esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory of Surrounding Rock Support Characteristics and Timing for Secondary Lining Support  
### Surrounding Rock Support Characteristics  
The typical displacement and support characteristic curves of the surrounding rock are shown in Figure 1. After excavating the tunnels, if the stiffness of the support structure is high or the support is applied too quickly, resulting in minimal deformation of the surrounding rock, the surrounding rock-support structure will remain balanced at point A. The support structure will need to provide a significant support stress (\(P_{\rm max}\)), while the surrounding rock itself will only need to bear the deformation pressure (\(P_{0}-P_{\rm max}\)) caused by elastic deformation (\(\mu_{0}\)). Under these conditions, the stiffness of the support structure is large, and the unloading and bearing capacity of the rock mass in the plastic zone cannot be fully utilized. If the support structure is sufficiently flexible, allowing for a certain amount of deformation in the surrounding rock, the equilibrium position of the surrounding rock-support structure will shift to point C in Figure 1.  
In this case, the deformation pressure borne by the support structure will be reduced to \(P_{\rm c}\). Conversely, if the stiffness of the support structure is too low or if the support is significantly delayed, causing the displacement to exceed the maximum displacement (\(\mu_{\rm max}\)) around the tunnel, the surrounding rock may experience relaxation and collapse. The deformation pressure exerted by the surrounding rock on the support structure will transform into loose pressure, at which point the installation of initial support becomes inappropriate. In practice, the optimal timing for support should be selected close to the left side of point D, such as at point C, where the deformation of the surrounding rock is  
Figure 1: Relationship between the ground reaction curve and the support characteristic curve.  
more substantial but without collapse, and the surrounding rock bears greater pressure (\(P_{0}-P_{\rm c}\)). At this stage, it is necessary to control the timing of support structure installation (surrounding rock deformation \(\mu_{0}\)) and the stiffness of the support structure (the slope of the support curve). Given that determining the reasonable stiffness and timing of support through theoretical calculations in actual construction is challenging, it is essential to initially install support to ensure that the surrounding rock does not loosen or collapse. The secondary lining should be applied after the deformation of the support structure has stabilized, as indicated by on-site monitoring. This approach ensures that the surrounding rock-support structure operates cooperatively around point C.
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 3 Methodology
### Benchmark Models
#### 3.6.2 CNN 2D-2 Layers [21]  
This model is a sequential Convolutional Neural Network with two layers. The first layer has 64 filters and a 2 \(\times\) 2 kernel size, using ReLU activation on input tensors of shape (3, 3, 2). The second layer comprises 32 filters and a 1 \(\times\) 1 kernel with ReLU activation. This architecture allows for hierarchical feature extraction, as the first layer captures local patterns while the second refines features. After the convolutional layers, feature maps are flattened into a one-dimensional vector and processed through a fully connected layer with 16 hidden units and ReLU activation. The network concludes with a dense output layer featuring two units and Softmax activation for probability distributions across target classes.
## 4 Results and Comparative Analysis
### Layered Summary of Findings
This multi-dimensional risk classification methodology not only enables fine-grained prioritization of mitigation efforts based on contextual threat severity, but also createsa robust mapping between identified attack vectors and their operational ramifications, allowing stakeholders to implement differentiated security policies across device types and deployment contexts, from tightly controlled hospital infrastructures to loosely managed Bring Your Own Device (BYOD) environments, where centralized enforcement may be absent and the attack surface significantly more volatile. Overall, the severity levels assigned in Table 5, represent not just theoretical exploit severity, but realistic attack feasibility to present a suitable and useable risk matrix for hardening mHealth platforms being made available and deployed in hybrid, resource constrained environments [45; 54]. Risk classification in this table is based on a combined evaluation of the CVSS v3.1 base score, the practical complexity of exploitation (e.g., attacker skill level and required access), and the assessed severity of potential impact. The risk estimation also considered real-world feasibility within the PHGL-COVID deployment context, including device configuration defaults, firmware limitations, and observed runtime behaviors during testing. As the comparative matrix shows, the security posture of the PHGL-COVID system cannot be accurately assessed by evaluating individual layers in isolation. Rather, high-confidence exploitation scenarios are enabled by the interplay of moderate-impact vulnerabilities across components, which reinforces the need for unified, cross-layer mitigation strategies.
## 3 Results
### Analysis of Ionospheric DVTEC Disturbances
#### 3.2.3 DVTEC Signal Period Analysis
The first maximum occurs between 01:00 UTC and 02:00 UTC, and the second between 03:00 UTC and 04:00 UTC. In these cases, we do not detect an at-a-glance relationship between the maximum peak currents and the maximum variations in the DVTEC of each station, since they occur at different times and locations. Finally, as mentioned previously, Figures 19-23 present the wavelet transform of the five studied stations. Observing these figures, we see that, for the day without AEA, the period of the DVTEC signal was less than 75 min, the peak-to-peak DVTEC amplitude was between 0.16 and 0.29 TECU and the spectral amplitude was between 0.1 and 0.2. On the other hand, for the day with AEA, we find periods lower than 100 min, peak-to-peak DVTEC amplitudes between 0.32 and 0.93 TECU, and spectral amplitudes between 0.25 and 1.4. Figure 21: Same as Figure 19 for PEBA station. Figure 20: Same as Figure 19 for MZGA station. Figure 23: Same as Figure 19 for TERO station. Figure 22: Same as Figure 19 for SRLP station.
# Biometrics for Internet-of-Things Security: A Review
## 4 Biometric-Based Systems for IoT-Oriented Authentication
### Single-Modal Versus Multi-Modal Biometric Authentication Systems
#### 4.1.4 Continuous Biometric Authentication  
Most of the biometric systems discussed above are generally referred to as static authentication systems. In a static authentication system, a user's identity is authenticated at the beginning of a session, for instance, by logging in the IoT device using a fingerprint or entering the room using an iris scan. As opposed to static authentication, there is continuous authentication. In a continuous authentication case, the authenticity of the user's identity is checked throughout the entire logon period.  
[PERSON] [68] proposed a method for evaluating a continuous keystroke dynamics system. Not only are the keystroke dynamics used at the beginning of a session, but they are continuously checked to monitor the user's status. In the proposed method, a penalty-and-reward function is designed. If a user types as they should, then they earn a reward, leading to an increase in the trust level. However, if the typing of the user does not comply with the template, a penalty is given, causing the trust level to decrease and the system's confidence in the user's authenticity to decrease. The user will be locked out of the system if the trust level is lower than a threshold. [PERSON] and [PERSON] [69] designed a continuous authentication system using mouse dynamics. In this work, the authors employed a public mouse dynamics dataset containing 49 users together with six machine learning algorithms (e.g., support vector machine and decision tree learning) to evaluate the proposed system. [PERSON] et al. [70] developed a fuzzy classifier-based continuous authentication system aiming to secure mobile devices. The proposed system uses behavioral biometric traits (e.g., touchscreen gestures) as unique features for authentication. Experimental results show that the proposed system achieves an EER of 11.5% on a private dataset, collected from 22 different users. [PERSON] et al. [71] presented a continuous authentication framework to address the threat of cheating in online tests. In the proposed framework, three biometric traits, namely face, mouse dynamics and keystroke dynamics, are collected and processed during the exam. Evaluated with offline datasets, the proposed framework exhibits encouraging results.
## 3 Results and Discussion
### Definition of Damage Variable
#### 4.1.3 Definition of the Damage Variable in Freeze-Thaw Action  
The initial state of fractured granite before freezing and thawing is considered as the non-damaged state. The damage and deterioration of granite caused by long-term freezing and thawing can be defined by the porosity \(n\) as its freeze-thawing damage variable \(D_{t}\).  
\[D_{t}=1-\frac{1-n_{i}}{1-n_{0}} \tag{29}\]  
where \(n_{i}\) is the saturated porosity of granite after different freeze-thaw cycles, while \(n_{0}\) is the initial saturated porosity.  
The total area of the T\({}_{2}\) spectrum curve measured by NMR is directly proportional to the pore volume in the rock sample [58]. Assuming that the apparent volume of the rock sample remains constant, the porosity is given by:  
\[n=\frac{V_{V}}{V}\times 100=\frac{C_{1}S_{T_{2}}}{V}\times 100 \tag{30}\]  
where \(V_{V}\) represents the pore volume of the rock sample, \(V\) is the total volume of the rock sample, \(C_{1}\) is a pending parameter, and \(S_{T_{2}}\) is the total area of the T\({}_{2}\) spectrum of the rock sample.  
Under freezing-thawing conditions, the total damage effect of fractured rock under loading can be equivalent to damage under two stages of loading. The first stage is the initial damage state caused by prefabricated fractures, while the second stage is the total damage state caused by freezing and thawing loads after prefabricated cracks. Therefore, the total damage variable \(D_{g}\) is defined as the combination of the initial damage caused by prefabricated cracks \(D_{f}\), the freeze-thaw cycle damage \(D_{t}\), and the load damage \(D_{t}\), based on the damage variables obtained under different conditions. The calculation formula is as follows:  
\[D_{g}=D_{f}+D_{t}+D_{l}-D_{f}D_{t}-D_{f}D_{l}-D_{t}D_{l}+D_{f}D_{t}D_{l} \tag{31}\]  
According to the generalized strain equivalence principle [59], the freeze-thaw damage constitutive relationship of fractured rocks under uniaxial compression can be expressed as:  
\[\sigma_{1}=E_{0}\varepsilon_{1}\big{(}1-D_{g}\big{)} \tag{32}\]
## 3 Results
### Reflectance Values at a Wavelength of 1700-2500 nm  
Table 13 presents selected descriptive statistics of the reflectance at a wavelength of 1700-2500 nm. In the assessment of differences in reflectance values at subsequent time points (before oil application, after 20 min, and after 60 min), a statistically significant difference (\(p<0.0001\)) was found between particular time points for the reflectance at a wavelength of 1700-2500 nm after the application of Oil A. It was found that at a wavelength of 1700-2500 nm after the application of Oil A (Table 14), the reflectance value after 20 min of exposure was statistically significantly lower compared with the value before exposure and with the values after 60 min and 120 min of exposure; as well, the reflectance value after 120 min of exposure was statistically significantly higher compared with the values after 20 min and 60 min of exposure. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Oil A: Conover–Iman post hoc test; \(p=\)**} \\ \hline
**Intertemporal comparison** & **A 0** & **A 20 min** & **A 60 min** & **A 120 min** \\ \hline
**A\_0** & & 0.0183 & 0.8402 & 0.3155 \\ \hline
**A\_20\_min** & 0.0183 & & 0.0299 & 0.0011 \\ \hline
**A\_60\_min** & 0.8402 & 0.0299 & & 0.2293 \\ \hline
**A\_120\_min** & 0.3155 & 0.0011 & & 0.2293 \\ \hline \multicolumn{4}{c}{**Oil B: Conover–Iman post hoc test; \(p=\)**} \\ \hline
**Intertemporal comparison** & **B\_0** & **B\_20\_min** & **B\_60\_min** & **B\_120\_min** \\ \hline
**B\_0** & & \(<\)0.0001 & 0.0108 & 0.4618 \\ \hline
**B\_20\_min** & & \(<\)0.0001 & & 0.0087 & \(<\)0.0001 \\ \hline
**B\_60\_min** & 0.0108 & 0.0087 & & 0.001334 \\ \hline
**B\_120\_min** & 0.4618 & \(<\)0.0001 & 0.0013 & \\ \hline \multicolumn{4}{c}{**Oil C: Conover–Iman post hoc test; \(p=\)**} \\ \hline
**Intertemporal comparison** & **C\_0** & **C\_20\_min** & **C\_60\_min** & **C\_120\_min** \\ \hline
**C\_0** & & \(<\)0.0001 & 0.1591 & 0.4558 \\ \hline
**C\_20\_min** & & \(<\)0.0001 & & 0.0040 & \(<\)0.0001 \\ \hline
**C\_60\_min** & 0.1591 & 0.0040 & & 0.0336 \\ \hline
**C\_120\_min** & 0.4558 & \(<\)0.0001 & 0.0336 & \\ \hline \multicolumn{4}{c}{**Oil D: Conover–Iman post hoc test; \(p=\)**} \\ \hline
**Intertemporal comparison** & **D\_0** & **D 20 min** & **D 60 min** & **D 120 min** \\ \hline
**D\_0** & & 0.0673 & 0.7403 & 0.1013 \\ \hline
**D\_20\_min** & 0.0673 & & 0.1310 & 0.0008 \\ \hline
**D\_60\_min** & 0.7403 & 0.1310 & & 0.0504 \\ \hline
**D\_120\_min** & 0.1013 & 0.0008 & 0.0504 & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Results of post hoc tests of the reflectance at a wavelength of 1000–1700 nm after the application of Oils A, B, C, and D.
## 5 Conclusions  
In this paper we have described a heterogeneous mobile sensor network for animal tracking made up of battery-powered and kinetic-powered nodes. The system tries to minimize the use of battery-powered nodes to a minimum which satisfies future real animal experiments and provides the means for animal localization. The system is based on the assumption that animals carrying kinetic-powered (secondary) nodes are tracked by their neighbors and their positions are approximated by the ones carrying battery-powered (primary) nodes. Therefore, primary nodes keep track of the information sent by secondary nodes and can download it to a base station when they enter its coverage range.  
Secondary nodes have been designed with a 433 MHz transmitter powered by a kinetic converter system made with coils and magnets. Primary nodes implement a 433 MHz receiver to gather secondary node information, a GPS to obtain its position periodically and a 166 MHz transceiver to communicate with the base station. Moreover, the primary node and base station communication interface has been designed over the same printed circuit board for cost saving. The system has proved to achieve a communication range of 100 m on the secondary-primary node communication and 700 m for the primary-base station link.  
We have tested the system in an animal environment (i.e. dogs and reindeers) showing good performances. In the future, we intend to introduce the system in a herd of reindeer in Northern Sweden to study the relationship between primary and secondary nodes needed to keep track of the herd and study its behavior. However, depending on the animal behavior and the requisites of the experiment, different parameters, such as acquiring data frequency of the GPS, the 166 MHz radio working cycle, or the time the primary node must listen to a secondary node, should be studied before testing the system in a real scenario.
###### Abstract  
The fourth member of the Triassic in the Tahe Olifield, as one of the key strata for elastic rock reservoirs, poses significant challenges to oil and gas exploration due to unclear identification of its depositional environments and sedimentary microfacies. Based on the guidance of sequence stratigraphy and sedimentological theories, this study comprehensively analyzed well logging data from more than 130 wells, core analysis from 9 coring wells (including lithology, sedimentary structures, and facies sequence characteristics), 3D seismic data (covering an area of 360 km\({}^{2}\)), and regional geological background. Combined with screening and settling method granularity experiments, the sedimentary characteristics of the sand body in the fourth member were systematically characterized. The results indicate the following: (1) In the Tahe Olifield, the strata within the fourth member of the Triassic are predominantly characterized by marginal lacustrine subfacies deposits, with delta-front subfacies deposits developing in localized areas. (2) From the planar distribution perspective, influenced by the northwestern provenance, a small deltaic depositional system developed in the early stage of the fourth member in the northwestern part of the Triassic Akekule Formation. This system was dominated by subaqueous distributary channel sand bodies, which were subjected to erosion and reshaping by lake water, leading to the formation of several stable sand bars along the lake shoreline. In the later stage of the fourth member, as the lake level continued to recede, the area of deltaic deposition expanded westward, and deltaic deposits also developed in the central to slightly eastern parts of the study area. Based on this, a depositional model for the fourth member of the Triassic in the Tahe Olifield has been established. (3) In the Tahe Olifield, the sand bodies within the fourth member of the Triassic system gradually pinch out into mudstone, forming lithological pinch-out traps. Among these, the channel sand bodies and long belt sand ridges, due to their good sorting and high permeability, become favorable reservoirs for oil and gas accumulation. This study clarifies the sedimentary model of the fourth member and reveals the spatial differentiation mechanism of sand bodies under the control of lake-level fluctuations and ancient structures. It can provide exploration guidance for delta lake sedimentary systems similar to the edge of foreland basins, especially for efficient development of complex lithological oil and gas reservoirs controlled by multistage lake invasion-like retreat cycles.  
Tahe Olifield; thin sand body; sedimentary facies; delta lake sediments; petroleum exploration +
Footnote †: journal: Applied sciences  
Article  
Sedimentary Characteristics of the Sandstone Intervals in the Fourth Member of Triassic Akekule Formation, Tarm Inglations for Petroleum Exploration
# Hybrid NHPSO-JTVAC-SVM Model to Predict Production Lead Time
## 3 Lead-Time Prediction Based on NHPSO-JIVAC-SVM
### Data and Preparation
#### 3.1.2 Feature Selection  
Feature selection (FS) of the machine learning model is essential. FS avoids the data dimensions problem and reduces learning difficulty. We performed feature engineering and removed irrelevant features.  
As shown in Figure 5, the FS steps were as follows:  
Step 1. The data are split into the training and test sets (8:2).  
Step 2. The random forest (RF) model is trained using a training set.  
Step 3. The importance score for each feature in the training set is calculated. Features are ranked by feature importance scores.  
Step 4. Suppose the model's accuracy and execution time are not satisfied. In that case, the feature with the minimum importance score will be deleted from the data set, and Steps 2 and 3 will be repeated until the desired number of features is obtained. Otherwise, the feature subset is obtained directly.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 5 Clustering Process Optimization
### Data Aggregation Phase
#### 5.3.3 Multipath-Based Data Aggregation  
In these schemes, the aggregated data are divided by the SNs into several parts before being sent to a single point via numerous paths [65]. These schemes aim to improve network robustness by sending small duplicate data packets to the BS via multiple paths. Multipath-based data aggregation typically relies on a ring topology that allows the partitioning of the SNs into several levels with respect to their distance from the BS (number of hops) [6].
# Noise Reduction in CWRU Data Using DAE and Classification with ViT
## 2 Research Method
### Denoising Autoencoder  
The autoencoder technique is a type of unsupervised learning method that involves training models to ignore noise in signals [26]. Auto-encoder models are categorized by the number of hidden layers and nodes [26]. The DAE is an example of such a technique, and its structure is shown in Figure 5[21]. The DAE structure contains the same number of nodes in the input and output layers but fewer nodes in the hidden layer. The learning method of the DAE involves adding random noise to the original data. The modified data are then added to the model as input data. Finally, the model is trained to restore the input data to its original form before the addition of noise [27].  
Figure 3: Short\(-\)time Fourier transform theory.  
The training method for the DAE is expressed in Equations (2)-(5) [8].  
(i) Add random noise to the original dataset \(x\):  
\[\widetilde{x}=x+noise \tag{2}\]  
Figure 4: Result of STFT ((**a**) normal; (**b**) ball; (**c**) inner; (**d**) outer).  
Figure 5: Learning method of the Denoising Autoencoder.  
1. The dataset \(\widetilde{x}\), generated by adding noise, is inserted as input data and encoded: \[h=f_{\theta}(\widetilde{x})=f(W\widetilde{x}+b)\] (3) 2. The encoded result is decoded and the model is trained to generate an output value as similar to the original dataset x as possible: \[x^{\prime}=g_{\theta}(h)=f\big{(}W^{\prime}h+b^{\prime}\big{)}\] (4) \[\min_{W,b,W^{\prime},b^{\prime}}L\big{(}x,x^{\prime}\big{)}=\frac{1}{2}x-x^{ \prime 2}\] (5)
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 4 Joint Optimization Framework for RIS Configuration and UAV Mobility
### Performance Evaluation and Trade-Off Analysis
#### 4.3.1 Throughput  
The system throughput is calculated using the Shannon capacity formula, which relates the achievable data rate to the available bandwidth and the received signal quality. It is expressed as  
\[\mathrm{Throughput}=B\cdot\log_{2}(1+\mathrm{SINR}), \tag{17}\]  
where \(B\) denotes the communication bandwidth, and SINR is the signal-to-interference-plus-noise ratio at the user.  
The SINR for the considered system model is given by  
\[\mathrm{SINR}=\frac{P\left|h_{d}+\sum_{m=1}^{M}g_{m}^{*}h_{m}e^{i\phi_{m}} \right|^{2}}{\sigma^{2}+I}, \tag{18}\]  
where \(P\) is the transmit power of the NTN node, \(\sigma^{2}\) is the noise variance, and \(I\) denotes the aggregate interference power. The term \(h_{d}\) represents the direct channel component, while the summation \(\sum_{m=1}^{M}g_{m}^{*}h_{m}e^{i\phi_{m}}\) accounts for the combined contribution of all \(M\) RIS elements. Here, \(g_{m}^{*}\) and \(h_{m}\) denote the \(m\)-th elements of the RIS-user and NTN-RIS channel vectors, respectively, and \(\phi_{m}\in[0,2\pi)\) is the phase shift applied by the \(m\)-th RIS element.  
The phase shifts \(\{\phi_{m}\}\) are dynamically optimized to achieve constructive interference at the receiver by maximizing the magnitude of the overall signal. This coherent combination of the direct and reflected signal components significantly enhances the SINR, thereby improving the system throughput.