esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Finite Element Simulation
### Finite Element Simulation Schemes  
The numerical simulation scheme is shown in Table 5. With the goal of further investigating the stability of the excavation surface with varying horizontal spacing between the two tunnels in the clay stratum, the parameters of numerical simulation schemes 1-4 are kept the same as the model test schemes. Based on schemes 1-4, the vertical spacing \(h\) between the two tunnels is reduced in schemes 5-7 from \(1D\) to 0.75\(D\) to study the stability of the excavation surface when the vertical spacing between the two tunnels differs in the clay stratum. In schemes 8-10, the diameter \(d\) of the existing tunnels is reduced from \(1D\) to 0.5\(D\) to study the stability of the excavation surface when the diameter ratios between the two tunnels vary.  
To simulate the excavation instability process, the initial ground stress equilibrium of the model is necessary to establish. Since this study primarily focuses on investigating the effect of the progressive instability of the excavation surface and does not require simulation of the excavation process of the existing and new tunnels, the corresponding soil layers in the two tunnels are removed during the modeling process. Furthermore, the two tunnels and the excavation panels are treated as existing underground structures, and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & \(\boldsymbol{\gamma}\) **(kN/m3)** & **E/MPa** & \(\boldsymbol{v}\) & \(\boldsymbol{c/kPa}\) & \(\boldsymbol{\varphi}\)\({}^{b}\) \\ \hline Clay & 19.8 & 0.063 & 0.3 & 0.555 & 11.034 \\ Excavation panel & 78.5 & 206,000 & 0.3 & — & — \\ Tunnel lining & 25 & 34,500 & 0.2 & — & — \\ \hline \hline \end{tabular} Note: \(\boldsymbol{\gamma}\) = Total bulk weight; \(E\) = Elastic modulus; \(\boldsymbol{v}\) = Poisson’s ratio; \(\boldsymbol{c}\) = Cohesion; \(\boldsymbol{\varphi}\) = Angle of in termal friction.  
\end{table}
Table 4: Structural material parameters [31; 33].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Conditions** & **Stratum** & \begin{tabular}{c} **Depth of** \\ **Tunnel C/D** \\ \end{tabular} & \begin{tabular}{c} **Existing Tunnel** \\ **Diameter d/D** \\ \end{tabular} & \
\begin{tabular}{c} **Vertical Spacing of Two** \\ **Tunnels h/D** \\ \end{tabular} &
\begin{tabular}{c} **Horizontal Spacing of** \\ **Double Tunnels L/D** \\ \end{tabular} \\ \hline
3 & Clay & 2 & 1 & 1 & 0 \\
4 & Clay & 2 & 1 & 1 & \(-1\) \\
5 & Clay & 2 & 1 & 0.75 & 1 \\
6 & Clay & 2 & 1 & 0.75 & 0 \\
7 & Clay & 2 & 1 & 0.75 & \(-1\) \\
8 & Clay & 2 & 0.5 & 1 & 1 \\
9 & Clay & 2 & 0.5 & 1 & 0 \\
10 & Clay & 2 & 0.5 & 1 & \(-1\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Finite element simulation schemes.  
Figure 9: 3D numerical model.  
the ground stress equilibrium stage is established using predefined fields. The instability of the excavation surface is simulated by repeatedly applying horizontal displacement loads to the shield excavation panels, and the displacement loads applied in each analysis step correspond to the experimental process.
# Multimode Guided Wave Detection for Various Composite Damage Types
## 3 Detection of Various Damage Types Using ABT-ABT on a 2-mm Quasi-Isotropic Composite Plate
### Damage Detection Using Multiple Pure-Mode Guided Waves
#### 3.3.2 Damage Detection Using Pure SH0 Mode  
Similarly, the pure SH0 mode generated by the adjustable ABT pair was used to detect the delamination simulated by the Teflon insert and the actual impact damage. Figure 14 shows a signal comparison between the pristine area, the delamination simulated by the Teflon insert, and the impact damage using pure SH0 mode. Strong wave packets of pure SH0 mode were observed for all three cases. It was found that large amplitude drops were clearly observed for both the Teflon insert and the impact damage. The frequency spectrums were calculated using time windowing on the acquired signals to obtain the main wave packet and remove the trailing waves. Similarly, amplitude drops were noted in the frequency spectrums. Therefore, SH0 mode was sensitive to both the delamination simulated by the Teflon insert and the actual impact damage. However, SH0 mode could not separate the delamination and impact damage since the same amplitude drop was observed for both cases.  
Figure 14: Signal comparison between the pristine area, the Teflon insert, and the impact damage using pure SH0 mode at 500 kHz on a 2-mm quasi-isotropic composite plate: (**a**) time-domain signals; (**b**) frequency spectrums.  
Figure 13: Signal comparison between the pristine area, the Teflon insert, and the impact damage using pure S0 mode at 500 kHz in 2-mm quasi-isotropic composite plate: (**a**) time-domain signals; (**b**) frequency spectrums.  
To distinguish between damage types, the pure S0 mode and SH0 mode had to be used together. This is because the pure S0 and SH0 modes were both sensitive to actual impact damage, whereas only the pure SH0 mode was sensitive to the delamination simulated by the Teflon insert. This was related to the motion characteristics of the respective mode shapes and the corresponding wavelengths. Thus, the use of the pure S0 and SH0 modes allowed for damage separation that would not have been possible otherwise.
# Fractional Sliding Mode Harmonic Control of an Active Power Filter
## 3 Design of the Fractional Sliding Mode Control  
### Definitions of Fractional Derivatives and Integrals  
We define \({}_{a}D_{t}^{a}\) as a generalization of a differential and integral operator, where \(a\) and \(t\) are the bounds of the operation, and \(\alpha\) is the order of fractional calculus. The commonly used three definitions are the Grunwald-Letnikov (GL) definition \({}_{a}D_{t}^{a}f(t)=\underset{h\to 0}{limh\to 0}{h}^{-\alpha}\sum_{j=0}^{ \infty}{(-1)^{j}\binom{\alpha}{j}(t-jh)}\), the Riemann-Liouville (RL) definition,  
\[{}_{a}D_{t}^{a}f(t)=\dfrac{1}{\Gamma(n-\alpha)}l_{a}^{t}\;\dfrac{f^{(n)}( \tau)}{(t-\tau)^{a-n+1}}d\tau\;\;n-1<\alpha<n,\]  
and the Caputo (C) definition.  
In order to simplify the notation, the fractional derivative of order \(\alpha\) is expressed as \(D^{\alpha}\) instead of \({}_{a}D_{t}^{\alpha}\), defined as the Caputo definionas  
\[aD_{t}^{\alpha}f(t)=\left\{\begin{array}{l}\dfrac{1}{\Gamma(n- \alpha)}\int_{a}^{t}\dfrac{f^{(n)}(\tau)}{(t-\tau)^{\alpha-n+1}}d\tau,\ \ \ \ \ n-1<\alpha<n\\ \dfrac{d^{n}}{dt^{n}}f(t),\alpha=n\end{array}\right.\]  
where \(f(\cdot)\) should be integrable, \(n\) is a positive integer, and \(\Gamma(\cdot)\) represents the Gamma function as \(\Gamma(n-\alpha)=\int_{0}^{\infty}\tau^{n-\alpha-1}e^{-\tau}d\tau\). The characteristic curve of the FOSMC and integer-order SMC are given in Figure 2, where the expression of the FOSMC close to equilibrium is \(t^{-\alpha}\) after arriving at the sliding manifold, and the expression of the integer-order SMC is \(e^{-t}\). It is obvious the chattering under the FO-SMC(\(\Delta\)) is less than that of the integer-order SMC (\(\Delta^{\prime}\)).
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 6 Non-Thermal Preservation Techniques
### Irradiation
The oxidation of lipids leads to hydro peroxides, aldehydes, ketones and various other products which affect taste, flavor, nutritional profile and acceptable conditions [87]. Irradiation also affects the color of the meat due to the susceptibility of iron in myoglobin, the main meat pigment. Myoglobin is adequately destroyed at a dose of 50 kGy or higher [86]. The optimal color of meat can be preservedduring the irradiation process by pre-slaughter antioxidant feeding, antioxidant addition, packaging technique, and appropriate temperature. Irradiation at a high dose results in unacceptable off odors and meat discoloration, whereas irradiation at a low dose results in fewer odor, flavor, and color issues [86]. Applications of irradiations in meat and fish at various wavelengths, such as 1.68 mWs/cm\({}^{2}\) for 140 s in shredded bullfrog, in chicken 1.95 mW/cm\({}^{2}\) for 120 s, fish meat 55.83 mJ/cm\({}^{2}\) and in beef at 164 mJ/cm\({}^{2}\), showed significant results. In fish and meat products such as chicken breast, fish fillets and beef, etc., microorganisms such as Murine norovirus-1, Hepatitis A virus and Enterobacteriaceae load were significantly reduced [87, 88]. In a database, the chicken meat microbial load reduction in the norovirus and hepatitis virus was 1.23 and 1.17 PFU/mL, while in fish, a microbial load reduction in various psychrophilic, mesophilic and enterobacteriaceae was observed significantly to levels of 0.26, 0.25 and 0.15/g [87, 89].
## 3 Results and Discussion
### Comparison with Other Configurations  
In order to evaluate the capacity of the HPFRC-RC configuration to resist impact loads, a wider study was carried out by including results of previous research. Eight additional tests with identical geometry, reinforcement arrangements (concrete cover 10 mm smaller), boundary conditions, and impact load were considered from two other studies [12; 22]. Among all the newly considered specimens, two were RC beams identical to series RC-U0 of this paper (only with a slightly different concrete strength,\(f_{c}\) = 48.5 MPa), four specimens consisted of reinforced SFRC beams (R-SFRC), and two specimens were reinforced HPFRC beams (R-HPFRC). In the R-SFRC beams, SFRC was used instead of conventional concrete. The SFRC consisted of a matrix with the same concrete as the companion RC beams and a certain amount of short straight steel fibers (\(J_{f}\) = 10 mm, \(d_{f}\) = 0.16 mm,\(f_{y}\) = 3000 MPa): two specimens contained 0.5% fibers by volume and two specimens 1.0%. In the R-HPFRC beams,  
Figure 8: (**a**) Maximum and residual deflection; (**b**) energy absorbed in first and second stages of tests, respectively; (**c**) weight loss of specimens.  
the conventional concrete was replaced with HPFRC with 1.6% fiber amount (\(l_{f}\) = 10 mm, \(d_{f}\) = 0.16 mm, \(f_{y}\) = 3000 MPa).  
The RC beams presented the same impact behavior as in the present paper. The R-SFRC beams developed flexural failure with opening of a shear plug (type I cracks) in the midspan. In addition to the shear plug, type II diagonal cracks opened in the webs but the bridging capacity of the SFRC was able to avoid their propagation. The residual width of diagonal cracks and shear plugs was larger in the specimens with 0.5% fiber content than with 1.0%. The R-HPFRC developed flexural failure mode with a major bending crack at midspan and no shear cracking at all.  
The results of all considered tests are represented in Figure 9 in terms of strength (maximum value of total reaction) and maximum midspan displacement produced by the impact. As it can be noted, the highest strength was obtained by the R-HPFRC beams followed by the R-SFRC beams with 1.0% fiber content and the R-SFRC with 0.5% fiber. The deformation capacity under impact can be evaluated by means of maximum deflection obtained at the midspan. From Figure 9, the average midspan deflection of HPFRC-strengthened RC specimens was 37% smaller than that of RC beams failing by brittle shear and severe fragmentation, while that of R-HPFRC specimens failing by flexure was 75% smaller than that of RC. The comparison of maximum deflection indicates that the HPFRC-RC beams showed an intermediate deformation capacity between RC and R-HPFRC. The smaller strength and higher deflection of RC-U55 with respect to RC-U35 was due to the full development of the additional resisting mechanism as stress ribbon by RC-U35 series.  
Figure 9: Comparison of results of the tests of the present paper and those from [12,22].
# A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks
## 3 System Model
### Radio Model  
The radio model [30; 32; 33] adopted in this paper is shown in Figure 2, and it contains a two-ray ground propagation model and free space model according to the distance from data transmitter to receiver.  
The energy \(E_{TX}\) consumed by the transmitter to deliver a \(q\)-bit packet can be calculated by:  
\[E_{TX}=\left\{\begin{array}{ll}q\zeta+q\varepsilon_{f}d^{2}&d<d_{0}\\ q\zeta+q\varepsilon_{m}d^{4}&d\geq d_{0}\end{array}\right. \tag{2}\]  
where \(\zeta\) is the energy consumed by the transceiver circuitry for a one-bit packet; \(\varepsilon_{f}\) and \(\varepsilon_{m}\) are the amplifier characteristic constants with regard to free-space propagation model and two-ray ground reflection model; \(d\) is the distance from the transmitter to the destination and \(d_{0}\) is the distance threshold which is computed by:  
\[d_{0}=\sqrt{\frac{\varepsilon_{f}}{\varepsilon_{m}}} \tag{3}\]  
where \(\varepsilon_{f}\) and \(\varepsilon_{m}\) are the amplifier characteristic constants.  
The energy consumed by the receiver to receive a \(q\)-bit packet can be calculated by:  
\[E_{RX}=q\zeta+q\varepsilon_{DA} \tag{4}\]  
where \(\zeta\) is the energy consumed by the transceiver circuitry and \(E_{DA}\) is energy of data aggregating for a one-bit packet.
## 3 Dual Sparsity Pruning Federated Learning Framework
### Computational Complexity Analysis  
To analyze the computational complexity of the algorithm, we primarily focus on two critical stages that contribute most significantly to the overall computational cost: the calculation of local KL divergence and the dual sparsity-based pruning process.  
For computing the KL divergence for client \(i\), we first need to calculate the probability distribution across classes by aggregating data points, which has a complexity of O(\(|\mathbf{D}_{i}|\)), where \(|\mathbf{D}_{i}|\) denotes the size of the local dataset. The efficiency of this computation can be enhanced through the utilization of optimized data structures such as hash tables.  
In the dual sparsity-based pruning phase, we construct a probability vector \(\mathbf{P}_{i}\in R^{m}\) to store the class-wise probabilities, where \(m\) represents the number of classes. According to Equation (5), computing the KL divergence requires accessing each element in vectors \(\mathbf{P}_{i}\) and \(\mathbf{Q}\), resulting in a time complexity of O(\(m\)). Subsequently, we apply linear transformations and reciprocal operations using Equations (6) and (7) to obtain \(p_{i}\), which incurs a constant time complexity of O (1). Following this, the elements of the binary matrix \(\mathbf{M}_{i}\) are sampled from the Bernoulli (\(p_{i}\)) distribution, introducing a relatively low computational cost. The final step involves the Hadamard product between the mask matrix and the accumulated gradients, which is an element-wise operation with a negligible computational overhead.
## 1 Introduction  
### Elderly Care: New Challenges Ahead and the Importance of Walking  
There is a constant increase in the number of individuals over the age of 65. Projections indicate this will approximately correspond to 1.5 billion in 2050 [1]. Consequently, pressure on national pension plans and long-term health care is increasing. Simultaneously, the number of working-age adults available to support elderly individuals is decreasing due to declining birth rates and increased longevity [1]. Health systems of individual governments should be prepared to address the fore-mentioned issues at the earliest.  
Unesco's estimates [2] indicate that the most common diseases that affect older women over 60 years correspond to unipolar depressive disorders, hearing loss, back and neck pain, Alzheimer's disease and other dementias, and osteoarthritis. Among older men, the main causes of disability include hearing loss, back and neck pain, falls, chronic obstructive pulmonary disease, and diabetes mellitus [2].  
Dementia is an umbrella term that indicates all diseases that are characterized by the progressive impairment of brain functions. Unfortunately, there are no cures for dementia to date. In order to decelerate or even prevent cognitive decline, doctors recommend living a healthy life by following a healthy diet, performing physical exercise, and challenging the brain via social engagement or intellectual stimulations. Thus, it is important to perform any activity that keeps the brain active and improves blood circulation. Specifically, physical exercise is an important part of a healthy lifestyle that increases the sense of wellbeing of an individual. Walking itself is a completely accessible way of physical exercise because it suits all abilities, it is free, it does not require equipment, and it can be performed anywhere. Moreover, walking in an outside environment leads to interaction between an individual and others, and thus it can correspond to a social activity that can improve an individual's mood. There is no definitive evidence from randomised trials although studies continue to explore the potential of walking as a preventive treatment for dementia as well as a disease-modifying treatment [3; 4]. A main question focuses on whether physical activity directly protects an individual from dementia by providing better oxygen supply or instead whether it offers indirect protection by reducing risks, such as hypertension, that impair cognition. In any case, walking must be considered as extremely important for elderly individuals due to its role in modifying the progress of the previously mentioned diseases e.g., diabetes [5], was scientifically proven by extant studies. Moreover, monitoring the right gait parameters during walking (Section 2.3) could aid in measuring balance, and thus prevent falls that are another cause of disability based on Unesco's estimates.
## 2 Materials and Methods
### Statistical Analysis  
In this study, the statistical analysis of the measured data was performed by using IBM SPSSv.22. A combined database was created from the collected questionnaire data and the IAQ measurements, by assigning each occupant's questionnaire to the respective indoor concentrations data (using the building and office code). As a first step, descriptive results about the survey, symptom prevalence, and pollutant concentrations were obtained. For identifying the potential associations between the IEQ parameters and the overall comfort, proportional odds ordinal logistic regression analysis was used, and results were expressed in odds ratios (OR). The Spearman correlation coefficient, a non-parametric measure of statistical dependence, was used to identify relations between the rankings of two variables. The Wilcoxon test, a non-parametric statistical hypothesis test, was used to compare the populations of two paired samples and Mann-Whitney U test was used to reveal the pollutant concentration variations among several Sick Building Syndrome (SBS) groups.
## 2 Methodology
### Analyzing the Results of the Delphi Survey
#### 2.4.2 Mann-Whitney U Test  
When the Kruskal-Wallis test yielded significant results, we performed the Mann-Whitney U test as a post hoc analysis to compare specific pairs of groups [28]. This non-parametric method is suitable for comparing two independent groups, and it provides a more conservative result compared to parametric tests. The Mann-Whitney U test allowed us to identify which pairs of groups differed significantly in their risk perception rankings [29].
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 6 Applications and Policy Implications
### Impact of Interventions  
It can be hard to measure the direct impact of individual safety interventions. When seat belts were introduced, for instance, there was no specific date at which all cars had seat belts--there was a gradual transition from optional equipment to mandatory installation, to mandatory usage laws, and finally to widespread compliance. Similar gradual adoption patterns apply to other safety technologies and legal changes, from ABS brakes to drunk-driving laws.  
If we take a closer look at recent decades, we can compare traffic fatalities to population to assess the efficacy of various safety interventions and countermeasures. Figure 10 presents this comparative analysis for both the United States and the United Kingdom from 1994 to 2022. The longitudinal data reveal divergent trends in road safety outcomes between these nations, despite similar levels of economic development and technological advancement. While both countries have implemented evidence-based safety measures since the 1990s, when U.S. fatality rates peaked at approximately 15.7 per 100,000 inhabitants, the rate disparity remains significant, with the U.S. currently experiencing about 12.9 deaths per 100,000 inhabitants compared with less than 3 in the United Kingdom.  
While exposure differences, particularly higher vehicle miles traveled (VMT) in the United States, contribute to this disparity, multivariate analyses suggest that this factor alone does not fully explain the variation. The U.S. built environment, characterized by auto-centric development patterns, creates systematic exposure risks by necessitating motor vehicle use across all demographic groups, including populations that may be more susceptible to crash involvement. Moreover, controlled studies examining fatality rates per vehicle mile traveled (VMT) indicate persistent disparities, suggesting fundamental differences in system design parameters, vehicle fleet characteristics, and regulatory frameworks between the two countries.  
Figure 10: Comparison of motor vehicle fatalities per 100,000 inhabitants between the United States and the United Kingdom (1994–2022). Data source: [126].  
The divergent trajectories, particularly the recent uptick in U.S. fatalities while the UK rates maintain a downward trend, underscore the critical role of systemic factors and policy interventions in determining road safety outcomes. This empirical evidence suggests that elevated U.S. fatality rates are not merely a function of increased exposure through higher mobility but rather reflect addressable systemic factors. These findings have important implications for the application of countermeasures and the transfer of successful safety interventions between jurisdictions, particularly in the context of emerging analytical methodologies and real-time crash prediction systems.
###### Abstract  
In many underground engineering projects, rocks are often subjected to cyclic loading and unloading, such as repeated excavation of roadway surrounding rock, which will lead to damage to underground rocks, and the energy of rocks also changes. Therefore, to study the energy evolution and damage characteristics of rocks under cyclic loading and unloading, different cyclic loading and unloading tests of bituminous sandstones under constant amplitude were conducted. Under cyclic loading and unloading, the lower limit stress was 40% of the rock peak intensity, the cyclic amplitude was 20-40% of the peak intensity, and the number of loading-unloading cycles was 10-30. The quantitative characterization of the damage degrees of bituminous sandstone was realized by the ultrasonic wave velocity and elasticity modulus methods. The energy evolution and damage characteristics of bituminous sandstone under different amplitudes and number of loading-unloading cycles were investigated through the energy dissipation method. Results showed that under cyclic loading and unloading, the ultrasonic wave velocity and elasticity modulus of bituminous sandstone decreased gradually; The damage variable shows a trend of rapid and then stable growth and has a power function relationship with the number of cycles; The input energy density and dissipation energy density curves were in L-shaped distribution, whereas the elastic energy density remained stable. The results of this study can provide some theoretical references to underground engineering construction.  
cyclic loading and unloading; rock damage; energy evolution; damage characteristics