esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Procedure  
#### 2.3.1 General Testing Protocol  
The visual assessment of the participants was performed using the digital COI-Sport Vision system (Centro de Optometria Internacional, Madrid, Spain; [[https://optosolutions.es/tratamientos/coisportvision](https://optosolutions.es/tratamientos/coisportvision)]([https://optosolutions.es/tratamientos/coisportvision](https://optosolutions.es/tratamientos/coisportvision)), accessed on 3 December 2024.). This system has been previously validated in studies evaluating visual and oculomotor skills in athletes, demonstrating its reliability for assessing parameters such as dynamic visual acuity, stereopsis, and anticipation time [26; 27; 28]. The calibration process followed the manufacturer's guidelines and included verifying the functionality of visual stimuli, optical alignment, and response recording accuracy. Additionally, the evaluator underwent specific training in the operation of the COI-Sport Vision system and adhered to a standardized protocol throughout the study. This included providing uniform instructions for all participants and conducting practice trials prior to formal assessments to ensure familiarity with the procedures. These steps minimized variability and ensured consistency in the measurements across all participants. Data collection was conducted mid-season to capture the athletes' visual and oculomotor skills during active competition, providing a realistic representation of their abilities under typical conditions. To minimize the potential influence of fatigue from training or matches, testing sessions were scheduled on rest days, ensuring that participants were not recovering from recent physical exertion. Additionally, participants were asked to confirm that they had not engaged in any strenuous physical activity within the 24 h preceding the assessments. This self-reported confirmation was supplemented by scheduling all testing sessions during mornings or early afternoons, times when cognitive and motor performance are typically optimal. To ensure consistency and minimize potential order effects, the sequence of tests was randomized for each participant. Additionally, practice trials were conducted for each test prior to the data collection to ensure participants were familiar with the procedures and equipment. All assessments were conducted in a dedicated testing room with consistent lighting and minimal distractions. The temperature and noise levels were monitored to maintain optimal conditions for performance. The full battery of tests, including breaks and practice trials, took approximately 45-60 min per participant. Participants were encouraged to report any discomfort or fatigue during the session, and additional breaks were provided as needed to ensure the quality of their performance.
# Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage
## 4 Solver by Alternating Direction Method of Multipliers (ADMM)
### **Z\({}_{i}(i=1,2,\cdots,5)\)** Sub-Problem Solving  
The objective function of the \(\textbf{Z}_{1}\) subproblem is:  
\[\textbf{J}_{\textbf{Z}_{1}}=u_{1}\big{(}\|\textbf{Z}_{1}\|_{p_{1}}^{p_{1}}\big{)} +\frac{\beta_{1}}{2}\|\textbf{Z}_{1}^{(k)}-\textbf{K}_{i\textbf{*}}\textbf{F} ^{(k)}-\overline{\textbf{Z}}_{1}^{(k)}\|_{2}^{2}. \tag{14}\]  
We adopted Lp-pseudonorm shrinkage to solve the Equation (14). In contrast to [PERSON] et al., [21] where the value of \(p\) was the same, we separated the \(p\) values of different gradients and directions ofthe image. The advantage of this operation lies in the fact that it can improve the performance of the deblurring image as described in Section 5.3. Lp-pseudonorm shrinkage is defined as \(\text{shrink}_{p_{1}}(\xi,\tau)=\max\{|\xi|-\tau^{2-p_{i}}|\xi|^{p_{i}-1},0\}(i=1,2,\cdots,5)\). According to the Lp-pseudonorm shrinkage rule, \(\mathbf{Z}_{1}\) can be updated as follows,  
\[\mathbf{Z}_{1}=\text{shrink}_{p_{1}}\Big{(}\mathbf{K}_{1}\mathbf{\ast}\mathbf{F}^{(k+1)}+ \overline{\mathbf{Z}}_{1}^{(k)},u_{1}/\beta_{1}\Big{)}. \tag{15}\]  
Similarly, we have:  
\[\mathbf{Z}_{i}=\text{shrink}_{p_{i}}\Big{(}\mathbf{K}_{i}\mathbf{\ast}\mathbf{F}^{(k+1)}+ \overline{\mathbf{Z}}_{i}^{(k)},u_{i}/\beta_{i}\Big{)}(i=2,3,4,5). \tag{16}\]
## 5 A Framework of RAG-Based LLMs for SLRs
The level of detail in the retrieved data can vary; for example, it can range from a broad description of an experimental setup to the specifics of a single experimental step. Depending on the nature and characteristics of the data to be extracted, either subtractive or abstractive methods may be employed [38]. In addition to enriching prompts with contextual background, domain-specific pre-trained models, such as SciBERT [61] and ChemGPT [62], can be utilized for reviews within their respective fields. To leverage large general-purpose models like GPT-4 [1] and LLaMA [2], domain-specific corpora may be employed to fine-tune these models for data extraction tasks. Furthermore, to facilitate the extraction of information from multimodal data sources--such as tables and images in the retrieved articles--multimodal language models can be incorporated into this process [63]. * _Stage 4: Information Synthesis_ Following data extraction, the synthesis phase integrates the extracted data from multiple articles to produce the desired output. Multiple LLMs, including multimodal LLMs, may be deployed to generate various outputs, including summaries, meta-analyses, trend analyses, or visual representations such as figures and tables, all of which provide a comprehensive overview of the compiled data [53; 54; 56]. A crucial function of information synthesis is to facilitate comparative analysis of data from different studies in a transparent and traceable manner, thus providing insights into critical aspects of a specific topic, such as its current status, existing gaps, emerging trends, and future research directions. Notably, the information synthesis phase can proceed without a distinct data extraction step, as the LLMs can directly utilize all selected papers to address specific user queries. Finally, the outputs of these synthesis processes may be combined by another LLM to produce a holistic review. To enhance efficiency and minimize computational demands, metadata from articles can substitute for full-text content during both data extraction and information synthesis processes. This framework is strategically designed to enable iterative and adaptive augmentation, wherein outputs from any stage can dynamically refine the inputs for preceding or subsequent stages through effective prompt engineering. Given the incremental nature of systematic literature reviews (SLRs), users should be able to rerun tasks at any stage, leveraging outputs from previous runs to enhance current inputs. While the applications of LLMs for document search and ranking are extensively studied [64], there is a need to focus more on data extraction from scientific papers and information synthesis to generate outputs that encompass various aspects through comparative analysis. To implement the framework of RAG-based LLMs for SLRs, the following steps must be undertaken for each SLR task: selecting an appropriate LLM, identifying an optimal training strategy, adopting suitable RAG techniques, refining prompt engineering to enhance prompt effectiveness, and selecting or creating datasets for model training and evaluation.
# C2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTM
## 5 Experimental Results
### Evaluation Metrics  
The C2B hybrid model is evaluated against a set of standard metrics identified in the literature [3; 10; 88; 89]. These metrics help determine the efficiency of the algorithm and allow the users to choose the system that best fits their use case. The metrics we use in our work for code retrieval are described below:  
* Precision: Precision is a classification metric that determines the percentage of predicted code snippets as true positives (TPs) and false positives (FPs). The precision  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Language** & **Training** & **Validation** & **Test** \\ \hline Java & 1,402,976 & 70,146 & 87,682 \\ Python & 1,274,986 & 63,833 & 79,787 \\ Javascript & 1,505,984 & 75,299 & 94,149 \\ Ruby & 192,825 & 9650 & 12,064 \\ Go & 10,000 & 5000 & 5000 \\ PHP & 99,986 & 4999 & 4999 \\ Total & 4,576,757 & 228,927 & 283,681 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CodeSearchNet dataset splits.  
value ranges from 0 to 1, where a value of 1 indicates no false positives. Precision is given by Equation (19). \[Precision(P)=TP/(TP+FP)\] (19) Recall: Recall is a classification metric that calculates the percentage of actual code snippets that are true positives (TPs) out of all actual positives (true positives, TPs, and false negatives, FNs). The recall value ranges from 0 to 1, where a value of 1 indicates no false negatives. Recall is given by Equation (20). \[Recall(R)=TP/(TP+FN)\] (20) Accuracy: Accuracy is a classification metric that calculates the proportion of correctly predicted code snippets, both true positives (TPs) and true negatives (TNs), out of all predictions made (true positives, true negatives, false positives, FPs, and false negatives, FNs). The accuracy value ranges from 0 to 1, where a value of 1 indicates perfect prediction. Accuracy is given by Equation (21). \[Accuracy(A)=(TP+TN)/(Total\#ofPredictions)\] (21) F1-score: The F1-score is a classification metric that combines precision and recall into a single value, providing a balance between the two. It is the harmonic mean of precision and recall, with the F1 value ranging from 0 to 1, where a value of 1 indicates perfect precision and recall. The F1 measure is given by Equation (22). \[F1-Score=(2\times P\times.R)/(P+R)\] (22)
## 3 Results and Discussion
### Uptake Experiment  
The overall results of the uptake experiments are summarized in Table 2. During the period of each exposure experiment, the concentration of GEM produced by the permeator was stable. Because of the air conditioning environment, the temperature during each experiment was also stable, fluctuating within \(\pm\) 0.6 K. An example of time series observations for temperature and pressure are shown in Figure 2. The inflow was kept constant, and the pressure difference between the inside and outside of the glass vessel was smaller than 1.6 Pa during exposure. The weight measurements of the vessel with the solution before and after the standard gas exposure showed a decrease of up to 5 g over the 4 h exposure and depended on the exposure period. Because evaporation influenced the volume of the uptake solution by only approximately 1%, we did not make any correction to the volume for the determination of Hg\({}^{0}\)aq and Hg\({}^{2+}\)aq concentrations. Even though it is not shown here, occasional temperature measurements of the exposed solution by a thermometer showed the differences in temperature between the solution and room air were approximately 1 K (lower for the solutions). It should be noted that the concentrations for Hg\({}^{0}\)aq and Hg\({}^{2+}\)aq discussed hereafter were blank corrected, and the pH measurements of the uptake solution after exposure demonstrated no change in pH. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Spiked Hg2+** & \(\lx@sectionsign\)** Purging Time** & \(n\) & \(\textdagger\)**Hg\({}^{0}\)aq** & \(\textdagger\)**Hg2+aq** & \(\textdagger\)**Hg2+aq** & **Recovery Yield** \\ \hline (pg) & (min) & & (pg L\({}^{-1}\)) & (pg L\({}^{-1}\)) & (\%) \\ \hline
150 & 15 & 5 & 23 \(\pm\) 5 & 328 \(\pm\) 46 & 64 \(\pm\) 14 \\
150 & 30 & 5 & 30 \(\pm\) 7 & 348 \(\pm\) 38 & 68 \(\pm\) 11 \\
150 & 60 & 4 & 12 \(\pm\) 2 & 375 \(\pm\) 29 & 73 \(\pm\) 8 \\
150 & 90 & 3 & 42 \(\pm\) 16 & 343 \(\pm\) 27 & 67 \(\pm\) 8 \\
150 & 120 & 2 & 27 \(\pm\) 10 & 446 \(\pm\) 25 & 86 \(\pm\) 6 \\
308 & 60 & 3 & 16 \(\pm\) 5 & 952 \(\pm\) 26 & 90 \(\pm\) 3 \\
308 & 90 & 3 & 40 \(\pm\) 4 & 1000 \(\pm\) 20 & 94 \(\pm\) 2 \\
308 & 120 & 3 & 32 \(\pm\) 5 & 1088 \(\pm\) 24 & 102 \(\pm\) 2 \\ \hline \hline \end{tabular} \(\lx@sectionsign\) The time individually spent for Hg\({}^{0}\)aq and Hg\({}^{2+}\)aq extraction. \(\textdagger\) Average \(\pm\) standard deviation (SD) of the blank corrected values.  
\end{table} 
Table 1: Results of the extraction test with 150 pg or 308 pg SRM 8610 spiking in 290 mL pure water. Figure 2: Time series plot of the temperature (T) and relative pressure (P) of the outflowing GEM standard gas from the glass vessel during the 4 h exposure experiment with a dilute nitric acid solution.
## 5 Algorithm Design
Since \(\vec{X}_{l}\) is a real number, which is inconsistent with the task offloading decision in integer form, we round it to convert the real number code into the integer code, so that the corresponding objective function value can be calculated and obtained. The detailed step of the proposed joint offloading decision and resource allocation optimization scheme, which consists of a task offloading decision algorithm based on the GWO algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method, and considers inter-satellite cooperation (GWOORAC), is expressed in Algorithm 1. ```
1:Initialize the grey wolf population, \(\vec{a}\), \(\vec{A}\), and \(\vec{C}\). Set \(t=1\). Set \(t^{\max}\). 2:Obtain \(f_{i,1}^{*}\) by (15), obtain \(f_{i,j}^{*}\) by (23), thus calculating the fitness of each grey wolf. The top three wolves in fitness are identified as \(\alpha\), \(\beta\), and \(\delta\), the other wolves are \(\omega\). 3:while\(1\leq t\leq t^{\max}\)do
4:for each \(l\in\mathcal{N}^{\omega}\)do
5: Update \(\vec{X}_{l}\). 6: Update \(\vec{a}\), \(\vec{A}\), and \(\vec{C}\). 7: For \(\vec{X}_{l}\), obtain \(f_{i,1}^{*}\) by (15), obtain \(f_{i,j}^{*}\) by (23), thus calculating \(U_{l}\). 8:if\(U_{l}<U_{a}\)then
9: update \(\vec{X}_{a}=\vec{X}_{l}\), \(U_{\alpha}=U_{l}\). 10:else if\(U_{l}>U_{a}\) and \(U_{l}<U_{\beta}\)then
11: update \(\vec{X}_{\beta}=\vec{X}_{l}\), \(U_{\beta}=U_{l}\). 12:else if\(U_{l}>U_{a}\) and \(U_{l}>U_{\beta}\) and \(U_{l}<U_{\delta}\)then
13: update \(\vec{X}_{\delta}=\vec{X}_{l}\), \(U_{\delta}=U_{l}\). 14:endif
15:endfor
16:\(t=t+1\). 17:endwhile
18:return\(\vec{X}_{a}\) and \(U_{a}\). ```  
**Algorithm 1** GWOORAC  
To sum up, the \(\vec{X}_{a}\) that is obtained finally is the optimal task offloading decision, i.e., \(y_{i,0}^{*}\), \(y_{i,1}^{*}\), and \(y_{i,j}^{*}\). The corresponding \(f_{i,1}^{*}\) and \(f_{i,j}^{*}\) are the optimal computing resource allocation. \(U_{a}\) is the minimum task completion delay. The complexity of Algorithm 1 is \(\mathcal{O}\big{(}t^{\max}N^{\mathsf{W}}N\big{)}\).
## 3 Methodology
### Machine Learning Model Development  
#### 3.4.1 Model Architecture  
The proposed model's architecture integrates LSTM layers for capturing temporal trends with dense layers for interpreting non-sequential features. Figure 4 illustrates the systematic framework structure, developed through theoretical foundations and empirical testing.  
Figure 4: Workflow for scenario generation and testing.  
The input layer receives refined features from Section 3.3 through multiple parallel streams handling temporal, occupancy, and environmental data. Feature normalization ensures consistent scale across inputs, while temporal embedding layers capture cyclical patterns in time-based features.  
Two stacked LSTM layers form the core of the temporal processing pipeline. The first layer contains 128 units and implements bidirectional processing to capture both forward and backward temporal dependencies. The second layer uses 64 units to identify higher-level temporal abstractions and global patterns. This dual-layer structure enables hierarchical feature extraction, with the first layer capturing basic temporal patterns and local dependencies, while the second layer identifies higher-level temporal relationships.  
Following the LSTM stack, two fully connected dense layers with 64 and 32 units, respectively, combine temporal features with static building characteristics. These layers leverage ReLU activation functions and enable non-linear feature interactions. The dense layer structure provides flexible mapping to output dimensions while maintaining the network's ability to capture complex relationships.  
The output layer employs linear activation across six units, predicting the required number of toilets, urinals, and washbasins for both female and male occupants. This design allows unrestricted range prediction while enabling specialized feature extraction for different fixture types. To control overfitting, dropout layers with a rate of 0.2 were strategically placed after each LSTM and dense layer.  
To control overfitting, dropout layers with a rate of 0.2 were strategically placed after each LSTM and dense layer. Figure 5 illustrates this complete model architecture, depicting the flow from the input layer through LSTM and dense layers to the final output. The diagram highlights the parallel processing paths and dropout mechanisms that enable robust prediction while preventing overfitting.
###### Abstract  
Leveraging data redundancy has long been recognized as an effective approach for concealing large amounts of secret data. In digital images, the 2D-pixel matrix inherently provides opportunities for redundancy, as each pixel is connected to its eight neighbors. Video segments, with their 3D structures, introduce an additional layer of redundancy known as temporal redundancy. Recent video steganography techniques have proposed utilizing this temporal redundancy for data concealment. This paper seeks to fully exploit the redundancy present in video segments by integrating both spatial and temporal redundancy through an Octree segmentation method. The video is divided into homogeneous, variable-sized 3D cubes to enhance redundancy in each region, thereby improving energy compaction in the 3D discrete cosine transform (3D-DCT) domain. Consequently, the hiding capacity is optimized because most of the signal's energy is concentrated in a few significant 3D-DCT coefficients, leaving a substantial portion of insignificant coefficients. These insignificant coefficients can be replaced with secret data without significantly affecting the quality of the carrier signal.  
video steganography; DCT; redundancy; capacity; PSNR; bpp  
United Arab Eminates, 1  
Footnote 1: [[https://doi.org/10.3390/asis010002](https://doi.org/10.3390/asis010002)]([https://doi.org/10.3390/asis010002](https://doi.org/10.3390/asis010002))
# Research Progress in the Construction and Application of In Vitro Vascular Models
## 2 Construction of In Vitro Vascular Models
### Construction of In Vitro Vascular Model Based on Non-Microfluidics-Based Methods
#### 2.2.1 Construction of Vascular Models In Vitro Using Templates
However, this method requires high mechanical properties of the hydrogel filled around the model to avoid tube collapse and structural damage to the hydrogel. Additionally, due to limitations in fabrication methods, template methods can only be used to construct simple branched, narrow, or single straight vessel structures, and cannot be used to construct complex vascular networks.
# Microbiome-Based Interventions for Food Safety and Environmental Health
## 5 Challenges and Limitations Associated with MBT
### Microbial Resistance and Stability Issues
Specific resistance genes, such as _msrC_, _vanX_, and _dfrA_, were detected in strains like _Enterococcus ficeum_, _Lactobacillus plantarum_, _Streptococcus thermophilus_, and _Lactococcus lactis_, which poses a significant threat to food safety [141]. The increasing incorporation of probiotic bacteria into food products to enhance health benefits has been laden with stability challenges, which frequently call into question their efficacy in commercial products [142]. The primary challenge in developing functional foods is maintaining high levels of viable probiotic bacteria during processing, with an additional difficulty in ensuring their safe delivery to the gut, where they can provide health benefits [143]. Due to instability, the viability of these probiotics can diminish significantly during preparation, shelf storage, in vivo application, and when passing through the human gastrointestinal tract, thereby compromising their intended therapeutic effects [142]. Following oral delivery, probiotics face severe conditions in the gastrointestinal tract, such as the highly acidic gastric fluids (pH 1-3) and a two-hour gastric emptying period, which significantly reduce their viability in the stomach [144]. The cytoplasmic pH and glycolytic enzyme activity of probiotics are reduced by the extremely acidic pH of the stomach, which has an impact on the F1 Fo-ATPase proton pump that is necessary for probiotic survival in acidic environments [145]. This presents a critical limitation for MBT, as maintaining the stability and viability of probiotic strains is essential to ensure their effectiveness in promoting health and preventing diseases [143]. Additionally, ensuring that probiotics remain active and potent throughout their shelf life and after ingestion involves overcoming significant technical and formulation hurdles, which adds to the complexity and cost of developing effective microbiome-based therapeutic products [127].
# Development of Quantum Private Queries Protocol on Collective-Dephasing Noise Channel
## 4 Security Analysis
### The Eva Attack  
Suppose there is a third-party eavesdropper [PERSON] in the communication process.  
If [PERSON] intercepts the initial quantum state that [PERSON] sends to [PERSON] in step (1): Because [PERSON] cannot tell which one of the four states \(\{|0_{dp}0_{dp}\rangle,|1_{dp}1_{dp}\rangle,|0_{dp}1_{dp}\rangle,|1_{dp}0_{ dp}\rangle\}\) that she has intercepted, and the measurement will cause the original quantum state to collapse, [PERSON] and [PERSON] will find the presence of the eavesdropper.  
If [PERSON] intercepts the detected particles declared by [PERSON] in step (3): Because [PERSON] declares two states in step (4), and [PERSON]'s choice of measurement base is random in step (2). [PERSON] does not know the measurement base chosen by [PERSON], then, [PERSON] cannot determine the result of the key obtained by [PERSON]. So, [PERSON]'s attack is invalid. Let us elaborate it with an example. Assuming the initial state is \(|0_{dp}0_{dp}\rangle\), when [PERSON] declares \(\{|\Phi^{+}_{dp}\rangle,|\Psi^{+}_{dp}\rangle\}\) and only [PERSON] makes a measurement with \"\(+\)\" base, then [PERSON] can get the result. To make the result intuitively, after omitting the denominator and the distribution probability, the formulas are expressed as:  
\[\langle\Phi^{+}_{dp}|0_{dp}0_{dp}\rangle|\Phi^{+}_{dp}\rangle=|\Phi^{+}_{dp}\rangle\]  
\[\langle\Phi^{-}_{dp}|0_{dp}0_{dp}\rangle|\Phi^{-}_{dp}\rangle=|\Phi^{-}_{dp}\rangle \tag{5}\]  
Third-party attack is a threat based on quantum state transmission. In our protocol, only [PERSON] prepares the initial quantum state and sends it to [PERSON], [PERSON] does not send the quantum state back to [PERSON]. If [PERSON] eavesdrops this process, it is meaningless for her to obtain the initial quantum state.