esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A
### Proof of \(df_{r}(t)/dt<0\) for \(\zeta>1\) Case  
The conclusion that the sign of (26) is negative will be proved as follows. First of all, \(p_{2}>p_{1}>0\) and \(\mathrm{e}^{-p1t}>\mathrm{e}^{-p2t}>0\) can be readily observed from (23). Considering that \(z_{1}=1/T_{G}>0\), there are three possible relationships among \(p_{1}\), \(p_{2}\), and \(z_{1}\), i.e., \(z_{1}>p_{2}>p_{1}\), \(p_{2}>z_{1}>p_{1}\), and \(p_{2}>p_{1}>z_{1}\). For the first case \(z_{1}>p_{2}>p_{1}\), the following inequality can be derived:  
\[\frac{z_{1}-p_{1}}{p_{2}-p_{1}}e^{-p_{1}t}>\frac{z_{1}-p_{1}}{p_{2}-p_{1}}e^{- p_{2}t}>\frac{z_{1}-p_{2}}{p_{2}-p_{1}}e^{-p_{2}t}>0 \tag{12}\]  
Based on (12) and the fact that \(G_{0}<0\), the sign of (26) can be obtained as negative using the following equation:  
\[\frac{df_{r}(t)}{dt}=G_{0}\cdot\left[\frac{z_{1}-p_{1}}{p_{2}-p_{1}}e^{-p_{1} t}-\frac{z_{1}-p_{2}}{p_{2}-p_{1}}e^{-p_{2}t}\right]<0 \tag{13}\]  
For the second case, \(p_{2}>z_{1}>p_{1}\), it is clear that the following inequalities are satisfied:  
\[\frac{z_{1}-p_{1}}{p_{2}-p_{1}}e^{-p_{1}t}>0,\frac{z_{1}-p_{2}}{p_{1}-p_{2}}e^ {-p_{2}t}>0 \tag{14}\]  
Therefore,  
\[\frac{df_{r}(t)}{dt}=G_{0}\cdot\left[\frac{z_{1}-p_{1}}{p_{2}-p_{1}}e^{-p_{1} t}+\frac{z_{1}-p_{2}}{p_{1}-p_{2}}e^{-p_{2}t}\right]<0 \tag{15}\]  
The analysis provided hereafter will indicate that the third case \(p_{2}>p_{1}>z_{1}\) would not exist under the prerequisite that \(H>DT_{G}/2=D/2z_{1}\). Similarly, this part will be proved by contradiction. Assuming that \(p_{2}>p_{1}>z_{1}\), the following inequality satisfies:  
\[2z_{1}<p_{1}+p_{2}=2\zeta\omega_{n}=\frac{(2H+DT_{G})}{2 HT_{G}} \tag{16}\]  
Inequality (16) can be further expressed as  
\[2\frac{1}{T_{G}}<\frac{(2H+DT_{G})}{2 HT_{G}}\Rightarrow H<\frac{DT_{G}}{2} \tag{17}\]  
Since (17) is contradictory to the prerequisite that \(H>DT_{G}/2\), the assumption that \(p_{2}>p_{1}>z_{1}\) is incorrect. In conclusion, \(df_{r}(t)/dt<0\) should always be satisfied under the condition that \(\zeta\geq 1\).
## 4 Proposed Approach
### Genetic Algorithm with Adaptive Mutation and Tabu Search (GAAM-TS)  
Each individual in the population encodes a complete sales route as a permutation of customer indices. The GA uses tournament selection, ordered crossover (OX), and elitism to evolve the population across generations. The adaptive mutation operator adjusts the mutation rate dynamically based on population diversity, helping to maintain exploration and avoid premature convergence.  
The fitness of each individual is evaluated using a composite cost function that combines the total geographic distance and the estimated travel time for the route, predicted by the LSTM model. Formally, the fitness is defined as follows:  
\[\text{Fitness}(\mathbf{R})=\frac{1}{\sum_{i=1}^{N-1}d(r_{i},r_{i+1})+\gamma \cdot T(\mathbf{R})}, \tag{3}\]  
where \(d(r_{i},r_{i+1})\) denotes the Haversine distance between consecutive customers, \(T(\mathbf{R})\) is the total predicted travel time of the route obtained via the LSTM model, and \(\gamma\in[0,1]\) is a trade-off parameter.  
To further enhance solution quality, we incorporate Tabu Search (TS) after each generation. TS performs local refinement by swapping customer positions within elite individuals. A tabu list stores recent solutions to prevent revisiting, and an aspiration criterion allows the overriding of the tabu status if a significant improvement is detected.  
The LSTM model is fully integrated within the GA evaluation process. Instead of relying solely on precomputed distance matrices, each candidate route is evaluated by querying the LSTM to predict context-aware travel times between consecutive customer locations, considering the estimated time of arrival at each leg. This dynamic integration ensures that the optimization process adapts to realistic temporal patterns influenced by traffic, hour, and day of the week.
## 3 Results and Discussion
### Adsorption and Desorption Experiments
#### 3.2.2 Effect of Flow Rate  
To determine the optimal flow rate, Cd(II) solution at pH 6 was applied to through the separation and enrichment column. Cd-I-MA-_co_-AN was used as an adsorbent, changing the flow rate between 0.5 and 5 mL min\({}^{-1}\). Figure 5 shows the flow rate's effect on the adsorption efficiency for Cd(II). The results show that the adsorption efficiency reached 94.3% when the flow rate was 0.5-2 mL min\({}^{-1}\). However, there was a decrease in adsorption efficiency when the flow rate of sample solution passed 2 mL min\({}^{-1}\). This can be attributed to the Cd(II) solution, which could not sufficiently contact the adsorbent [34]. Hence, the flow rate of 2 mL min\({}^{-1}\) was chosen for the column adsorption process.  
Figure 3: FTIR spectrum of Cd-I-MA-co-AN before and after elution.
## 3 Results
### The Study Participants' General Characteristics and Homogeneity Testing
#### 3.2.2 Group Comparison at Each Point of Time for a Change in AI Competency
The data comprehension learning score of the experimental group right after the program increased by 0.71 \(\pm\) 0.89, compared to the pre-program score, and the control group's score increased by 0.23 \(\pm\) 0.64. The difference in the change was not statistically significant (\(t\) = 1.93, \(p\) = 0.061). Additionally, the experimental group's data comprehension learning score in the 12 th week of the program increased by 0.61 \(\pm\) 0.63, compared to the pre-program score, whereas the control group's score decreased by \(-\)0.07 \(\pm\) 1.55. The difference in the change was not statistically significant (\(t\) = 1.83, \(p\) = 0.074). The experimental group's machine learning score right after the program increased by 0.71 \(\pm\) 0.94, compared to the pre-program score, and the control group's score increased by 0.30 \(\pm\) 0.90. The difference in the change was not statistically significant (\(t\) = 1.41, \(p\) = 0.166). The experimental group's machine learning score in the 12 th week of the program increased by 0.78 \(\pm\) 0.67, compared to the pre-program score, and the control group's score increased by 0.10 \(\pm\) 1.49. The difference in the change was not statistically significant (\(t\) = 1.88, \(p\) = 0.068). The experimental group's AI ethics score right after the program increased by 0.91 \(\pm\) 1.03, compared to the pre-program score, and the control group's score increased by 0.35 \(\pm\) 0.89. The difference in the change was not statistically significant (\(t\) = 1.87, \(p\) = 0.069). The experimental group's AI ethics score in the 12 th week of the program increased by 0.93 \(\pm\) 0.98, compared to the pre-program score, and the control group's score increased by 0.26 \(\pm\) 1.46. However, the difference in the change was not statistically significant (\(t\) = 1.68, \(p\)= 0.101) (Table 4).
## 2 Analysis of Previous Studies
### Application of Data Sampling Techniques  
Recent advancements in the construction of large databases have led to various ML studies being conducted on diverse topics. It has been shown that for the application of these ML algorithms, the quality of data, particularly the balance of data, is crucial, and several cases applying the SMOTE technique have been identified.  
[PERSON] et al. conducted research to predict the turnover rate of crucial nurses to ensure the quality of nationwide healthcare services. They selected 18 variables from a database and applied four ML algorithms after balancing the distribution of data between variables through data sampling [25].  
[PERSON] et al. devised methods to reduce customer churn in the telecommunications industry by analyzing customer behavior and predicting churn variables through ML after addressing the data imbalance issue by sampling open data related to the telecommunications industry [26].  
[PERSON] et al. conducted research on enhancing network intrusion detection. They applied the SMOTE technique to address the class imbalance issue in basic data. This ensemble voting classifier significantly enhances the accuracy and precision of network intrusion detection systems [27].
## 2 Related Works
### Wireless Recharging Strategies  
As mentioned above, due to the breakthrough in energy transfer technology, there have been many studies that employ mobile Wireless Charging Vehicles (WCVs) to prolong the lifetime of WSNs [33; 34; 35]. In [33], the authors envisioned employing a mobile vehicle to periodically visit each node and charge it wirelessly. By always keeping the energy level of each node above the minimum threshold, the network can run permanently. The objective of this method is to maximize the ratio of the time for the WCV on staying at the service station to the time of the recharging cycle, and the authors proposed a near-optimal solution. [PERSON] et al. proposed an optimal data gathering method for the cluster-based network by introducing the Energy-Harvesting nodes (EHs) [34]. The idea of this algorithm is to find the relationship between the best location of CHs and the best location of EHs. However, it assumes that the EHs can harvest energy from the environment and the location of them are adjustable. This is unrealistic in real applications.  
[PERSON] et al. considered the movement costs and capacity constraints of the WCV [14]. In their method, the sensor nodes are organized into clusters and one data gathering vehicle as well as multiple charging vehicles are introduced into the network. To achieve the aim of balancing energy consumption and reducing data collection latency, they formulated recharge optimization into a Profitable Traveling Salesmen Problem with the constraints of battery capacity and designed a simple Greedy Algorithm (GA) as well as a three-step Adaptive Algorithm (AA) to solve this problem. In [35], the author employed some Mobile Data Collectors (MDCs) to visit each nodes and collect their data. Different from other mobile Sinks based data gathering strategies, these MDCs can also recharge nodes. To minimize the energy consumption and satisfy the packet-delay constraint, they try to find out the optimal path of these MDCs, so a two-phase path selection algorithm was proposed.  
Considering the cost of data collection as well as the computing overhead for routing, we combine the static data collection method with the wireless mobile charging strategy. In each annulus, there is a WCV that is only responsible for recharging the cluster headers or the candidate cluster headers. In addition, with the help of the candidate cluster headers, the sensed data is uploaded to the static sink through a few hops in a distributed manner. By balancing the energy consumption between the common nodes and CCHs, the network lifetime can be effectively prolonged.
# Evaluation of BDS-2 and BDS-3 Satellite Atomic Clock Products and Their Effects on Positioning
## 2 Methods
### Ionosphere-Free PPP and Receiver Clock ISB
Thus, when the broadcast clock is aligned to B1I/B3I IF combination, the correction of hardware delay discrepancy is required to apply with TGD\({}_{B1I/B3I}^{s}\) and can be expressed as follows:  
\[d\overline{t}_{IF}^{s}=dt^{s}+b_{IF}^{s}=\left(dt^{s}+b_{B3I}^{s}\right)_{ \textit{{\footnotesize{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{ \it{\it{{\it{           }}}}}}}}}}}}}}}+\frac{f_{1}^{2}}{f_{1}^{2}-f_{3}^{2}}{ \rm TGD}_{B1I/B3I}^{s} \tag{11}\]  
where \(\left(dt^{s}+b_{B3I}^{s}\right)_{\textit{{\footnotesize{\it{\it{\it{\it{\it {\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{                                                        0. \)}}}}}}}}}\) is the broadcast clock offset. With above PPP model, we can calculate the station coordinates to analyze the performance of positioning or fix the coordinates to calculate other parameters, such as the receiver clock offset. In this paper, the receiver clock offsets of BDS-2 and BDS-3 are treated as two independent white noise processes. Thus, the receiver ISB can be derived by difference of the two clock offsets.
# Analysis on Response of a Single Pile Subjected to Tension Load Considering Excavation Effects
## 5 Validation of the Proposed Approach
### Case One  
[PERSON] [33] carried out full-scale tests on a reinforced concrete pile to investigate the load-displacement behavior of the pile under uplift condition. The pile was embedded in a thick fine sand layer overlain by a thin silty clay layer with a thickness of 1.2 m. The depth of the water table was 1.2 m.  
The length and diameter of the concrete pile were 12 m and 0.53 m, respectively. The saturated soil unit weight was 18.4 kN/m\({}^{3}\). Hence, the effective unit weight of the soil was set as 8.4 kN/m\({}^{3}\). The Poisson's ratio of the surrounding soil was 0.35. The average SPT blow counter was 11, and the effective friction angle was 30\({}^{\circ}\). The shear modulus of the surrounding soil was 20 MPa, and the shear modulus of the pile was 30 GPa. For the proposed method, the ratio of lateral earth pressure coefficient \(K\) to lateral earth pressure coefficient at rest \(K_{0}\) was set as 1.5. The friction angle of the pile-soil interface \(\delta\) was 0.75 times the effective friction angle of the surrounding soil.  
Figure 6 shows the comparison between the measured load-displacement curve and the calculated curve derived from the proposed approach. The calculated results from [PERSON] [34] and [PERSON] et al. [30] are also presented in this figure. It can be seen in Figure 6 that when the load is less than 300 kN, the load-displacement curve derived from the proposed approach agrees well with the measured curve and the calculated results from [PERSON] and [PERSON] [34]. The results derived from [PERSON] et al. [30] underestimate the load. When the load is larger than 300 kN, the proposed approach and the approach of [PERSON] et al. [30] underestimate the load, while the approach of [PERSON] and [PERSON] [34] slightly overestimates the load. In general, there is a good agreement between the proposed approach, the measured values, and the calculated results from [PERSON] and [PERSON] [34] and [PERSON] et al. [30].
# A Survey of Generative AI for Detecting Pedophilia Crimes
## 2 Background
### Comparative Technical Analysis
#### 2.4.1 Architectural and Performance Differences  
The primary architectural distinction between the models lies in their core components, training methodologies, and performance optimizations.  
* ChatGPT is built upon the GPT architecture, which has evolved through several versions [12]. Its foundation consists of a series of transformer encoder layers, each utilizing a multi-head self-attention mechanism and a Feedforward Neural Network (FNN) [13]. The model is further aligned with human preferences using Reinforcement Learning from Human Feedback (RLHF) to enhance safety and produce more helpful responses [12; 13].  
* Gemini is also based on a decoder-only Transformer architecture [12]. Its design includes specific modifications for efficient training and inference on Tensor Processing Units (TPUs) and employs multi-query attention [13]. A key architectural feature is the integration of Retrieval-Augmented Generation (RAG), which grounds its responses in retrieved information to improve factual accuracy [12]. Gemini is designed as a natively multimodal system, capable of processing a combination of text, images, audio, and video [12]. In terms of performance, it is noted for prioritizing computational efficiency, potentially outperforming ChatGPT in speed and energy consumption [12].
* LLaMA, released by Meta AI, also uses the Transformer architecture but with several technical differentiators [13]. It employs Root Mean Square Layer Normalization (RM-SQLN) instead of traditional layer normalization, and it uses the Swish-Gated Linear Unit (SwiGLU) activation function [13]. For positional information, it utilizes a Rotary Position Embedding (RoPE) scheme [13]. Regarding its context window, the original LLaMA was trained with a 2 K token context length, which was extended to 4 K tokens for LLaMA2 [13]. The practical implications of its computational requirements are significant; while compact models like LLaMA 7B can be run on local machines, more extensive versions demand impractical processing times, in the order of minutes per answer [13].
# Tissue Engineering Strategies for Retina Regeneration
## 4 Biomaterials and Scaffolds Used for Tissue Engineering
### Scaffold Free Cell Sheets Using Thermoresponsive Polymers  
Thermoresponsive polymers are stimuli-responsive smart materials that show reversible hydrophilicity/hydrophobicity around a critical temperature [115] (Figure 2). This can be used to prepare cell monolayers or sheets for implantation without any supporting matrix. The approach can enable the preparation of intact, scaffold-free monolayer cell sheets along with the deposited ECM through phase separation. During transplantation, the ECM supports faster attachment of host tissue without any additional coating. Poly(\(N\)-isopropylacrylamide) (PNIPAAm) is one of the most popular thermoresponsive polymers which allows cell sheet harvest by temperature reduction from 37-20 \({}^{\circ}\)C [116]. [PERSON] et al. [117] has shown that RPE forms cell sheets over PNIPAAm and exists as a monolayer structure with intact cell-to-cell junctions after transplantation [117]. Functional three-dimensional (3D) tissues can also be fabricated using thermoresponsive polymers by layering cell sheets. Micro-patterning technology combined with cell sheet technology can be used to create more complex 3D functional tissues [118].
# Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification
## 3 Damage Identification Method Based on FFDV Monitoring
### Validation of FFDV's Rotation-Angle Change Rate Damage Identification Index  
Further tests were conducted to validate the applicability of the  
Using the method for calculating the FFDV of the structure, the FFDV of the test beam was obtained under various conditions. FFDV chromatography was performed on the rotation-angle change rate, as shown in Figure 35.  
Based on Figure 35, when the structure was undamaged, the structural rotation angle changed continuously with the typical structural deformation coordination characteristics, and no abnormal rotation-angle change was observed. After the damage, the overall rotation-angle change characteristics of the structure's FFDV showed no significant change. However, an abnormal rotation-angle change rate was observed in the damaged member.  
Figure 35: Diagram of FFDV and rotation-angle change rate chromatography.  
The peak value of the rotation-angle change rate was significantly higher than the average rotation-angle change rate of the structure, and the abnormal characteristics of the rotation-angle change rate of the damaged member were prominent. This verifies that the rotation-angle change rate index is highly sensitive to damage location.
# Research of Enzyme-Induced Carbonate Precipitation on Strength Behavior of Reinforced Sand
## 3 Results and Discussion
### Micro-Mechanism of EICP-Reinforced Sand
#### 3.2.2 Influence of EICP on Pore Distribution of Soil  
The analysis of the influence of EICP on the pore structure of soil is based on image processing of CT scan results. As shown in Figure 11, slices obtained from the CT scans were 3D reconstructed using Avizo software (Version 2020.1) to generate the pore space distribution model. The pore distribution was then transformed into a Pore Network Model (PNM) by extracting the geometric characteristics of the pores. Connectivity analysis was employed to distinguish between isolated and connected pores. Both the pore space distribution model and the PNM were used to characterize various pore properties.  
Table 2 presents the pore statistical information and pore volume distribution in plain soil (CT0) and EICP-reinforced samples with cementation solution concentrations of 0.5, 1, 1.5, and 2 mol/L (CT1, CT2, CT3, and CT4, respectively). For pore size distribution statistics, the pores are categorized by size into three groups: large (>1.4 \(\times\) 10\({}^{8}\)\(\upmu\)m\({}^{3}\)), medium (4.4 \(\times\) 10\({}^{7}\)-1.4 \(\times\) 10\({}^{8}\)\(\upmu\)m\({}^{3}\)), and small (<4.4 \(\times\) 10\({}^{7}\)\(\upmu\)m\({}^{3}\)). Table 2 shows that as the cementation solution concentration increases from 0 mol/L to 2 mol/L, the porosity decreases significantly from 30.92% to 15.01%, a reduction of 51.5%. Furthermore, it is evident that both large and medium pores decrease noticeably, while small pores tend to increase. This phenomenon is specifically illustrated in the statistical data presented in Figure 12.  
Figure 11: Steps for imaging processing of CT test results.  
Figure 12: Variation in pore volume in EICP-reinforced sand at \(D_{\rm r}\) = 0.7.  
The results in Figure 10 show a decrease in total pore volume as \(C\) increases. Specifically, both large and medium pores decrease in volume with higher \(C\), while small pores initially increase from 0 (plain soil) to 1.5 mol/L. This is due to large and medium pores storing more solution, facilitating crystal aggregation around adjacent particles. As crystal size increases, it blocks pore throats, subdividing large or medium pores into smaller ones. The reduction in large pores indicates particle bonding and bridging effects. However, from \(C\) = 1.5 to 2 mol/L, small pores decrease in volume. This suggests that higher concentrations of the reaction solution promote more calcium carbonate precipitation, leading to the blocking of smaller pores.
# Time-Frequency Analysis of Daily Activities for Fall Detection
## 1 Introduction
High accuracy was achieved with 96.9% sensitivity and 97.1% specificity for classifying fall events from low-rigor activities with low impact forces such as walking, sit-to-stand, stand-to-sit, sit-to-lie activities. A key limitation is the algorithm was not evaluated in comparison to activities with high impact forces such as running or jumping. Unlike our previous study, the purpose of this study to analyze daily activities and fall events using both time and frequency domains, especially from more rigorous activities. Further, this study proposes a fall detection algorithm using only the acceleration signal, eliminating the need for a gyroscope to confirm fall events. This method reduces the overall cost of a sensor unit, amount data collected, data storage requirements, but, perhaps most importantly, battery consumption and size requirements, all critical for a commercial system.