esa-sceva/satcom-chunk-collection huggingface dataset:
# Resilient Modulus Characterization of Compacted Cohesive Subgrade Soil
###### Abstract  
Soil investigations concerning cyclic loading focus on the evaluation, in particular, of design parameters, such as elastic modulus, Poisson's ratio, or resilient modulus. Structures subjected to repeated loading are vulnerable to high deformations, especially when subgrade soils are composed of cohesive, fully-saturated soils. Such subgrade soils in the eastern part of Europe have a glacial genesis and are a mix of sand, silt, and clay fractions. The characteristic of, e.g., Young modulus variation and resilient modulus from repeated loading tests, is presented. Based on performed resonant column and cyclic triaxial tests, an analytical model is proposed. The model takes into consideration actual values of effective stress \(p^{\prime}\), as well as loading characteristics and the position of the effective stress path. This approach results in better characterization of pavement or industrial foundation systems based on the subgrade soil in undrained conditions. The recoverable strains characterized by the resilient modulus \(M_{r}\) value in the first cycle of loading was between 44 MPa and 59 MPa for confining pressure \(\sigma^{\prime}{}_{3}\) equal to 45 kPa, and between 48 MPa and 78 MPa for \(\sigma^{\prime}{}_{3}\) equal to 90 kPa. During cyclic loading, cohesive soil, at first, degrades. When pore pressure reaches equilibrium, the resilient modulus value starts to increase. The above-described phenomena indicate that, after the plastic deformation caused by excessive load and excess pore water pressure dissipation, the soil becomes resilient.  
dynamic loading; cyclic loading; resilient response; resilient modulus; shear modulus +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Network Training and Validation  
Artificial neural networks (ANNs) are based on a design inspired by the connections that naturally occur between neurons in the human brain [42]. The idea is to create machines that can perform tasks that cannot be carried out with traditional algorithms because they depend on an undefined and uncertain number of factors. By simulating human brain processing, complex information such as images, sounds, and large databases can be analyzed for patterns and irregularities [43]. This can be used for classification and regression tasks, where the patterns found are correlated with a desired output. In this way, the network \"learns\" what output to provide according to the input it receives [13]. ANNs consist of simple elements called perceptrons that simulate the processing activity of a neuron. Like neurons, perceptrons form a layered network, with an input layer, an output layer, and the layers between them called hidden layers. The latter can have several layers, and in each layer, the number of perceptrons can vary, either increasing or decreasing. On the other hand, the input layer must have several perceptrons equal to the input data, while the output layer can vary depending on the task to be performed [44].  
In all layers, the perceptrons have one or more inputs \(x\), which can be external or come from other perceptrons. These inputs are weighted with a weight \(w\), to determine which inputs should be more important for the calculation of the output \(y\), which is given by the following equation:  
\[y=\sum_{j=1}^{d}w_{j}x_{j}+w_{0} \tag{11}\]  
where \(d\) is the number of perceptron inputs, and \(w_{0}\) is the weight given to the bias \(x_{j}\), which always has a value of 1. This establishes a relationship between the input and the output of each perceptron, which is adjusted so that the network provides a desired output for a given input. Therefore, an iterative training process is applied, starting from random values of \(w\) and determining the error between the obtained output and the desired one, and adjusting the parameters depending on the difference to minimize the error in each iteration. The result is a trained network capable of inferring an output corresponding to the desired result for any new input.
# An Overview on Push-Based Communication Models for Information-Centric Networking
## 6 Conclusions  
The increasing deployment of low-cost sensing devices and the proliferation on wireless technologies offer significant opportunities for the development of a next generation of Internet services. ICN publish/subscribe content-oriented architecture brings benefits for next generation Internet environments, in particular for large-scale mobile heterogeneous environments. IoT environments fall into such categories, bringing in extra challenges, such as more heterogeneity due to resource-constrained devices; massive volumes of small data; wider heterogeneity in traffic types.  
This paper contributes with guidelines to raise awareness to the need to further study and develop ICN pull/push-based communication approaches, in particular in the context of IoT scenarios. While pull-based models are relevant in the context of communication between cloud applications and services and IoT gateways/brokers, the communication between IoT devices and gateways requires push-based communication support, given that pull-based models reduce the network performance due to the frequent broadcast of Interest packets. For this purpose, this paper proposes to consider the combination of Long-lived Interests and special Data packets (such as pData). Long-lived interests can support periodic data communication while at the same time assisting IoT devices in reducing resource consumption; special data packets support event-triggered communications between authorized devices. The combination of these two solutions is also relevant to achieve mitigation of data broadcast storms due to frequent interest forwarding.  
Both authors have been involved in the discussion of push-based principles for ICN; literature review, and guidelines.  
Work developed in this paper is funded by FCT strategic project COPELABS, references UID/MULTI/04111/2013, UID/MULTI/04111/2016, and UID/MULTI/04111/2019.  
**Acknowledgments:** [PERSON] thanks the insightful exchange of ideas concerning ICN and IoT held in 2017/2018 with [PERSON] (Siemens AG); [PERSON] (Siemens AG); [PERSON] (Siemens AG and Technical University of Munich); [PERSON] (Technical University of Munich).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Processing  
For data processing, raw files in.txt and.tmp format were initially taken from the SCADA system, processed, and concatenated into a unified file. The steps performed for data cleaning and filtering to ensure data integrity are described below.  
* Variables with null values exceeding 10,000 records were removed from the dataset, as these could potentially skew the analysis.
* Variables that, based on domain knowledge, do not contribute significant features to the analysis were eliminated.
* Null values per complete row were imputed, representing a maximum of 7 non-significant records in the WT2 variables.
* Data were filtered to include only those with an active power greater than 0 and less than 1600, ensuring the WT is within the guaranteed operating range.  
Figure 1: General flow chart of the methodological process used in this study.  
* The operating mode of the WT must be 5, which indicates, in this case, that it is not in power limitation or maintenance.
* The wind speed range was set between 3 m/s and 25 m/s, as these are the start and cut-off values of the GOLDWIND GW70/1500 WT, ensuring the data reflects typical operating conditions.
* Temperature outliers were eliminated: specifically, IGBT temperatures higher than 120 \({}^{\circ}\)C, considering the average is 60 \({}^{\circ}\)C.
* Temperatures should be greater than 0 \({}^{\circ}\)C, since no sub-zero temperatures have been recorded in the study area, which has an average historical temperature of approximately 15 \({}^{\circ}\)C.  
This approach ensures that the data used for analysis and model training are consistent and high-quality, which is essential for accurate and reliable results.  
After data processing, the final set was reduced to 317,323 records and 55 variables. That is, 17.85% of the data were eliminated.
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 4 Thermal Hazards of Lithium-Ion Battery
### Thermal Hazards of Typical Commercial Lithium-Ion Batteries
#### 4.4.2 Lithium iron phosphate  
LFP is the positive electrode with the best thermal stability among the current commercial LIB cathode materials. [PERSON] et al. compared the thermal stability of various electrode materials at high temperature, and LFP showed lower oxygen and heat release [174]. [PERSON] et al. used an ARC to analyze the thermal stability of LCO, NMC and LFP with LiBOB and LiPF\({}_{6}\) electrolytes, and the thermal stability of LFP was found to be the best in both systems [175]. The high thermal stability of LFP is rooted in its strong structural stability during charging and discharging [176]. However, due to the poor conductivity of LFP, the migration rate of lithium ions is very low, therefore surface carbon coating, and Cu\({}^{2+}\) or Mg\({}^{2+}\) plasma doping are required for modification [177; 178]. Furthermore, the modification of LFP is still a research focus. How to increase the electrochemical performance of LFP while maintaining the high level of safety is a key concern of researchers.
# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 5 Simulation Results
### The Resource Control Scheme Performance Comparison
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**(d)**\(SNR_{\textit{clk}}\) = 5 dB} \\ \hline
**3D Video** & \multicolumn{2}{c}{**SoftCast**} & \multicolumn{2}{c}{**Distortion**} & \multicolumn{2}{c}{**Proposed Resource**} \\
**Sequences** & & & & **Resource** & **Algorithm** & **Control** & **Scheme** \\ \cline{2-7}  & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline Balloons & 36.8079 & 0.9526 & 35.8347 & 0.9512 & 36.2920 & 0.9613 \\ Kendo & 36.8431 & 0.9389 & 35.5959 & 0.9398 & 36.6900 & 0.9568 \\ Newspaper & 36.1632 & 0.9460 & 35.0736 & 0.9391 & 35.3860 & 0.9479 \\ Dancer & 32.4094 & 0.8434 & 30.9076 & 0.8469 & 31.6366 & 0.8695 \\ gtFly & 36.0398 & 0.8898 & 34.4277 & 0.9028 & 36.0597 & 0.9268 \\ PozzanHall2 & 40.0081 & 0.9584 & 38.4855 & 0.9466 & 39.8148 & 0.9566 \\ PozzanStreet & 34.8735 & 0.9192 & 33.8732 & 0.9069 & 34.8194 & 0.9270 \\ Shark & 35.0505 & 0.8880 & 34.0345 & 0.9067 & 34.9220 & 0.9307 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(Cont.\)  
Figure 10: Performance comparison of different schemes under multiple \(SNR_{\textit{clk}}\) and bandwidth values: (a) Kendo; (b) PozzanStreet.
# Multi-Channel Surface EMG Spatio-Temporal Image Enhancement Using Multi-Scale Hessian-Based Filters
## 3 Results and Discussions
For simulated signals, the ground truth (MUAP location) is already known. On the basis of TP, TN, FP, and FN values, the sensitivity, specificity, and accuracy of these algorithms was calculated for EMG signal, with a signal-to-noise ratio in the range of 50 dB (good-quality EMG) to 5 dB (low-quality EMG). The results of this analysis, for a set of simulated EMG signals, for each algorithm analyzed in this study, are shown in Figure 8a-c. Accuracy is also calculated for another group of sEMG signals of 12 sets, with a signal-to-noise ratio in the range of 5 to 50 dB, as shown in Figure 9a-d From the results, it is evident that the multi-scale filter has the highest accuracy, and has little variations as compare to the other methods, so it should be preferred over other methods. As the Hermite shape filter has almost the same shape as that of the MUAP, it more resembles the MUAP in the sEMG signals and shows good performance at higher noises. However, it has a drawback that it cannot retain the polarity of the MUAP, and also, in case of bidirectional propagation of the sEMG, it creates a distortion to the MUAP and some of the non-propagating noise is also detected as MUAP, which reduces its performance at a low SNR. These results show that the proposed filter can enhance the sEMG images, which will facilitate its further processing for studying muscle anatomy and physiology. Figure 8: (**a**) Accuracy, (**b**) specificity, and (**c**) sensitivity of the proposed multi-scale Hessian-based filter compared with various algorithms analyzed in this study for the enhancement of a set of simulated EMG signals. From the results, it is evident that the multi-scale Hessian filter has a higher accuracy, sensitivity, and specificity for a signal with singal to noise ratio (SNR) greater than 10 dB, so it should be preferred over other methods.
# Optical Emission Spectroscopy as a Diagnostic Tool for Characterization of Atmospheric Plasma Jets
## 4 Some Examples of Using OES in Practical Applications of APPJs  
In the introduction, some possible applications of the APPJ were mentioned to outline its increasing importance. Although the aim of this paper was to give some examples of using OES for basic characterization of the APPJ plasma, we here further report some examples of using OES in combination with practical applications of the APPJ. For example, [PERSON] et al. [64] used OES for characterization of the APPJ for wound healing. The authors found that relative intensities of reactive species increased 2-3 times when the plasma jet was in contact with the epidermis. The authors concluded that the epidermis reacted as a floating electrode, causing a locally enhanced electric field at the interface. Additionally, [PERSON] et al. [65] used OES to characterize the APPJ used for wound healing. OES was used to find the most optimal parameters in terms of the flow rate, the distance of the APPJ from the surface, and gas composition. No differences in active species generation were found when the metallic or organic substrate were used. The authors conclude that the secondary electron emission plays a small role in the chemistry induced in the jet. [PERSON] et al. [66] used OES to investigate the correlation between the intensity of OH radicals in the APPJ with various O\({}_{2}\) addition to Ar feed gas and inactivation of bacteria. With the help of OES the authors concluded that the trace amount of O\({}_{2}\) addition into working gas enhanced the OH radicals formation what also speeded up the killing of bacteria and endospore. Another example reported the application of OES in the case when the APPJ was used for the deposition of thin films from styrene and methyl methacrylate precursors [67]. OES was used to investigate the degree of monomer dissociation versus the power and a carrier gas flow rate. In addition, [PERSON] et al. [68] used OES to characterize the APPJ for deposition of Cu thin films on a polymer, where a Cu wire inserted inside the discharge tube acted as the evaporation source. Enhancement in the Cu film deposition rate and increased purity was obtained by H\({}_{2}\) gas addition to Ar feed gas. Furthermore, increased rotational temperature was observed when adding H\({}_{2}\). The authors concluded that atomic hydrogen produced by the plasma plays an important role in heating the gas to promote the evaporation of Cu atoms. The last example is the application of OES in combination with the APPJ for polymer surface modification. [PERSON] et al. [69] used OES to investigate the influence of water admixture (OH radicals) on the functionalization of polymer surfaces. The authors found that the influence of water admixture on polymer surface modification strongly depends on the type of the carrier gas.
## 3 Learning Malware Detection
### Training Details  
The proposed method is implemented using the PyTorch 2.0 framework [74]. During training, we employed a learning rate of \(1\times 10^{-4}\), which was adjusted every two epochs using a weight decay of \(1\times 10^{-4}\). We utilized the Adam optimizer with the objective function set to minimize the cross-entropy loss. Furthermore, we resized all training and testing images to dimensions of \(160\times 160\times 3\) for the main branch and \(20\times 20\) for auxiliary branches. We applied various augmentation techniques to prevent overfitting [70,75], such as random flipping, blur, and rotation. Algorithm 1 demonstrates the training detail of the proposed DAMN.  
```
1:Input: Training set \(D_{train}\), validation set \(D_{val}\)
2:Output: Trained CNN model \(M\)
3:Initialize CNN model \(M\) with random weights
4:Initialize learning rate \(\eta_{0}\), initial batch size \(B_{0}\), number of epochs \(N_{epochs}\), learning rate decay factor \(\alpha\)
5:Initialize epoch counter \(e=1\)
6:for\(i=1\) to \(N_{epochs}\)do
7:if\(i\mod 2=0\)then
8: Update learning rate: \(\eta_{i}=\alpha\cdot\eta_{i-1}\)
9: Sample mini-batches \(B_{train}\) from \(D_{train}\) with augmentation
10:for each mini-batch \(B_{train}\)do
11: Compute loss \(L\) using forward pass of \(M\) on \(B_{train}\)
12: Update weights of \(M\) using backpropagation and gradient descent with learning rate \(\eta_{i}\)
13:for each mini-batch \(B_{val}\in D_{val}\)do
14: Compute validation accuracy using forward pass of \(M\) on \(B_{val}\)
15: Compute average validation accuracy for this epoch
16:if average validation accuracy \(>\) best validation accuracy then
17: Update best validation accuracy: \(best\_val\_acc=\) average validation accuracy
18: Save current weights of \(M\) as best weights: \(best\_weights=M\).get\(\_weights\)()
19: Set weights of \(M\) to best weights: \(M\).set\(\_weights(best\_weights)\)
```  
**Algorithm 1** Learning malware classification with DAMN.  
All models were trained for 50 epochs with a fixed batch size of 64. Our experiments were conducted on a machine equipped with an AMD Ryzen 3200G central processing unit (CPU) clocked at 3.6 GHz, 16 GB of random-access memory, and a Nvidia GeForce GTX 1060 (6 GB) graphical processing unit (GPU). These hardware specifications provided sufficient computational resources for effectively training and evaluating our models.
# Influence of Variotropy on the Change in Concrete Strength under the Impact of Wet-Dry Cycles
## 3 Results
These changes are directly related to the increase in the degree of aggressiveness of the saturation aqueous medium to pH 5. The drop in strength characteristics was already recorded after the first 50 cycles of moistening and drying, and up to the 250 th cycle, it was not so intense. Starting from the 400 th cycle, the rate of decrease in strength characteristics noticeably increases. This can be explained by the fact that, up to 400 cycles, the drop in strength is due only to the degree of aggressiveness of the medium at pH 5, and starting from 400 cycles, according to the tests already presented earlier at pH 7, the destructive effect of wetting and drying cycles is added to the aggressive medium. Thus, the resulting drop in strength characteristics in this aggressive environment after 600 cycles of wetting and drying is noticeably higher than at pH 7 (a neutral environment). Table 6 shows the changes in strength characteristics in percentage terms. According to the data from Table 6, the maximum loss of cubic compressive strength for vibrated concrete was 36%, for centrifuged 28%, and for vibrocentrifuged 21%. As for the drop in prismatic compressive strength, it was 37% for vibrated concrete, 26% for centrifuged concrete, and 20% for vibrocentrifuged concrete. Here, as in a neutral medium, the smallest strength losses, much smaller than those of centrifuged (up to 7%) and especially vibrated (up to 17%) concretes, are observed in vibrocentrifuged concretes. Figures 10 and 11 show the dependences of the change in cubic and prism compressive strengths of vibrated, centrifuged, and vibrocentrifuged concretes in a moistening medium with pH 10. Figures 10 and 11 clearly show that the change in cubic and prism compressive strengths of vibrated, centrifuged, and vibrocentrifuged concretes is similar to that of the curves presented in Figures 6 and 7 (neutral medium with pH 7). Up to cycle 200 inclusive, an increase in strength characteristics is also observed, and then, starting from cycle 250, the curve goes down. Table 7 shows changes in strength characteristics in percentage terms. Based on the data presented in Table 7, after 600 cycles of moistening and drying, the loss of cubic compressive strength for vibrated concrete was 23%, for centrifuged, 18%, and for vibrocentrifuged, 15%. The loss of prism compressive strength for vibrated
# Recycled Eco-Concretes Containing Fine and/or Coarse Concrete Aggregates. Mechanical Performance
## 2 Materials and Methods
### Statistical Analysis  
To evaluate the influence of replacing natural aggregate with recycled aggregate on concrete properties, two different techniques were used, depending on the property studied. First, to study the mechanical properties of the concrete, an analysis of variance (ANOVA) was carried out for each property and age studied, carrying out a total of four analyses. The statistical software \"R\", version 4.0.5, was used to make the calculations.  
The proposed model (Equation (1)) to carry out the analyses corresponds has two factors (% substitution of coarse aggregate and % substitution of fine aggregate) and interactions.  
\[\mathrm{Y_{ijk}}=\mu_{11}+\alpha_{i}+\beta_{j}+\alpha\beta_{ij}+\varepsilon_{ ijk},i=1,\ldots,4;j=2;\,k=1,\ldots,3 \tag{1}\]  
This model obtains a value for the response variable \(\mathrm{Y_{ijk}}\) (strength studied in each case) by adding different values: (i) \(\mu_{11}\) is the average value corresponding to the reference mix (HP) in each case; (ii) \(\alpha_{i}\) quantifies the relative effect corresponding to the first factor (% coarse aggregate substitution); (iii) \(\beta_{j}\) quantifies the relative effect corresponding to the second factor (% fine aggregate substitution); (iv) \(\alpha\beta_{ij}\) is the relative effect due to the interaction that occurs when simultaneously substituting both fractions; and (v) \(\varepsilon_{ijk}\) indicates the perturbation of the model.  
To check whether the analyses carried out are valid, the homoscedasticity and normality assumptions must first be checked using the Bartlett and Shapiro-Wilk tests, respectively. Table 5 shows the \(p\)-values of both tests. As can be seen, both assumptions are met in all analyses, so the analyses are therefore considered valid.  
Figure 5: Mixing process.  
Once the initial assumptions were tested, the model calculates the model coefficients, indicating whether or not these values are significant. In other words, for what percentages of substitution is strength variation relevant or not from a statistical point of view (\(p\)-value < 0.05).  
In the density study (apparent as well as fresh-state), expressions were also observed that relate the total percentage of substituted aggregate (%) with the density (D) studied in each case. In both cases, the model proposed for the optimal adjustment was obtained using linear regression (Equation (2)), in both cases obtaining very good correlation coefficients (R\({}^{2}\) > 0.8):  
\[\text{D}=\text{a}\%+\text{b} \tag{2}\]  
This model returns a density value through an affine function with parameters including the y-intercept (b), which corresponds to the reference value for the mix without recycled aggregate (RC) and the gradient (a) that adjusts the line to the data obtained through a least-squares fitting.