baseband to reduce the amount of data to be processed subsequently. Then, pulse compression is performed and combined with the reconstructed reference signal, which concentrates the energy of target echo and improves the range resolution. Then, the pulses are realigned according to the peak position of the direct wave signal after pulse compression.  
After pulse alignment, we divide the pulses into different groups according to the CF of pulses and, then, perform coherent integration according to the CPI we set for the signals in each group, which can be used to filter out fixed clutter and to improve moving target detection performance. The coherent integration method based on Radon-NUFFT will be introduced in Section 4. Then, the constant false alarm rate (CFAR) detection method is used to detect targets.  
Figure 4: Flow chart of the signal processing method.  
Figure 5: Sketch map of simultaneously multi-beamforming.  
Finally, after target detection, an amplitude comparison direction measurement is implemented by taking advantage of the beams that may contain echo of targets. Combined with the azimuth and bistatic range of the target, the actual position of the target can be obtained.
# A Fast Wavelet-Based Bridge Condition Assessment Approach Using Only Moving Vehicle Measurements
## 3 Method for Bridge Damage Detection
### Identifying Bridge Damage Location  
Compared with a healthy bridge, the change in wavelet energy can be used as an indicator of the presence of bridge damage. However, it only implements the first level of bridge SHM. In order to achieve the second level of bridge SHM, i.e., to localize the bridge damage, the measured acceleration is evenly divided into portions (e.g., ten) as shown in Figure 2. In fact, each portion signal is the response when the vehicle moves on the corresponding beam segment. For the sake of clarity, the measured vehicle accelerations for the undamaged bridge and damaged bridge are labeled as \(\tilde{q}_{v}^{u}\) and \(\tilde{q}_{v}^{d}\), respectively. Based on Equation (27), their wavelet energies are calculated as \(E_{energy}^{u}\) and \(E_{energy}^{d}\), respectively. Both signals, \(\tilde{q}_{v}^{u}\) and \(\tilde{q}_{v}^{d}\), are divided into ten portions, labeled as \(\tilde{q}_{v,i}^{u}\) (\(I\) = 1\(\sim\)10) and \(\tilde{q}_{v,j}^{d}\) (\(j\) = 1\(\sim\)10). Similarly, those component signals of \(\tilde{q}_{v,i}^{u}\) and \(\tilde{q}_{v,j}^{d}\) can also be used to obtain the corresponding wavelet energy, labeled as \(E_{energy,i}^{u}\) and \(E_{energy,j}^{d}\), respectively. A damage location index is proposed and expressed as  
\[ID_{i+1}=\Delta_{i+1}-\Delta_{i}\ (i=1\sim 9) \tag{28}\]  
where  
\[\Delta_{i}=\left|E_{energy,i}^{d}-E_{energy,i}^{u}\right|/\left|E_{energy}^ {d}-E_{energy}^{u}\right|\ (i=1\sim 10) \tag{29}\]  
Theoretically, when a bridge is damaged, all component acceleration responses vary to some extent, as well as their corresponding wavelet energies. For the bridge damage at the beam segment No. \(i\), it is found that the change in \(\Delta_{i}\) is great while this is not obvious in \(\Delta_{i-1}\). Therefore, the bridge damage at beam segment No. \(i\) has the maximum value of \(ID_{i}\). In this way, the index \(ID\) can be used to approximately indicate the bridge damage location.  
Figure 3 shows the process of the fast wavelet-based bridge damage detection method. In this method, a reference acceleration associated with the healthy bridge conditions is required for a baseline. Based on the description in Section 3.2, the change of wavelet energy can be used to indicate the bridge conditions. When bridge damage is determined, Equation (29) is then used to identify the damage location.  
Figure 2: Divide the vehicle acceleration signal.
# Using an Artificial _Physarum polycephalum_ Colony for Threshold Image Segmentation
## 4 Algorithm Steps of APPCA
### Step 1: Initialization  
The threshold image segmentation at the start of the solving process is initialized from three main aspects, i.e., the original image, the artificial _Physarum polycephalum_ colony algorithm, and the system simulation parameters.  
First, the original image needs to be formatted before segmentation, where the original color image should be transformed into a grayscale image with a gray level of \(L\).  
Second, the initialization of the artificial _Physarum polycephalum_ colony algorithm sets the parameters of the APPC. The maximum number of hyphae \(S\), the possibility \(p_{s}\) of social learning, and the possibility \(p_{r}\) of free learning should be set first. The artificial _Physarum polycephalum_ colony algorithm can search for the optimal solutions in the whole solution space and the artificial hyphae can move from one pixel to another to connect them into a segmented image. The segmented image for hyphae \(k\) can be seen as a feasible solution. The fitness in Equation (5) can be set to zero at the start.  
In the process of initialization, _Ite_ = 1, the artificial _Physarum polycephalum_ colony should be randomly encoded as the feasible solutions to threshold image segmentation and the algorithm can easily adjust the initialization parameters on a personal computer to fit the requirements of threshold image segmentation, and obtain good performance. Assuming there are \(S\) hyphae in the APPC, each hyphae indicates a feasible solution. For a hyphae \(k(k=1,2,3,\ldots,S)\), it can produce a random segmented vector \(x^{k}\) of the original image:  
\[x^{k}(1)=\Big{\{}x^{k}_{i}(1)\Big{|}i=1,2,3,\ldots,HV\Big{\}}, \tag{14}\]  
where \(x^{k}_{i}=\{0,1\}\) is the selecting bit of pixel \(i\) calculated by hyphae \(k\). For a hyphae \(k\), if pixel \(i\) is selected as a segmented region, then \(x^{k}_{i}=1\); otherwise, \(x^{k}_{i}=0\). Therefore, in subsequent iterative calculations, the optimal solution \(x^{*}\) can be calculated in Equation (14) and compared by \(S\) hyphae according to the objective function in Equation (5).  
Third, the system simulation parameters should be set during initialization. The simulation time is set as \(t=0\). The simulation end conditions should be predefined here, including the maximum simulation time \(T_{max}\), maximum iteration steps \(Ite\_max\), and minimum iteration error threshold \(e_{th}\). If any of these conditions are met, the iterative calculation can be ended.
# Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
## 4 Failure Prediction and Container Migration Strategies
### Container Migration Strategies  
Let N denote the total number of devices in an edge network. If \(K\) (\(K<N\)) devices are predicted to be faulty, the encoding method for each chromosome in the GA-based migration strategy is as follows:  
\[Chromosome=\left[n_{i},n_{j},\ldots,n_{k}\right]_{k} \tag{10}\]  
Figure 1: Standard LSTM neuron structure.  
Figure 2: Two-layer GRU model.  
Among them, \(n_{i},n_{j},\ldots,n_{k}\) represent the unique identification ID of the healthy device, and each device number corresponds to the container on the faulty device one-to-one. In other words, the container on the faulty device is migrated to the device with the corresponding serial number.  
According to the coding method, it is necessary to use an evaluation function to calculate the fitness of each individual using the following formula:  
\[fitness=\frac{1}{\left(\theta\big{(}U_{balance}^{total}+V_{balance}^{total} \big{)}+\gamma Mig_{cos\,t}^{total}\big{)}\right.} \tag{11}\]  
Subsequently, according to the fitness calculated in the previous step, the classic roulette algorithm is used to screen and select excellent individuals. The following formula is used:  
\[P_{i}=\frac{f_{i}}{\sum_{i=1}^{NP}f_{i}} \tag{12}\]  
In this equation, \(f_{i}\) indicates the fitness of an individual and \(NP\) is the total number of individuals.  
The selected individuals avoid falling into a local optimal solution through single-point crossover and single-point mutation. Finally, when the number of iterations reaches a preset number, the algorithm terminates, and the individual with the largest fitness function value is output as the optimal migration strategy.  
Algorithm 3 describes the container migration strategy algorithm based on a GA. After initializing the population, for each generation of individuals, it first calculates the fitness value (lines 5-9) according to Equation (11), and then performs selection, crossover, and genetic operations (lines 10-18). After the number of iterations is reached, the output is a heuristic migration strategy.  
```
Input:

EN: Edge network deployment, including container list and device list

FEN: A list of faulty nodes, including all predicted faulty devices in the current time slice

Output:

\(\text{A}_{t}\): optimal migration strategy

Procedure:
```
1: Initialize population List P = {p\({}_{1}\), p\({}_{2}\),..., p\({}_{s}\)}
2: Initialize evolutionary parameter t = 0, the maximum number of iterations T
3: while t < T do
4: s = len(p)
5: for i = 1 to s do
6: MigCost \(\leftarrow\) calMigCost(Fen)
7: loadBalance \(\leftarrow\) calLoadBalance(EN - Fen)
8: fitness = 1/(\(\gamma\)MigCost + 6 loadBalance)
9: end for
10: for i = 1 to S/2 do
11: crossover operation to p(i)
12: end for
13: for i = 1 to S do
14: variation operation to p(i)
15: end for
16: for i = 1 to S do
17: P(i + 1) = P(i)
18: end for
19: t = t + 1
20: end while
21: output(MS) ```  
**Algorithm 3** Migration strategy based on a GA.
## 3 The Proposed Method
### Discriminative Feature Selection  
In general, the extracted entropy features can be directly combined into a longer vector to comprehensively quantify operational status of rolling bearings. However, the high dimensionality and redundancy of the original features will increase the computation time and decrease the pattern recognition accuracy. Thus, it is necessary to select the most representative features to form a low-dimensional discriminative feature vector for fault diagnosis.  
The proposed discriminative feature selection is based on the distance principle: the larger distance between different classes and the smaller distance within the same category of one feature. This feature is regarded as discriminative for fault diagnosis. To be specific, denote the feature set as \(f_{n}\in R^{l}\) (\(n=1\), \(2\),..., \(N\)), where \(N\) is the total number of samples and \(J\) is the feature dimensionality. The discriminativity of the \(j\)th feature is  
\[D\Big{(}f_{j}\Big{)}=\frac{Dis^{\text{inter}}\Big{(}f_{j}\Big{)}}{Dis^{\text{ intra}}\Big{(}f_{j}\Big{)}} \tag{6}\]  
where \(Dis^{\text{inter}}\) and \(Dis^{\text{intra}}\) are the interclass distance and the intraclass distance for the \(j\)th feature, respectively, calculated as Equation (7).  
\[\begin{array}{l}Dis^{\text{inter}}\Big{(}f_{j}\Big{)}=\frac{1}{Q(Q-1)}\sum_ {a,p=1;q\
eq p}^{Q}\Big{|}\frac{1}{N_{a}}\sum_{i=1}^{N_{q}}f_{i,j}{}^{q}-\frac {1}{N_{p}}\sum_{i=1}^{N_{p}}f_{i,j}{}^{p}\Big{|}\\ Dis^{\text{intra}}\Big{(}f_{j}\Big{)}=\frac{1}{Q}\sum_{q=1}^{Q}\Big{(}\frac{1 }{N_{q}}\big{(}\frac{1}{N_{q}}\big{)}\sum_{i,k=1;q\
eq k}^{N_{q}}\big{|}f_{i,j }{}^{q}-f_{k,j}{}^{q}\Big{|}\Big{)}\end{array} \tag{7}\]  
where \(f_{i,j}{}^{q}\) are samples for the \(q\)th (\(q=1\), \(2\),..., \(Q\)) class and \(N_{q}\) is its number.  
The discriminativity \(D(f_{j})\) measures ease of classifying the \(Q\) classes using the \(j\)th feature. A larger discriminativity is, the easier to classify the \(Q\) classes that a feature is. Thus, \(D(f_{j})\) (\(j=1\), \(2\),..., \(J\)) is sorted in descending order, and the number of features for fault diagnosis is increased one by one. When the desired accuracy is achieved, the first few features are selected as the most discriminative features.
# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 5 Experimental Results and Analysis
### Robustness Analysis  
To assess the robustness of the proposed method, SDebrisNet was tested on the SDD dataset with different SNRs. The noise in a space surveillance platform mainly includes  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**SFM** & **SFEM** & **TFM** & **Model Size (MB)** & \(\mathbf{F_{\beta}\uparrow}\) & \(MAE\downarrow\) & \(CE\downarrow\) \\ \hline  & & & 48.44 & 0.724 & 0.143 & 1.316 \\ ✓ & & & 43.04 & 0.711 & 0.158 & 1.407 \\  & ✓ & & 74.32 & 0.729 & 0.138 & 0.946 \\  & & ✓ & 69.00 & 0.871 & 0.057 & 0.615 \\ ✓ & ✓ & & 68.92 & 0.763 & 0.065 & 1.082 \\ ✓ & & ✓ & 63.60 & 0.979 & 0.036 & 0.387 \\  & ✓ & ✓ & 94.88 & **0.989** & **0.035** & **0.383** \\ ✓ & ✓ & ✓ & 89.48 & 0.988 & **0.035** & 0.384 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The ablation study of SDebrisNet on the SDD dataset.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Methods** & **Backbone** & **Model Size (MB)** & **Inference Speed (FPS)** & **GPU** \\ \hline MGA [59] & ResNet-101 & 350 & 14 & RTX 1080 TI \\ DCF [60] & ResNet-101 & 274 & 28 & RTX 2080 TI \\ STM [45] & ResNet-101 & 194 & 100 & RTX 2080 TI \\ Ours & MobileNetV3 & 89 & 77 & RTX 2080 TI \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the recent saliency detection methods.  
Figure 8: Saliency detection results of SDebrisNet without different components: all of the frames are collapsed by a max operator.  
thermal noise, shot noise, dark current noise, and stray light noise, which could affect the accuracy of space debris saliency detection. The first three noises are caused by the statistical nature of photodetection or photodiode in sensor systems [61], which can be represented as the Gaussian white noise model generally. The stray light noise results from accidental perturbations of diffuse reflection from the Earth, Moon, and other nebulae [62]. It also can be modeled using two Gaussian function dimensions. The detection results of SDebrisNet on the SDD dataset with different SNRs are shown in Table 8. Based on the test results, it can be observed that the centroid error increases as the SNR decreases in this experiment, and the false alarm rate also increases accordingly. The centroid error obtained by our method does not exceed 0.9 pixels in this experiment.