esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Generalized autoregressive conditional heteroskedasticity (GARCH) is one of the most popular models for time-series forecasting. The GARCH model uses a maximum likelihood method for parameter estimation. For the likelihood method to work, there should be a known and specific distribution. However, due to uncertainties in time-series data, a specific distribution is indeterminable. The GARCH model is also unable to capture the influence of each variance in the observation because the calculation of the long-run average variance only considers the series in its entirety, hence the information on different effects of the variances in each observation is disregarded. Therefore, in this study, a novel forecasting model dubbed a fuzzy linear regression sliding window GARCH (FLR-FSWGARCH) model was proposed; a fuzzy linear regression was combined in GARCH to estimate parameters and a fuzzy sliding window variance was developed to estimate the weight of a forecast. The proposed model promotes consistency and symmetry in the parameter estimation and forecasting, which in turn increases the accuracy of forecasts. Two datasets were used for evaluation purposes and the result of the proposed model produced forecasts that were almost similar to the actual data and outperformed existing models. The proposed model was significantly fitted and reliable for time-series forecasting.  
forecasting; fuzzy linear regression; fuzzy logic; sliding window; time series Article
# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 3 Results
### Experimental Comparison
This decline can be attributed to the fixed threshold approach's limited recognition ability in semi-indoor and semi-outdoor scenarios, where satellite signal features exhibit less distinct variations. In contrast, the other networks that utilize training models demonstrate improved accuracy across all four scenarios. For semi-indoor and semi-outdoor scenes, the accuracy of the CNN + BiLSTM network is significantly higher than that of the other algorithms. This indicates that this network's ability to extract temporal and spatial information during state transitions is markedly superior to that of other methods, such as the stacked model. In the case of the latter four methods, the use of multi-source feature data results in significantly better accuracy across all four scenes compared to scene classification algorithms that rely on single-source information. This improvement is attributed to the inherent homogeneity of single sensor information; data from different sensors can characterize scene features from multiple perspectives. Figure 17: Comparison of accuracy in different scenarios.
## 4 Result Analysis  
### Validation  
In this section, the previously derived model and optimization algorithm will be verified by comparing the data of the experiment and the open literature.  
To verify the accuracy of the present method, an SSSS partially covered composite plate is taken into account. The geometry and material properties of the simply supported square composite plate are as follows: \(L=W=0.4\) m, \(h_{b}=0.005\) m, \(h_{d}=0.0025\) m, \(h_{c}=0.0005\) m, \(\rho_{b}=\rho_{c}=7800\) kg/m\({}^{3}\), \(\rho_{d}=2000\) kg/m\({}^{3}\), \(\beta=0.38\), \(E_{c}=E_{b}=207.0\) GPa, \(G_{d}=4.0\) MPa, and \(\mu_{c}=\mu_{b}=0.334\). The partially covered plate with the patch location is shown in Figure 5. The natural frequency and loss factor of the first mode for the partially covered composite plate, as shown in Table 1, are compared with the numerical results of the open literature.  
Figure 4: Structure of multi-population genetic algorithms.  
It can be seen that the results obtained in this paper are in good agreement with those in the literature [22]. This validation ensures the accuracy in analyzing the partially covered sandwich plate. The position of the patch is on the center of the sandwich plate.  
As seen in Figure 5, a cantilever composite plate with a partially constrained damping layer is used for modal testing. The material of the base plate and constrained layer are both selected aluminum alloy 5052. The material of the damping layer is selected aluminum foam. The material and geometric parameters of the composite plate are shown in Table 2. The equipment employed for modal testing includes an impact hammer (PCB MH03 with sensitivity of 10 mV/l bf) and an accelerometer (DYTRAN 3032A with sensitivity 10.00 mV/g). The signals are recorded with a data acquisition system supported by the B&K corporation. The position coordinates, \(a\), \(b\), \(c\), \(d\), of the patch are 0.085, 0.085, 0.185, and 0.185, respectively. Table 2 shows the detailed parameters of the composite plate (material and geometric) [40].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Base Plate**} & \multicolumn{2}{c}{**Damping Layer**} & \multicolumn{2}{c}{**Constrained Layer**} \\ \hline Length \(L\)/m & 0.25 & 0.08 & 0.08 \\ Width \(W\)/m & 0.25 & 0.08 & 0.08 \\ Height \(h\)/m & 0.004 & 0.004 & 0.004 \\ Density \(\rho\)/(g\(\cdot\)cm\({}^{-3}\)) & 2.72 & 0.60 & 2.72 \\ Elasticity modulus /GPa & 69.3 & 12 & 69.3 \\ Poisson ratio \(\mu\) & 0.33 & 0.33 & 0.33 \\ Loss factor \(\eta\) & / & 0.25 & / \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material and geometric parameters of composite plates.  
Figure 5: Experimental composite plate.  
After the collection and analysis of experimental data, the experimental and theoretical calculation results are listed in Tables 3 and 4. It can be clearly seen that the difference between the experiment, calculation and error results are also counted in the table. All the errors are within the acceptable range, which fully shows the accuracy of the theoretical model in this study.
## 2 Analysis and Design of the Active Phase Shifter Circuit  
As outlined in the basic principles of phased array systems [18; 19], the phase shifter plays a crucial role in the overall system, which imposes stringent requirements on parameters such as phase shifting accuracy and amplitude fluctuations. To maintain high phase accuracy and low amplitude variation across a wide frequency band, this paper presents the design of an active phase shifter tailored for phased array system applications.
# Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks
## 4 Implementation of AggOR Scheme
### Complexity Analysis  
In order to testify the validity and efficiency of AggOR, we analyze its complexity in terms of computation, message and storage complexity. The computation complexity of Algorithm 2 (finding candidate gathering node set) is \(O(\max_{\forall i\in[1,m]}(|CP_{i}\cup N_{i}|))\), where \(m\) is the number of sensor nodes in the network. It means in the worst case, a node \(v_{i}\) visits all its candidate parent nodes \(CP_{i}\) and its neighbor nodes \(N_{i}\) to find its candidate gathering nodes \(CG_{i}\). Accordingly the computation complexity of Algorithm 1 (gathering area construction) is \(O(\max_{\forall i\in[1,m]}|CP_{i}\cup N_{i}|+|CG_{i}|)\). Since the number of elements in \(CP_{i}\), \(N_{i}\) and \(CG_{i}\) are all less than \(m\), the complexity of our algorithms is \(O(m)\).  
In the process of transmission hierarchy diagram construction, sink starts flooding hello message and other nodes broadcast it after reception. Thus the control message cost is \(m\). When constructing gathering areas, node \(v_{i}\) sends join message to its potential gathering node, and after receiving acceptance message from it, \(v_{i}\) sends acknowledgement message to join the gathering area. Hence in this process the message cost is \(3m\). Overall, the message complexity of AggOR is \(O(m)\).  
For the control information, since each node stores its overlapping rates of sensing area with nearby nodes, candidate parent node set, neighbor node set, level and remaining energies of its candidate gathering nodes, the storage complexity is \(O(m)\). In addition, for the data packets, every member node only carries its own data, while each gathering node caches the data from its member nodes. Considering that data aggregation is implemented at one-hop distance, the number of data packets collected by a gathering node is usually far less than \(m\), with the storage complexity \(O(m)\).  
To sum up, compared with EEUC, whose message complexity is \(O(m)\), and HEER, whose message complexity is \(O(m)\) and computation complexity is exponential, our distributed scheme AggOR has relatively low complexities of computation, message and storage.
# Boundary-Aware Hashing for Hamming Space Retrieval
## 4 Experiments
### Ablation Study  
We investigate three variants of BAH: (1) BAH+T is a common operation using the deep hashing methods, adding a tanh activation function behind the hash layer to learn the hash codes using the BAH; (2) BAH-C is the BAH variant without using the \(c_{ij}\) Equation (1), it is worth noting that the \(c_{ij}\) is only valid for multi-label datasets; (3) BAH-Q is the BAH variant without using the \(l_{2}\) loss Equation (4), i.e., \(\alpha=0\).  
From Figures 4-6, Tables 2 and 3, BAH outperforms BAH+T by 5.08%, 4.45%, 8.42% and 7.29% in average of R@\(H\leq 2\) on MS-COCO, NUS-WIDE, CIFAR-10 and ImageNet datasets, respectively. As previously stated, the tanh function exists in the saturated region. This is the reason that BAH+T to aggregate similar data is far weaker than that of BAH--it is worth noting that the re-ranking performance of BAH on MS-COCO is lower than that of BAH+T. However, BAH+T limits the effectiveness of the search while BAH balances both effectiveness and accuracy. Thus, BAH is more suitable for Hamming space retrieval in different application scenarios. Taking everything into account, BAH outperforms BAH  
Figure 6: Comparison of R@\(H\leq 2\), P@\(H\leq 2\), F1-score, and ratio of returned zero images for BAH and other methods on the ImageNet dataset. (**a**) R@\(H\leq 2\) on ImageNet. (**b**) P@\(H\leq 2\) on ImageNet. (**c**) F1-score on ImageNet. (**d**) Zero images on ImageNet.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**ImageNet**} \\ \cline{2-5}  & **16 Bits** & **32 Bits** & **48 Bits** & **64 Bits** \\ \hline DHN [23] & 0.3243 & 0.4796 & 0.2151 & 0.1237 \\ DSH [15] & 0.6373 & 0.7185 & 0.4586 & 0.3252 \\ HashNet [25] & 0.1763 & 0.5722 & 0.5725 & 0.5592 \\ DCH[7] & 0.5894 & 0.6643 & 0.6254 & 0.6071 \\ GreedyHash [42] & 0.6541 & 0.7116 & 0.6444 & 0.5982 \\ CSB [26] & 0.6084 & 0.6921 & 0.6224 & 0.5913 \\ BAH & **0.7063** & **0.7263** & **0.7244** & **0.7245** \\ \hline BAH+T & 0.7034 & 0.7109 & 0.7075 & 0.7000 \\ BAH-Q & 0.6863 & 0.7354 & 0.7206 & 0.6844 \\ \hline \hline \end{tabular}
\end{table}
Table 3: MAP Results of Re-ranking within Hamming Radius 2 (MAP@\(H\leq 2\)) for different bits on the ImageNet dataset.  
C on MS-COCO and NUS-WIDE datasets. It is thus clear that \(c_{ij}\) leverages multi-label information, which can effectively trade off between accuracy and effectiveness on multi-label datasets. BAH outperforms BAH-Q by huge margins of 11.43%, 20.81%, 15.11%, and 10.42% in average R@\(H\leq 2\) on MS-COCO, NUS-WIDE, CIFAR-10, and ImageNet datasets, respectively. This result validates that the \(l_{2}\) loss helps aggregate relevant data effectively into Hamming ball, leading to an enhancement of the pruning efficiency by BAH.
## 3 Results
### Reproducibility of the Measures of RV and LV Strain  
The reproducibility of strain measurements was assessed in all the images obtained at the different HRs for 19 of the study participants. The results included sixty-seven strain calculations. A high degree of reproducibility was observed for LVGLS (intraclass correlation coefficient [ICC] = 0.96; 95% confidence interval [CI], 0.93-0.97) as well as for RVGLS (ICC = 0.91; 95% CI, 0.86-0.94) and RVfwLS (ICC = 0.91; 95% CI, 0.85-0.94).
## 3 The PII\({}^{1/2}\)DD\({}^{1/2}\) Control Scheme
### \(Pl^{\lambda}D^{\mu}\) Frequency Response  
In general, the \(Pl^{\lambda}D^{\mu}\) controller is not a commensurate-order system, especially if \(\lambda\) and \(\mu\) are obtained by optimization. However, even if the factorization (14) cannot be applied, for low frequencies the magnitude slope tends towards \(-\lambda 20\) dB/dec and the phase to \(-\lambda\pi/2\) rad, while for high frequencies the magnitude slope tends towards \(+\mu 20\) dB/dec and the phase towards \(+\mu\pi/2\) rad [47].  
Tuning the three gains \(K_{p}\), \(K_{\beta}\) and \(K_{fd}\) and the two orders \(\lambda\) and \(\mu\) it is possible to modify the magnitude plot, with independent slopes at low and high frequencies, and consequently the phase plot, with independent asymptotic values at low and high frequencies. Figure 2 shows in yellow with \(Pl^{\lambda}D^{\mu}\) controller frequency response for \(K_{p}=1\), \(K_{fh}=4\), \(K_{fd}=0.015\), \(\lambda=0.8\) and \(\mu=0.6\). These example parameters provide a frequency response which is comparable to the one of the PID of Section 4.2 (blue) in the range \(10\div 1000\) rad/s, but with different magnitude slopes and asymptotic phase values outside this range.
# Integrated Image Processing Toolset for Tracking Direction of Metal Grain Deformation
## 3 Integrated Image Processing Toolset
### Grain Boundary Extraction Algorithm Based on Moore-Neighbor  
The last task was to extract the grain boundary, which would be used to observe the grain deformation. The displacement information on the boundary point was utilized as the input information for analyzing the geometric deformation of the grain and for predicting the force direction. To provide a tracking flexibility, the built-in image labeler application in MATLAB was utilized. This allowed us to track a specific grain and extract the boundary for a further analysis. The application functions as a labeling toolset to draw a bounding box on a specific grain target. The export information contains the bounding box coordinates corresponding to a specific image file, including the GB location \(GB(x,y)\).  
To extract the boundary, the Moore-Neighbor tracing mechanism was integrated into the toolset. This algorithm performs continuous pixel-by-pixel tracing on binary images until specific commands are satisfied. The binarization process using GD-HBT was used to support the edge-detection algorithm. The Moore-Neighbor tracing algorithm, which was updated using [PERSON]'s stopping criteria, was used to trace the boundary location [57; 58; 59]. Identical to the implementation of 8-connected pixels in computer graphics (Figure 7), the Moore-Neighbor tracing algorithm consists of a set of eight pixels that share a vertex or  
Figure 6: Fast normalized cross-correlation (f-NCC) algorithm for grain matching: (**a**) reference image: stitched 50-scope, (**b**) target image: 200-scope, (**c**) normalized cross-correlation analysis, (**d**) plot of target image location on the reference image, (**e**) surface plot of correlation map, and (**f**) stacking analysis of 200-scope image (bright) on stitched 50-scope image (dark).  
edge with the starting pixel \(P\). These neighboring pixels (\(P1:P8\)), located in \(P(x\pm 1,y\pm 1)\), determine the trajectory, either clockwise or counterclockwise, of pixel \(P\), which serves as a sensor. Every time pixel \(P\) hits a black \(\{1\}\) pixel declared as a grain boundary, a sequence signal \(B(b_{1},b_{2},b_{3},\ldots,b_{k})\) is stored to shape the contour map of the grain boundary. This process is repeated until pixel \(P\) returns to a predetermined initial location. Because the loop terminated when pixel \(P\) returned to the initial position, mistaken results appeared owing to a discontinuity of the target shape, which caused pixel \(P\) to return to the initial position before successfully tracing all the grain regions. To address this weakness, [PERSON]'s stopping creation was utilized by modifying the termination command, which stopped tracing if pixel \(P\) returned to the initial position in the same manner as the initial visits. This improvement significantly improved the Moore-Neighbor algorithm, providing a robust contour extraction algorithm for the tracking of the grain boundary.
## 2 Establishment of Evaluation System  
### Determination of Evaluation Index  
At present, the regulations on the braking performance of vehicles in various countries require the braking system of vehicles to have good braking efficiency, excellent lateral stability and an excellent braking swing angle. In order to put forward a scientific and reasonable evaluation index of the braking timing sequence, after sorting out the regulations on vehicle braking performance in the United States, Europe and China, we read relevant references [5; 6; 7; 8; 9; 10] and discussed the issue with relevant experts in the industry. Those investigations suggest that the evaluation index of the braking timing sequence of a semi-trailer train can follow the principles of scientificity, safety, feasibility and measurability. At the same time, combined with the analysis of the braking stability of the semi-trailer train in reference [11], the following items are proposed as the evaluation indexes for evaluating the braking timing sequence of the semi-trailer train.  
1. [label=(0)]
2. Braking distance: When the vehicle is at a certain speed, the driver starts to take braking measures until the vehicle stops completely. The smaller the braking distance, the better the braking performance of the car. At present, most semi-trailer trains are equipped with ABS. References [12; 13] show that ABS can reduce the braking distance of the vehicle by controlling the slip rate of the vehicle during braking. Because the braking distance is more intuitive, it is widely used to evaluate the braking efficiency.
3. Yaw angle: The yaw angle around the z-axis is generated at the center of mass of the vehicle. If the braking force is unevenly distributed or the braking timing sequence is improper, the vehicle will swing laterally. Therefore, in order to ensure the lateral stability of the semi-trailer truck, the yaw angle should be used as an evaluation index.
4. Jackknife angle: The angle formed by the longitudinal axis of the tractor and the longitudinal axis of the semi-trailer. The incorrect braking timing sequence will produce a large jackknife angle, which will cause the semi-trailer train to fold and cause serious traffic accidents.
5. Lateral acceleration: The acceleration along the lateral axial direction of the vehicle during braking. Greater lateral acceleration will cause lateral instability of the vehicle during braking, and, in severe cases, the vehicle will roll over.
6. Brake deviation: The phenomenon of the vehicle running to one side due to an uncoordinated braking force or improper braking timing sequence. When the braking deviation is serious, the vehicle will slip sideways, causing the driver to lose control of the vehicle and causing serious traffic accidents.
## 3 Lightweight Cryptography for Smart Objects
### Public-Key (Asymmetric) LWC Algorithms
#### 3.2.3 Performance Comparison of Available Implementations of Public-key Cryptographic Algorithms  
Hereafter, we recall the performance results, presented in [44], of RSA and ECC public-key different available implementations, with benchmarks obtained in constrained devices (namely an 8-bit Arduino Uno board), such as TinyECC and Wiselib. Table 3 shows implementation results for RSA public-key encryption, when the private key is held in SRAM or in ROM. In Table 4, a performance comparison in the ECDSA signature algorithms, using TinyECC and Wiselib implementations, is presented. Finally, a ROM footprint comparison is shown in Table 5.  
\begin{table}
\begin{tabular}{r r r r r} \hline \hline \multirow{2}{*}{**Cure parameters**} & \multicolumn{2}{c}{**Execution time (ms)**} & \multicolumn{2}{c}{**Memory footprint (bytes)**} & \multicolumn{1}{c}{**Comparable RSA**} \\ \cline{2-5}  & **TinyECC** & **Wiselib** & **TinyECC** & **Wiselib** & **key length** \\ \hline
128r1 & 1858 & 10774 & 776 & 732 & 704 \\
128r2 & 2002 & 10615 & 776 & 732 & 704 \\
160k1 & 2228 & 20164 & 892 & 842 & 1024 \\
160r1 & 2250 & 20231 & 892 & 842 & 1024 \\
160r2 & 2467 & 20231 & 892 & 842 & 1024 \\
192k1 & 3425 & 34486 & 1008 & 952 & 1536 \\
192r1 & 3578 & 34558 & 1008 & 952 & 1536 \\ \hline \hline \end{tabular}
\end{table}
Table 4: ECDSA signature performance: TinyECC versus Wiselib implementations.  
\begin{table}
\begin{tabular}{r r r r r} \hline \hline \multirow{2}{*}{**Key length (bits)**} & \multicolumn{2}{c}{**Execution time (ms)**} & \multicolumn{2}{c}{**Memory footprint (bytes)**} \\ \cline{2-5}  & **Key in SRAM** & **Key in ROM** & **Key in SRAM** & **Key in ROM** \\ \hline
64 & 66 & 70 & 40 & 32 \\
128 & 124 & 459 & 80 & 64 \\
512 & 25089 & 27348 & 320 & 256 \\
1024 & 109666 & 218367 & 640 & 512 \\
2048 & 1587559 & 1740267 & 1280 & 104 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RSA private key operation performance.
## 5 Methodology
### Texture Feature Extraction
#### 5.1.2 Calculation of the Gray-Level Co-Occurrence Matrix (GLCM)  
A GLCM is a one-dimensional square matrix, where each element in the row (\(i\)) and column (\(j\)) directions records a relative occurrence frequency (\(M_{ij}\)) of a pair of pixels with the same element value, separated by a certain pixel distance (\(D\)) in one orientation (\(\theta\)) [23; 28]. Figure 4 shows an illustration of GLCM production at different orientations and pixel distances for a pixel of interest in the square matrix, along with an example of statistics for \(M_{ij}\) at 90\({}^{\circ}\) (vertical direction) and one pixel distance. Figure 4b shows the GLCM of an example detailed image, where the image size is 7-by-7 (but not limited to square) and the number of gray levels is 8. In the GLCM, the element (3, 0) has a value of 1, because there is only one instance in the example detailed image where the vertically adjacent pixels (2, 1) and (1, 1) have gray-level values of 3 and 0, respectively.  
Figure 4: Illustration of GLCM production: (**a**) A pair of pixels with the same element value separated by the different pixel distances in 4 orientations. (**b**) Relative occurrence frequency recording of gray levels of digital images, using a GLCM with \(D=1\) and \(\theta=90^{\circ}\).  
Except for the four necessary orientations of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\), several studies have indicated that the different pixel distances (\(D\)) have a great influence on the accuracy of GLCM-based texture description, and demonstrated that one pixel distance (\(D\) = 1) can lead to better accuracy of the feature extractions [23; 34]. This paper introduces an adjacent distance of bordering on pixels instead of striding over pixels into the calculation of the GLCMs.