esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation on Shear Behavior of Dune Sand Reinforced Concrete Deep Beams
## 2 Experimental Program
### Test Setup and Procedure  
The experiment was conducted according to the procedure suggested by the standard method for testing concrete structures GB/T50152-2012 [45]. A 2000 kN servo-hydraulic actuator with a maximum displacement range of 100 mm was equipped for static loading. It applied a symmetrical four-point load to the beam at a displacement control rate of 0.1 mm/min. The beam was supported by a roller post and a pin bracket 150 mm from the end of beam, with a clear span of 1800 mm between the supports. Loading plates (size 150 \(\times\) 200 mm) were placed at the loading point to prevent stress concentration damage. To measure the beam deflection at midspan, three linear variable differential transformers (LVDTs) were placed on both sides of the upper end of the beam and in the center of the lower section. Five pairs of strain gauges were placed at equal distances from the bottom of the beam's longitudinal reinforcement. Each of the four stirrups on both sides of the beams with a smaller shear span-to-depth ratio (\(a/d\) = 0.7 and 1.05) and each of the six stirrups on both sides of the beams with a larger shear span-to-depth ratio (\(a/d\) = 1.4) received a pair of strain gauges. To measure concrete strain, strain gauges were installed at a 45\({}^{\circ}\) angle on both sides of the beam and vertically on the beam symmetry line. The details of the test setup are illustrated in Figure 5.
# Multimodal Few-Shot Learning for Gait Recognition
## 2 Method
### Convolutional Neural Network  
Given each sensing mode, our proposed CNN includes three identical networks that function independently, and the outputs of these three networks are concatenated. Each network contains three one-dimensional (1D) convolutional layers with 32, 64, and 128 filters, and the convolutional layers are followed by a batch normalization layer. Each filter in the first convolutional layer has a size of \(20\times(w\cdot 2)\), while the sizes of filters are \(20\times 32\) and \(20\times 64\) for the second and third convolutional layers, respectively. More specifically, for the first convolutional layer, the width of each filter is equal to that of the standard format \((w\cdot 2)\). We slide each filter across the height of the input and compute the dot product between the filter and the input, resulting in a series of scalar values. This convolution operation is repeated for the 32 filters, and the resulting series of scalar values are stacked horizontally, whereby the width of the output becomes equal to the number of filters. The stride of all convolutional layers is set to 1, and the padding size is set such that the height of the output is the same as that of the input. Similarly, for the second and third convolutional layers, the width of the filters equals the number of filters in the previous convolutional layer. Therefore, the shape of the feature map is \(87\times 32\), \(87\times 64\), and \(87\times 128\) after each convolutional layer. The last feature map is flattened, then the size of the feature vector is \(87\cdot 128\). The feature vectors of the three networks are concatenated to form one vector, followed by two fully connected layers. We use a rectifier linear unit (ReLu) as the activation function for every convolutional layer and the first fully connected layer to avoid the vanishing gradient phenomenon [44].
# The Perpetual Vector Mosquito Threat and Its Eco-Friendly Nemeses
## 5 Eco-Friendly _Lysinibacillus sphaericus_ and _Bacillus thuringiensis_ subsp. _israelensis_
### Brief History of Lysinibacillus (Formerly Bacillus) sphaericus (Ls)
#### 5.2.2 Bti, the Most Robust and Effacacious Natural Mosquito Larvicidal Bacterium Known  
To date, the Bti ONR 60A serotype H-14 strain is the most widely used and environmentally safe bacterial larvicide, primarily targeting _Aedes_, _Anopheles_, and _Culex_ species [252]. It is also used to control _Simulium_ species (blackflies) that vector _Onchocera volulus_, the etiological agent of river blindness (onchocerciasis) [221]. River blindness is endemic in Africa, and the disease is also known to occur in at least six countries in the Americas (Brazil, Colombia, Ecuador, Venezuela, Guatem, and Mexico), where the parasite was introduced as a result of the slave trade [253]. Although significantly less prevalent than mosquito-borne viral and parasitic diseases, onchocerciasis is listed as an NTD. Recent estimates of onchocerciasis are still alarming. At least 390,000,000 people in 31 countries required preventative treatment with ivermectin, 14.6 million of those infected presented with skin diseases, and 1,150,000 had vision impairments [3; 254].  
Interestingly, it must be noted that the expanded host range for Bti includes other dipterans, such as the Mexican and Mediterranean fruit flies and fungus gnats [255; 256], and pea and potato aphids, which are hemipteran and homopteran pests, respectively [257; 258; 259; 260], and coleopteran cotton boll weevil and leaf beetle [261; 262]. Interestingly, non-arthropod targets of Bti are the cercariae stages of human and avian parasitic flukes _Schistosoma mansoni_ and _Trichobillahara szidata_, respectively, which are susceptible to the water soluble M-exotoxin [263], the intermediate _Oncomelanin_ snail host of _Schistosoma japonicum_, when assayed at unusually high concentrations (900 ng/mL) [264], and the root-knot nematode, _Meloidogyne incognata_[265].
# Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management
## 3 Methodology
### Optimization in Machine Learning  
#### 3.2.1 Least Squares Support Vector Machine for Classification  
The least squares SVM (LSSVM), proposed by [PERSON] et al. [53], is an enhanced ML technique with many advanced features. Therefore, the LSSVM has high generalizability and a low computational burden. In a function estimation of the LSSVM, given a training dataset \(\{x_{k},y_{k}\}_{k=1}^{N}\), the optimization problem is formulated as follows:  
\[\min_{\omega,b,\sigma}(\omega,\epsilon)=\frac{1}{2}\|\omega\|^{2}+\frac{1}{2}C \sum_{k=1}^{N}e_{k}^{2} \tag{1}\]  
subject to \(y_{k}=\langle\omega,\rho(x_{k})\rangle+b+e_{k},\ k=1,\ldots N\)  
where \(J(\omega,\epsilon)\) is the optimization function; \(\omega\) is the parameter in the linear approximation; \(e_{k}\in\mathbb{R}\) are error variables; \(C\geq 0\) is a regularization constant that represents the trade-off between the empirical error and the flatness of the function; \(x_{k}\) is the input patterns; \(y_{k}\) are prediction labels; and \(N\) is the sample size.  
Equation (2) is the resulting LSSVM model for function estimation.  
\[f(x)=\sum_{k=1}^{N}\alpha_{k}K(x,x_{k})+b \tag{2}\]  
where \(\alpha_{k},b\) are Lagrange multipliers and the bias term, respectively; and \(K(x,x_{k})\) is the kernel function.  
The Gaussian radial basis function (RBF) and the polynomial are commonly used kernel functions. RBFs are more frequently used because, unlike linear kernel functions, they can classify multi-dimensional data efficiently. Therefore, in this study, an RBF kernel is used. Equation (3) is the RBF kernel.  
\[K(x,x_{k})=\exp(-\|x-x_{k}\|^{2}/2\sigma^{2} \tag{3}\]  
Although the LSSVM can effectively learn patterns from data, the main shortcoming is that the predictive accuracy of an LSSVM model depends on the setting of its hyperparameters. Parameter optimization in an LSSVM includes the regularization parameter (C) in Equation (1) and the sigma of the RBF kernel (\(\sigma\)) in Equation (3). The generalizability of the LSSVM can be increased by determining optimal values of \(C\) and \(\sigma\). In this investigation, the enhanced FA, which is an improved stochastic, nature-inspired metaheuristic algorithm, was developed to finetune the above hyperparameters \(C\) and \(\sigma\).
## 3 Results  
Twenty-four individuals with PD were recruited; however, only 18 of these completed the study. Out of the 18, 10 participants were allocated to the HOME group and 8 participants to the CON group. Of the six individuals who withdrew from the study, two were from the HOME group due to the restrictions associated with the COVID-19 pandemic, and four were from the CON group due to lack of time and family issues. Demographic data are shown in Table 1. There were no significant differences between the two groups at baseline with regard to demographics, or duration and severity of disease, assessed using the Modified H&Y scales (Table 1). \begin{table}
\begin{tabular}{l c c} \hline \hline
**Demographic** & **HOME Group (_n = 10_)** & **CON Group (_n = 8_)** \\ \hline Age (years (SD)) & 68.60 \(\pm\) 6.67 & 68.88 \(\pm\) 6.73 \\ \hline Body mass index (kg/m\({}^{2}\) (SD))) & 23.66 \(\pm\) 3.36 & 23.83 \(\pm\) 4.23 \\ \hline Men/women \(n\) (\%) & 6 (60)/4 (40) & 4 (50)/4 (50) \\ \hline [PERSON]’s disease duration (years) & 5.08 & 4.80 \\ \hline Modified Hoehn and Yahr scale & 2.6 & 2.58 \\ \hline Medical comorbidities (_n_, \%) & & \\ Hypertension & 4 (40\%) & 4 (50\%) \\ Diabetes mellitus & 3 (30\%) & 2 (25\%) \\ Dyslipidemia & 2 (20\%) & 1 (12.5\%) \\ Heart disease & 1 (10\%) & 1 (12.5\%) \\ \hline Mini-Mental State Examination & 29.4 \(\pm\) 5.02 & 29.25 \(\pm\) 4.72 \\ \hline Education (_n_ (\%)) & & \\ High School & 6 (80) & 5 (62.5) \\ \(>\)High school & 1 (10) & 2 (25) \\ \(<\)High school & 3 (30) & 1 (12.5) \\ \hline Taking L-DOPA as a single medication (_n_) & 6 & 5 \\ \hline Taking L-DOPA with other medications (_n_) & 4 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of demographic of individuals with PD between the home-based exercise group (HOME) (_n_ = 10) and the control group (CON) (_n_ = 8). In addition, individuals with PD in the HOME group did not significantly differ from the CON group with regard to the amount of PD medication, and no statistical differences were seen between the groups before the exercise treatment when assessed using the MDS-UPDRS motor part and MDS-UPDRS rigidity items, FAR-p, gait characteristics, FRT, TUG, 10 MWT and FES-I (Table 2). The comparison of primary and secondary outcomes between the groups revealed significant improvements in the HOME group in those outcomes when compared to the CON group after the 10-week exercise regime, specifically in the MDS-UPDRS rigidity item (\(U=15.0\), \(p<0.017\)), step length (\(U=21.0\), \(p<0.033\)), gait velocity from the 3 m force distribution platform (\(U=14.5\), \(p<0.023\)) and FES-I (\(U=23.0\), \(p<0.047\)), as shown in Table 3. For the within group comparisons, the Wilcoxon signed-rank tests showed no statistical differences in any outcomes in the control group between the baseline and 10-week time point. However, a significant improvement was seen between the baseline and after 10 weeks within the HOME group for FAR-p (\(Z=-2.67\), \(p<0.008\)), step length (\(Z=-1.17\), \(p<0.041\)), FRT (\(Z=-0.95\), \(p<0.012\)), fast speed of 10-MWT (\(Z=-1.72\), \(p<0.008\)) and FES-I (\(Z=-2.527\), \(p<0.012\)) (Table 4).
###### Abstract  
Magnetic mesoporous silica nanoparticles (Fe\({}_{5}\)O\({}_{4}\)-MSNs) were successfully synthesized with a relatively high surface area of 568 m\({}^{2}\)g\({}^{-1}\). Fe\({}_{3}\)O\({}_{4}\)-MSNs were then modified with poly(2-diethyl) aminoethyl methacrylate (PDEAEMA) brushes using surface-initiated ARGET atom transfer radical polymerization (ATRP) (Fe\({}_{3}\)O\({}_{4}\)@MSN-PDMAEMA). Since the charge of PDEAEMA is externally regulated by solution pH, tertiary amines in the polymer chains were quartenized using 2-iodoethanol to obtain cationic polymer chains with a permanent positive charge (Fe\({}_{5}\)O\({}_{4}\)@MSN-QPDMAEMA). The intensity of the C\(-\)O peak in the C1s X-ray photoelectron spectrum increased after reaction with 2-iodoethanol, suggesting that the quaternization process was successful. The applicability of the synthesized materials on the removal of methyl orange (MO), and sunset yellow (E110) dyes from an aqueous solution was examined. The effects of pH, contact time, and initial dyes concentrations on the removal performance were investigated by batch experiments. The results showed that the Fe\({}_{3}\)O\({}_{4}\)@MSN-PDMAEMA sample exhibited a weak adsorption performance toward both MO and E110, compared with Fe\({}_{3}\)O\({}_{4}\)@MSN-QPDMAEMA at a pH level above 5. The maximum adsorption capacities of MO and E110 using Fe\({}_{3}\)O\({}_{4}\)@MSN-QPDMAEMA were 294 mg g\({}^{-1}\) and 194.8 mg g\({}^{-1}\), respectively.  
magnetic mesoporous nanoparticles; polybase brushes; polymer quaternization; dyes removal; kinetics; isotherms +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Antioxidant and Detoxification Pathways: CAT and GST Activities  
The mechanisms involved in the counteraction of toxicant- and reactive oxygen species (ROS)-mediated injuries include the activation of enzymes catalysing phase-II reactions and ROS-scavenging processes, such as GST and CAT [71]. As can be observed in Figure 3, selected treatments had an overall slighter impact on CAT and GST activities compared to lysosomal responses. GST levels measured in the digestive gland of mussels did not vary among treatments (Figure 3A); in gills, a significantly higher GST activity compared to either control or all other treatments was noted in mussels exposed to 0.1 mg/L CHR (Figure 3B). In digestive glands, the CAT activity decreased significantly compared to control only in mussels exposed to MP (Figure 3C); CAT levels measured at this treatment, however, did not change significantly compared to the other tested conditions. In gills, the exposure to 0.1 mg/L CHR induced significantly lower CAT activity compared to all the other treatments, including control (Figure 3D).  
GST and CAT levels measured in this study suggest that MP, CHR-MP and 0.73 ng/L of freely dissolved CHR had an absent or limited impact on the processes governing antioxidant and detoxification pathways in mussels. The lack of GST modulation in mussel digestive gland is in line with previous observations obtained in mussels exposed for 7 days to virgin and pyrene-contaminated MP [18] and may be related to either the low stimulation of biotransformation processes in response to physical factors or a limited ROS-scavenging role played by the enzyme in this tissue, as previously postulated [19]. Accordingly, in response to higher CHR concentrations, GST was upregulated in gills,  
Figure 3: Specific activity of the enzymes GST and CAT (mean \(\pm\) SEM, \(n=3\)) measured in mussels after 7 days of exposure to \(5\times 10^{3}\) units/L of polystyrene microplastics (MP) and chrysene-loaded microplastics (CHR-MP), 0.1 mg/L and 0.73 ng/L of freely dissolved CHR (without MP); (**A,B**), GST activity in mussel digestive gland (**A**) and gills (**B**); (**C,D**), CAT activity in mussel digestive gland (**C**) and gills (**D**). Different letters denote statistically significant differences (\(p<0.05\), ANOVA—Bonferroni _post-hoc_ comparison) among treatments.  
where it shows generally higher basal levels, likely reflecting a greater involvement in ROS-scavenging activity and/or phase-II metabolism of waterborne toxicants.  
Interestingly, CAT activity in the digestive gland was significantly inhibited in the presence of plain MP, while it did not show changes in response to CHR-loaded particles. Previous studies highlighted a sensitively greater CAT inhibition and ROS production in mussels exposed to virgin MP compared to fluoranthene-incubated MP [72; 73]. These findings may suggest that below specific toxicity thresholds, the sorption of organic chemicals may act as a compensating, rather than cumulative, factor with respect to some MP-related alterations of mussel antioxidant defences.
###### Abstract  
Diagenesis has been demonstrated to significantly affect porosity development in shale reservoirs, however, the effect of diagenetic modifications on shale pore structures is still unclear. For clarifying this issue, this paper focuses on the Upper Ordovician to Lower Silurian Wufeng and Longmaxi shales, which are the only commercially gas-produced shale plays in China. This study aims to reveal the influence of diagenetic alterations on the WF-LMX shale reservoir quality by integrating total organic carbon (TOC) content, X-ray diffraction (XRD), low-temperature gas (N\({}_{2}\)) and carbon dioxide (CO\({}_{2}\)) adsorption experiments, field-emission scanning electron microscopy (FE-SEM), energy dispersive X-ray (EDS), and cathodoluminescence (CL) analyses. Three major shale lithofacies were identified, mainly including siliceous, siliceous-argillaceous mixed, and argillaceous shale; the siliceous shale has a relatively high TOC content. The organic pores, intergranular pores, intragranular pores, and fractures are generally developed in the WF-LMX shales. The pore volume (PV) and specific surface area (SSA) of micropores, mesopores, and macropores of siliceous shales are higher than those of mixed shales and argillaceous shales. The TOC content has a strongly positive correlation with PV and SSA for micropores and mesopores. After combustion, the PV and SSA of micropores and mesopores were decreased, whereas the PV and SSA of macropore were significantly increased. In the siliceous shale, organic pore is the dominant pore type due to the fact that a large amount of antibgenic microcrystalline quartz aggregates can protect organic pores from compaction. The argillaceous shale has high clay and low TOC content, and the dominant pore type is pores between clay flakes. The siliceous shale has a relatively high TOC content, large PV and SSA, and so are the desert lithofacies for shale gas exploration.  
Keywords: shale lithofacies; diagenetic modifications; reservoir; pore types and structure; combustion; the Wufeng-Longmaxi shales +
Footnote †: journal: Applied sciences
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
###### Abstract  
Privacy-preserving record linkage (PPRL) aims to link records from different data sources while ensuring sensitive information is not disclosed. Utilizing blockchain as a trusted third party is an effective strategy for enhancing transparency and auditability in PPRL. However, to ensure data privacy during computation, such approaches often require computationally intensive cryptographic techniques. This can introduce significant computational overhead, limiting the method's efficiency and scalability. To address this performance bottleneck, we combine blockchain with the distributed computation of secret sharing to propose a PPRL method based on blockchain-coordinated distributed computation. At its core, the approach utilizes Bloom filters to encode data and employs Boolean and arithmetic secret sharing to decompose the data into secret shares, which are uploaded to the InterPlanetary File System (IPFS). Combined with masking and random permutation mechanisms, it enhances privacy protection. Computing nodes perform similarity calculations locally, interacting with IPFS only a limited number of times, effectively reducing communication overhead. Furthermore, blockchain manages the entire computation process through smart contracts, ensuring transparency and correctness of the computation, achieving efficient and secure record linkage. Experimental results demonstrate that this method effectively safeguards data privacy while exhibiting high linkage quality and scalability.  
privacy-preserving record linkage; Bloom filter; secret sharing; blockchain; smart contracts 2025  
3
# Study on Effects of Energy Deposition and Thermal Shock Wave by Electron Beam Irradiation
## 3 Energy Deposition of Electrons in Materials
### Introduction of Experimental Conditions  
When the low-energy electron is vertically incident into the target material, its penetration \(R_{0}\) in the target is expressed by the empirical Formula (1). It is assumed that the initial incident electron energy is \(E_{0}\) and its energy attenuation is \(E(r)\) at \(r\) away from the target surface (the corresponding mass thickness is \(\rho_{r}\)) [27]. Then the distance that the electron with energy \(E(r)\) continues to travel in the target is shown by:  
\[E(r)=\exp\{\frac{K_{3}-\sqrt{K_{3}^{2}-4K_{4}\ln[Z^{K_{2}}(R_{0}-\rho_{r})/K_{1 }]}}{2K_{4}}\} \tag{4}\]  
So, the following formula can be obtained:  
\[R^{\prime}(r)=R_{0}-\rho_{r}=\frac{K_{1}}{Z^{K_{2}}}E^{K_{3}-K_{4}\ln E} \tag{5}\]  
where the unit of \(E\) is MeV, and \(K_{1}\), \(K_{2}\), \(K_{3}\), and \(K_{4}\) are fitting constants, with values of \(K_{1}=0.963\), \(K_{2}=0.315\), \(K_{3}=1.250\), and \(K_{4}=0.112\), respectively.  
In order to analytically calculate the energy deposition of electrons in the material, the transmission coefficient of electrons in the target material is also needed, which is denoted by \(\xi(r)\) and given by Formula (3). On the basis of a large number of experimental data, [PERSON] [28] summed up a universal formula for the transmission coefficient of electrons' vertical incidence on a flat plate target material under low energy relativity theory.  
\[\begin{array}{l}\xi=\frac{1+\exp(-m\eta_{0})}{1+\exp\left[\mu\left(\frac{K _{0}}{K_{0}}-n_{0}\right)\right]}\\ n=9.2Z^{-0.2}+16Z^{-2.2}\\ n_{0}=0.63Z+0.27\\ Z=\frac{\sum n_{i}A_{i}Z_{i}/\sum_{i}n_{i}A_{i}}\end{array} \tag{6}\]  
where \(R_{0}\) is the electron penetration depth; \(R\) is the distance from a point in the target to the incident surface. \(n_{i}\) represents the weight percentage of the component.  
In a thin layer \(\rho\mathrm{d}r\) at a position with a mass thickness of \(\rho_{r}\) from inside the target to the target surface, if \(\mathrm{d}r\) is assumed to be small enough and the backscattering coefficients of the front and back surfaces to compensate each other, the energy deposition of electrons per unit mass thickness in \(\mathrm{d}r\) is  
\[Q(r)=-q(Z)\frac{\mathrm{d}[\xi(r)E(r)]}{\rho\mathrm{d}r} \tag{7}\]  
\(q(Z)\) is the surface reflection correction coefficient.
# \(\alpha\)-Fluctuating Nakagami-\(m\) Fading Model for Wireless Communications
## 5 Simulation and Results' Analysis  
To prove the correctness of the performed analytical work, numerical simulations as well as experimental verification were performed. To this extent, the results obtained in Sections 3 and 4 (see Theorems 1-4 and the respective corollaries) were used to evaluate the corresponding characteristics (i.e., PDF, CDF, MGF, raw moments, AoF, outage probability, ABER, and EC) analytically, and were compared with the ones obtained via brute-force numerical integration (used in their definitions). All results were accompanied (where applicable) by the corresponding derived high-SNR asymptotics (depicted with dashed blue lines) and Monte Carlo simulation with \(10^{5}\) samples.  
Since channel performance is completely defined by the set of channel parameters, they were chosen in such a way as to correspond to as many practical scenarios as possible,covering both heavy and light fading (described by \(m\)), shadowing (controlled by \(m_{s}\)), and nonlinear distortions (\(\alpha\)).  
One has to emphasize the limitations implied on the parameters. It is clear that, due to the initial physical model (see Section 3.1), no limitations are implied on the nonlinearity coefficient \(\alpha\) (except for positivity, i.e., \(\alpha>0\)), but the shadowing coefficient is assumed to be \(m_{s}>1\) (due to the limitations of the original model). Moreover, the results derived in Sections 3 and 4 are evaluated with restriction \(\frac{m_{s}\alpha}{2}>1\) in the cases where first-order moments are used, and \(\frac{m_{s}\alpha}{2k}>1\) in the cases where \(k\)-order moments are needed. The restriction on the fading parameter \(m\) is generally due to the fact that the baseline model is induced by the basic Nakagami-\(m\) channel, thus \(m\geq 0.5\).