esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In a topic describing the characterization of gait variability, entropy metrics help quantify irregularity and variability in gait patterns due to aging, injury, or neurological conditions [16]. In assessing gait stability, entropy analysis provides insight into gait stability and adaptability under various conditions or perturbations. Differences in entropy measures between healthy individuals and those with gait disorders or pathological conditions can aid in diagnostic or prognostic evaluations [26]. Moreover, entropy-based analysis can elucidate how individuals adapt their gait patterns in response to environmental changes or interventions [14]. The complex interaction between temporal and spatial control of joint motion during walking supports setting up stable gait patterns [27]. However, these patterns depend on factors such as age, pathology, and the specific type of perturbation involved. Older adults and individuals with pathologies often favor reduced variability and increased stability, adopting a slower gait. In contrast, healthy young adults show more variability, but remain more stable at different speeds [28; 29]. However, the study of dynamic stability in response to sudden changes in speed remains an area that is lacking in comprehensive research. Park's study [14] is the only paper addressing a similar issue, examining how sudden changes in gait speed affect gait dynamics. It is worth noting that that study did not treat these changes as perturbations; instead, the speed changes (increase) occurred gradually, allowing the subjects time to adjust. However, to date, no studies have been conducted that comprehensively assess the stability, regularity, and complexity of gait among various perturbations involving both location and type of speed, including acceleration and deceleration. To address this gap, the primary aim of this study was to investigate how different perturbation possibilities, including their timing (Initial Contact, Mid-Stance, and Pre-Swing) and direction (acceleration or deceleration of the one belt of the treadmill), impact the regularity of the center of the body mass (CoM) movement. Since the regularity of time series is described using sample entropy, an additional purpose of this paper was closely related to the behavior of this parameter. Accordingly, the next objective was to examine the values of SampEn in the anterior-posterior (AP), medial-lateral (ML), and vertical directions, depending on the length of the vector \(m\) for each type of perturbation possibility.
## 4 Binary Network Problem
### A Network Behavior Recovery  
To indicate the applicability of the information-theoretic approach in the binary network area, an example may be useful. In an economic-behavioral network, the efficiency of information flow is predicated on discovering or designing protocols that efficiently scale free patterns. In many ways this is like a transportation network where the emphasis is on design and efficiency in routing the traffic flows (for example, see [23] and the references therein). To carry this information flow analogy a bit farther, consider the problem of determining least-time point-to-point traffic flows between sub networks, when only aggregate origin-destination volumes are known. Given information about the network protocol in the form of a matrix \(A_{\bar{y}}\) composed of binary elements, traffic flows may be estimated from the noisy aggregate traffic data. If the amount of unknown origin to destination routes is much larger than the amount of origin-destination data, then we have an ill-posed linear inverse problem of the type first introduced in Section 2. If we write the inverse problem as \[y_{i}=\sum_{j=1}^{N}A_{y}p_{j}\,, \tag{1}\]  
where, \(y_{i}\) and \(A_{ij}\) are known, \(p_{j}\) is unknown and \(\sum_{j=1}^{N}p_{j}=1\), we may make use of the CR family of entropic divergence measures Equation (1) and write the problem as the following constrained conditionally optimization problem:  
\[I(\mathbf{p},\mathbf{q},\mathbf{\
u})\big{|}\sum_{l=1}^{n}\lambda_{i}\left( \sum_{j=1}^{N}\bigl{(}A_{ij}p_{j}-y_{i}\bigr{)}\right),\sum_{j=1}^{n}p_{j}=1\,, p_{j}\geq 0 \tag{2}\]  
This is just the solution to a standard problem when a function must be inferred from insufficient sample-data information. Thus network inference and monitoring problems have a strong resemblance to an inverse problem in which key aspects of a system are not directly observable. Details of the application of information-theoretic entropic methods to this type of network information flow problem are discussed in [PERSON] and [PERSON] (2015, 2007) [24,25] and [PERSON], [PERSON], and [PERSON] [5,6]. Finally, it is worth emphasizing that in actual networks, the flows from one node to another will themselves affect node-to-node capacities that may impact deterministic or statistical predictions [26]. Finally, it is interesting that network theory presents a model for producing scale free patterns that are manifestations in the physics world of least-time-free energy consumption. In other words if economic systems did not consume energy in the least time, these patterns would not be present.
## 1 Introduction
[11] carried out uniaxial compression tests on red sandstone, conducted time-frequency analysis on extracted AE characteristic signals, and proposed a method to extract AE characteristic signals during deformation and failure based on the difference between the AE signal duration ratio and the rise time. [PERSON] et al. [12] monitored the distribution characteristics of the time-dependent dominant frequency and the amplitude of AE signals generated by red sandstone specimens in different cracking states under uniaxial compression. In addition, they defined four AE signal patterns and discussed the fracture mechanism in the failure process. Overall, relevant literature all over the world provide valuable references for the monitoring of rock instability failure [13; 14; 15]. However, the variation characteristics and evolution law of monitoring information (e.g., displacement field, strain field, and AE) in sandstone failure should not be analyzed separately when studying the failure law of sandstone and the identification of premonitory deterioration information based on the fusion of DIC-AE multi-source information. Otherwise, the applicability, reliability, and validity of various monitoring methods are likely to be restricted. Considering this fact, the extraction, recognition, inversion, and fusion mechanisms of multi-source heterogeneous information require further research. In this study, red sandstone specimens with prefabricated cracks of different inclination angles were taken as the research object. The displacement and strain fields in digital images and the time and frequency domains of the AE signals were synchronously monitored. Additionally, premonitory deterioration information was identified by fusing the monitored information from the digital images and AE signals. Furthermore, the relationship between the instability failure of red sandstone and the obtained digital images and AE signals was revealed, and finally, the forewarning of red sandstone failure by levels was realized.
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 4 Results and Discussion
### Minimal Subset Size Simulations  
In this trial, performance requirement constraints are added to the geometric algorithm and the downdate algorithm so that they can be used to search for the minimal subset which satisfies the requirement. Firstly, the LNAV performance requirement is considered to check if it can be met by the dual-constellation navigation system. The 2 drms, HPL and subset size of the final subset constructed by each algorithm during the simulation are illustrated in Figure 7. As the final subset size in each algorithm is well below the visible satellite number during the simulation, this means the LNAV requirements can be well satisfied by the GPS/BDS-2 single-frequency navigation system. Among the three satellite selection strategies, the proposed algorithm reaches the smallest final subset size with an average value of 6.54, about 1.00 smaller than that of the geometric algorithm and 1.54 smaller than that of the downdate algorithm, at a cost of little increased 2 drms value that still within the requirement. The average consumed times of these algorithms are close and all less than 0.01 s.  
Figure 7: 2 drms, HPL and subset size of the final subset constructed by each algorithm under LNAV requirement. (**a**) 2 drms value; (**b**) HPL value; (**c**) Subset size.  
Secondly, the horizontal requirement of APV-I is utilized here to analyze the capability of the GPS/BDS-2 single-frequency navigation system in aerial navigation. The 2 drms, HPL and subset size of the final subset constructed by each algorithm are illustrated in Figure 8. As we can see, the accuracy requirement is satisfied by the three algorithms during the simulation. However, the integrity requirement is violated by them during the 9-10 h. The final subsets at these times all reach the all-in-view subset but still cannot entirely meet the requirements for not enough satellites in view or poor geometries. However, the curves in Figure 8c show that the proposed algorithm can maintain a relatively smaller subset during the simulation, with an average subset size of 10.77, which is 3.02 smaller than that of the geometric algorithm and 2.54 smaller than that of the downdate algorithm. Additionally, the three algorithms reach similar average consumed time, as shown in Table 5.  
Figure 8: 2 drms, HPL and subset size of the final subset constructed by each algorithm under APV-I horizontal requirement. (**a**) 2 drms value; (**b**) HPL value; (**c**) Subset size.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**LNAV**} & \multicolumn{2}{c}{**APV-I**} \\ \cline{2-5}
**Algorithms** & **Subset Size** & **Consumed Time (s)** & **Subset Size** & **Consumed Time (s)** \\ \hline Geometric algorithm & 7.5417 & 0.0043 & 13.7917 & 0.0128 \\ Downdate algorithm & 8.0833 & 0.0023 & 13.3125 & 0.0052 \\ Proposed algorithm & 6.5417 & 0.0062 & 10.7708 & 0.0121 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average subset size and consumed time of each algorithm in the minimal subset size trial.
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 3 Identifying the Problem
### The Problem in the Context of Industry 4.0
#### 3.4.4 CAD Model Security Gaps
Unless such security measures are being followed in the distribution and supply chain of a proprietary product's CAD files, these files can be acquired by unauthorized parties, thus violating the confidentiality of the CAD model. It will then be possible for an unauthorized party to reproduce the product and put it on the market, compromising the business of the original producer of the product. The translated CAD model information, such as the STL file and the slicer output, are especially prone to malicious attacks. Although various CAD programs could make use of their own proprietary CAD model file formats, the translated CAD model information is typically in a standard and non-proprietary format that can be interpreted by the digital interface of manufacturing platforms. It is therefore possible to intercept this information, either during the distribution phase, or in-line to the digital interface of the manufacturing hardware, and introduce malicious changes that could affect the structural integrity or the proper functioning of the end product. This would result in compromising the integrity of the CAD model, since adherence to the product's design specifications can no longer be guaranteed. During all the phases from a product's design to its manufacturing, the relevant parties need to have access to this information. For example, two teams of engineers need to have access to a product's CAD model during the design phase to ensure that the product will fit within a certain space as part of a larger system designed by one engineering team, while the other engineering team ensures that any required changes to the product's CAD model remain within the limitations of the product's design. When the product needs to be manufactured, the translated CAD model information needs to be sent to a manufacturer. It is thus apparent that the availability of the product's information is a crucially important aspect throughout a product's design and manufacturing cycles. This influences the level and type of security measures that can be utilized to secure the product's design information, since an optimal balancing point between confidentiality, integrity and availability (CIA) is required to have a practical yet efficient security solution in terms of these three CIA pillars. Since the manufacturing equipment concerned can be classified under OT-type systems, it can be expected that the traditional IT security measures would possibly be insufficient or even incompatible to provide an effective security solution in this context, as has been discussed in Section 2.2.
###### Abstract
[PERSON] et al. [23] raised the fast global optimization feature using the PSO, adjusted the SVM regression model's parameters, and used the combination model. They then compared the predicted outcomes with the measured values of the BPNN. The PSO algorithm was utilized by [PERSON] et al. [24] to optimize the SVM model's parameters. The optimal parameter combination was determined to be penalty factor c = 1.42 and kernel parameter o = 1.15. The optimal parameter combination was then substituted into the SVM model to create the PSO-SVM, which was then employed in the quantitative assessment of landslide susceptibility in the study area. The SVM optimization approach was the focus of [PERSON] et al. 's analysis [25], which demonstrated the algorithm's high intrusion detection accuracy. An SVM-based image segmentation approach based on the texture and color of the picture as feature vectors was proposed by [PERSON] et al. [26]. In order to increase the SVM robustness to measurement uncertainty, [PERSON] et al. [27] concentrated on evaluating the impact of each parameter's uncertainty on the forecast accuracy of the SVM. [PERSON] et al. [28] proposed a novel combinatorial neural network algorithm for the prediction of PM10 and PM2.5. The algorithm incorporates the use of the PSO technique to optimize the parameters of the neural network model. In summary, there has been little research on the systematic numerical simulation of the dust-removal efficiency of high-temperature wire-plate ESP in magnetic field environments. Additionally, there are no reports on the prediction of ESP magnetic field effects under different working conditions using the PSO-SVM and BP neural network models. Therefore, this article conducted numerical simulations to evaluate the dust-removal efficiency of high-temperature wire-plate ESP under various operating conditions in a magnetic field environment. The simulation data were then combined with the PSO-SVM and BP neural network models to predict the magnetic field effect under different operating conditions. The obtained results can serve as a reference for further enhancing the dust-removal performance of ESP and utilizing intelligent algorithms to predict the capture performance of PM2.5 in ESP.
## 3 Results and Discussion
### Evaluation of Quantitative Yield  
When comparing all selected log-sawing patterns, it was found that all three chosen patterns remarkably affected the overall quantitative yield of beech timber with and without bark. This is also demonstrated by the quantitative yield results presented in Table 2.  
Based on our results, the highest quantitative yield was observed for cant sawing at 84% (which is marked in Table 2, in red color), which was, on average, 17.3% higher compared to other types of cutting. Other studies [4; 6] show that cant sawing is characterized by obtaining timber with the maximum dimensions. In other research [17], it was discovered that round sawing and cant sawing had a higher quantitative yield compared to through-and-through sawing. The yield of through-and-through sawing was lower by 3.84% compared to round sawing and 3.73% compared to cant sawing. In general, the quantitative yield was higher than that reported in other papers [20; 22; 23].  
Through-and-through sawing provided slightly lower results than cant sawing, at approximately a 12% lower yield (the average value of yield was 72.28%). The reason for this could be the greater tapering of the log in the V1-marked cut, which led to more waste in obtaining a parallel surface along the entire length or a smaller diameter of the cut. This result is also supported by a study [2] that applied through-and-through sawing to Masson pine (_Pinus massoniana_ Lamb.), and its quantitative yield reached the lowest average value of 53.3%. A similar result for the quantity yield (around 57.29%) with the live sawing of the oak logs was observed [7].
# Smart Energy Meters for Smart Grids, an Internet of Things Perspective
## 2 Sensing Techniques  
Sensing systems for smart meters measure the energy being consumed. Depending upon the type and usage of the energy supplied, the sensing systems are of varied types; however, the most common types of measured variables in the energy domain are electricity, heat, and gas, with electricity being the most common; heat, in most cases, is a derived variable from either the electric or the gas supply, but due to its impact, it is usually measured separately. In some cases, it is the heat that is directly being supplied to the consumer, with district heating in different European countries being a prime example [43]. While an integrated smart meter to measure all the quantities has not been reported, individually these variables are increasingly being measured, monitored, processed, and controlled for smart energy scenarios [44]. Currently, an integrated application scenario is also not being implemented at the physical level; rather, these variables are processed at a higher level of integration and abstraction, typically at the applications layer and in the cloud [45; 46], leading to the obstruction of the working mechanism, which might translate  
\begin{table}
\begin{tabular}{c p{227.6 pt}} \hline \hline
**Reference** & **Comments** \\ \hline
[37] & Review of how lives of people are affected by the use of smart meters, highlighting 13 socio-psychological variables \\ \hline
[38] & Extensive review of evolution of smart metering \\ \hline
[39] & Review and explanation of hardware-oriented security scheme implementation on smart meters \\ \hline
[40] & Survey highlights the key security issues of AMIs and focuses on how key management techniques can be utilized for safeguarding AMI \\ \hline
[41] & Focus upon distributed generation, microgrids, smart meter deployment, energy storage technologies, and the role of smart loads in primary frequency response provision \\ \hline
[42] & Review of different data-based algorithms, including grid delay and delay tolerance, to improve distribution system models \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._to data underutilization and other data collection artifacts. Smart meter designers usually think of measurement technique as an external factor, which is exemplified by the fact that most of the smart meter research takes off the shelf components, whereas in industry different types of meters in different scenarios result in different designs. This points to the fact that the sensing technique is an important factor in the overall design which is often overlooked in the reviews and research. Thus, to make smart meters more relevant and integrated, the review is presented for each type of energy supply sensing technique in the following section. The smart meter as a physical node is described in Figure 3.
## 2 Literature Review
### Traditional VPN Classification Methods  
Prior to exploring the machine learning-based approaches, it is crucial to be aware of the conventional approaches to VPN classification. Port-based classification or flow-based analysis and Deep Packet Inspection (DPI) are frequently used in these techniques [28]. These techniques have, however, a number of drawbacks, such as the requirement for thorough payload inspection, vulnerability to evasion strategies, and reliance on well-known port numbers [2].
# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 4 Experiments
### Datasets and Models  
This study uses the val2017 subset of the COCO dataset (Common Objects in Context) [35]. This subset contains 5000 high-quality annotated images across 80 object categories, covering a diverse range of natural scene images. The variety in the image data provides a comprehensive and representative basis for evaluation.  
The experimental framework in this study is based on OpenMMLab's MMSegmentation [36] toolkit. This open source image segmentation toolkit is well regarded for its modular design, flexibility, and ease of use. It also utilizes pre-trained models trained on several public benchmark datasets, providing a solid technical foundation for our research. In the experiments, we selected three different semantic segmentation models: DeepLabV3 [24], PSPNet [37], and BiSeNetV1 [38]. All models employ ResNet-50 as the backbone network and are pre-trained on the COCO-Stuff 164K dataset. Detailed configuration parameters and performance comparisons for each model are shown in Table 1. In all experiments, we extracted saliency maps from the model bottleneck.
# A Direction-Sensitive Microwave Sensor for Metal Crack Detection
## 2 Sensor Design
### Realization of Crack Direction Detection  
For conventional single-port microstrip sensors, when there exists a crack parallel to the current direction, the sensors will have a blind region for crack detection and also cannot realize crack direction detection. So, a patch resonator with two feeding ports is proposed to improve the detection performances for crack detection.  
Firstly, the working principle of the sensor with a single feeding port is analyzed. Figure 3 shows the schematic diagrams of the current path on the ground plane for the cases with and without a crack. When the crack on the ground plane is perpendicular to the current direction, the current path length is obviously extended, as shown in Figure 4b. However, when the crack direction is parallel to the current direction, as shown in Figure 4c,  
Figure 3: Equivalent model of the sensor with crack on the ground plane. (**a**) Physical model. (**b**) Equivalent Circuit.  
the crack has no effect on the current path length. Additionally, the resonant frequency of the sensor will not vary even though there is a crack on the ground plane. Therefore, the sensor with a single feeding port is unable to detect cracks which are parallel to the current direction.  
For the sensor proposed in this paper, two feeding ports are employed to generate perpendicular polarization currents which overcome the drawbacks of a single-port sensor. As shown in Figure 5, the crack parallel to the ox axis is defined as a horizontal crack, and the crack along the oy axis is denoted as a vertical crack in this paper. The horizontal and vertical cracks can be detected by analyzing the resonant frequency of Port 1 and Port 2, respectively.