esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Modeling of Single Pile Behaviors Due to Groundwater Level Rising
## 6 Conclusions  
Finite element analysis with fully coupled flow deformation was used to determine the pile-soil response with change of groundwater level. The change of pile capacity was explained by pile-soil interaction, stress distribution along the pile and development of plastic points. In summary:  
1. Groundwater rises caused both an upward movement of the ground and the pile foundation. The pile settlement relative to the ground surface increased with groundwater level.
2. A reduction of stress distribution along with the pile with rising groundwater level was caused by soil swelling around the pile. The pile shaft friction was reduced It was accompanied by development of plastic points (failure points).
3. Groundwater rises caused a loss in pile foundation capacity. For any percentage of pile settlement/diameter in the analysis, the percentages of pile load reduction have not reach to 35%, the pile that was working load with factor of safety less than 2.0, the pile stability should be concerned when the groundwater level is changed. In this study, the evaluation of pile stability due to groundwater level rising for preliminary guidelines to protect existing structures are proposed.  
Further study will be necessary by extending the scope of study to the magnitude of pile capacity reduction which may depend on rate of groundwater rising that related to various scenario of groundwater usage. Moreover, series of full field and physical model tests will provide the validity of the findings in this study.  
Conceptualization and methodology, H.P. and P.L.; numerical simulations, H.P.; validation, formal analysis, writing--original draft preparation, H.P. and P.L.; writing--review and editing, P.L. and U.S.; visualization, H.P.; supervision, project administration, U.S.; funding acquisition, H.P. and U.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Thailand Science Research and Innovation (TSRI) under the \"Research and Researchers for Industries (RRi) Grant No. PHD58I0003\" collaborating with BMS construction Company Limited and King Mongkut's Institute of Technology Ladkrabang Research Fund Grant No. KREF046012 for the financial sponsorship (PLAXIS Software).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank [PERSON] for numerical instructions for geotechnical and [PERSON] for recommendation of groundwater modeling.  
The authors declare no conflict of interest.
## 4 Evaluation
### Ablation Study  
We conduct experiments on BlockMatch to evaluate the contributions of the contrastive training framework and root operand model pre-training task. For observation, a detailed examination of the specific configurations of BlockMatch variants is provided in Table 3. In the Table, a checked circle () indicates this BlockMatch variant uses the corresponding technique, whereas a cross () denotes that this BlockMatch variant does not use the corresponding technique.  
**Contrastive Training Framework.** Figure 11 illustrates that BlockMatch outperforms BlockMatch_triplet_ in basic block similarity detection evaluations across various compilers  
Figure 10: Results of multiple scenarios with different pool size.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **BlockMatch** & **BlockMatch\({}_{triplet}\)** & **BlockMatch\({}_{wIo. ROM}\)** \\ \hline Contrastive Training Framework & \(\bigcirc\) & \(\times\) & \(\bigcirc\) \\ Triplet Loss Training & \(\times\) & \(\bigcirc\) & \(\times\) \\ Root Operand Model & \(\bigcirc\) & \(\bigcirc\) & \(\times\) \\ Masked Language Model & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The setting of BlockMatch variants.  
and optimizations. For example, when the number of basic blocks in the pool increases to 1000 in the cross-optimization scenario, the recall@1 of BlockMatch\({}_{triplet}\) drops to 0.537. In contrast, BlockMatch maintains a recall@1 of 0.671. This highlights the effectiveness of our contrastive training framework, which utilizes large-batch hard samples for more comprehensive model training. Consequently, BlockMatch converges towards optimal parameters and achieves higher performance.  
**Root Operand Model.** The root operand model is used to help our model comprehend the root semantics of unseen operands. The results presented in Figure 11 clearly demonstrate that BlockMatch consistently outperforms BlockMatch\({}_{w/o,ROM}\) in terms of recall@1 and MRR for different pool sizes (ranging from 1 to 1000), particularly in the cross-compiler scenario. These findings suggest that our root operand model pre-training task mitigates the impact of OOV operands on our model, which makes our model perform better on binary code similarity detection.
# Wideband Spectrum Sensing Based on Reconfigurable Filter Bank in Cognitive Radio
## 5 Simulation Results and Analysis
### Wideband Spectrum Sensing Based on RFB  
At three different sensing times, taking 2048 samples in the dataset of NE sub-band 1, 300 MHz-710 MHz frequency band, the sub-bands ISPs are 12.27%, 47.51%, and 80.38% respectively. After the sampled data is processed by the FB, the energy detection method is employed for each sub-band to perform spectrum sensing. The detection threshold is determined by Equation (13) at different false alarm probabilities, and the detection probability \(P_{d}\) is determined by Equation (15). ROC curves on the wideband spectrum sensing are shown in Figure 9, with different sub-bands numbers divided by RFB, and the \(P_{d}\) is higher, the detection performance is better. \(P_{f}\) is preset for changing the threshold \(\lambda\) according to Equation (14).  
As can be seen from the ROC curve, the wideband spectrum sensing based on the FB performs much better than the direct energy detection method. Furthermore, the optimal number of sub-bands divided by the RFB is not necessarily the number that the \(P_{d}\) hits the maximum, but relates to the ISP of the sub-bands. In Figure 9a, when the sub-bands ISP is at a low level, fewer FB sub-bands are needed to reach the requirements of detection performance, and eight sub-bands are suitable. But in  
Figure 8: Amplitude spectrum of eight sub-bands.  
Figure 9: ROC curve for sub-bands with different ISPs. (a) ISP is 12.27%; (b) ISP is 47.51%; (c) ISP is 80.38%.  
Figure 9c, when the sub-bands ISP is at a higher level, more FB sub-bands are essential to achieve the required detection accuracy, and the optimal \(M\) is 128. In Figure 9b, the \(P_{d}\) rises along with the bigger \(M\), but when the \(M\) becomes greater than 64, no further improvement of \(P_{d}\) is identified. Consequently, the appropriate number of RFB sub-bands is selected by weighing the detection performance and the computational complexity.
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## 2 Problem Modeling
### Seamless Coverage Model  
This section mainly focuses on the seamless coverage provided by the LEO satellite constellation and, to simplify the analysis process, on the orbital plane coverage provided by the LEO satellite constellation. The coverage of the orbital plane of a single LEO satellite is shown in Figure 3. In order to achieve seamless coverage on the orbital plane, it is necessary to ensure that there are overlapping parts in the coverage area of two adjacent satellites, and the size of the overlap is affected by the latitude difference between the adjacent satellites and their interference avoidance strategy.  
The coverage model of the LEO satellite is shown above. The arc \(AB\) is the coverage corresponding to all beams, and the arc \(AD\) corresponds to the off beams. \(C\) is the center of the arc \(AB\), \(\chi\) is the pitch angle, and the number of off beams is \(K\). The order of shutting beams is from the high latitude to the low latitude, and the set of off beams can be represented by \(\{b_{1},b_{2},\cdots,b_{K}\}\). Both the pitch angle \(\chi\) and the number of off beams \(K\) are functions of the satellite latitude \(\psi\). The latitude of the upper and lower boundaries of the LEO satellite coverage area are \(\psi_{up}\) and \(\psi_{down}\), separately. The elevation angles of points \(B\) and \(D\) relative to the LEO satellite are functions of the latitude of the LEO satellite, and the elevation angle expressions of \(D\) and \(B\) are expressed as follows  
\[\alpha_{up}(\psi)=\chi(\psi)+(2K(\psi)-N)\mu_{b}, \tag{7}\]  
Figure 3: The coverage of LEO satellite in orbit plane.  
\[\alpha_{down}(\psi)=\chi(\psi)+N\mu_{b}. \tag{8}\]  
The expressions of \(\psi_{up}\) and \(\psi_{down}\) are shown as below  
\[\psi_{up}(\psi)=\psi-\arcsin(\frac{R_{e}+h}{R_{e}}sina_{up}(\psi))sign(\alpha_{ up}(\psi)), \tag{9}\]  
\[\psi_{down}(\psi)=\psi-\arcsin(\frac{R_{e}+h}{R_{e}}sina_{down}(\psi))sign( \alpha_{down}(\psi)). \tag{10}\]  
For any elevation angle \(\alpha\) the LEO satellite, the corresponding latitude \(\psi^{{}^{\prime}}\) expression is shown as  
\[\psi^{{}^{\prime}}=\psi-\arcsin(\frac{R_{e}+h}{R_{e}}sina)sign(\alpha). \tag{11}\]  
To achieve seamless coverage in the orbital plane, it is necessary to ensure that there is coverage overlap between neighboring satellites. The constraint can be expressed as  
\[\psi_{up}(\psi)-\psi_{down}(\psi+\delta)\geq\epsilon, \tag{12}\]  
where \(\epsilon\) is the size of overlap, and \(\delta\) is the latitude difference between neighboring satellites.
## 4 Biosensors in the Field of Healthcare
### Wireless Power Transfer to Medical Implants
#### 4.3.1 Near-Field WPT  
Near-field (NF) WPT is the most popular form of energy transfer for short distances, ranging from millimetres (mm) to centimetres (cm). NF-WPT can be divided based on the working principle into subcategories: inductive coupling (NFIC) (Figure 8A), capacitive coupling (NFCC) (Figure 8B), and magnetic resonant coupling (NFMRC) (Figure 8C). NFIC has been widely used to supply wireless power to various devices. For example, medical wearable devices, smartphones, and electric toothbrushes. Highly efficient power transfer for short distances and the simple implementation of two coils for transmitting and receiving power are the most common advantages of NFIC. In contrast, NFIC mandates  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
precise alignment and short distance WPT that limits their use in medical deep tissue implantations [143]. To overcome these drawbacks, a new concept was proposed by [PERSON] et altermed NFMRC, in 2007. NFMRC technology exhibits a longer distance of energy transmission, up to a few metres, when compared to inductive coupling. Typically, the system consists of four coils: source coil, transmitter coil, receiver coil, and load coil [143; 144]. Despite its advantages, like higher frequency bandwidth, optimal impedance matching, and a high Q-factor, its structural complexity poses a problem due to the requirement of multiple coils. Thereby, it becomes unsuitable for bio-implants [145]. Ultimately, NFCC is a WPT scheme that relies on an electrical field to deliver power. The system requires at least two parallel plates separated by a medium to form a coupling capacitor. These plates act as transmitter and receiver coils. Although this scheme is less sensitive to electromagnetic interference and the misalignment of plates, it is hindered by a greater bulk and low power output. An in vitro comparison between conductive and capacitive WPT by [PERSON] et al. showed that NFIC offers higher power transfer efficiency than NFCC [146; 147] that could be suitable for powering future vascular implants.
## 2 Materials and Methods
### Libraries Preparation for RNA-Seq and Bioinformatic Data Analysis  
For RNA-Seq 3 samples per condition were analyzed (N = 3). Sequencing libraries were prepared with TruSeq Stranded Total RNA kit (Illumina, San Diego, CA, USA) using 200 mg total RNA. Qualities of sequencing libraries were assessed with 4200 TapeStation with the DNA1000 reagent kit. RNA processing was carried out using Illumina NextSeq 500 Sequencing. FastQ files were generated via Illumina bcl2 fastq2 (Version 2.17.1.14--[[https://support.illumina.com/downloads/bcl2](https://support.illumina.com/downloads/bcl2) fastq-conversion-software-v2-20.html]([https://support.illumina.com/downloads/bcl2](https://support.illumina.com/downloads/bcl2) fastq-conversion-software-v2-20.html) (accessed on 30 June 2019) starting from raw sequencing reads produced by Illumina NextSeq sequence. Quality of individual sequences were evaluated using FastQC software (see Code Availability 1) after adapter trimming with the cutadapt software. Reads were computed using the STAR/RSEM software ([PERSON], 2019) using Gencode Release m24 (GRCm38) as a reference, using the \"-strandness forward\" option and details of raw reads are reported in Table 1. Differential expression analysis for mRNA was performed using R package DE-Seq2 ([PERSON], 2019), selected because of its superior performance in identifying isoforms differential expression ([PERSON], 2019). Coding and non-coding genes were considered differentially expressed and retained for further analysis with 1 log2(Nichoid sample/control sample) \(\mid\)\(\geq\) 1 and False Discovery Rate (FDR) \(\leq\) 0.1. This choice is motivated by the decision to maximize the sensitivity of this analysis in order to perform a massive screening and identify candidate genes to be validated with a wider sample population with real-time analysis.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Number of** & **Uniquely Mapped** & **Number of Reads** & **Number of Reads** \\  & **Input Reads** & **Reads Number** & **Mapped to** & **Mapped to Too** \\  & & & **Multiple Loci** & **Many Loci** \\ \hline CTR1\_S1 & 19,289,882 & 11,795,170 & 1,493,375 & 37,166 \\ CTR2\_S2 & 23,623,412 & 14,720,267 & 1,856,515 & 38,488 \\ CTR4\_S4 & 23,096,785 & 14,219,220 & 1,538,618 & 25,945 \\ NIC1\_S6 & 13,593,139 & 90,44,496 & 833,151 & 15,608 \\ NIC2\_S7 & 21,106,591 & 11,528,324 & 2,345,397 & 27,963 \\ NIC4\_S9 & 30,114,286 & 16,176,352 & 1,197,447 & 36,228 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of sequencing reads before and after STAR/RSEM alignment and quantification.
# Integrating Visual Cryptography for Efficient and Secure Image Sharing on Social Networks
###### Abstract  
The widespread use of smart devices, such as phones and live-streaming cameras, has ushered in an era where digital images can be captured and shared on social networks anytime and anywhere. Sharing images demands more bandwidth and stricter security than text. This prevalence poses challenges for secure image forwarding, as it is susceptible to privacy leaks when sharing data. While standard encryption algorithms can safeguard the privacy of textual data, image data entail larger volumes and significant redundancy. The limited computing power of smart devices complicates the encrypted transmission of images, creating substantial obstacles to implementing security policies on low-computing devices. To address privacy concerns regarding image sharing on social networks, we propose a lightweight data forwarding mechanism for resource-constrained environments. By integrating large-scale data forwarding with visual cryptography, we enhance data security and resource utilization while minimizing overhead. We introduce a downsampling-based non-expansive scheme to reduce pixel expansion and decrease encrypted image size without compromising decryption quality. Experimental results demonstrate that our method achieves a peak signal-to-noise ratio of up to 20.54 dB, and a structural similarity index of 0.72, outperforming existing methods such as random-grid. Our approach prevents size expansion while maintaining high decryption quality, addressing access control gaps, and enabling secure and efficient data exchange between interconnected systems.  
image security; lightweight encryption; visual cryptography; privacy-preserving
## 4 Results and Discussion
### Comparison of Simulations and Experiments  
Based on the experimental and simulated particle motion images, the relationship between the vertical and horizontal dispersion distances of the quartz sand particles with time could be obtained and compared, as shown in Figures 13 and 14, respectively.  
According to Figures 13 and 14, the dispersion distance of solid particles in the horizontal direction was smaller than that in the vertical direction at the same moment. The simulated dispersion distances in both directions were in reasonable agreement with the experimental data, further proving the validity  
Figure 14: Particle horizontal dispersion distance at different times.  
Figure 13: Particle vertical dispersion distance at different times.  
of the numerical model. As depicted in Figure 13, within 0-3 ms after the shock wave acted on the solid particles, the vertical dispersion distance showed an accelerating growth trend, indicating that the vertical velocity of the particles gradually increased during this period. The vertical dispersion distance then increased almost uniformly within 3-6 ms, and the increase slowed after 6 ms, which means that the vertical velocity of the particles gradually decreased. The larger the particle size, the more obvious the velocity drop. Figure 14 presents that after a period of acceleration (0-3 ms), the horizontal dispersion distance basically showed a linear growth trend, and the horizontal velocity of the particles hardly attenuated within 10 ms.  
The motion law of the solid particles in the vertical and horizontal directions is different, which is ascribed to the different forces in the two directions. The solid particles are mainly subjected to drag force, air resistance, and gravity in the vertical direction, while mainly subjected to drag force and air resistance in the horizontal direction. In the initial stage after the interaction, the drag force of the fluid on the solid particles is greater than the gravity and air resistance, resulting in the accelerated motion of the particles. With the passage of time, when gravity dominates, the upward dispersion velocity of the solid particles gradually slows until the particles fall. Furthermore, since the airflow is mainly in the vertical direction, the vertical drag force is significantly greater than the horizontal drag force, causing a greater vertical dispersion distance of the solid particles.
## 4 Artificial Neural Network
### Model Performance Evaluation  
In this study, the widely used and well-established ratio of 80:20 was used to divide the data into training and testing subsets, respectively. As discussed earlier, the training data are used to construct the model network and the testing data are used for validating the performance of the network [49]. The network uses the training data to learn and map the hidden relationships. Thereafter, it was applied to the testing dataset and the predicted outputs were compared with the measured results (targets) to estimate the predictive strength of the model. For this study, three statistical indices were utilised to assess the accuracy of the developed model: (1) root mean square error (RMSE); (2) mean absolute per cent deviation (MAPD); and (3) coefficient of determination (R\({}^{2}\)). All these indices are predominantly used for evaluating the accuracy of any data-driven modelling technique [49].  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left[\left(R_{ai(m)}-R_{ai(p)}\right) \right]^{2}} \tag{12}\]_Appl. Sci._**2020**, _10_, 8633  
\[\mathit{MAPD}(\%)=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{R_{ai(m)}-R_{ai(p)}}{R_{ ai(m)}}\right|\times 100 \tag{13}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(R_{ai(p)}-R_{ai(m)}\right)^{2}}{\sum_{i=1}^{n} \left(R_{ai(m)}-\overline{R}_{a(m)}\right)^{2}} \tag{14}\]  
where \(n\) denotes the number of data samples; \(R_{ai(m)}\) is the \(i\)th measured surface roughness; \(R_{ai(p)}\) is the \(i\)th predicted surface roughness; and \(\overline{R}_{a(m)}\) is the mean value of measured surface roughness. The values of the statistical indices for the training and testing datasets are summarised in Table 5.  
For the training dataset, the values of RMSE, MAPD and R\({}^{2}\) were 0.127, 4.69 and 0.95, whereas for the testing dataset the values were 0.204, 8.12, and 0.88, respectively, which depicted the satisfactory performance of the developed model.  
The scatter and error plots for the training and testing datasets are shown in Figures 9 and 10. The perfect prediction between the data plotted on the abscissa (measured response) and the ordinate (predicted response) is depicted by the perfect prediction line (x = y) in the scatter plot. Additionally, the error plots for the training and testing datasets were created by computing the difference between the measured and simulated response for each data point. From Figures 9 and 10, it can be seen that the developed ANN model produced the outputs close to the observed measurement with approximate \(\pm 10-15\%\) error.
# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
## 7 Energy-Saving Techniques and Security Mechanisms  
### Energy-Saving Technologies  
In precision farming applications sensor nodes are usually powered by low-energy batteries that are difficult or impossible to recharge or replace. This is considered to be a major disadvantage to maintain a real-time monitoring system. Energy-saving techniques is vital to maintain the system's efficiency in smart farming. This kind of techniques can provide battery life extension by reducing the amount of communication between the nodes and the base station, while minimizing the redundant data in the network. Energy preservation techniques for precision agriculture systems are presented as a separate architectural level covering the sensing and networking procedures of smart farming.  
In the Sensor layer, the proposed energy-saving approach is an on/off process which is based on the selection of a subset of nodes that will remain active for a certain period of time, while others remain inactive. Following this assumption, SWORD (sleep/wake on redundant data) is an energy preserving scheme that can be used to collect data on soil moisture [19]. The SWORD algorithm performs data control by removing redundant data so as to minimize energy consumption and increase the life of sensor nodes in the network.  
In the Network layer, data transmissions and receptions can also be scheduled based on the sleep/awake periods of sensor nodes at predetermined intervals. For this purpose, \(A^{2}\)S, an automated agricultural precision tracking system can be used [46]. Based on this energy-saving technology, whenever the sensing period is set by the application server, the sink node keeps the schedule and it spreads the sleep order message over its network every sensing period. Each time a node receives the sleep message, it sets the sleep timer's end time to the value of the duration field included in the message. When the meter time ends, the node detects the environment and battery voltage level and sends the data to the source. Then, he expects the next sleep request message.  
Moreover, another energy-saving scheduling technique that can be deployed in the Network layer involves the use of unmanned flying vehicles in an agricultural crop monitoring system. Based on this scheme, the node on the unmanned flying vehicle wakes the ground nodes to retrieve the measured data. To perform this function, a coded radio signal is sent via a transmitter to the ground nodes. The nodes are in an inactive state, except for a small receiver waiting to receive the trigger signal.  
Furthermore, taking advantage of APTEEN hierarchical routing protocol, a time division multiple access technique can be implemented as a scheduling method. Based on this technique, messages are sent to put some nodes in sleep mode so as to avoid packet collisions between sensor nodes belonging to different clusters. In addition, carrier sense multiple access technique is another alternative method, which is equally effective for avoiding collisions.