esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Non-Associative Flow Rule  
In the literature, the major geotechnical problems were analyzed in the geomaterials governed by the associative flow rule. However, it is recognized that the plastic flow in the limit state behaves as a non-associative feature for real soils [34; 46]. At present, there are two main research ideas to consider the non-associative characteristics of materials in the limit analysis: (1) use the non-associative flow rule directly for the calculation without changing the yield criterion, and (2) keep the associative flow rule unchanged and modify the parameters in the yield criterion or introduce a variable to represent the non-associative characteristics. In practice, the calculation process of the former is usually complex, and the trickier point is that the non-associative flow method does not satisfy the basic assumptions of the limit analysis principle, which makes it difficult to continue the analysis. The latter has a clear idea and method and is more suitable for combining with the upper-bound principle. Therefore, following the second research idea, a nonlinear dilatancy coefficient  
Figure 1: The modified power law strength envelope and corresponding tangential line.  
\(\zeta\) proposed by [PERSON] and [PERSON] [34] is introduced in this work to improve the strength criterion, and the shear strength parameters can be modified by the following expression:  
\[\left\{\begin{array}{l}c^{*}=\zeta c\\ \tan\varphi^{*}=\zeta\tan\varphi\end{array}\right. \tag{6}\]  
where \(c^{*}\) and \(\varphi^{*}\) represent the shear strength parameters of the materials that satisfy the non-associative flow rule; \(\zeta\) denotes the dilatancy coefficient and is defined as:  
\[\zeta=\frac{\cos\varphi\cos\psi}{1-\sin\varphi\sin\psi} \tag{7}\]  
where \(\psi\) is the dilatancy angle during the plastic shearing failure, which ranges from 0 to \(\varphi\). According to Equation (7), the coefficient \(\zeta\) reaches the maximum value, namely 1, when \(\psi=\varphi\), which is also the case that the plastic flow rule changes from non-associative to associative. Such an introduction of the dilatancy coefficient makes the M-C criterion applicable to the non-associative material. Accordingly, the P-L criterion is also revised to portray the nonlinearity and non-associative flow characteristics of the materials.  
\[\tau=\zeta c_{0}\bigg{(}1+\frac{\sigma_{n}}{\sigma_{t}}\bigg{)}^{\frac{1}{n}} \tag{8}\]  
Applying the derivation operation to Equation (8) about \(\sigma_{n}\), the tangent slope yields:  
\[\tan\varphi_{t}^{*}=\frac{\mathrm{d}\tau}{\mathrm{d}\sigma_{n}}=\frac{\zeta c _{0}}{m\sigma_{t}}\bigg{(}\frac{\sigma_{n}}{\sigma_{t}}+1\bigg{)}^{(1-m)/m} \tag{9}\]  
By rearranging Equations (4), (8), and (9) and eliminating the stress components from the equations, an expression that only contains the shear strength parameters \(c_{t}^{*}\) and \(\varphi_{t}^{*}\) is expressed as:  
\[c_{t}^{*}=\frac{m-1}{m}\zeta c_{0}\bigg{(}\frac{m\sigma_{t}\tan\varphi_{t}^{* }}{\zeta c_{0}}\bigg{)}^{1/(1-m)}+\sigma_{t}\tan\varphi_{t}^{*} \tag{10}\]
## 3 Proposed System
### Hardware  
We developed a pair of proof-of-concept devices that includes a transmitter and a receiver device. Throughout the paper, we limit our attention to a smartwatch worn on the wrist as a transmitter and a smartphone touched by the finger of the same hand as a receiver. In the transmitter device, a commercial surface transducer was embedded in an off-the-shelf smartwatch (Galaxy Gear I) for the purpose of generating the mechanical signals. The surface transducer, which does not make acoustic signals by itself because it does not have any moving parts (_i.e._, cones), creates a tiny expansion/contraction of the metal parts to make the attached surface mechanically vibrate. When the transducer is attached to the skin around the ear, the generated tiny vibration is transmitted to the inner  
Figure 1: Proposed system that transmits data from a smartwatch to a touch device by modulating high-frequency mechanical waves. The signal is mechanically generated by the watch-mounted bone-conductor transducer.  
ear through the bones of the skull. In our case, the vibrations from the transducer are intended to flow to the tips of the fingers through the hand structure, which acts as a waveguide. To amplify the electrical signals sent to the transducer, we incorporated a 2.5-W Class-D mono audio amplifier, PAM8302A, by Diodes Incorporated. Figure 2 shows the amplifier and surface transducers used in our system, and Figure 3 shows the developed system. A data acquisition system (NI-6343 by National Instrument) was used for input signal generation.  
At the receiver side, a piezoelectric, originally used as a sound pickup, was attached to the front side of the smartphone to collect the transmitted vibrations. The same module of the data acquisition system, but with an isolated electrical ground, was employed to capture and analyze the collected signals.
# Model Checking Resiliency and Sustainability of In-Vehicle Network for Real-Time Authenticity
## 5 Formal Analysis of MAuth-CAN
### Model Checking Analysis of Theorem 1
In order to compute the minimum TB, we present Lemma 2:  
If the CAN authenticator makes it to address all consecutive attack messages, TB is not necessarily longer than 4 \(\times\) T\({}_{\text{tx}}\). In our model, for a given authentication time, denoted by T\({}_{\text{auth}}\), and transmission time, denoted by T\({}_{\text{tx}}\), we can measure the maximum communication delay, using the clocks ECUAuth_Q1.x and ECUAuth_Q2.x on the locations ECUAuth_Q1.TxAck and ECUAuth_Q2.TxAck. For given T\({}_{\text{auth}}\) = 1 and T\({}_{\text{tx}}\) = 2, we check the worst-case time for the AFR message to arrive all ECUs. We use the following queries:  
\[\text{{CTL-Property-3: sup}}\{\text{ECUAuth\_Q1.TxAck}}:\text{ECUAuth\_Q1.x} \tag{4}\]  
\[\text{{CTL-Property-4: sup}}\{\text{ECUAuth\_Q2.TxAck}}:\text{ECUAuth\_Q2.x} \tag{5}\]  
\"sup{expr}: list\" in UPPAAL MC returns the maximum value of variables in \"list\". That is, the expression in list is evaluated only on the states that satisfy expr (a state predicate) that acts like an observation. Model checking shows that the worst-case response time of the AFR is always 8 (4 \(\times\) T\({}_{\text{tx}}\)). That is consistent with the illustration in Figure 4, thus we can conclude that 4 \(\times\) T\({}_{\text{tx}}\) is the minimum TB. In the results of model-checking for CTL-Property-1, 2, 3, and 4, we prove Lemma 1 and Lemma 2. Consequently, Theorem 1 is proved by the proofs of Lemma 1 and Lemma 2. In this section, we show that MAuth-CAN is resilient to masquerade attack using consecutive adversary messages if the authenticator reads incoming message using 2 size of reception queue and the authentication time is less than the message transmission time. In particular, it is shown that the consecutive two messages of AFR sent by the authenticator can prevent the flooding of adversary messages by preempting CAN bus. However, it is true only if the AFR messages is successfully transmitted to other nodes. In next section, we will show that MAuth-CAN is sustainable to BoA even if CAN priority is not secure and attacker can utilize the highest priority of CAN.
## 3 Results
### Practical Application of the Methodology at Rail Crossing P5567
#### 3.2.3 Possibility of Avoiding Collisions by the Passenger Vehicle Driver at Crossing P5567  
The possibility of avoiding collisions by the driver of a passenger vehicle can be again assessed in regard to the time for the approach of the rail vehicle and the crossing time of the slowest vehicle. Calculated values regarding the methodology are shown in the table. The assessment of the possibility of avoiding collisions by the driver of the road vehicle is suggested in Table 9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{3}{*}{**Speed of the Rail Vehicle km.h\({}^{-1}\)**} & \multicolumn{3}{c}{**Surface Adhesion**} \\  & **Dry** & **Wet** & **Slippery** \\  & **0.15** & **0.10** & **0.05** \\  & & **Stopping of the Rail Vehicle** & \\ \hline
10 & Yes & Yes & Yes \\
20 & Yes & Yes & Yes \\
30 & Yes & Yes & Yes \\
40 & Yes & Yes & Yes \\
50 & Yes & Yes & Yes \\
60 & Yes & Yes & Yes \\
80 & Yes & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 7: Possibilities of avoiding collisions at crossing P5967 in the direction of Horni Dvoristé.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**Speed of the Rail Vehicle km.h\({}^{-1}\)**} & \multicolumn{3}{c}{**Surface Adhesion**} \\  & **Dry** & **Wet** & **Slippery** \\  & **0.15** & **0.10** & **0.05** \\  & & **Stopping of the Rail Vehicle** & \\ \hline
10 & Yes & Yes & Yes \\
20 & Yes & Yes & Yes \\
30 & Yes & Yes & Yes \\
40 & Yes & Yes & Yes \\
50 & Yes & Yes & Yes \\
60 & Yes & Yes & Yes \\
80 & Yes & Yes & No \\ \hline \hline \end{tabular}
\end{table}
Table 8: Possibilities of avoiding collisions at crossing P5967 in the direction of Ceske Budejovice.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**Direction of the Vehicle**} & \multirow{3}{*}{**Direction of the View**} & \multicolumn{3}{c}{**S\({}_{t}\)**} & \multicolumn{1}{c}{**T\({}_{s}\)**} & \multicolumn{1}{c}{**T\({}_{s}\)**} \\  & & **[m]** & **[s]** & **[s]** \\ \hline Velesin & Horni Dvoristé & 56 m & 4.680 s & 2.52 s & No \\  & Ceske Budéjovice & 34 m & 1.53 s & No \\  & Horni Dvoristé & 373 m & 4.752 s & 16.785 s & Yes \\  & Ceske Budéjovice & 71 m & 4.752 s & 3.198 s & No \\ \hline \hline \end{tabular}
\end{table}
Table 9: Security of perspective ratios for drivers of road vehicles at crossing P5569.  
Table 8 suggests that the distance of perspective ratios is quite insufficient except in one case. This exception indicates the situation when the driver of the road vehicle is moving in the direction of Skridla and the rail vehicle is reaching the rail crossing from Horni Dvoriste. Thanks to the sufficient perspective ratios, the driver of the road vehicle can either safely cross the rail crossing or wait in case of an approaching rail vehicle and thereby avoid the collision. In that respect, other perspective ratios are wholly insufficient and thus the driver can be in danger of collision even if he did not notice an approaching rail vehicle upon his arrival at the rail crossing [3].
# Impact of Grounding Modeling on Lightning-Induced Voltages Evaluation in Distribution Lines
## 2 Induced-Lightning Modeling
### EM Fields Computation  
The EM fields are computed analytically considering the approach proposed in [24] and validated in [25]. The method requires as input the knowledge of the channel-base current, the return stroke height and the return stroke velocity. It can be applied both to perfect electric conductor ground and soil characterized by a finite conductivity. The only assumption required is the Transmission Line model for the attenuation of the current along the channel. The main advantage of this approach consists of the possibility of dealing with analytical formulas, which guarantee a fast solution and a low computational effort.
## 2 Building Blocks
### Attention-Aware Multi-Modal Fusion Block  
The Attention-Aware Multi-modal Fusion Block is a computational module introduced in Reference [8] to adaptively re-weight feature channels from different modalities, therefore highlighting discriminative features and suppressing irrelevant ones. The module is based on the Attention mechanism [21] that produces significant performance improvements. The module is comprised of multiple stages. In the first stage, a global average pooling operation is performed to abstract global spatial information of each channel. Pooled features are then processed in a bottleneck where linear and non-linear operations are applied in order to learn the interactions between channels. The concatenated channel-wise statistics are then multiplied by the corresponding input features. The final fused features are obtained by an element-wise summation of the re-weighted features. The Attention mechanism has been extensively used in Remote Sensing applications; however, there exist only a small number of scenarios where the Attention block has been used as a way to fuse the features extracted from models' encoders [22; 23; 24; 25]. [PERSON] et al. [22] developed a multilevel attention mechanism through adversarial learning to detect oil palm trees. [PERSON] and [PERSON] [23] created a new method to fuse hyperspectral images with attention. [PERSON] et al. [24] used a attention-based fusion block to better detect different remote sensing objects. [PERSON] et al. [25] introduced a multilevel features fusion method with attention to improve the segmentation accuracy of pixels near object boundaries. As shown, something similar to the previously proposed Attention-Aware Multi-modal Fusion Block is presented; however, major differences exist between the proposed solutions, and only the core idea of fusing and enhancing features with attention is preserved.
# Time-Sensitive Networking Technologies for Industrial Automation in Wireless Communication Systems
## 4 IEEE 802.11 Technology for TSN  
The wireless TSN uses not only 5 GS but also WLAN technology as lower layer. In 2021, IEEE 802.11 working group released draft 8.0 document for 802.11 ax which is recent wireless MAC protocol to support high efficiency under dense networks [32; 33]. In 802.11 ax, orthogonal frequency division multiple access (OFDMA) and triggered transmission was proposed to reduce latency and to increase throughput. Table 3 lists the relationships between the TSN features (such as latency, reliability, resource management) and the 802.11 ax technologies. New capabilities in 802.11 ax enables wireless TSN because they reduces latency and channel contention. This section introduces 802.11-based technologies highly related to TSN. Among studies from 802.11b to 802.11 be, we introduce technologies for time synchronization, low latency, transmission reliability, and traffic scheduling.  
Figure 4: Uplink synchronization [25].
# A Novel Methodology for Planning Urban Road Safety Interventions
## 3 Novel Methodology
### Cost-Benefit Analysis for Road Safety Interventions
#### 3.4.4 Prioritization of Interventions  
By estimating benefits from reduced social costs of accidents and comparing them with intervention costs, the Benefits/Costs (B/C) ratio serves as a tool for selecting optimal project solutions.  
Using the B/C ratio is rooted in the economic concept of opportunity cost, which evaluates the trade-offs of investing limited resources. This method focuses on maximizing safety benefits per unit of expenditure, directing resources toward the most impactful interventions. The B/C ratio is advantageous as it identifies solutions that provide the greatest reduction in accident-related social costs while minimizing missed opportunities.  
The decision-making process performs in two stages, as follows:  
1. Site-specific optimization, in which for each identified critical point, the intervention with the highest B/C ratio is selected as the most cost-effective solution;
2. Network-wide prioritization, in which across all critical points, interventions are ranked by their B/C ratios to establish a priority hierarchy, identifying sites with the greatest need for action.  
Before implementing prioritized interventions, field inspections are conducted to confirm the necessity of the proposed solutions.
# ICRSSD: Identification and Classification for Railway Structured Sensitive Data
## 2 Methods
### Canonical Data Identification
#### 2.1.1 Sensitivity Measure Based on Information Entropy and Maximum Discrete Entropy  
Information entropy [12] quantifies the average information content per message and reflects the uncertainty of a data source. Therefore, in the context of massive data, using information entropy as a quantitative metric of information content often achieves good results. If the information entropy is defined as \(H\), the information entropy of a random variable \(X=(x_{1},x_{2},\ldots,x_{n})\) can be expressed as:  
\[H(X)=-\sum_{x_{i}\in D_{X}}p(x_{i})\log_{2}p(x_{i})\quad(i=1,2,\ldots,n) \tag{1}\]  
Figure 2: Canonical data identification diagram.  
The maximum discrete entropy represents the maximum value of information entropy, which takes its maximum value when the random variable \(X\) is taken to \(n\) different values with equal probability. The maximum discrete entropy is calculated as follows:  
\[H_{\text{max}}(X)=\log_{2}n \tag{2}\]  
The information entropy of an attribute is used to indicate the amount of information about the attribute. The maximum discrete entropy of an attribute is a metric that evaluates the highest uncertainty. According to information theory, data with higher uncertainty carries more information and is considered more sensitive. Therefore, we can measure the sensitivity of an attribute by measuring the distance between the information entropy of the attribute and the maximum discrete entropy of the attribute. The attribute sensitivity is calculated as follows:  
\[H_{\text{S}}(X)=\frac{H_{\text{max}}(X)-H(X)}{H_{\text{max}}(X)} \tag{3}\]  
According to Equation (3), we can get the initial sensitivity \(H_{\text{S}}(X)\) for each attribute \(X\) ( \(0\leq H_{\text{S}}(X)\leq 1\)). The smaller the value of \(H_{\text{S}}(X)\), the more information attribute \(X\) carries, which indicates a higher level of sensitivity for attribute \(X\). At this point, the quantification of data sensitivity in a massive data environment can be completed. Thus, a solid foundation is laid for the subsequent intelligent identification of sensitive attributes.
###### Abstract  
Fragility fractures, caused by low-energy trauma, are a significant global health concern, with 158 million people aged 50 and over at risk. Hip fractures, a common issue in elderly patients, are often linked to underlying conditions such as osteoporosis. This study proposed a cost-effective solution using a non-wheeled smart walker with load sensors to measure gait parameters, addressing the high cost of traditional gait analysis equipment, the prototype used PASCO load cells PS2200 for force measurement, eliminating the need for Arduino UNO or microcontroller-based hardware. A lightweight amplifier PS2198 amplified the signal, which was transmitted via USB to a personal computer. PASCO capstone software was used for data recording and visualization. The smart walker was tested on forty volunteers divided into two equal groups: those with osteoporosis and those without, by performing a 10 m walk test three times. ANOVA comparing spatiotemporal parameters (TSPs) of the two participant groups (\(\alpha\) = 0.05) showed that significant differences lay in terms of time taken to complete the walk test (\(p\) < 0.01), left step length (\(p\) = 0.03), walking speed (\(p\) = 0.02), and stride length (\(p\) < 0.02). The results indicate that this smart walker is a reliable tool for assessing gait patterns in individuals with osteoporosis. The proposed system can be an alternative for time consuming and costly methods such as motion capture, and for socially stigmatizing devices such as exoskeletons. It can also be used further to identify risk factors of osteoporosis.  
Keywords:fracture prevention; bone health; mobility aid geriatric care; spatiotemporal analysis; osteoporo-porotic fractures; hip fracture; elderly healthcare; biomechanical assessment; fall prevention; osteoporosis management; physical therapy; musculoskeletal disorders; aging population; medical technology; clinical research; bone density; patient rehabilitation; functional mobility; osteoporosis diagnosis +
Footnote †: journal: Applied Sciences
# The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
## 2 Related Work
Each experiment's unique device characteristics, such as architecture, sampling rate, wavelength, and light intensity, significantly impact data acquisition. Signal processing, often the first step, includes critical tasks like filtering, windowing, and feature extraction. Data annotation, crucial for comparing expert and algorithmic results, is prominently featured in datasets like CB_InVivo, CSL, and MIMIC-III [55; 56; 57]. However, many datasets lack comprehensive descriptions, artifact labels, and expert annotations, affecting the result metrics and applicability. The quality labels assigned by different experts further complicate the determination of universally effective features. Despite the diversity in SQIs, there is no universally accepted standard for their application, leading to variations in how signal quality is measured and interpreted. Furthermore, despite extensive research in SQIs, there is a notable lack of studies investigating how CF affects signal quality evaluation. Understanding the influence of CF on SQIs is vital for accurately assessing the cardiac and vascular parameters as it directly impacts the blood vessel responses and alters the PPG waveform. This insight is essential for advancing monitoring technologies and emphasizes the need for an in-depth understanding of the factors affecting SQI, particularly concerning CF. The foundation of this study is rooted in a previous investigation [35] that evaluates the vascular depth-dependent changes in MW-PPG signals due to CF variations. This prior work highlights the significant individual variability in signal responses due to skin and vascular properties, demonstrating that PPG signals exhibit hysteresis and wavelength-dependent responses to CF changes. The study underscores how different wavelengths respond distinctively to varying CF levels. Building on these findings, the current research quantitatively analyzes the impact of CF on various SQI metrics. This study aims to refine the PPG signal quality assessment criteria by identifying the optimal CF ranges based on these metrics. This comprehensive analysis bridges the gap between qualitative observations and quantitative insights, enhancing the accuracy and reliability of PPG-based health monitoring systems by providing clear guidelines on the optimal CF levels for obtaining high-quality signals. To the best of our knowledge, this is the first study of its kind to provide a quantitative analysis of the impact of CF on SQI metrics.
# Combination of LiCs and EDLCs with Batteries:
###### Abstract  
The research presented in this paper proposes a hybrid energy storage system that combines both electrolytic double-layer capacitors (EDLCs) also known as supercapacitors (SCs) and lithium-ion capacitors (LiCs) also known as hybrid capacitors (HCs) with a battery through a multiple input converter. The proposal was verified in simulation and validated by implementing a laboratory prototype. A new hybridisation topology, which reduces the amount of resource requirement when compared to the conventional hybridisation topology, is introduced. An electric vehicle (EV) current profile from previous research was used to test the performance of the proposed topology. From the results obtained, the hybridisation topology proposed in this research had the lowest cost per unit power at 14.81 $/kW, the lowest cost per unit power to energy, and available power to energy ratio, both at 1:1.3, thus making it a more attractive hybridisation topology than the two conventional alternatives. The multiple input converter built had efficiency values in excess of 80%. The key take away from this paper is that using the proposed hybridisation topology, the battery is less often required to supply energy to the electric vehicle, and so, its cycle life is preserved. Furthermore, since the battery is not used for the repeated acceleration and deceleration in the entire driving cycle, the battery's cycle life is further preserved. Furthermore, since the battery is not the only storage device in the energy storage system, it can be further downsized to best fit the required base load; therefore, leading to a more optimized energy storage system by reducing the weight and volume of space occupied by the energy storage system, while also achieving better efficiencies.  
b  
battery; DC-DC converter; electric vehicle; hybrid capacitor; hybrid energy storage; supercapacitor