esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Establishment of Wedge Sliding Body Model
### Limit Equilibrium Model of Wedge Sliding Body
Thus, the equation between normal stress (\(\sigma_{v}\)) and buried depth (\(z\)) of upper surface of unit body can be obtained:  
\[\sigma_{v}(z)\ =\ (q-\frac{Q_{0}}{P_{0}})e^{-P_{0}z}+\frac{Q_{0}}{P_{0}}(0\leq z \leq H), \tag{6}\]  
If it is assumed that the compressive stress (\(F\)) acting on the top of wedge sliding body by soil body (DCcd-EFfe) is:  
\[F\ =\ \sigma_{v}|_{z\ =\ H}\left[L_{e}+D_{0}\tan(\frac{\pi}{4}-\frac{\varphi}{2} )\right], \tag{7}\]  
The relationship equation between normal stress (\(\sigma_{v}\)) and buried depth (\(z\)) can be expressed as:  
\[\sigma_{v}|_{z\ =\ H}\ =\ \frac{Q_{0}}{P_{0}}\Big{(}1-e^{-P_{0}H}\Big{)}+qe^{-P_ {0}H}, \tag{8}\]  
The expression relationship of compressive stress (\(F\)) can be obtained by combining Equations (8) and (7) as:  
\[F\ =\ \bigg{[}\frac{Q_{0}}{P_{0}}\Big{(}1-e^{-P_{0}H}\Big{)}+qe^{-P_{0}H} \bigg{]}\Big{[}L_{e}+D_{0}\tan(\frac{\pi}{4}-\frac{\varphi}{2})\Big{]}_{0}, \tag{9}\]  
The shear stress (\(T_{1}\)) on the slip surface (AaCc) of the wedge sliding body according to assumption 4 is expressed as:  
\[T_{1}\ =\ \frac{L_{\mathrm{e}}}{\cos(\frac{\pi}{4}-\frac{\varphi}{2})}c+N_{1} \tan\varphi, \tag{10}\]  
The shear stress (\(T_{1}\)) can be obtained by combining Equations (7), (8) and (10) as:  
\[T_{1}\ =\ \frac{\tan(\frac{\pi}{4}-\frac{\varphi}{2})}{\tan(\frac{\pi}{4}- \frac{\varphi}{2})+\tan\varphi}\bigg{[}\frac{L_{e}}{\cos(\frac{\pi}{4}-\frac{ \varphi}{2})}c+\Big{(}F+G-2T_{2}\cos(\frac{\pi}{4}-\frac{\varphi}{2})\Big{)} \frac{\tan\varphi}{\sin(\frac{\pi}{4}-\frac{\varphi}{2})}\bigg{]}, \tag{11}\]  
The shear stress (\(T_{2}\)) at the buried depth z on the sliding surface A(a)C(c) based on assumption 3 is expressed as:  
\[T_{2}\ =\ \frac{1}{6}\gamma\lambda{D_{0}}^{3}\tan\varphi\tan(\frac{\pi}{4}- \frac{\varphi}{2})+\frac{1}{2}[c+\lambda\sigma_{v}|_{z\ =\ H}\tan\varphi]{D_{0}}^{2}\tan(\frac{\pi}{4}-\frac{ \varphi}{2}), \tag{12}\]  
Based on the above analysis, Equations (2), (9), (11) and (12) can be substituted into Equation (3) to obtain the active stress (\(P\)):  
Figure 3: Loosening earth pressure calculation model. \[P\ =\ \frac{l_{0}}{\tan\beta}\sigma_{v}|_{z\ =\ H}+\frac{1}{2}\gamma l_{0} ^{2}-\frac{\left[\frac{l_{0}}{\cos\beta}c+\frac{\tan\varphi}{\sin\beta}(F+G+2T_{2 }\cos\beta)\right]}{(\tan\beta+\tan\varphi)\cos\beta}-\frac{\tan\varphi}{6\cos \beta}\lambda{D_{0}}^{2}[\gamma D_{0}+3\sigma_{v}|_{z\ =\ H}]-\frac{c}{2\cos\beta}, \tag{13}\]
## 6 Applications in Generating Highly Complex Scenarios
### Optimized Generation Method for Highly Complex Scenarios  
This study searches for high-complexity urban intersection scenarios based on genetic algorithms. As shown in Figure 14, the method first encodes the input scenario parameters and then generates new scenarios through an evolutionary process of crossover, mutation, and selection. Fitness is calculated based on a complexity index, and once the complexity of a scenario exceeds a set threshold, it is stored in the scenario library. Unlike the goal of traditional optimization searches, which seek the optimal solution, this study aims to generate as many solutions as possible that meet the conditions, so a complete replacement selection strategy is used. Under this strategy, the parent individuals will be completely replaced by their offspring. In each generation, the algorithm only evaluates the newly generated offspring and does not retain any parent individuals. In the genetic algorithm setup, the population size is set to 10, with a crossover rate of 0.8 and a mutation rate of 0.01. The algorithm is configured to run for a maximum of 10,000 iterations.  
Figure 14: The optimization search framework guided by complexity metrics.  
As depicted in Figure 15, the scenario parameters include initial lanes \(\{l_{1},l_{2},\ldots,l_{N}\}\), initial positions \(\{x_{1},x_{2},\ldots,x_{N}\}\), initial velocities \(\{v_{1},v_{2},\ldots,v_{N}\}\), and driving characteristic parameters \(\{p_{1},p_{2},\ldots,p_{N}\}\). Here, \(x\) represents the initial position of each vehicle in its lane, defined using Frenet coordinates along the lane centerline. The driving characteristics parameter \(p\) assumes the values \(1\), \(2\), and \(3\), corresponding to conservative, normal, and aggressive driving behaviors, respectively. These characteristics are predefined by the scenario model embedded in PanoSim. In this experiment, the total number of vehicles, \(N\), is set at \(10\).  
As depicted in Figure 14, we have implemented a segment encoding strategy, taking into account that the initial position, initial speed, and driving characteristic parameters represent different types of variables. This strategy organizes the chromosome into several distinct segments, with each segment containing genes of a single type. Specifically, each chromosome is divided into four segments: the initial lane sequence, the initial coordinate sequence, the initial speed sequence, and the driving characteristic sequence. Each segment contains genes that correspond to the respective parameter values for each vehicle, arranged in the appropriate order. Consequently, the total length of each chromosome is \(N*4\). In addition, given the strict requirements for vehicle position matching, and to prevent unreasonable vehicle configurations resulting from large-scale modifications, we employed a small-scale single-point crossover strategy. This strategy allows for minor adjustments based on the parent gene, ensuring that changes remain controlled and reasonable.
# Coordination in Emergency Response System Design:
## 7 Case Study: Hazmat Transportation in Chengdu City
### Building the Hazmat Transport Network for Chengdu City  
There are 1284 nodes and 2073 edges in the urban road network of Chengdu, which also contains 20 fire stations and 62 ambulance station. There are 326 primary schools. We assume a radius of 800 m around a school defines the \"sensitive area\" that needs to be avoided by hazmat trucks. In Figure 7a, the green parts constitute these sensitive nodes and edges that cannot be included in the HMTN, which we denote by \(G^{H}\left(V^{H},E^{H}\right)\). The red parts in Figure 7b become unconnected due to the removal of the sensitive nodes and edges. After also removing these unconnected parts, the resulting HMTN, with 636 nodes, is depicted in Figure 7c. Figure 8 shows the existing network as well as the HMTN (in thicker lines).  
Figure 9 depicts each node in terms of its response and wait times. Since we do not have access to the targets aspired to by the Chengdu City officials, we use their averages as the coverage targets. The average of _RTAS_ is 4.78 min, the corresponding _RTFS_ average is 6.63 min, and the WT average is 3.20 min. Based on these values, currently 302 nodes fall into the poor response area and 40 nodes are in the adequate response area, whereas the  
Figure 8: The location of the existing fire and ambulance stations.  
Figure 7: Building the HMTN of Chengdu: (**a**) green: sensitive area; (**b**) red: unconnected nodes and edges; (**c**) HMTN.  
response targets are satisfied for the remaining 294 nodes. That means only 46.2% of the city can receive a coordinated and timely response in case of a hazmat transport incident.
# A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data
## 3 Taxonomy of Anomaly Detection Techniques
### Anomaly Detection Learning Modes
#### 3.4.1 Supervised Anomaly Detection  
Classification  
The SVM is a separate hyperplane formally defined as a discriminative classifier. With smart transportation [33], SVM is used to provide drivers with real-time alerts and instructions on the streets' anomalies to ensure a safe driving experience. Similarly, in smart cities [49], SVM is utilized for predicting anomalies and attacks that target the IoT systems.  
The Naive Bayes classification method is based on the Bayesian Theorem and is primarily compatible when the dimensionality of the input is high. Bayesian techniques have been used in smart homes [34] to propose a two-tier intrusion detection system using a machine learning approach to classify records and monitor suspicious patterns across the network in the service provider's data center. Additionally, [PERSON] et al. [50] used the naive base classifiers to propose a self-constructing feature clustering method for anomalydetection.  
The K-Nearest Neighbor (k-NN) algorithm is an example of supervised machine learning methods utilized for solving classification and regression issues by assuming similarities in devices deployed in a proximate location. The k-NN has been implemented in the industrial system to detect cyber-physical attacks for cyber manufacturing systems by [PERSON] et al. [12]. Similarly, k-NN was used to propose a self-constructing feature clustering method for detecting anomalies by [50].  
Figure 5: Collective anomaly for heart rate monitoring signal.  
Figure 6: IoT anomaly detection learning modes.  
Regression algorithms use the input features to predict the data's output values faded into the system. For example, in intelligent transportation, a regression-based model was developed by [PERSON] et al. [32] to detect contextual anomalies in the air traffic control framework by identifying the correlation between accidents and resource measurements in the log reports.  
The Decision Tree approach constructs regression or classification techniques in a tree structure. This approach separates the dataset into small groups, while at the same time an associated decision tree is increasingly constructed in the industrial system. A decision tree is used to predict anomalies and attacks in smart cities' IoT systems [49].
## 3 Results
### Chromate Anion
850 cm\({}^{-1}\)[85], it is possible that this band is associated with the start of the reduction of the Cr\({}^{6+}\) to Cr\({}^{3+}\) and represents the Cr-OH symmetric and antisymmetric stretching mode and the shift is due to the restricted space within the LDH interlayer space. The IR interlayer CrO\({}_{4}\)\({}^{2-}\)\(v_{3}\) (Cr-O) mode was reported at 870 cm\({}^{-1}\) by [PERSON] et al. [86], while [PERSON] and [PERSON] [87] found the same band at 866 cm\({}^{-1}\). After heating between 100 and 200 \({}^{\circ}\)C, a split of this band was observed, and two new bands appeared around 874 and 930 cm\({}^{-1}\), supporting a change in the anion symmetry from \(T_{d}\) to C\({}_{30}\) as a result of the interaction between the CrO\({}_{4}\)\({}^{2-}\) and the LDH hydroxide layers similar to what was observed in the Raman spectra by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [18]. [PERSON] et al. [88] also observed in the mid-IR spectrum of intercalated CrO\({}_{4}\)\({}^{2-}\) two bands at 866 and 917 cm\({}^{-1}\). The tetrahedral chromate ion is characterized by two distinct IR-active vibrational modes: the antisymmetric stretch (\(v_{3}\)) and the symmetric deformation mode (\(v_{4}\)). The \(v_{4}\) is expected to be observed around 330 cm\({}^{-1}\), i.e., outside the mid-IR range and therefore not observed. The two bands represent the triply degenerate (\(F_{2}\)) mode of the chromate. With C\({}_{30}\) symmetry, the antisymmetric stretching vibration splits into two modes (\(A_{1}+E\)) ([65], p. 414). The XPS Cr 2p high-resolution scan of the chromate-LDH provided evidence that upon aging, the nature of the interlayer anion changed, resulting in the reduction of Cr\({}^{6+}\) to Cr\({}^{3+}\). Since the Raman spectrum of the same sample was taken shortly after synthesis, the spectrum did not show any significant evidence of this reduction [18]. Instead, the Raman spectrum is dominated by the CrO\({}_{4}\)\({}^{2-}\) anion with a reduced symmetry, as has also been observed in the infrared spectra of fresh chromate-LDH samples. The XPS data were obtained about 6 months after the Raman spectra, and by then, approximately 60% of the Cr\({}^{6+}\) was reduced to Cr\({}^{3+}\).
## 3 Proposed Schemes
### Delay Minimization Routing (DMR) Scheme
#### 3.1.3 Best Forwarder Node Selection and Information Transition  
When a source node broadcasts an information packet, it first checks whether the physical distance between itself and any of the minor sink node is less than than the defined communication range. If it is so, the source node hands over the information to the minor sink node directly. Otherwise, the source node selects a node that acts as a forwarder node and advances the information of the source node to the minor sink node. The forwarder node is the one that is nearest to the minor sink node and is recognized by the source node based on the information acquired during the exchange of hello bags among the nodes. The minor sink node further forwards the information to the major sink as shown in Figure 5.  
Figure 4: Hello packet format.  
Figure 3: Minor sink \(S_{2}\) and \(S_{4}\) packet forwarding.
# Low-Intensity Whole-Body Vibration: A Useful Adjuvant in Managing Obesity? A Pilot Study
## 3 Results
### Hormonal Components  
The circulating levels of testosterone and GH (Tables 4 and 5) did not differ in time, group and group \(\times\) time. Plasma irisin did not show any differences in time, group and group \(\times\) time, whereas change in salivary irisin was observed in Group 2 (\(p<0.01\)) as compared to the control group (Table 6).  
The analysis shows increased salivary irisin levels in both groups PRE and POST at T1 and T9. In particular at time 1, group 2 showed a greater percent delta value (\(\Delta\)%) compared to group 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**T1**} & \multicolumn{2}{c}{**T9**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Salivary Irisin (ng/mL)**} & \multicolumn{2}{c}{**Salivary Irisin (ng/mL)**} \\ \cline{2-7}  & **PRE (SD)** & **POST (SD)** & **\(\Delta\)\% PRE (SD)** & **PRE (SD)** & **POST (SD)** & **\(\Delta\)\% POST (SD)** \\ \hline
**Group 2** & 4.26 (3.31) & 10.80 (5.70) & 839.14 (1984.41) & 2.99 (2.10) & 8.79 (3.80) & 478.01 (823.44) \\ \hline
**Group 4** & 25.15 (0.68) & 43.09 (40.67) & 578.72 (2630.94) & 22.06 (29.46) & 71.48 (246.77) & 509.58 (351.21) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Salivary irisin levels PRE and POST at Time T1 and T9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Time**} & **Group 1** & **Group 2** & **Group 3** & **Group 4** \\ \cline{3-5}  & & **Mean (SD)** & **Mean (SD)** & **Mean (SD)** & **Mean (SD)** \\ \hline \multirow{2}{*}{**GH [\(\mu\)g/L]**} & Post & 0.29 (0.28) & 0.36 (0.32) & 0.40 (0.32) & 0.58 (0.69) \\ \cline{2-5}  & Pre & 0.55 (0.95) & 1.02 (2.16) & 0.57 (0.74) & 0.33 (0.46) \\ \hline \multirow{2}{*}{**Testosterone** [nmol/L]**} & Post & 7.14 (6.91) & 10.49 (12.04) & 7.06 (7.11) & 6.67 (6.83) \\ \cline{2-5}  & Pre & 6.6 (7.28) & 8.39 (10.18) & 8.24 (8.40) & 5.81 (5.75) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean and standard deviation of the four groups of hormonal components. GH, growth hormone (\(\mu\)g/L); testosterone.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Factor** & **F** & \(P\) & **Partial \(\eta^{2}\)** \\ \hline \multirow{2}{*}{**GH [\(\mu\)g/L]**} & Time & 0.23 & 0.63 & 0.003 \\  & Group & 0.39 & 0.76 & 0.02 \\  & Group \(\times\) Time & 0.87 & 0.46 & 0.46 \\ \hline \multirow{2}{*}{**Testosterone** [nmol/L]**} & Time & 0.1 & 0.75 & 0.001 \\  & Group & 0.57 & 0.64 & 0.02 \\ \cline{2-5}  & Group \(\times\) Time & 0.13 & 0.94 & 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hormonal components of the four groups and relative significance levels and effect size (partial \(\eta^{2}\)). Statistically significant data are highlighted in bold. GH, growth hormone (\(\mu\)g/L); testosterone.
## 3 Application
### Application of Foam Dust Reduction in Shanjiaoshu Coal Mine  
(1) 21,129 mining ventilation roadways  
The 21,129 working faces located in the second level #12 coal at the southern mining area of the Shanjiaoshu coal mine. The thickness of the coal seam was 3.3 m, with a dip angle of 6-10. An AKFC-92A mine dust sampler installed approximately 4-5 m from the back of the excavation ventilation side was used to test the parameters of total mine dust and respirable mine dust. Among all the data collected, six of eight groups of data were valid. The detailed data is provided in Table 6.  
Figure 7: Comparison of the dust-reduction efficiency of water mist and foam for respirable dust.  
A comparison of the efficiency of water spray and foam for dust control is shown in Figure 8.  
Table 6 and Figure 6 reveals an obvious and significant effect of dust control. The water consumption was sharply reduced with the foam technology for dust control. The efficiency of total mine dust reached 72%, with a 67% efficiency of respirable mine dust, which is far greater than the efficiency of water spray. The foam technology for dust control not only effectively reduced the density of mine dust in underground excavation working faces but also purified the health conditions of the workplace.  
21,108 transportation roadways  
The excavation working faces of 21,108 transportation roadway were located in the second mining area of the Shanjiaoshu coal mine, with a total roadway length of 1100 m. A fully mechanized roadheader (EBZ160C) was adopted. After the site measurements, a comparison of the efficiency of water spray and foam for dust control was performed, with the results shown in Figures 9 and 10.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \
\begin{tabular}{c} **Density of Total** \\ **Mine Dust** \\ **mg/m\({}^{3}\)** \\ \end{tabular} &
\begin{tabular}{c} **Density of Total** \\ **Respiratory Mine** \\ **Dust** \\ **Usingt Water** \\ **Usingt Water** \\ **Usingt Water** \\ **Usingt Water Foam** \\ **Usingt Air Foam** \\ **  
According to Figures 9 and 10, the foam technology for the dust control increases dust suppression. The efficiency of foam technology for total mine dust control at the point of the driver is 2.10 times as efficient as water spray. The efficiency of containing respiratory dust is 2.37 times as efficient as water spray. In the ventilation area (within 5 m), the efficiency of foam technology for total mine dust control is 0.62 times greater than the efficiency of water spray, and the efficiency of containing respiratory dust is 1.50 times greater than water spray.
# Dynamic Risk Assessment in Cybersecurity: A Systematic Literature Review
## 4 Results
### Artificial Intelligence/Machine Learning
#### 4.1.2 Attack Trees
[31] proposed a dynamic risk management response system (DBRMS) which aims to quantify the risk of the monitored system and to produce response plans consecutively, in order to address cyber threats. The authors utilised an attack graph generator which uses as input information about connections among devices, a vulnerability inventory, and a business model of the organisation (e.g., crucial assets), and produces as output all possible attack scenarios. A Markov chain is used to determine the probability that an attack will succeed, based on the attack path, along with properties associated with the difficulty of exploiting a vulnerability. The impact is calculated based on the consequences of the exploited vulnerability that resides in the business device. The threat quantification module, through the use of elementary risk, calculates the risk value of an event based on the probability and the impact. Using data collected from scans in a SCADA system, the authors conducted experiments to demonstrate that the proposed model is dynamic, since it can adapt to different input data and produces suitable response plans in an automated way. [PERSON] et al. [32] recommended a security assessment approach based on an ontology and an attack graph for ICS. The system's assets, vulnerabilities, attacks, counter-measures, and relationships between them are represented by an ontology using OWL. The created security ontology represents attacks that pose a threat to assets (based on identified vulnerabilities) using SWRL rules. The attack graph is subsequently created through the utilisation of an efficient algorithm, harnessing the inference capabilities of the security ontology. The evaluation of the algorithm in different topologies and network sizes showed that the proposed approach is suitable for enterprise networks. [PERSON] et al. [33] presented a method to assess risk based on an attack graph and two security indicators for smart city infrastructures. The first indicator shows the importance (type of node, running services) of the node which is being attacked, and the second one (topological) presents the number of connected nodes that are going to be affected, also taking into consideration their importance. The attack graph can be constructed as an output of penetration testing. In the next step, the total value of the system at risk is calculated based on the aforementioned indicators. Afterwards, the protective measures are applied in order to eliminate the most critical vulnerabilities. Then, the total risk value of the system is recalculated (the value is reduced).
## 2 Application of BRBS in the Detection of Diabetic Nephropathy Biomarkers
### Urinary Albumin  
Urinary albumin detection reflects abnormal protein leakage in the kidney and can understand early kidney disease and kidney injury. Pathological increases are seen in diabetic nephropathy, hypertension, and preeclampsia during pregnancy [18]. If the albumin in the urine is in the range of 30-200 mg/L, it belongs to microalbuminuria. Early diabetic nephropathy is a mild increase in urinary albumin excretion (microalbuminuria), which gradually progresses to massive albuminuria and eventually kidney failure, requiring dialysis or kidney transplantation. Up to now, microalbuminuria is still the best non-invasive biomarker for detecting diabetic nephropathy [19].  
[PERSON] [20] successfully constructed a MIP-ECS based on a dual-signal strategy for human serum albumin (HSA) detection in urine (Figure 1). Firstly, the glassy carbon electrode (GE) substrate was modified with AuNPs and polythionine-methylene blue (PTH-MB) sequentially. Then, a dual-functional monomer molecularly imprinted polymer (MIP) was prepared by the electropolymerization method using human serum albumin (HAS) as the TM, HQ, and o-phenylenediamine o-PD as functional monomers. This biosensor showed a dual-signal oxidation of Fe(CN)\({}_{6}\)\({}^{4-}\) and PTH-MB at 0.20 V and \(-\)0.22 V, respectively. When HSA was captured on MIP, the adsorbed HSA on the electrode surface would hinder electron transfer, reducing the total intensity of dual-signal currents with the increase of HSA concentration. The limit of detection (LOD) of this sensor can reach 3.0 \(\times\) 10\({}^{-11}\) g L\({}^{-1}\). Moreover, the selected AuNPs and PTH-MB both have excellent electrocatalytic performance, in which the methylene blue (MB) in PTH-MB molecules acts as an electrocatalyst, which can enhance the electron transfer efficiency of electrochemical reactions on the electrode surface. Furthermore, the synergistic effect between AuNPs and PTH-MB effectively enhances the current signal, thus improving the sensitivity of the biosensor [21]. In practical detection, this biosensor has good selectivity and repeatability, achieving the detection of HSA in urine, and providing a potential universal platform for clinical protein detection.