esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Optimization of Service Migration and Resource Allocation in Mobile Edge-Cloud Computing
## 5 Performance Evaluation
### Comparison Experiments  
To evaluate our solution in a dynamic migration and resource allocation environment, we benchmarked it against four distinct schemes.  
* **Follow-Avg scheme:** This scheme targets the user's current location for migration if a task remains incomplete and there is residual time. It then allocates computing resources equally among all tasks on the same server.
* **PSO scheme:** In this scheme, migration targets and resource allocation decisions are treated as particles within a particle swarm optimization (PSO) algorithm, using average response delay as the fitness function. Decisions are made for each time slot state.
* **PPO scheme:** Proximal policy optimization (PPO) is employed, a method from online reinforcement learning within the DRL spectrum, to determine service migration and resource allocation.
* **DDPG:** Deep deterministic policy gradient (DDPG) utilizes the actor-critic framework of DRL to derive migration and resource allocation strategies.
# Electrically Conductive Nanocomposite Fibers for Flexible and Structural Electronics
## 3 Results and Discussion
### Acceptance Criteria  
In order to obtain a composite fiber that can be used in textbooks as a conductive element or sensor, the material must have suitable electrical and mechanical properties and it must be possible to continuously extrude it to obtain fibers of a limitless length.  
The first criterion considered in the study was the ability to obtain a continuous fiber with a constant diameter (0.1 mm error allowed) of 0.2 mm, 0.5 mm, and 1 mm, depending on the used nozzle during extrusion, and a minimum length of 0.5 m.  
The mechanical criteria considered included the elasticity and extensibility of the developed fibers. The fibers with adequate elasticity were those that, after repeated bending with a bending radius of 0.5 mm, did not crack and no damage visible to the naked eye was observed on their surface. Additionally, the minimum elasticity that the developed fibers should exhibit, should allow the fiber to be stretched by a minimum of 40% without damage.  
The last of the considered criteria that the developed fibers should meet was the criterion of low resistivity. It was assumed that fibers with a diameter of 0.2 mm should have a resistivity below 0.2 \(\Omega\)m. It was also essential that the fibers possessed constant resistivity in the function of voltage in the range of 0 V to 30 V.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Material Amount [vol. \%]**} \\ \cline{3-5}  & & **SBS** & **Graphite** & **CNT** \\ \hline \multirow{8}{*}{C} & G1 & 95 & 5 & 0 \\  & G2 & 90 & 10 & 0 \\  & G3 & 80 & 20 & 0 \\  & G4 & 60 & 40 & 0 \\  & G5 & 55 & 45 & 0 \\  & G6 & 50 & 50 & 0 \\  & C1 & 98 & 0 & 2 \\  & C2 & 95 & 0 & 5 \\  & C3 & 90 & 0 & 10 \\  & M1 & 96 & 2 & 2 \\  & M2 & 93 & 2 & 5 \\  & M3 & 90 & 5 & 5 \\  & M4 & 85 & 10 & 5 \\  & M5 & 80 & 15 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the composite materials.
# Precise Identification of Coal Thickness by Channel Wave Based on a Hybrid Algorithm
## 4 Results
### Obtained Slowness Distribution  
After the performance of 1000 evolution generations by the GA, the optimal inversion error decreased from 55.3 ms (Figure 4a) to 27.6 ms (Figure 4b). The randomly generated initial population  
Figure 3: The time–frequency distribution of S5–R7. Results of the time–frequency analysis applied to data from source station S5 recorded at receiver station R7.  
Figure 2: The geological map of the survey area and the ray coverage map combined with realized survey geometry (location of all source and receiver stations).  
(slowness model) can quickly approach the global optimal solution according to the genetic operator, therefore, the value of the _RMSE_ has a large descending range. The SIRT's strong local search ability was then used for further search on the basis of the initial solution determined by GA. The slowness result obtained by use of the GA was regarded as a reliable initial model for the SIRT. The result of the final inversion of slowness (after 500 iterations) was acquired with an error of 15.3 ms (Figure 4c). Since this step is a further local search for the object function's global optimal solution, the value of _RMSE_ generally decreases by a small margin.  
The slowness of channel wave propagation has a good correspondence with the medium, and the slowness of channel wave propagation in a coal seam is higher than that in rock strata. As can be seen from Figure 4c, the low slowness region corresponding to blue color was mainly distributed  
Figure 4: Inversion results of slowness. (**a**) Initial model with iterations _Iter_ = 1; (**b**) result from the genetic algorithm (GA) inversion after 1000 iterations; (**c**) final result from the application of simultaneous iterative reconstructive technique algorithm (SIRT) tomography after 500 iterations;  
around 350-550 m and the triangular region in the left side. Therefore, it was preliminarily estimated that the average coal thickness in this region was lower than that in the surroundings. In addition, the range of the thin coal area from Figure 4a-c became clearer and more accurate, and the final slowness distribution result also had a good correspondence with the fault.
# Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm
## 3 Problem Description  
This paper addresses the collaborative challenges and privacy protection issues faced by multiple data collectors during the process of power load forecasting. In the power grid, each user independently holds their own power load data, and due to privacy concerns, it is difficult to directly share their original data. Federated learning is a reasonable choice to solve this problem. In the \(k\)-th round of federated learning, each client \(i\) performs a local model update:  
\[\Delta\theta_{i}^{k+1}=\text{SGD}(\theta^{k},D_{i},\eta_{l})-\theta^{k},i=1, 2,\ldots,n \tag{9}\]  
The global model is updated as  
\[\Delta\theta^{k+1}=\sum_{i=1}^{n}\frac{|D_{i}|}{|D|}\Delta\theta_{i}^{k+1}= \kappa_{1}\Delta\theta_{1}^{k+1}+\kappa_{2}\Delta\theta_{2}^{k+1}+\ldots+ \kappa_{n}\Delta\theta_{n}^{k+1} \tag{10}\]  
where \(D=D_{1}\cup D_{2}\cup\ldots\cup D_{n}\), and \(\kappa_{i}=\frac{|D_{i}|}{|D|}\). When federated training reaches the stable optimal solution \(\theta^{*}\), the global model update approaches zero, i.e., \(\Delta\theta=0\). At this point, two situations may occur:  
1. \(\Delta\theta_{1}=\Delta\theta_{2}=\ldots=\Delta\theta_{n}=0\), indicating that all clients have consistent data distributions and the optimal solution is achieved simultaneously.
2. The local model updates \(\Delta\theta_{i}\) are not all zero, which is caused by inconsistent data distributions during the training process. This scenario more accurately reflects practical applications of federated learning. In this case, the local model updates may not guide the global model toward optimization, resulting in generally lower performance and accuracy for the global model.  
Data quality is a critical foundation for load-forecasting modeling in power systems. Existing methods often assume that user load data are independently and identically distributed. In reality, while the power consumption data of different users are independent, the assumption of identical distribution does not hold true. Moreover, although the FL model keeps the training data locally, it requires uploading model parameter updates. Attackers can infer users' sensitive information from these uploaded model parameters, making it challenging to effectively ensure data privacy.
# Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation
## 5 The Proposed VLSI Architecture and Implementation
### The Algorithm Implementation and Pipeline Architecture  
The results of VLSI implementation based on KF and proposed filtering algorithms are evaluated in terms of hardware approach. The experimental path of the two-dimensional model environment is the research environment of this article. In research, \(f=1/\Delta_{k}=1\) is the sampling frequency. By adding random components with \(\sigma_{k}^{\mathrm{u}}\)= 0.045 and \(\sigma_{k}^{\mathrm{v}}\)= 4.5 to the MD, model describing analytic stabilizing motion of the MD with noise is based on Equation (3). The model describing observation location of the MD is taken in Equation (5). The VLSI architecture is shown in Figure 5. The improved project illustrates the proposed approach which use plenty of shifters to replace multipliers and dividers. Pipeline architecture is used to design the circuit to increase efficiency. Moreover, KF has matrix so much, like \(\mathbf{K}_{k}\), \(\mathbf{\overset{\kappa}{P}}_{k}\), \(\mathbf{\overset{\kappa}{P}}_{k}\). For example, as shown in Equations (6), (7) and (10), in the case where the x-axis is independent of the y-axis, this matrix just has some items with values. As shown in Equations (25) and (26). Therefore, these registers can be omitted with a value of zero in the proposed VLSI design to decrease the circuit area. As a result, the approaches in this article can decrease the circuit complexity, and the architecture of design also can process the problems of floating-point-number computation by using shifters. The circuit is designed by using four-stage structure which can decrease processing time, where stage1, stage2, stage3, and stage4 are control input, use PKD-based and DKF-based approaches to judge steady, compute KG, \(\mathbf{\overset{\kappa}{S}}_{k}\), \(\mathbf{\overset{\kappa}{P}}_{k}\), \(\mathbf{\overset{\kappa}{x}}_{k}\) and \(\mathbf{\overset{\kappa}{P}}_{k}\), respectively.  
\[\mathbf{\overset{\kappa}{P}}=\begin{bmatrix}\tilde{P}_{11}&0&\tilde{P}_{13}&0 \\ 0&\tilde{P}_{22}&0&\tilde{P}_{24}\\ \tilde{P}_{31}&0&\tilde{P}_{33}&0\\ 0&\tilde{P}_{42}&0&\tilde{P}_{44}\\ \end{bmatrix} \tag{25}\]  
\[\mathbf{\overset{\kappa}{P}}=\begin{bmatrix}\hat{P}_{11}&0&\hat{P}_{13}&0\\ 0&\hat{P}_{22}&0&\hat{P}_{24}\\ \hat{P}_{31}&0&\hat{P}_{33}&0\\ 0&\hat{P}_{42}&0&\hat{P}_{44}\\ \end{bmatrix} \tag{26}\]
# Relative Positioning in Remote Areas Using a GNSS Dual Frequency Smartphone
## 4 Results
### Test 5
At southern European latitudes, such as those in this study, under minimal solar conditions, even for baselines of the order of 100 km, maximum positioning errors using the double-difference technique are about 20 cm, and the RMS of about 5 cm ([PERSON] (2008) [27]). Those values are consistent with this study. But during times of disturbed ionospheric conditions, during solar maximum even at mid-latitudes maximum errors around 100 cm and RMS of tens of cm can be expected for baselines on the order of 100 km ([PERSON] (2008) [27]). Thus, for equatorial regions where the ionosphere is \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CASC**} & \multicolumn{3}{c}{**CEUT**} & \multicolumn{3}{c}{**GAIA**} & \multicolumn{3}{c}{**VIGO**} \\ \cline{2-13}  & **E** & **N** & **U** & **E** & **N** & **U** & **E** & **N** & **U** & **E** & **N** & **U** \\ \hline AVE & 0.068 & 0.038 & 0.042 & 0.086 & 0.035 & 0.133 & \(-\)0.008 & \(-\)0.005 & 0.048 & \(-\)0.016 & \(-\)0.040 & \(-\)0.031 \\ STD & 0.091 & 0.033 & 0.044 & 0.030 & 0.018 & 0.040 & 0.026 & 0.025 & 0.057 & 0.014 & 0.017 & 0.026 \\ RMS & 0.010 & 0.003 & 0.004 & 0.012 & 0.002 & 0.019 & 0.001 & 0.001 & 0.008 & 0.001 & 0.003 & 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Statistics results from the Test 5. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CASC**} & \multicolumn{3}{c}{**CEUT**} & \multicolumn{3}{c}{**GAIA**} & \multicolumn{3}{c}{**VIGO**} \\ \cline{2-13}  & **E** & **N** & **U** & **E** & **N** & **U** & **E** & **N** & **U** & **E** & **N** & **U** \\ \hline AVE & 0.084 & 0.240 & 0.195 & \(-\)0.117 & 0.441 & 0.329 & \(-\)0.191 & 0.010 & 0.296 & \(-\)0.355 & \(-\)0.054 & 0.223 \\ STD & 0.168 & 0.033 & 0.076 & 0.209 & 0.077 & 0.098 & 0.083 & 0.006 & 0.117 & 0.123 & 0.064 & 0.049 \\ RMS & 0.035 & 0.059 & 0.044 & 0.057 & 0.200 & 0.118 & 0.043 & 0.000 & 0.101 & 0.141 & 0.007 & 0.052 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Statistics results from the Test 6.  
much more disturbed, even during solar minimum, we do not expect baselines hundreds of km long to achieve the accuracy presented in this paper, although we do expect the low-cost mobile phone solution still function at a level close to that provided by much more expensive equipment.
# Vision Transformer-Based Tailing Detection in Videos
## 4 Experiments and Analysis
### Experimental Settings
#### 4.2.2 Method 2: CNN-Based Method  
The CNN exhibits better performance than that of the handcrafted rule-based method because CNNs can robustly learn tailing patterns from the images of multiple views, and handle the noisy localization generated by the object detector, during the extensive training process. The CNN can also handle the perspective effect illustrated in Figure 6c,d, and perform better than rule-based method in the partial occlusion situation depicted in Figure 6. The tailing event detection result with various CNN architectures are summarized in Table 1. The number written next to model, i.e., 512, represents the input frame size 512 \(\times\) 512.  
* Training configurations. We trained all the CNN architectures used in this experiment for 100 epochs using SGD [45] with momentum = 0.9, weight decay = 0.0001, and a batch size of 64. We performed a grid search with a learning rate (lr) in [0.001 0.01]. We use MultiplicativeLR as the lr scheduler.  
From the experimental results, we can make a few observations. First, CNN performance did not significantly increase as the depth of the model is increased. The simple-CNN shows the best detection accuracy at 63.54%. On the other hand, if we compare the recall of the tailing detection, ResNet-101 shows the best performance among all CNN-based models, while slightly outperforming ResNet-50. The recall drops as the ResNet architecture is further deepened to ResNet-152. The general consensus is that the larger the convolutional neural network, the better the performance [46]. However, according to the experimental results, there is no significant gain with increasing model size. We consider that this is due to two reasons, (i) overfitting on small datasets, and (ii) spatial inconsistency. In general, as the size of the model increases, with a sufficient dataset, the capacity of the model increases, and so does its performance. However, in the case of small datasets, as the model capacity increases, overfitting may incur. Secondly, as the network size is increased, the spatial information becomes inconsistent due to the increase in the receptive fields of the deeper filters. We believe that these two reasons constitute the main reasons behind the low performance of deeper networks such as ResNet-152, in comparison with simpler-CNN in terms of accuracy and precision.
# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 4 Proposed Architecture
### Merging-Squeeze-Excitation Feature Fusion
#### 4.2.1 Pre-Merging  
We use the pre-merging step to initially combine feature maps \(\mathbf{A}^{(n)}\) from all \(N\) branches together and to produce the pre-merged feature maps \(\mathbf{B}\in\mathbb{R}^{1\times W\times C}\), which will be used along with \(\mathbf{A}^{(n)}\) to compute the channel weights. The feature maps \(\mathbf{B}\) can be expressed as \(\mathbf{B}=[\mathbf{b}_{1};\mathbf{b}_{2};\ldots;\mathbf{b}_{C}]\), where the row vector \(\mathbf{b}_{c}\in\mathbb{R}^{1\times W}\) is expressed as \(\mathbf{b}_{c}=[b_{1,c},b_{2,c},\ldots,b_{W,c}]\) and \(b_{w,c}\) is a value at the \(w\)-th data point of the \(c\)-th channel. Several feature merging methods are available [42]. Here, we investigate and compare the following methods:  
* Addition merging creates feature maps \(\mathbf{B}\) by using the element-wise addition. The value \(b_{w,c}\) is obtained from \[b_{w,c}=\sum_{n=1}^{N}a_{w,c}^{(n)},\] (3) where \(a_{w,c}^{(n)}\) is a value at the \(w\)-th data point of the \(c\)-th channel of \(\mathbf{A}^{(n)}\).
* Maximum merging creates feature maps \(\mathbf{V}\) by using the element-wise maximum operation. The value \(b_{w,c}\) is obtained from \[b_{w,c}=\max\Big{\{}a_{w,c}^{(1)},a_{w,c}^{(2)},\ldots,a_{w,c}^{(N)}\Big{\}}.\] (4)  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**CNN Model**} & \multicolumn{2}{c}{**PAMAP2**} & \multicolumn{2}{c}{**DaLiAc**} & \multicolumn{2}{c}{**DSAD**} \\ \cline{2-7}  & **W** & **C** & **W** & **C** & **W** & **C** \\ \hline LeNet5 & 69 & 120 & 144 & 120 & 25 & 120 \\ AlexNet & 8 & 256 & 17 & 256 & 2 & 256 \\ VGG16 & 9 & 512 & 18 & 512 & 3 & 512 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The width \(W\) and the number of channel \(C\) of the feature maps \(\mathbf{A}^{(n)}\) obtained from LeNet5, AlexNet, and VGG16 by using the PAMAP2, DaLiAc, and DSAD datasets.  
* Minimum merging creates feature maps **B** by using the element-wise minimum operation. The value \(b_{w,c}\) is obtained from \[b_{w,c}=\min\Bigl{\{}a_{w,c}^{(1)},a_{w,c}^{(2)},\ldots,a_{w,c}^{(N)}\Bigr{\}}.\] (5)
* Average merging creates feature maps **B** by using the element-wise averaging operation. The value \(a_{w,c}\) is obtained from \[b_{w,c}=\frac{1}{N}\sum_{n=1}^{N}a_{w,c}^{(n)}.\] (6) For a future usage, we denote the merging operation as \(\mathbb{F}_{Merge}(\cdot)\). Specifically, we have \[\textbf{B}=\mathbb{F}_{Merge}\bigl{(}\textbf{A}^{(1)},\textbf{A}^{(2)},\ldots, \textbf{A}^{(N)}\bigr{)}.\] (7)
## 4 Conclusions
\begin{tabular}{l l} \(a\) & softness parameter [N/m] \\ \(C\) & capacitance [F] \\ \(C^{*}\) & dimensionless capacitance [-] \\ \(C_{n}{}^{*}\) & dimensionless capacitance located at \(n\)'s turn cell [-] \\ \(cs\) & compressive strain [-] \\ \(e\) & elementary charge [C] \\ \(eE_{o}\) & applied voltage [eV] \\ \(f\) & frequency of light [1/s] \\ \(H\) & magnetic field strength [gauss] \\ \(h\) & Planck's constant [Js] \\ \(I\) & current density [A] \\ \(I_{d}\) & diode current [A] \\ \(I_{ph}\) & photocurrent [A] \\ \(I_{r}\) & light intensity [cd] \\ \(I_{S}\) & reverse-bias saturation current [A] \\ \(I_{sh}\) & shunt current [A] \\ \(k\) & Boltzmann constant [J/K] \\ \(k_{I}\) & glass factor [1/m] \\ \(m\) & mass of electron [kg] \\ \(N\) & recombination in the \(n\)-\(p\)-type semiconductor [1/m\({}^{3}\)] \\ \(N_{d}\) & diode ideality factor [-] \\ \(n\) & pairs of regions [-] \\ \(R\) & resistance [\(\Omega\)] \\ \(R^{*}\) & dimensionless resistance [-] \\ \(R_{a}\) & arithmetical mean deviation of the assessed profile of surface roughness [m] \\ \(R_{u,E}\) & arithmetical mean deviation of the assessed profile of electric current by sensing [A] \\ \(R_{s}\) & series resistance [\(\Omega\)] \\ \(R_{sh}\) & shunt resistance [\(\Omega\)] \\ \(R_{y}\) & maximum height of the assessed profile of surface roughness [m] \\ \(R_{y,E}\) & maximum height of the assessed profile of electric current by sensing [A] \\ \(R_{z}\) & Japanese industrial standard based on the five highest peaks and lowest valleys over the entire sampling length of the assessed profile of surface roughness [m] \\ \(R_{z,E}\) & Japanese industrial standard based on the five highest peaks and lowest valleys over the entire sampling length of the assessed profile of electric current by sensing [A] \\ \(R_{ou}\) & representative resistance [\(\Omega\)] \\ \(T\) & transmitted probability [-] \\ \(T_{a}\) & absolute temperature [K] \\ \(T_{i}\) & transmitted probability at \(i\)-ordered pair of regions [-] \\ \(T_{out}\) & transmitted probability at the outlet of \(n\) pairs of regions [-] \\ \(t\) & response time [s] \\ \(t^{*}\) & dimensionless time [-] \\ \(t_{1}\) & response time based on \(C\) and \(R\) [s] \\ \(t_{2}\) & response time based on the speed of carrier diffusion at deeper regions than the depletion layer [s] \\ \(t_{3}\) & response time based on the speed of carriers in the inner depletion layer [s] \\ \end{tabular}
# High Reliable Uplink Transmission Methods in GEO-LEO Heterogeneous Satellite Network
## 2 System Model
### Uplink Transmission Model of NOMA Scheme  
Recognizing that there exists a significant beam gain gap between the beam-center user and beam-edge user, NOMA can be applied to save the spectral resource where two users are allocated within one resource block. Let \(x_{1}\) and \(x_{2}\) denote uplink signals of \(U_{1}\) and \(U_{2}\), respectively. The received signal at the LEO satellite can be expressed as  
\[y=h_{1}x_{1}+h_{2}x_{2}+n, \tag{3}\]  
where \(n\sim\mathcal{CN}(0,\sigma^{2})\) denotes the background noise. Assume that the signal power and power factor for the beam-edge user are denoted as \(P\) and \(\alpha\), respectively. We have \(\mathbb{E}[|x_{2}|^{2}]=\alpha P\) and \(\mathbb{E}[|x_{1}|^{2}]=(1-\alpha)P\). It is noteworthy that the signal of beam-center user should be demodulated first at the successive interference cancellation (SIC) receiver since the channel condition of the beam-center user is much better. In this case, the signal-to-interference-noise ratios (SINRs) of \(U_{1}\) and \(U_{2}\) at the SIC receiver-based LEO satellite can, respectively, be computed as  
\[SINR_{1}=\frac{|h_{1}|^{2}(1-\alpha)P}{|h_{2}|^{2}\alpha P+\sigma^{2}} \tag{4}\]  
and  
\[SNR_{2}=\frac{|h_{2}|^{2}\alpha P}{\sigma^{2}}. \tag{5}\]
# PLI-VINS: Visual-Inertial SLAM Based on Point-Line Feature Fusion in Indoor Environment
## 6 Conclusions  
In this paper, a visual-inertial SLAM algorithm based on point-line feature fusion for various indoor environments is proposed. Compared with the visual inertial SLAM algorithm based on point features, the proposed PLI-VINS uses the combination of point and line features to increase the robustness of the visual inertial SLAM system. This PLI-VINS is built on VINS-Mono and evaluated using EuRoC datasets. Different from the existing work, the PLI-VINS makes use of the advantages of different features and sensors, and effectively integrates point, line and IMU data by improving the quality of the extracted line features, thus improving the robustness and accuracy of the system. A comparison with the existing similar work shows that this paper can achieve the highest accuracy in most indoor situations.  
In the future, this paper will improve the system by looking for more methods to constrain between 3D lines, and introduce line features into the initialization process, or effectively add line features into the word bag model and dense map of point and line features. These works will further improve the system, will be more suitable for indoor environments, improve the accuracy of camera motion trajectory estimation and the stability of system operation.  
Conceptualization, Z.Z., T.S., B.X.; methodology, Z.Z.; investigation and validation, Z.Z. and T.S.; writing--original draft preparation, Z.Z.; writing--review and editing, Z.Z., T.S., Y.L. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China (61701056) and Fundamental and Frontier Research Program of Chongqing Science and Technology Bureau (cstc2021 jcyj-msxmX0348); supported by the Action Plan for High Quality Development of Postgraduate Education of Chongqing University of Technology (gzlcx20223088, gzlcx20223063, gzlcx20223075).  
Not applicable.  
The authors declare no conflict of interest.