esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Test Stimulus Compression Methods for Ultra-Large-Scale Integrated Circuits
## 4 Scan Chain Optimization-Based Compression Methods
### Broadcast Scan Structure
[PERSON] utilized heuristic algorithms to reorder scan cells to enhance the fault coverage of the broadcast scan mode [77]. [PERSON] introduced an additional broadcast scan method and proposed a reconfiguration method to improve the compression efficiency of testing [78]. To simultaneously enhance the compression efficiency and fault coverage of testing, [PERSON] proposed a new layout and routing method for the broadcast scan mode [79]. Subsequently, [PERSON] made improvements for channel delay faults under the broadcast mode [80]. In light of the limitations of the physical location and layout of scan cells, [PERSON] optimized the wiring of the cells [81]. Considering that the physical location of scan cells is fixed before scan vector input, the above reconfiguration and optimization methods can effectively improve compression efficiency but introduce the consumption of wiring resources. Therefore, some research scholars have made other design improvements around the Illinois scan structure, including multi-fan-in optimization [82], pin optimization [83], RTL-level optimization [84], and low-power optimization [85]. The scan tree structure is another typical structure used in broadcast scan, which is also effective for compressing test stimuli and reducing test time. Compared to Illinois scan  
Figure 5: Schematic diagram of broadcast scanning structure [30,76]. structure, scan tree can effectively reduce test application cost but not test power [86]. In the scan tree structure, scan cells are connected in a tree-like structure, with compatible cells connected in parallel and incompatible cells connected in series. During testing, test data are input into the root node of the scan tree, and then propagated to every node, or scan cell, of the scan tree. The test data of the scan cells at the same level are the same. Compared to multiple scan chains, the probability of broadcasting test vectors in the scan tree is higher. Compared to a single scan chain, the longest scan chain in the scan tree has a shorter length, resulting in shorter test time. [PERSON] and others preprocessed the original test set according to the characteristics of the scan tree to achieve better compression effects [76]. [PERSON] [87] and others proposed a coarse compatibility method based on the compatibility between scan cells, and [PERSON] [88] proposed an extended compatibility tree method, both of which can effectively reduce test time. Other researchers have optimized the scan tree structure to improve compression effects, including a hybrid structure of reconfigurable scan chains and scan trees [89], scan forest structures [90], and low-power scan forest structures [91].
# A Compact Triple Band Antenna Based on Multiple Split-Ring Resonators for Wireless Applications
## 4 Fabrication and Measurement  
The proposed Multi-SRR-based antenna was fabricated, as can be seen in Figure 13. The measured S11 was evaluated by comparing the simulated S11 results of HFSS and CST, as shown in Figure 14. These simulations showed a strong correlation, underlining the accuracy of the design, which confirmed good agreement with the measurement result. Table 3 presents a comparison of the simulated and measured values for return loss and bandwidth of the proposed multi-SRR antenna. The results show good agreement in band positioning, with some variation in bandwidth. In Band 1, the measured bandwidth (23.5%) slightly exceeds the simulated bandwidth (14.55%), covering important standards such as Wi-Fi 6, WLAN, RFID and 5G. Band 2 also shows a larger measured bandwidth (18.9%) compared to the simulated result (14.28%), successfully covering the 5G and WiMAX midbands. For Band 3, the simulated bandwidth is wider (54.13%), but the measured range (20.47%) still covers important applications such as Wi-Fi 5/6/6E, WLAN and RFID.  
As can be seen in Figure 14, a distinct increase in frequency was also observed in the third band. This frequency shift is attributed to manufacturing tolerances, especially due to the compact dimensions of SRR 3 and SRR 4, as we mentioned in the previous section (current distribution). These components are very sensitive to even small manufacturing deviations, which can have a significant impact on the resonant frequency. In addition, the limitations of the VNA at 7 GHz in our laboratory also contribute to the lower accuracy of measurements in higher frequency bands. The slight frequency deviation has minimal impact and does not compromise the overall performance of the antenna. Overall, the  
Figure 14: Comparison of S11 return loss values for the simulated CST, simulated HFSS and fabricated antenna.  
Figure 13: Picture of the fabricated antenna (**a**) Front side (**b**) Back side.  
measured results match well with the simulations, confirming the antenna's suitability for multi-standard wireless applications. This is further illustrated in Table 7.  
The proposed design allows selective activation of resonant elements, enabling application-specific optimization. Table 8 shows how the proposed multiband antenna can be optimized for different applications by selectively activating specific SRRs. Each device type uses a unique combination of SRRs to target required frequency bands. This flexibility supports various wireless standards like Wi-Fi, Bluetooth, Zigbee and 5G NR
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 3 Evaluation of Different Knowledge-Based Approaches
### Assessment Results
#### 3.3.7 Instance-Based Regression Formalisms  
Instance-based methods, encompassing case-based, regression, and kernel function models, lack a formalized language for explicit knowledge expression, relying on storing and retrieving instances or patterns. However, they excel in accuracy by leveraging similarity among instances or patterns for predictions or classifications. While they can handle a wide variety of instances, these methods may suffer from data sparsity or biased instance representation, impacting their coverage. They generalize well within their trained instance spaces, but face limitations in adapting to unseen or significantly different instances. These methods provide moderate explainability based on instance similarity, but might struggle with complex explanations in high-dimensional or non-linear spaces.  
Lacking explicit mechanisms for uncertainty or confidence levels associated with instances or predictions, they may not adequately encode uncertainty. Handling inconsistencies via proximity measures for instance retrieval might not inherently resolve conflicts in contradictory instances. Confidence estimation relies on proximity measures or statistical evaluations, but it is not inherent to these methods. Although accommodating new knowledge might be challenging without extensive retraining or model modification, they scale well by relying on efficient instance retrieval and comparison techniques.  
In summary, instance-based methods excel in accuracy and scalability, effectively capturing similarities within instance spaces. However, they may lack explicit mechanisms for dealing with uncertainty, inconsistent knowledge, or accommodating new knowledge without significant retraining. Their generalizability might be limited to the scope of the training instances.
# Adaptive Subset-Based Digital Image Correlation for Fatigue Crack Evaluation
## 2 Automated Determination Algorithm of Adaptive Subset Sizes
Next, the matching distance \(D\) between \(S_{1}\) and \(S^{\prime}_{1}\) is computed using \(O_{1}\) and \(O^{\prime}_{1}\), which is given by:  
\[D=\sqrt{\left(x_{i}-x^{\prime}_{i}\right)^{2}+\left(y_{i}-y^{\prime}_{i} \right)^{2}}, \tag{2}\]  
where \((x_{i},y_{i})\) and \((x^{\prime}_{i^{\prime}},y^{\prime}_{i})\) are the spatial points of \(O_{i}\) and \(O^{\prime}_{i}\) on \(R_{q}\) and \(R^{\prime}_{q}\), respectively. Now, \(C_{\text{NCC}}\) is iteratively calculated by increasing \(S_{1}(j)\), i.e., \((2M_{j}\)+1) \(\times\)\((2M_{j}\)+1). Here, \(M_{j}\) (\(j\) = 1,2,3... \(m\)) is the size parameter of \(S_{i}\), and \(S_{i}(j)\) should be smaller than the ROI. Then, \(D\) can be obtained depending on \(S_{1}(j)\). When \(S_{i}(j)\) has small values, \(D\) typically fluctuates, as shown in Step (2) of Figure 1, because the lack of distinctive speckle features within the \(S_{i}\) makes it difficult to find the exact location of \(S^{\prime}_{i}\). On the other hand, \(D\) will converge after \(S_{i}(j)\) exceeds a certain value, which physically implies that sufficient speckle features are secured within the subset. The threshold value can be considered as the minimum converging size \(V_{1}\), which is determined when the derivative of \(D\) (\(D^{\prime}\)) becomes 0, as shown in Step (2) of Figure 1.  
Step (3) The establishment of a convergence map: As for \(O_{i}\) (\(i\) = 2,3... \(n\)), \(V_{i}\) can be obtained by repeating Step (2), as described in Step (3) of Figure 1. Then, \(V_{i}\) is assigned at the corresponding \(O_{i}\) within the ROI, which is called the convergence map. Physically, \(V_{i}\) in the convergence map means the minimum subset size for the proper DIC analysis with respect to each \(O_{i}\) within the ROI. Step (4) The determination of adaptive subset sizes: In the last step, \(q\) number of the convergence maps can be obtained from \(R_{q}\) and \(R^{\prime}_{q}\), as shown in Step (4) of Figure 1. The reason why the multiple convergence maps are used in this algorithm is that the random measurement noises caused during the image acquisition process can be minimized through averaging. If the random measurement noises are more dominant than the speckle features within a certain size of subset, \(V_{i}\) will be increased. Thus, for each pair of reference images, \(V_{i}\) might be different depending on the random measurement noises even at the same \(O_{i}\). Then, \(V_{i}\) can be assumed to follow the normal distribution because \(V_{i}\) depends on random measurement noises. Finally, the adaptive subset size \(A_{i}\) is statistically determined by summing the SD (\(\sigma\)) and mean (\(\mu\)) with respect to the \(q\) number \(V_{i}\) of each \(O_{i}\).
## 1 Introduction  
Shooting performance from a standing position is one of the most complex activities, requiring accuracy, precision, and consistency [1]. The shooter aims at a target and the efficiency is mainly based on shooting score [2]. However, previous evidence has shown that shooting performance must be analysed at a more specific level, describing the movement and the interaction between the feet and the ground [1, 2]. A biomechanical approach often includes kinematical, kinetical, and electromyography behavioural parameters responsible for a more efficient performance [3].  
Most of the previous research aiming to describe the associations between shooting performance and biomechanics has been carried out in Olympic sports [1, 3, 4, 5, 6, 7, 8, 9]. Specifically, elite shooters have been found to produce smaller body-swap amplitudes indicated by the centre of pressure movements during standing [4, 6] and shooting activities [5, 7]. Furthermore, postural balance has been shown to discriminate between successful and unsuccessful shooters, i.e., unsuccessful shooters have worse postural balance during shooting activity [5]. In general, a recent systematic review has shown that the most important factor affecting shooting performance is rifle stability, while some physiological or psychological factors, such as heart-rate stability and anxiety/stress, seem to have small influence on shot score [9].  
Although a great effort has been made in order to investigate the most important determinants for better shooting efficiency in sports, less evidence has been provided for ground reaction forces during shooting performance in special police. Special police officers are trained to perform specific shooting tasks and demands at maximal level [10]. While shooting training is part of the everyday activities among special police officers, the associations between shot score and produced forces beneath different foot regions remain unclear. A quality shooting position should ensure the highest level of stabilization of the abdomen, leg, and feet muscles and generate a significant amount of ground reaction forces through the feet. Recently, a study by [PERSON] [1] showed that plantar forces during shooting performance might be associated with the shot score. However, the study was conducted among four rifle shooters and the possibility of low statistical power cannot be excluded. By examining how ground reaction forces may be associated with shooting performance, the new biomechanical approach to training protocols and better shooting efficiency may be incorporated within the police system.  
Therefore, the main purpose of the study was to examine the associations between plantar pressure distribution patterns and shooting performance among special police officers. We hypothesized that lower peak ground reaction forces beneath the feet would be associated with better shooting performance.
## 4 Experimental Result and Analysis  
### Dynamic Characteristics  
Through a white noise frequency sweep on the model before and after an earthquake of the same intensity, the natural frequencies of the model at different stages are obtained from the time-history data collected using the acceleration sensor (Table 4). The first two shaking modes of the model were translational in the X and Y directions and the third mode was torsional. The first-order natural shaking frequency was significantly smaller than the second order, indicating that the model stiffness was more significant in the Y direction than in the X direction. After experiencing the first frequent level eight earthquake, the natural frequency of the model did not change, indicating that the model was in an elastic state. After the second frequent level eight earthquake the natural frequency of the model decreased slightly, indicating that the model was still in an elastic state. Therefore, the model was in an initial state without damage before each seismic loading. Meanwhile, the seismic response test data of the model obtained before and after the seismic wave delay and amplification could be compared laterally.
## 2 Materials and Methods
### Convolutional Neural Networks (CNN)  
CNNs are specialized deep learning models that process data within a grid structure. While time series data have a one-dimensional grid topology, image data typically have a two-dimensional grid of pixels. CNNs use convolutions in at least one of their layers,  
Figure 4: CEEMDAN decomposition of solar irradiance for the year 2023.  
a specific type of linear operation that replaces the generic matrix multiplication used in other neural networks. Over time, various CNN architectures have emerged, such as multichannel and multi-head, although the basic structure remains essentially the same [47].  
CNN models are versatile and capable of handling multiple input data formats, including 1D, 2D, and nD, where data typically consists of 1 to n channels [48]. This study employed a 1D input data format within the proposed hybrid model (see Figure 5). For this purpose, the time series data were treated as a one-dimensional grid, where each variable is represented as a distinct channel, allowing the CNN to learn the spatial relationships between variables while preserving the temporal sequence. The sliding window size should ensure that the model effectively captures the temporal features encountered. Therefore, our CNN has three convolutional layers, kernel sizes of 3, and stride of 1, which were optimized to balance computational efficiency and the ability to learn hierarchical patterns. This enables the CNN to extract meaningful features that are passed on to downstream components in the hybrid encoder-decoder model, improving the overall predictive accuracy of the model.  
The application of CNNs in this research follows that in the cited literature, which the authors have used to extract and identify patterns in meteorological variables to improve solar irradiance forecasting. We intend to take advantage of the ability of CNNs to automatically learn hierarchical features from raw input data, such that the model could effectively capture complex temporal dependencies and spatial correlations within the data. The integration of CNNs into the hybrid model can complement other components by providing complex feature extraction, improving the overall predictive accuracy of solar irradiance forecasts.
# Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing
## Materials and Methods
### Datasets  
#### 2.2.1 Landsat  
Landsat time-series (1999-2016) images were downloaded from the US Geological Survey (USGS) website. We selected images with minimal cloud cover and acquisition dates within the growing season. The image ultimately met the criteria in Table 1. The Landsat TM and ETM+ data, which had higher spatial resolution (30 m), could more accurately express the spatial distribution characteristics of post-fire vegetation recovery. Therefore, we used these images to extract the fire areas and hierarchical burn severities and then compared the results with the MODIS data to verify the vegetation regeneration. The EVI enhances the vegetation signal by adding blue bands to correct for the effects of soil background and aerosol scattering, a procedure that was developed to resolve some of the  
Figure 1: Location of the study area (the background image is a thematic mapper (TM) image from 2000).  
Figure 2: Average temperature and precipitation diagram between the Tulihe (Yakeshi and Hulunbuir) and Xinlin (DaXingAnLing and Heilongjiang) meteorological stations from 2000 to 2016 via the national meteorological science data-sharing service platform ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/))).  
limitations of the NDVI. The resulting coefficient was used to correct the variability in soil and canopy background reflectance, as shown in Equation (1):  
\[\text{EVI}=2.5\times\frac{\rho_{NIR}-\rho_{RED}}{\rho_{NIR}+(6\times\rho_{RED}-7.5\times\rho_{BLUE})+1}. \tag{1}\]  
where EVI is the enhanced vegetation index and \(\rho_{BLUE}\), \(\rho_{RED}\) and \(\rho_{NIR}\) are reflectance values of the blue, red and _NIR_ bands, respectively.
## 3 Routing Algorithm for TFBN  
The process of delivering and receiving messages between two systems is described by a routing algorithm. To shift from a source node to a destination node, it streamlines the designation of path by using a message incorporated in the network. The execution of the interconnection network, which is a critical component in the MPC system, is heavily influenced by message routing expertise and outline of the router. To route a message from the source to its objective in a TFBN, the routing method is divided into three parts [27].  
* Phase I: The message in the source BM is directed to a suitable gate node for routing to the higher level network.
* Phase II: The message is directed through the higher level network. The routing procedure is reversed after reaching the highest level, and the message is routed to a gate node in the destination network.
* Phase III: The message is sent from the destination BM's gate node to the target node within the BM.  
The routing mechanism is the same, but the reverse in Phase I and Phase III. The only difference is that Phase I occurs in the source BM while Phase III occurs in the destination BM. When the packet is created in the source node, the destination is verified. In case of being the source and the destination nodes in separate BMs, all the three phases will  
Figure 2: A \(4\times 4\) higher level network of a TFBN. A total of 5 levels can be interconnected with \(4\times 4\) basic modules.  
be applicable. If the source and destination nodes are in the same BM; however, the routing mechanism will only work within that BM. IOnly Phase 1 will be used in this case. A standard preordained dimension-order routing protocol is used to make the routing procedure easier, with the message being sent vertically first and then horizontally.
## 4 The Influence of Cable Length on the Overvoltage Distribution of Permanent Magnet Motor
### The Influence of Cable Length on the End-to-Ground Voltage Distribution  
When studying the influence of the length of the connecting cable at the end of the permanent magnet wind turbine on the overvoltage distribution, the influence of other conditions is ignored. For the input PWM pulse of the permanent magnet wind turbine, the input is set to a single pulse, the rising edge is set to 2 \(\mu\)s, and the pulse amplitude is set to 2800 V. The influence of different cable lengths on the voltage distribution characteristics of the motor end, winding to ground and inter-turn, is studied. According to the theory, the critical cable length is 300 m, and the maximum voltage should be 5.6 kV. Figure 11 shows the maximum ground voltage of the first-turn conductor under different cable lengths.  
It can be seen from Figure 11 that as the cable length increases, the maximum ground voltage at the end of the motor increases when the cable length is less than 300 m, and the maximum voltage changes rapidly with the length, about 10 V/m. After the cable length is greater than 300 m, the maximum voltage increases slowly, only 1.7 V/m, so the critical cable length is determined to be near 300 m. The simulation results are close to the theoretical results.  
Figure 12 below is the distribution evolution diagram of the maximum value of the ground voltage of each turn conductor in the four coils of the generator stator winding under different cable lengths.  
It can be seen from Figure 12 that when the cable length is below 300 m, the first turn of the ground voltage of each conductor of the winding is slightly lower than the last turn, with a difference of about 0.25 kV. When the cable length is 300 m and above, the maximum ground voltage of each conductor of the winding is the same as the first turn and the last turn. The maximum ground voltage appears at the end turn of the coil, about 5.4 kV, which is 3.6% different from the theoretical 5.6 kV.