esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Theoretical Analysis of Halden Reactor Creep Tests on M5 Cladding Material
## 3 Experimental Results and Discussion
### Effect of C&C and Resulting Temperature Increment  
Because of C&C, one expects the cladding temperature to increase as the C&C progresses. In IFA-663, the time-period under stress is 2700 h (see Figure 9 in HWR-755). Considering that we found a C&C of 3.73 \(\upmu\)m/1000 h (see Equation (3)), the expected C&C would amount to 3.73 \(\times\) 2.700 = 10 \(\upmu\)m. With a Zr C&C conductivity of 0.02 W/cm \({}^{\circ}\)C and a fuel LHR of 40 kW/m (indicative value for an 8% enriched fuel in the HBWR), this would result in a cladding temperature increment of 8 \({}^{\circ}\)C. A similar result applies to IFA-617 (see Figure 18 in HWR-658). One should note that, here, one assumes that crud and oxide have similar thermal conductivity, which requires further assessment when a complete model is developed.  
The 8 \({}^{\circ}\)C temperature increment caused by corrosion and crud (C&C) deposition corresponds to the maximum value reached at the end of the creep period as the deposition progresses over time. To account for this gradual buildup, a simplified approach assumes  
Figure 3: Variations in the secondary creep rate in IFA-617 and IFA-663 experiments vs. C&C rate (factor \(K\)).  
an average increment of 4 \({}^{\circ}\)C over the 2700 h of the test. This approximation is based on the following rationale:  
* Gradual Progression of C&C Deposition: the deposition and associated temperature effects develop progressively, starting from negligible levels at the beginning of the test. Using half the maximum increment reflects this time-dependent growth without overestimating the impact;
* Conservative and Realistic Simplification: By averaging the temperature increment, the model avoids overstating the effects while maintaining a reasonable approximation;
* Limited Impact on Results: Sensitivity analysis demonstrates that variations in C&C deposition rates have only a moderate influence on creep rates, making this simplification adequate for capturing overall trends;
* Established Methodology: Averaging temperature increments over time is a practical and commonly adopted approach in cladding studies when detailed time-dependent data are unavailable.  
This approach strikes a balance between simplicity and accuracy, ensuring that the earlier results remain valid. A more detailed time-dependent correction would likely yield similar conclusions.
## 3 Methodology
### Feature Extraction
#### 3.4.1 TF-IDF  
Term frequency-inverse document frequency (TF-IDF) is a statistical technique for feature extraction that represents an improvement over the bag of words by assessing the importance of a word both within a document and across the entire corpus [19].  
TF-IDF is the product of res exterm frequency (TF), which measures how often a word appears in a document, and inverse document frequency (IDF), which measures the importance of a word in a document relative to its occurrence across the entire corpus. TF-IDF is calculated by the following functions:  
\[w_{x,y} = TF_{xy}\ \times\ IDF_{t}\] \[IDF_{t} = log(\frac{N}{df_{x}+1})\]  
where \(w_{x,y}\) is the weight of term x in document y, \(TF_{xy}\) is the term frequency of x in document y, \(N\) is the total number of documents in the corpus and d\(f_{x}\) represents the number of documents that contain the term x.
# MZAP--Mobile Application for Basketball Match Tracking and Digitalization of Endgame Reports
## 5 MZAP Android Application--Match Report Preview and Digital Signing
### Security Mechanisms for PDF Match Endgame Reports
#### 5.2.4 Report Updates and Version Control  
MZAP endgame reports are inherently immutable; that is, they are never deleted from the system. When a revision or update is required, the existing report is marked as invalid on the MZAP server rather than being modified or overwritten. In such cases, the current record is flagged as invalid by an administrator, and a new report is subsequently generated, locked, encrypted, digitally signed, and uploaded to the MZAP server for archival. During this process, new passwords are generated, and the entire signing and uploading procedure is repeated to ensure data integrity and authenticity.  
For each updated report, a corresponding changelog is created and linked to the UUID of the match to which the report pertains. This enables robust traceability and supports auditability.  
Each report is annotated with several metadata fields embedded as custom properties within the PDF document. These include the version number of the MZAP application used to generate the report, a timestamp indicating the exact time of generation, a server-managed document version number that is incremented with each update, and the UUID of the associated match. These fields, shown in Figure 23, collectively support version control, provenance tracking, and data consistency across the archival system.
# Deep Learning-Based Autoscaling Using Bidirectional Long Short-Term Memory for Kubernetes
## 2 Related Works  
In this section, we review the recent studies related to autoscaling applications conducted using a time-series analysis technique in the cloud system. Time-series analysis has applications in many area such as weather forecasting, earthquake prediction, and mathematical finance. It uses previous and current observed values to predict future values [21]. In this study, it is used to predict future workload or required resources [22]. An example is the number of requests arriving at the system within one-minute intervals. Table 1 presents a review of important autoscaling methods. [PERSON] et al. [11] presented an architecture called ELASTICDOCKER, which uses a reactive approach based on threshold-based rules to perform scaling actions. ELASTICDOCKER vertically scales both memory and virtual CPU cores resulting from the workload. The disadvantage of vertical scaling is that it has limited resources for hosting the machine capacity. To address this problem, ELASTICDOCKER performs live migration of the container when the hosting machine does not have enough resources. The experimental result showed that this approach helps reduce the cost and improve the quality of experience for end-users. In Kubernetes (KSS), the Horizontal Pod Autoscaler (HPA) [12] is a control loop that reactively scales the number of pods based on CPU utilization, regardless of whether the workload or application is performing. It maintains an average of CPU utilization across all pods at a desired value by increasing or decreasing the number of pods, for example, \(Target_{CPU}=80\%\). The traditional approaches for time-series analysis and prediction include statistical methods such as autoregression (AR), moving average (MA), autoregressive moving average (ARMA), and autoregressive integrated moving average (ARIMA). [PERSON] et al. [6] proposed an architecture using a reactive workload component for hybrid clouds, which classifies the incoming workload into the base workload and trespassing workload. The first workload is handled using ARIMA, while the second is handled using a public cloud. However, the limitation of reactive approach is that it only can react to the change in workload after it actually happens. Thus, the system has to spend a certain amount of time reconfiguring to adapt to the new workload. In contrast, the proactive approach analyzes the historical data and forecasts the future resource required for the scaling action. We review some recent studies using time-series analysis for the proactive approach below. [PERSON] and [PERSON] [13] proposed an autoscaling platform, called the cloud resource prediction and provisioning scheme (RPPS), which can scale web applications in hybrid clouds. RPPS uses ARMA to analyze the past resource usage (CPU utilization) and forecast the future usage to provide the required resources. For the evaluation, they compared RPPS with the reactive approach of Kubernetes HPA [12] and showed that their approach achieved better results with varying workload.
## 2 Results  
### (p)ppGpp- and DksA-Dependent Genes in Y. enterocolitica Identified Using RNA-Seq  
Previous reports have shown that (p)ppGpp and DksA have extensive regulatory roles in _Escherichia coli_ and _Xanthomonas citri_[9; 10]. Our previous studies on _Y. enterocolitica_ also showed that the regulatory patterns of (p)ppGpp and DksA at the phenotypic and genotypic levels are complex, showing both synergistic and antagonistic actions [27]. To better understand the function of (p)ppGpp and DksA in cellular processes in _Y. enterocolitica_, RNA-Seq was conducted to determine the transcription profiles of wild-type and mutant strains, including _Y. enterocolitica_\(\Delta\)_dksA_, _Y. enterocolitica_\(\Delta\)_relA\(\Delta\)_spoT_, and _Y. enterocolitica_\(\Delta\)_dksA_relA\(\Delta\)_spoT_.  
As shown in Table S2, approximately 23.96 million, 26.06 million, 25.49 million, and 25.61 million raw reads were generated in WT, \(\Delta\)_dksA_, _ArelA\(\Delta\)spoT_, and \(\Delta\)_dksA_relA\(\Delta\)spoT_, respectively, using Illumina sequencing. After removing low-quality reads, approximately 23.58 million, 25.69 million, 25.07 million, and 25.32 million clean reads were retained, respectively. The matching rate of these clean reads to the reference genome ranged from 97.72 to 98.15%, with a unique matching rate ranging from 93.64 to 95.92%. Saturation analysis showed that most clean reads were close to saturation at 40% of the mapping reads, indicating that the number of sequencing reads covered most of the expressed genes (Figure S1). In the coverage analysis, each curve had no biased peaks, indicating no sequencing bias (Figure S2). These results showed that the transcriptome sequencing results were reliable and could be used for subsequent bioinformatics analysis.
## 4 Proposed Scheme
### Pseudonym ID and Domain Key Updating Phase  
The pseudonym ID and domain key updating phase begins immediately once a vehicle joins or leaves the network. The TA is responsible for securely disseminating the updated domain key to domain members every time a vehicle joins a 5G-enabled vehicular network domain. Since newly joined vehicles cannot listen to the above communication, backward secrecy is preserved. Similarly, once a vehicle leaves a domain, TA updates the domain key to prevent the key from being reused on the old vehicle, thus ensuring forward secrecy. When the domain membership changes, the proposed scheme provides a pseudonym ID update to prevent adversaries from tracing authorized vehicles by linking multiple messages to the same sender. In this phase, the TA has to execute the batch leave or batch join process depending on the vehicle's action.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 3 NP Properties and Characterization Methods
### Physical Properties and Their Characterization
#### 3.1.4 Dynamic Light Scattering (DLS)  
DLS is a technique employed to determine the hydrodynamic size of MNPs present in a liquid solution. It examines the patterns of light scattered by particles, which is crucial for understanding their behavior in colloidal suspensions.
## 2 Materials and Methods
### Selecting Dust Events
#### 2.2.2 MODIS Data  
Next, MODIS satellite images and tools for analyzing the remote sensing data were used to map these events. MOD021 km Terra Sensor MODIS (36 bands) data were downloaded from website [53]. MODIS Characterization in this study including: Spatial resolution: 1 km, temporal resolution: 5 min, MODIS collection 6.1, possessing level 1B.  
For the detection of dust storms, MODIS images were downloaded at desired dates and transformed into HDVI format with ENVI5.1 software (5.1 version, L3 harris Geospatial Company, Broomfield, CO, USA, 2013). Geometrical corrections and brightness temperatures of the bands were calculated. After receiving and pre-processing MODIS satellite data, the Brightness Temperature Difference (BTD) method [54] was used to detect the presence of dust. This method considers the brightness temperature difference between band 31 (11 \(\upmu\)m) and 32 (12 \(\upmu\)m) to detect dust storms; the value of zero is considered as the threshold for dust detection (where values less than 0 K indicate dust).  
\[\text{D}=\text{BT31}-\text{BT32};\hskip 28.452756 pt\text{Dust if}<0\text{ K} \tag{1}\]  
In this study, because the Aerosol Optical Depth (AOD) of the region on these dates had many missing values due to the presence of clouds, the Ackerman index was used instead.
## 2 Target Structure and Numerical Modeling
### Material Model  
When selecting a material model for blast analysis, various material properties--in particular, the strength increasing effect due to the strain rate--must be considered. Therefore, we selected the following material model from LS-DYNA:  
For the concrete, the *MAT_CSCM_CONCRETE material model was applied; this captures the nonlinear material behaviors of concrete, as well as its stiffness degradation arising from damage, erosion, and the strain rate effect [25, 26]. A compressive strength (\(f_{\text{ck}}\)) of concrete was selected as 39.23 MPa, and 1.05 was used as the ERODE parameter, to simulate erosion of the concrete element in the blast analysis.  
To model the steel plates in the girders, rebars, and cables, the *MAT_PLASTIC_KINEMATIC material model was applied. This model considers the strain rate effect, isotropy, and kinematic hardening of the material, and it assumes a bi-linear material nonlinearity. The material constants C and \(p\) (used in the Cowper-Symonds model, which describes the strain rate effect) were determined using the following equation, developed for high-strength steel [27]. Usually, the C value of 40 is applied to normal structural steel, but this value is known to overestimate the strain rate effect of high-strength steel. Therefore, the experimentally validated Equation (1) is used to calculate the C value in order to prevent overestimation of the strain rate effect.  
\[\text{C}=\left\{\begin{array}{cc}92000\text{-}\exp\Bigl{(}\frac{\sigma_{0}} {564}\Bigr{)}-194000,&\sigma_{0}>270\text{ MPa},\\ 40,&\sigma_{0}\leq 270\text{ MPa},\end{array}\right.\,p=5. \tag{1}\]  
where, \(\sigma_{0}\) is the yield stress (MPa). In addition, the strengths selected for the steel plates in the girders were varied according to the thickness. The input parameters used for each material model are listed in Table 3.  
Figure 4: Cable model: (**a**) Cable no. 37, (**b**) Cable no. 53, and (**c**) Cable no. 68.  
Figure 5: Pylon model: (**a**) concrete part and (**b**) rebar.
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
###### Abstract  
In modern wargaming, accurately predicting the locations of the opponent units is crucial for effective strategy and decision making. However, situational data provided by tactical wargame systems present significant challenges: high redundancy across consecutive frames and extreme data sparsity, with units occupying only a small fraction of the overall map. Traditional convolutional neural networks (CNNs) struggle to extract meaningful patterns from such data. To address these limitations, we propose an enhanced location prediction neural network (ELP-Net) that integrates graph neural networks (GNNs) and transformers, combining the robust representation learning capabilities of GNNs with the temporal dependency modeling strength of transformers. By capturing complex inter-node relationships, our model effectively reduces the impact of data repetition and sparsity, achieving robust location predictions in dynamic and sparse wargaming environments. Experimental results demonstrate that our approach significantly improves the prediction accuracy (the combined use of both the GNN and transformer modules results in a 6.4% average performance boost), highlighting its potential to advance intelligent decision making in wargaming applications.  
wargaming; location prediction; graph neural networks; transformers; decision making +
Footnote †: journal: Applied sciences  
1
## 3 Results and Analysis
### Slope Flow Velocity, Rainfall Intensity, and Slope Gradient Fitting Equation  
An intensification of precipitation strength results in an expansion of surface water flow volume, subsequently accelerating the flow velocity of slope runoff. An increase in slope magnitude signifies a greater inclination of the terrain, which, under the influence of gravitational forces, amplifies the energy of water traversing the slope and further accelerates the flow velocity of slope runoff. The combined action and mutual constraints of these factors ultimately impact the velocity of water flow along the slope. To delve deeper into the impact of rainfall intensity and slope gradient on slope flow velocity, the rainfall intensity and slope gradient tangent values were denoted as X and Y, respectively. A binary fitting analysis of the rainfall intensity and slope gradient tangent value was subsequently conducted. The fitted surface is illustrated in Figure 5, and the fitting equation is as follows:  
\[\mathrm{v}=0.057\mathrm{ln}\mathrm{X}+0.033\mathrm{ln}\mathrm{Y}-0.053\ \ \ ( \mathrm{R}^{2}=0.908)\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Rainfall** \\ **Intensity** \\ **(mm-h\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Gradient** \\ **(\({}^{\circ}\))** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Discharge** \\ **Velocity** **(m-s\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Reynolds** \\ **Number** \\ **(\(Re\))** \\ \end{tabular} & \begin{tabular}{c} **Froude** \\ **Number** \\ **(\(Fr\))** \\ \end{tabular} & \
\begin{tabular}{c} **Manning** \\ **Coefficient** \\ **(\({}^{\circ}\))** \\ \end{tabular} &
\begin{tabular}{c} **Resistance** \\ **Coefficient** \\ **(\({}^{\circ}\))** \\ \end{tabular} \\ \hline \multirow{4}{*}{50} & 10 & 0.124 & 43.421 & 1.143 & 0.017 & 0.427 \\  & 20 & 0.140 & 76.094 & 1.453 & 0.025 & 0.397 \\  & 30 & 0.149 & 108.447 & 1.773 & 0.025 & 0.341 \\  & 40 & 0.155 & 207.893 & 2.036 & 0.030 & 0.292 \\ \hline \multirow{4}{*}{80} & 10 & 0.131 & 46.240 & 1.445 & 0.016 & 0.402 \\  & 20 & 0.156 & 93.630 & 1.756 & 0.021 & 0.387 \\  & 30 & 0.165 & 125.573 & 1.990 & 0.025 & 0.312 \\  & 40 & 0.189 & 249.569 & 2.236 & 0.028 & 0.265 \\ \hline \multirow{4}{*}{110} & 10 & 0.155 & 50.124 & 1.729 & 0.014 & 0.383 \\  & 20 & 0.177 & 138.843 & 2.018 & 0.017 & 0.356 \\ \cline{1-1}  & 30 & 0.192 & 205.076 & 2.482 & 0.017 & 0.271 \\ \cline{1-1}  & 40 & 0.226 & 302.162 & 2.980 & 0.021 & 0.192 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Slope runoff hydraulic parameters under different slope gradients and rainfall intensities.
## 2 Experimental Works
### Density and Porosity Tests  
The 13 specimens were tested following the suggested method of the ISRM [6] in order to determine dry mass (maintained at 105 \({}^{\circ}\)C for 24 h) and saturated mass (submerged for 48 h). The hydrostatic scale method was used in order to calculate the total volume of the samples. Dry density (\(\rho_{\rm dry}\)), saturated density (\(\rho_{\rm sat}\)), and n were obtained separately by the following Equations (12)-(14):  
\[\rho_{\rm dry}=\frac{m_{\rm dry}}{V_{\rm T}}=\frac{m_{\rm dry}}{\frac{m_{\rm sat }-m_{\rm sub}}{\rho_{\rm w}}}=\frac{m_{\rm dry}\rho_{\rm w}}{m_{\rm sat}-m_{ \rm sub}}, \tag{12}\]  
\[\rho_{\rm sat}=\frac{m_{\rm sat}}{V_{\rm T}}=\frac{m_{\rm sat}}{\frac{m_{\rm sat }-m_{\rm sub}}{\rho_{\rm w}}}=\frac{m_{\rm sat}\rho_{\rm w}}{m_{\rm sat}-m_{ \rm sub}}, \tag{13}\]  
\[\rm n=\frac{V_{\rm v}}{V_{\rm T}}100=\frac{m_{\rm sat}-m_{\rm dry}}{m_{\rm sat }-m_{\rm sub}}100, \tag{14}\]  
where \(m_{\rm dry}\) is the dry mass (g), \(m_{\rm sat}\) is the saturated mass (g), \(m_{\rm sub}\) is the submerged mass (g), \(V_{\rm v}\) is the pore (void) volume (cm\({}^{3}\)), \(V_{\rm T}\) is the bulk volume (cm\({}^{3}\)), and \(\rho_{\rm w}\) is the water density (g:cm\({}^{-3}\)).  
The mean values of \(\rho_{\rm dry}\), \(\rho_{\rm sat}\), and n were 2.63 g:cm\({}^{-3}\), 2.65 g:cm\({}^{-3}\), and 2.5% respectively.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 3 Optimization
### The Use of GA
The GA approach to adaptive phase zeroing is significantly faster than random search and gradient methods, which was proven in the work [166] and later in [167] as applied to satellite communication antennas. The GA was developed to optimize the number and size of cells for a circular symmetric grid. Moreover, numerical optimization based on GAs has been used to develop multilayer broadband radar absorbers [168]. In [169], a procedure for synthesizing radio-absorbing materials to reduce the radar cross section in the broadband frequency range is presented. GAs have also been widely used to optimize microstrip circuits, as in [170; 171].
## 3 Algorithm Design
### Performance Analysis
#### 3.1.2 Delay Violation Probability  
To obtain the delay violation probability for any accessed user \(i\), denoted as \(P_{\text{out}}^{(i)}(\mathbf{x})\), based on the definition in Equation (4), we need to derive the delay violation probability for user \(i\) in any given period \(l\) during video playback. We only need to determine its probability density function \(p(D_{\text{M},l}^{(i)}=t_{l})\). Using the law of total probability, we can expand the caching status of the MEC server \(\mathbf{A}_{c,l}\) and the FoV positions of all accessed users \(\mathbf{A}_{c,l}\), which can be expressed as:  
\[p(D_{\text{M},l}^{(i)}=t_{l})=\sum_{\mathbf{A}_{c,l}}P(\mathbf{A}_{c,l})\sum_{ \mathbf{A}_{c,l}}P(\mathbf{A}_{r,l})p_{D_{\text{M},l}^{(i)}}(D_{\text{M},l}^{ (i)}=t_{l}|\mathbf{A}_{c,l},\mathbf{A}_{r,l}) \tag{13}\]  
where \(P(\mathbf{A}_{c,l})\) and \(P(\mathbf{A}_{r,l})\) are the probability of \(\mathbf{A}_{c,l}\) occurring and that of \(\mathbf{A}_{c,l}\) occurring, respectively, satisfying:  
\[P(\mathbf{A}_{c,l})=P(G_{l}=\sum_{(n,m)}a_{c,l}(n,m)) \tag{14}\]  
\[P(\mathbf{A}_{r,l})=\sum_{\begin{subarray}{c}(n_{1}^{(1)},m_{1}^{(1)})\\ \text{if }x_{1}=1\end{subarray}}\sum_{\begin{subarray}{c}(n_{1}^{(2)},m_{1}^{(2)} )\\ \text{if }x_{2}=1\end{subarray}}\cdots\sum_{\begin{subarray}{c}(n_{l}^{(l)},m_{l}^{ (l)})\\ \text{if }x_{l}=1\end{subarray}}\prod_{\begin{subarray}{c}i=1,2,-l\\ i\text{if }x_{i}=1\end{subarray}}P_{\text{F}}(n_{l}^{(i)},m_{l}^{(i)}) \tag{15}\]  
Here, \(P_{\text{F}}(n_{l}^{(i)},m_{l}^{(i)})\) represents the probability that the viewpoint of user \(i\) falls within the tile at coordinate \((n_{l}^{(i)},m_{l}^{(i)})\) at the start of the \((l+1)\)-th period. As described in Section 2.2.2, since the FoV prediction is obtained by the cloud server using the video's saliency map, \(P_{\text{F}}(n_{l}^{(i)},m_{l}^{(i)})=P_{\text{F}}(n_{l},m_{l})\) holds for all users. The last term in Equation (13) is the conditional probability density function of the duration \(D_{\text{M},l}^{(i)}\) of the computation and transmission stage of user \(i\) in period \(l\). Given \(\mathbf{A}_{c,l}\) and \(\mathbf{A}_{r,l}\), it follows from Equations (7) and (12) that \(D_{2,l}^{(i)}\) and \(D_{5,l}^{(i)}\) are constants. Therefore, we have:  
\[\begin{array}{ll}p_{D_{\text{M},l}^{(i)}}(D_{\text{M},l}^{(i)}=t_{l}| \mathbf{A}_{c,l},\mathbf{A}_{r,l})=&\int_{0}^{t_{l}-D_{2,l}^{(i)}-D_{5,l}^{(i )}}p_{D_{3,l}^{(i)}}(t_{2}|\mathbf{A}_{c,l},\mathbf{A}_{r,l})\\ &\cdot p_{D_{4,l}^{(i)}}(t_{l}-D_{2,l}^{(i)}-D_{5,l}^{(i)}-t_{2}|\mathbf{A}_{c,l},\mathbf{A}_{r,l})dt_{2}\end{array} \tag{16}\]  
Substituting Equations (14)-(16) into (13), and then using the integral relationship between the probability and its density function, we can obtain \(P(D_{M,l}^{(i)}>D_{\text{th}})\), the delay violation probability of accessed user \(i\), by substituting it into Equation (4).