esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Theoretical Procedures
#### 2.2.2 Preparation of Grid and Docking Modeling  
Utilizing the receptor grid generation module, the binding sites in BSA, blind docking has been performed, with the grid size set to 110, 110 and 110 along the x-, y- and z-axes, with 0.375 A grid spacing. The establishment of a cubic grid in the active space delineates the active site of the BSA protein. A grid box was generated at the centroid of this active site to facilitate docking calculations, ensuring accurate positioning for ligand interaction studies [32].  
For molecular docking studies, Aspirin was selected as a reference ligand. The primary objective of this investigation was to evaluate the inhibitory potential of our synthesized molecules on the BSA protein relative to that of Aspirin. This comparison aims to provide insights into the efficacy of these synthesized compounds in modulating BSA protein activity. Molecular docking was conducted using the Glide program (Grid-Based Ligand Docking with Energetics) integrated within Maestro [33]. During the docking procedure, binding modes that exhibited the highest Glide scores were selected for further analysis. The Glide XP module was employed to evaluate the docking results. The XP scores of the synthesized molecules were compared to the XP score of Aspirin, a known anti-inflammatory agent and inhibitor of BSA protein, to assess the relative binding efficacy of the synthesized compounds [34]. The molecular interactions were examined using Discovery Studio software v 2024 [35]. The top-performing compounds were selected for subsequent investigations into their pharmacokinetic and toxicological profiles, with the goal of evaluating their potential for therapeutic application.
## 3 Results and Discussion
### SnowSense Service for the Island of Newfoundland  
For the entire SnowSense service based on the GNSS in situ measurements, EO and modelling, spatially-distributed maps of the SWE were generated and run-off estimates were derived for several river locations in the two main catchments Humber River and Exploit River. Figure 2 gives an example of a SWE map, which was modelled and assimilated for the entire island of Newfoundland for the 15 March 2018. Such a SWE map was provided for each day.  
The simulated and assimilated run-off results were validated against four run-off gauge measurements in total. Measurements (raw data) are made available by the Water Resources Management Divisions. As for the catchments of the Humber River and the Exploits River, a strong interest in flood forecast was predominating; the run-off results are presented for the gauge stations at the lower parts of the two catchments.
# Detection of Malware by Deep Learning as CNN-LSTM Machine Learning Techniques in Real Time
## 1 Introduction
### Contribution
In recent years, malicious software (or \"malware\") has become increasingly pervasive and damaging to computer systems worldwide. Every day, thousands of new malicious programs are created. Figure 2 depicts annual data from malware attacks over the last decade, indicating that more than 900 million pieces of malware were in circulation in 2019, representing a nearly 2000% increase from 2010. Even the smallest businesses may lose millions of dollars due to malware infestations. The identification of viruses requires more than merely routing protocols, which is a major drawback. Therefore, machine learning is used by researchers and antivirus vendors to detect and classify infections. Many studies have focused on binaries as a subset of malware because of how frequently they are used to infect systems. Malware evaluation may be conducted both statically and dynamically. The malware's dynamic behavior is analyzed in a sandbox while static analysis is utilized to extract features of the virus that may be used for detection or classification by machine learning [13].
# Post-Earthquake Damage Inspection of Wood-Frame Buildings by a Polarimetric GB-SAR System
## 3 Results  
A number of non-contact measurements were conducted by the developed GB-SAR system on the wood wall specimens to check the system performance and to image the concealed damage to the wood structures. This section presents the results of a 1D survey and two 2D surveys on those two damaged wall samples.
# Unsupervised Vehicle Re-Identification Method Based on Source-Free Knowledge Transfer
## 4 Conclusions  
In this paper, we propose an unsupervised vehicle re-identification method that leverages source-free knowledge transfer for data augmentation, aimed at enhancing the performance of unsupervised vehicle re-identification. We achieve this by employing a source-free knowledge transfer module to generate \"source-like samples\" and utilizing a joint training strategy to facilitate target domain adaptation, ensuring robust model generalization across diverse environmental conditions.Our approach undergoes comprehensive experimental validation, conducted on two crucial benchmark datasets in the vehicle re-identification domain, namely VeRi776 and VehicleID. When compared to existing unsupervised vehicle re-identification methods, our method exhibits superior performance, indicating its capability to accurately identify vehicles across different environmental settings. Ablation experiments further corroborate the effectiveness of each proposed component. Looking forward, we will continue to explore cross-domain challenges in unsupervised vehicle re-identification to further enhance its performance and applicability.  
Conceptualization, Z.S. and D.L.; methodology, Z.S., D.L. and Z.C.; software, D.L. and Z.C.; validation, Z.S., D.L. and Z.C.; formal analysis, Z.S.; investigation, Z.S.; resources, Z.S.; data curation, D.L. and Z.C.; writing--original draft preparation, D.L. and Z.C.; writing--review and editing, Z.S.; visualization, W.Y.; supervision, Z.S. and W.Y.; project administration, Z.S. and W.Y.; funding acquisition, W.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Fujian Science and Technology Plan: the special project of central government guiding local science and technology development (No. 2022L3003). Fujian Provincial Department of Education project (JAT200039).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Feed Ratio**} & \multicolumn{3}{c}{**VeRi776**} \\ \cline{2-4}  & **Rank-1 (\%)** & **Rank-5 (\%)** & **mAP (\%)** \\ \hline
1:5 & 67.4 & 76.5 & 31.3 \\
1:4 & 69.5 & 78.9 & 33.2 \\
1:3 & 72.6 & 80.9 & 36.1 \\
1:2 & 74.4 & 82.1 & 37.9 \\
1:1 & 73.6 & 81.7 & 37.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The impact of progressive joint training strategy on performance.
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 6 Experiments and Numerical Results
### Benders Decomposition  
As discussed earlier, the exact solution approach using the CPLEX solver is not able to solve mid-size or larger instances of the problem. To overcome this, we use the Benders decomposition algorithm, enhanced with a couple of improvement techniques, namely warm-start and the e-optimality approach. We run the base model as in the previous case using the Benders-based approach. Using this, we are able to solve larger instances of the problem. The results are reported in Table 3. As shown in this table, the BD approach is able to solve larger instances of the problem. For n = 10, the Benders-based solution is extremely quicker than the CPLEX solver while obtaining the same results, since the Benders approach is an exact approach. Aligned with the results of small instances as in Table 2, as the value of alpha increases, the total cost increases, which in some cases can lead to less hub facilities/hub links due to the less desirability with the higher hub transportation cost. Regarding the Modified Benders Algorithm (with preprocessing enhancements), the run time is improved compared to the classic Benders approach, which enables our algorithm to solve instances of 45 nodes in less than two hours. Since the initial exact algorithm does  
not solve instances larger than 10 nodes, we use the Benders solution to study the effect of congestion.
###### Abstract  
Introduction: Bone-age assessment using posteroanterior left hand-wrist radiographs is indispensable in pediatric endocrinology and forensic age determination. Traditional methods--Greulich-Pyle atlas and Tanner-Whitehouse scoring--are time-consuming, operator-dependent, and prone to inter- and intra-observer variability. Aim: To systematically review the performance of AI-based models for automated bone-age estimation from left PA hand-wrist radiographs. Materials and Methods: A systematic review was carried out and previously registered in PROSPERO (CRD42024619808) in MEDLINE (PubMed), Google Scholar, ELSEVIER (Scopus), EBSCOhost, Cochrane Library, Web of Science (WoS), IEEE Xplore, and ProQuest for original studies published between 2019 and 2024. Two independent reviewers extracted study characteristics and outcomes, assessed methodological quality via the Newcastle-Ottawa Scale, and evaluated bias using ROBINS-E. Results: Seventy-seven studies met inclusion criteria, encompassing convolutional neural networks, ensemble and hybrid models, and transfer-learning approaches. Commercial systems (e.g., _BoneXpert(r)_, _Physis(r)_, _VUINO Med(r)-BoneAge_) achieved mean absolute errors of 2-31.8 months--significantly surpassing Greulich-Pyle and Tanner-Whitehouse benchmarks--and reduced reading times by up to 87%. Common limitations included demographic bias, heterogeneous imaging protocols, and scarce external validation. Conclusions: AI-based approaches have substantially advanced automated bone-age estimation, delivering clinical-grade speed and mean absolute errors below 6 months. To ensure equitable, generalizable performance, future work must prioritize demographically diverse training cohorts, implement bias-mitigation strategies, and perform local calibration against region-specific standards.  
bone age assessment; artificial intelligence; X-rays; skeletal maturity
## 5 Simulation Experiments
### Sensitivity Analysis of Parameters
Figure 8: Box plot corresponding to statistical data in experiment scenario 1. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**TLISOA vs.** & **Optimum** & **Average** & **Median Value** & **Worst Value** & **Standard** & **Average** \\  & **Value** & **Value** & **Median Value** & **Worst Value** & **Deviation** & **Convergence Time/s** \\ \hline SOA & 3.05\% & 6.72\% & 8.19\% & 9.33\% & 41.67\% & 13.76\% \\ CAM-GA & 11.90\% & 14.13\% & 13.63\% & 16.58\% & 46.15\% & 13.30\% \\ A-QCDPSO & 3.81\% & 14.29\% & 15.38\% & 20.68\% & 61.47\% & \(-1.24\%\) \\ PSO-AWOA & 11.26\% & 9.09\% & 9.64\% & 8.75\% & \(-5.00\%\) & 11.25\% \\ CESMA & 5.50\% & 7.65\% & 8.28\% & 11.81\% & 37.31\% & 14.96\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of algorithms for scenario 2. Figure 9: Box plot corresponding to statistical data in experiment scenario 2.
## 2 Structural Reliability
### First-Order Reliability Method  
The FORM is an analytical approximation of the probability integral, obtained by linearizing a limit-state function transformed into the standard normal space at an optimal point. In the theory of structural reliability, the optimal point, also known as the design point or most probable point, is computed via mathematical programming. By transforming the random variables from the original space into the standard normal space \((\mathbf{u}=\mathrm{T}(\mathbf{x}))\), the failure probability integral can be written as:  
\[P_{f}=\int_{G(\mathbf{u})\leq 0}\varphi_{n}(\mathbf{u})d\mathbf{u} \tag{2}\]  
where \(G(\mathbf{u})\) is the limit-state function transformed into the standard normal space, and \(\varphi_{n}(\cdot)\) is the \(n\)-variate standard normal density function for the random vector. The FORM linearizes the limit-state function \(G(\mathbf{u})\) at a point \(\mathbf{u}^{*}\) that is obtained by solving a constraint optimization problem, defined as:  
\[\underset{\mathbf{u}*}{\arg\min}\left\{\left\|\mathbf{u}\right\|\right|G( \mathbf{u})=0\left.\right\} \tag{3}\]  
The design point, \(\mathbf{u}^{*}\), indicates a location on the linearized limit-state function that is at the least distance from the origin in the standard normal space. The linearized limit-state function at \(\mathbf{u}^{*}\) can be expressed as:  
\[G(\mathbf{u})=\
abla G(\mathbf{u}*)(\mathbf{u}-\mathbf{u}*)=\left\|\
abla G( \mathbf{u}*)\right\|(\beta-\alpha\mathbf{u}) \tag{4}\]  
where \(\
abla G(\mathbf{u}*)=\
abla_{\mathbf{u}*}G(\mathbf{u}*)=(\partial G/\partial u _{1}^{*},\partial G/\partial u_{2}^{*},\ldots,\partial G/\partial u_{n}^{*})\) denotes the gradient row vector. The linearization represents the failure domain \(G(\mathbf{u})\leq 0\), which is the half-space defined by \(\beta-\alpha\mathbf{u}\leq 0\). The reliability index \(\beta=\alpha\mathbf{u}*\) is interpreted as the minimum distance from the origin to the limit-state surface in the standard normal space, and \(\alpha=-\
abla G(\mathbf{u}*)/\left\|\
abla G(\mathbf{u}*)\right\|\) denotes the normalized negative vector at the design point (see Figure 1).  
Considering the half-space defined by the reliability index, an approximate value for the failure probability can be defined as:  
\[P_{f\mathrm{FORM}}\approx\Phi(-\beta) \tag{5}\]  
where \(\Phi(\cdot)\) denotes the cumulative distribution function of the standard normal distribution. Since the limit-state function is approximated by a linearized function in the standard normal space at the design point, accuracy problems arise when the limit-state function is strongly nonlinear. The SORM that includes a curvature correction has been developed to enhance the accuracy of the FORM.  
Figure 1: Illustration of the first-order reliability method (FORM) and second-order reliability method (SORM) for a reliability problem.
## 3 Selection of Suitable Radio Frequency Protocol  
The northern regions of Quebec have an arctic climate with very cold winters and short, much cooler summers. As a result, the electronic components must be carefully chosen. Furthermore, winter train operations in northern Quebec are marked by slower speeds, exposing electronic systems and freight to more extended periods in temperatures as low as \(-40\ ^{\circ}\)C.  
Most of the railway maintenance employees are called upon to work on the railway track to ensure site maintenance: the necessary equipment is loaded on board a truck moving on the railway track (Hi-Rail pickup) (Figure 4), which drives the employees to the site of the intervention.  
Figure 3: Telecommunication network in the Nanavut.  
While the employees are busy carrying out maintenance operations around the Hi-Rail pickup, one is in radio contact with the nearest train to alert the employees to get to safety when a convoy is coming. Therefore, the idea was to add a second layer of security for the employees with an automatic alert system. Note that the Hi-Rail pickups do not have any geolocate systems: these are rudimentary trucks. The solution consists of installing a transmitter in the locomotive pulling the wagons and a receiver in the Hi-Rail pickup. The locomotive's transmitter gets the locomotive's GPS coordinates using a GPS board and sends them wirelessly to the Hi-Rail pickup, which also acquires its own GPS coordinates. Then, it can calculate the distance between the train and itself. Finally, the receiver sends wirelessly this distance to a watch equipping the employees, which converts the distance information into a visual and audio alert that evolves when the train approaches. Before describing the solutions, it is necessary to study the GPS coverage in the isolated place where the Nordic trains run.
# A Velocity-Aware Handover Trigger in Two-Tier Heterogeneous Networks
## 2 Related Work
### Handover Triggering  
To the best of the authors' knowledge, there are no investigations in the literature that survey the performance limits of the sudden change of velocity in handover performance, since the majority velocity-based handover improvement approaches concentrate on velocity estimation and not its variety. In the work of [PERSON] et al. [11], the authors began to evaluate velocity after an impressive number of HOFs occurred. The authors calculated velocity in view of the received signal strengths (RSSs) of two diverse BSs, with which the handover margin (HM) was balanced accordingly. The numerical outcomes demonstrate that handover triggering velocity-based methods can essentially decrease the call drop rate, particularly when the velocity is high, which contributes to improved handover performance.  
In parallel, there is a diverse handover trigger idea which depends on criteria other than the velocity, for example, RSS-based activating systems. This idea was utilized by [12] for a log-normal fading environment, where the handover algorithm was examined based on the level crossing of the difference between the RSSs. A strategy based on an improved velocity handover triggering system was proposed in [13]. The proposed scheme effectively limits the system load to avoid link crash. In addition, the introduced scheme augments the resource usage. Consequently, UE can utilize the system to maximum capacity. This triggering technique additionally decreases handover latency and the handover dropping rate. Various handover triggering schemes are location-based, such as in [14] where a handover management system for LTE femtocells was discussed. They used a modified self-organizing map (SOM) to learn the locations of the indoor environment. The triggering technique can also benefit from a prediction model, as proposed in [15].  
Furthermore, in [16] the authors investigated multiple guidelines like signal strength and available bandwidth in the selection of the optimal target cell, which could increase the success rate and thereby reduce blocking. For enhancing the quality of the video and multimedia traffic, the work in [17] was found. The authors of this work proposed techniques for triggering handover based on the quality of multimedia information. However, different issues exist, such as estimating the correct amount of data for handover triggering.
# Leveraging Large Language Models for Sensor Data Retrieval
## 5 Evaluation
### Results and Discussion
#### 5.3.2 Execution Time  
These experiments were performed using an A100-SXM4 GPU with 40 GB of RAM. Table 3 presents the execution times for each model in the indexing and retrieval phases. Notably, the processing times for indexing a table was consistently low, typically ranging between 60 and 160 milliseconds. This efficiency enabled the indexing of the entire dataset comprising 1299 files using all proposed models in less than 20 min.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **P@1** & **MMR** \\ \hline _UAE-Large-V1_ & 0.33 & 0.51 \\ _bge-large-en-v1.5_ & 0.37 & 0.51 \\ _bge-base-en-v1.5_ & **0.90** & **0.94** \\ _bge-small-en-v1.5_ & 0.75 & 0.85 \\ _ember-v1_ & 0.48 & 0.62 \\ _gte-large_ & 0.73 & 0.83 \\ _gte-base_ & 0.67 & 0.77 \\ _all-MiniLM-L6-v2_ & 0.52 & 0.69 \\ \hline \hline \end{tabular}
\end{table}
Table 2: P@1 and MMR for each model in the data retrieval task. Best results are boldfaced.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Indexing (s)** & **Retrieval (hh:mm)** \\ \hline _UAE-Large-V1_ & 184 & 01:27 \\ _bge-large-en-v1.5_ & 217 & 01:28 \\ _bge-base-en-v1.5_ & 128 & 01:27 \\ _bge-small-en-v1.5_ & 127 & 01:28 \\ _ember-v1_ & 216 & 01:28 \\ _gte-large_ & 218 & 01:28 \\ _gte-base_ & 127 & 01:26 \\ _all-MiniLM-L6-v2_ & 86 & 01:26 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Indexing (seconds) and retrieval (hours and minutes) execution time by model.  
Variations in the time taken for word embedding calculations were observed among different models, influencing their overall speed. Notably, larger models generating embeddings of size 1024 and smaller models of size 768 exhibited similar duration for the indexing process. Conversely, the smallest models, specifically _all-MiniLM-L6-v2_ with a size of 384, were notably faster, indexing all datasets in just 86 s.  
In contrast, the retrieval operation demanded substantially more time than the indexing phase. On average, the entire retrieval process across all models required approximately 12 h, averaging around 1 h and 27 min per model. Unlike the indexing phase primarily relying on embedding calculations, the retrieval process involves a series of steps outlined in Section 3. This includes retrieving the top-k comparable columns for each column in the query table, computing similarity scores, and re-ranking the results. The duration increases with the number of indexed datasets. It is notable that the choice of model does not significantly impact the average time spent on the retrieval process, ranging from 3.2 to 5.9 s per query. Nevertheless, this time range can be considered reasonable for a production environment.
## 5 Parameter Study and Discussion
### Influence of the Parameters of the Freezing/Thawing Stratum on the Dynamic Responses
#### 5.1.2 Effect of the Freezing and Thawing Depth  
With the change in seasons, the active layer will freeze or melt, and the depth of the active layer will change with the temperature. Three different depths of the active layer, \(h_{2}=1.5\) m, \(3.0\) m and \(4.5\) m, were selected to investigate the influence of the depth of the active layer on the amplitude of the pile foundation.  
Figure 6 shows the amplitude-frequency curves of the vertical vibration of the pile foundation under different freezing and thawing depths, taking \(T=-0.35\)\({}^{\circ}\)C. Figure 6a shows that, in Case 2, with increasing freezing depth, the resonant frequency of the pile cap increases slightly, while the amplitude decreases significantly. In Case 4, with increasing thawing depth, the amplitude of the pile cap increases considerably, while the resonant frequency decreases. Because the resonance frequencies of the permafrost with the thickness of the upper active thawing layer (Case 4, Figure 6b) decrease and the amplitudes increase, it is not conducive to the reduction of the resonance of the pile foundation when the thawed depth of the active layer increases due to climate warming. Nevertheless, since the resonance frequencies of the seasonally frozen soil with the thickness of the upper active freezing layer (Case 2, Figure 6a) increase and the amplitudes decrease, the increase in the frozen depth tends to be beneficial to structural vibration due to the drop in temperature. In short, the existence of a frozen soil layer can restrain the foundation vibration. Whether  
Figure 5: Comparison of the amplitude–frequency curves of the pile foundation with/without the mass of the pile cap. (**a**) Case 1; (**b**) Case 2; (**c**) Case 3 and (**d**) Case 4.  
seasonally frozen soil or permafrost, it is necessary to keep the frozen state as long as possible to prevent the active layer from thawing.
# Lightweight Detection Method of Obfuscated Landing Sites Based on the AST Structure and Tokens
## Proposed System
### Lightweight AST
#### 3.2.1 Obfuscation-Type Classification Accuracy  
**Theorem 1**.: _The proposed lightweight AST enables determination of the type of obfuscation algorithm._  
**Proof of Theorem 1.** Each obfuscation algorithm has its own syntax structure. This syntax structure is generated by each obfuscation algorithm and is used to restore the original code. The original code has an effect on AST nonstructural nodes; however, it has no effect on the syntax structure. The proposed lightweight AST is defined by AST, where all of the AST nonstructural nodes are removed. A lightweight AST has AST structural nodes only. In other words, a lightweight AST has its own syntax structure according to each obfuscation algorithm. Therefore, we can identify the type of obfuscation algorithm being used by employing the lightweight AST.