esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Single Molecule Electrical Measurements: Biomolecule Detection
### Indirect Sensing by Single Molecule Electrical Measurements  
Indirect sensing involves detecting the specific molecular marker by using analytical probes, which are designed to selectively bind to the target marker and serve to amplify the electrical signal by nanodevices (Figure 3). By using probe molecules, target marker molecules specifically form a host (probe)--guest (target) conjugate, resulting in the signal amplification of target molecules so that they are easily discriminated from the host only (probe) and no-target other molecules. In addition to signal amplification, some probe molecules work as chemical scavengers in order to purify or exclude molecules that interfere with detection other than guests. The use of these probe molecules enables the specific and selective detection of guests, resulting in an improvement of the sensing accuracy and sensitivity.  
Up to now, there have been the reports on identifications of various chemical species, including nucleobases and nucleotides such as DNA and RNA [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24], amino acids and peptides [25; 26; 27; 28; 29], proteins [30; 31; 32; 33], carbohydrates [34; 35; 36], biocompatible polymers [37; 38; 39; 40], second messenger molecules and organic molecules [41; 42; 43; 44; 45], ions [46; 47; 48; 49; 50; 51; 52], toxic nanoparticles [53], and viruses [54; 55]. Among these bimolecular sensing, based on the DNA and amino-acid identification method, single-molecule electrical sequencing methods are developed [56; 57; 58; 59; 60; 61; 62] (Table 1). These molecules are important target biomolecules for medical applications and thus detailed studies on these target groups are described in the following sections.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target Sample** & **Device** & **Detection** & **Sample** & **Reference** \\ \hline Nucleotide (DNA) & Nanopore (Bionanopore) & Ion current signal characterization & DNA & Review: [7; 8; 9; 10; 11] \\ \hline Nucleotide (epi DNA, 5 mC, 5 hmC) & Nanopore (Bionanopore) & Ion current signal characterization & DNA & [13; 14; 15] \\ \hline Nucleotide (DNA) & Nanogap & Tunnel current signal characterization & DNA & [16; 17; 18; 19] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Biomolecule-sensing species: device, target, and tools for single-molecule detection.  
Figure 3: Indirect sensing of target molecule/particle by nanodevices. In this sensing mode, a probe molecule that selectively binds to the target molecule is required. DNA aptamer (**a**) and complementary nucleotides (**b**) are used as probe molecules. In addition to nucleotide probe molecules, peptide chains and monoclonal antibodies (**c**) are used to measure the binding form of target molecules and particles using nanodevices, and identify the differences in electrical signals.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
# Parallel Computation of CRC-Code on an FPGA Platform for High Data Throughput
## 3 CRC Computational Process
### Serial CRC Generation Process  
The CRC generation process is basically a modulo-2 arithmetic of two binary polynomials. For a given message M and a generator polynomial G, both can be expressed in polynomial form as [18]:  
\[M(x)\ =\ \sum_{0}^{m-1}l_{i}x^{i}, \tag{1}\]  
\[G(x)\ =\ \sum_{0}^{g-1}g_{i}x^{i}, \tag{2}\]  
where \(m\) and \(g\) are the highest degree of the corresponding polynomial, while \(g_{i}\) and \(l_{i}\) are binary coefficients that can be either 1 or 0 at bit position x\({}^{i}\). For example, a 32-bit polynomial for CRC-32 can be expressed by both binary formats as 2'b1000001001100001001110110111 or G(x) = x32 + x26 + x23 + x22 + x16 + x12 + x11 + x10 + x8 + x7 + x5 + x4 + x2 + x1 + x0. The CRC value of the message \(M\) can be obtained by the following equation [16]:  
\[CRC[M(x)]\ =\ M(x)\cdot x^{g}\ mod\ G(x), \tag{3}\]  
The hardware implementation and the corresponding software algorithm are shown in Figure 3 and Algorithm 1, respectively.  
After computing the CRC, it is appended to the original message for later use. Since the method only processes one bit at a time, it is labeled as a bitwise CRC algorithm. The time complexity of the algorithm is proportional to the size of the message data stream that renders it unpractical to use for big chunks of data.
# A Location-Time-Aware Factorization Machine Based on Fuzzy Set Theory for Game Perception
## 4 Experiments and Results in Discussion
### Experimental Analysis
LTFM effectively considers the similarity information between feature interactions and labels through fuzzy set theory, which enhances the ability of LTFM to capture positive feature interaction information. We can observe that the projection of location and time information and the use of similarity information bring about a 5% and 11% performance improvement with respect to FM.
# Investigating a Toolchain from Trajectory Recording to Resimulation
## 3 Results
### Scenario 2  
In scenario 2, two cars are passing each other, one going straight and one performing the LTAP maneuver. The corresponding recorded data are presented in detail in Figures 4 and 5. Note that for the comparison only one of the cars going straight has been considered.  
Scenario 2 describes the LTAP maneuver. The trajectories in Figure 8a of the simulation and recording match well. For car 2, both the recorded and simulated trajectories are in the middle of the lane. For the left-turning vehicle car 1, the simulation again shows an idealized curve in the middle of the lane, while for the recorded vehicle, slight deviations of 0.1 m to 1 m can be recognized.  
However, the progressions of the velocities present obvious deviations. Especially for car 1 in Figure 8b, a clear difference between simulation and recording can be observed. While in the recording the left-turning vehicle brakes and comes to a stop to let car 2 pass, the simulated car 1 hardly reduces its speed and performs its maneuver without the influence of car 2. It is clear that the behavioral models for lane change and vehicle following included in the simulation are not sufficient to represent such a scenario, as they only consider vehicles immediately next to or in front of them. However, the version of the simulation used does not yet include any functions to represent the observed behavior.  
Nevertheless, such maneuvers are a common cause of accidents because of the intersecting trajectories. In Figure 8c, it can be seen that the simulated car 2 reduces its speed and reacts to car 1 as it enters the intersection to avoid a collision. This behavior is realistic, analogous to scenario 1, except for the offset of about 1 s due to the lack of reaction time. The implemented behavioral models seem to be suitable for the targeted  
Figure 7: Results for Scenario 1: (**a**) Recorded (blue) versus simulated (green) vehicle trajectories; (**b**) Recorded (blue) versus simulated (green) velocities of the leading vehicle; (**c**) Recorded (blue) versus simulated (green) velocities of the following vehicle.  
simulation of critical scenarios (accidents and near-accidents) and the resulting braking behavior. However, evasive maneuvers are not considered. The large scatter in the velocity for the recorded car 2 is again due to the ML-based tracking of the trajectories.
# Robustness of Cyber-Physical Supply Networks in Cascading Failures
## 5 Numerical Simulation
### The Cascading Process under Physical Node Failure  
#### 5.2.1 The Cascading Process of One Node Failure  
Next, we investigated the relationship between \(R\) and \(\gamma\) with different values of \(\alpha\) in cyber-physical supply networks, and the results are presented in Figure 9. First, as \(\gamma\) changes from 0 to 1, the robustness of the whole network decreases. \(R\) descends with the increase in \(\gamma\). The reason is as follows: One firm's failure can cause losses to its upstream and downstream firms, resulting in further failures of these firms. The successive failures of firms are caused by loads being less than the lower bound. A smaller \(\gamma\) could make the network more robust to cascading failure. The higher the value of the lower bound, the greater the cascading size [14].  
Figure 8: The relationship between robustness and the number of failure nodes with different values of \(\alpha\).  
Figure 7: The effect of \(\varphi\) and \(\delta\) on \(R\) under several node failures.  
Moreover, when the cyber-physical supply networks face node attacks, a critical threshold can determine whether the network will have cascading failures. When \(\gamma\) is less than or equal to \(\gamma^{*}\), the network will not have cascading failures, and when \(\gamma\) is greater than \(\gamma^{*}\), cascading failures can be triggered. As shown in Figure 9, when \(\alpha=0.5\), the critical threshold \(\gamma^{*}=0.85\) and \(\gamma>\gamma^{*}\) will lead to cascading failures in the network. The critical threshold is also observed in \(\alpha\). When \(\alpha\) is less than \(\alpha^{*}\), the increase of \(\alpha\) is beneficial to the cascading robustness of the network. When \(\alpha\) is greater than or equal to \(\alpha^{*}\), the raising effect of node load expansion on cascading robustness will be saturated.  
Figure 10 presents the heatmaps of \(R\) within the parameter space \([\alpha,\gamma]\). The critical thresholds, \(\alpha^{*}\) and \(\gamma^{*}\), divide the heatmaps into two zones: the sensitive and safety zones. In the sensitive zone, the cascading robustness of the network varies with the setting of \([\alpha,\gamma]\). When the capacity parameter space \([\alpha,\gamma]\) falls into the safety zone, the cascading robustness reaches the maximum value of 1, which means that cascading failure will not occur. The larger the safety zone, the lower the capacity extension costs to prevent cascading failures on the network [43].  
Figure 10: The effect of \(\alpha\) and \(\gamma\) on \(R\).  
Figure 9: Relation between \(R\) and \(\gamma\) with different values of \(\alpha\).
# A Durability Prediction Method for Historical Square Rebar Reinforced Concrete Buildings
###### Abstract
The sole factor evaluated in the study relevant to the considerations given above was the influence of the old concrete on the durability of historical RC structures. [PERSON], [PERSON] and [PERSON] [23; 27] performed a comparison study on the historical design theory in China with the current design standards for beam bending and shear behaviors. The historical design theory adopted an allowable stress calculation method, and the design safety factors were 3.55-4.00. The historical design method was more conservative than the current design method, however, it did not consider the earthquake action, and some structural configurations were primary. In another study was performed by [PERSON] et al. [25], a corrosion-induced concrete cracking experiment was conducted for square rebar concrete specimens and corrode products of the historical square rebars were analyzed. Moreover, the ratio of the critical depth and the section length of the rebars were investigated; however, they did not establish an applicable calculation method. [PERSON] et al. [24] performed experimental and numerical investigation on the bond-slip behavior of square rebar and concrete, and the experimental results revealed that the performance was substantially weaker than that of the current deformed round rebar. Subsequently, [PERSON] et al. [28] performed a study for experimental and theoretical analysis for the shear capacity of the square rebar reinforced concrete beam. The experimental results illustrated that this type of beam had a critical crack that propagated to the upper surface of the beam which caused the beam failure under shear load. They proposed a modified arch-truss model to quantitatively evaluate the relationship between the crack development and the shear capacity of the square rebar reinforced concrete beam. According to the comprehensive literature review, an adaptive durability assessment method for square rebar RC historical buildings was not found. Only approaches for round rebar reinforced concrete structures may currently be used to anticipate and analyze the durability of historical RC buildings;nevertheless, the accuracy and reliability have not been proven. Consequently, in the current paper, a durability prediction system for square rebar reinforced concrete buildings is proposed, which can be used for the durability prediction for the relevant type of historical buildings.
# Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information
## 3 Methods
### CNN-BILSTM Model  
Given the above analysis of the CNN and BILSTM models, we considered the correlation between traffic flow and speed for obtaining a more precise forecast. The proposed CNN-BILSTM model selected the spatial features of the traffic flow data through CNN, extracted the temporal features for the input of BILSTM, and, productively, forecast the traffic flow through the fully connected layer.  
Prior to introducing the model algorithm process in detail, we initially constructed the historical traffic flow and speed data matrix. Let \(x_{t}^{o}\) represent the traffic flow of the observation point \(o\) at time \(t\), where the historical flow data matrix of the observation point from \(t-n\) to \(t\) can be expressed as \(Y_{t}^{o}=\left[x_{t-n}^{o},x_{t-n+1}^{o},\cdots,x_{t}^{o}\right]\) (a total of \(m\) detection points including point \(o\)), and the flow matrix contains the flow data of other detection points, as follows:  
\[Y_{t}^{m}=\begin{bmatrix}Y_{t-n}^{m}\\ Y_{t-n+1}^{m}\\ \vdots\\ Y_{t}^{m}\end{bmatrix}^{T}=\begin{bmatrix}x_{t-n}^{1}&x_{t-n+1}^{1}&\cdots&x_ {t}^{1}\\ x_{t-n}^{2}&x_{t-n+1}^{2}&\cdots&x_{t}^{2}\\ \vdots&\vdots&\ddots&\vdots\\ x_{t-n}^{m}&x_{t-n+1}^{m}&\cdots&x_{t}^{m}\end{bmatrix} \tag{12}\]  
As stated above, let \(s_{t}^{o}\) denote the vehicle speed of observation point \(o\) at time \(t\), and establish the speed matrix as follows:  
\[H_{t}^{m}=\begin{bmatrix}H_{t-n}^{m}\\ H_{t-n+1}^{m}\\ \vdots\\ H_{t}^{m}\end{bmatrix}^{T}=\begin{bmatrix}s_{t-n}^{1}&s_{t-n+1}^{1}&\cdots&s _{t}^{1}\\ s_{t-n}^{1}&s_{t-n+1}^{1}&\cdots&s_{t}^{2}\\ \vdots&\vdots&\ddots&\vdots\\ s_{t-n}^{m}&s_{t-n+1}^{m}&\cdots&s_{t}^{m}\end{bmatrix} \tag{13}\]  
where \(Y_{t}^{m}\) and \(H_{t}^{m}\) denote the traffic flow and speed of the selected detection point at time \(t\), respectively.  
The specific prediction algorithm steps of the CNN-BILSTM model structured in this paper are stated below:  
1. Preprocess the traffic flow data as well as speed data, fill in missing values, and delete outliers.
2. Substitute the data into the model and classify the data, of which 90% is used for training, and the remainder is used as the test data. Thereafter, the data are normalized.
3. Set the time step window length to 7, add 1 each time, and then traverse all the data.
4. The processed data serve as the input into the CNN model to extract spatial characteristics.  
Figure 2: BILSTM model structure diagram.  
(5) The traffic flow spatial feature data output by the CNN network is used as the input of the BILSTM network model to further extract the spatial features of the data. (6) Define the super parameters of the model for network training prediction. The general framework of the CNN\(-\)BILSTM model is shown in Figure 3:
# A Multi-Resolution Approach to GAN-Based Speech Enhancement
## 5 Experiments and Results
### Performance of Multi-Scale Discriminator  
#### 5.2.1 Objective Results  
The goal of these experiments is to show the efficiency of the multi-scale discriminator compared to the conventional single discriminator. As shown in Table 3, we evaluated the performance of the multi-scale discriminator while varying \(q\) of the multi-scale loss \(L_{D}(q)\) in Equation (12), which means varying the number of sub-discriminators. Compared to the baseline proposed in [26], the progressive generator with the single discriminator showed an improved PESQ score from 2.5898 to 2.6514. The multi-scale discriminators  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & \(\sum_{n\geq p}\mathbf{L_{1}(G_{n})}\) & **PESQ** \\ \hline AECNN [26] & \(p=16k\) & 2.5873 \\ \hline \multirow{4}{*}{**Proposed**} & \(p=8k\) & 2.6257 \\  & \(p=4k\) & 2.6335 \\  & \(p=2k\) & 2.6407 \\  & \(\mathbf{p}=\mathbf{1}\mathbf{k}\) & **2.6516** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of results between different architectures of the progressive generator. The best model is shown in bold type.  
Figure 4: Results of AB preference test. A subset of test samples used in the evaluation is accessible on a webpage [[https://multi-resolution-SE-example.github.io](https://multi-resolution-SE-example.github.io)]([https://multi-resolution-SE-example.github.io](https://multi-resolution-SE-example.github.io)).  
outperformed the single discriminators, and the best PESQ score of 2.7077 was obtained when \(q=4k\). Interestingly, we could observe that the performance was degraded when the \(q\) became below \(4k\). One possible explanation for this phenomenon would be that since the progressive generator faithfully generated the speech below the 4 kHz sampling rate, it was difficult for the discriminator to differentiate the fake from the real speech. This let the additional sub-discriminators a little bit useless for performance improvement.
## 2 Materials and Methods
### Methods
#### 2.2.2 Extraction of RCSS  
In this step, the RCSS extraction process is performed and, in order to ensure independence from operator performance effects, this process is automatically carried out. The proposed method is based on the NC method, which utilizes the scattergram between the near-infrared (NIR) bands of subject and reference images. This method locates the centers of the water and land-surface data clusters based on local maxima in the scattergram, where the water cluster center is not far from the origin, and the land-surface cluster center is near the center of the scattergram [19]. The coefficients for an NC line are computed once the above centers are determined, as shown in Equations (1) and (2):  
\[\mathrm{a} = \frac{y_{Lmax}-y_{Wmax}}{x_{Lmax}-x_{Wmax}} \tag{1}\] \[\mathrm{b} = y_{Lmax}-a*x_{Lmax} \tag{2}\]  
where a is the gain, b is the offset, \(x_{Wmax}\) and \(x_{Lmax}\) are the respective water and land-surface data cluster centers of the subject image, and \(y_{Wmax}\) and \(y_{Lmax}\) are the respective water and land-surface data cluster centers of the reference image. Afterwards, a half vertical width (HVW) is defined via Equation (3):  
\[\mathrm{HVW} = \sqrt{1+\ a^{2}}\ (HPW) \tag{3}\]  
where HPW is a half perpendicular width, which is commonly set to 10. Finally, the pixels inside the HVW are selected as the NC set, and they can be expressed via Equation (4):  
\[\mathrm{NC} = \left|y_{NIR}-a\ x_{NIR}-b\right|\ \leq\mathrm{HVW} \tag{4}\]  
Figure 4: Flowchart of the proposed method.  
where \(x_{NIR}\) represents the pixels of the NIR band in the subject image and \(y_{NIR}\) represents the pixels of the NIR band in the reference image.
## 1 Introduction
This advancement is particularly relevant for domains requiring management systems, where rapid adaptation to evolving technologies is crucial. By addressing these goals, this paper aims to advance the field of OE by leveraging fine-tuned LLMs, like GPT-4 and Mistral 7B, to enhance the automation and accuracy of ontology generation. It seeks to bridge the gap between theoretical principles and real-world applications by developing domain-specific datasets and applying the models to practical scenarios, such as SAR missions during wildfires. Through a detailed comparative analysis, the study provides actionable insights, offering guidance for selecting and optimizing models for specialized tasks. By highlighting the importance of domain-specific training and exploring hybrid solutions, the paper sets a benchmark for integrating LLMs into complex, knowledge-driven domains, ultimately aiming to inspire further innovation and provide practical tools for researchers and practitioners. While this study focuses on fine-tuning large language models (LLMs) for ontology engineering, it builds upon the foundation of existing research in automated ontology construction, knowledge representation, and semantic reasoning. A comprehensive review of prior work in these areas, including relevant methodologies and applications of LLMs in ontology engineering, is provided in Section 2. This ensures that our contributions are well grounded within the broader context of the existing literature. The structure of the paper is as follows. Section 2 presents the related work. Section 3 describes the proposed approach. Section 4 presents the experiments and results, and Section 5 discusses the findings with respect to the research hypotheses. Finally, Section 6 summarizes the key findings of this study and identifies needs for future research.
## 3 Results  
Descriptive data, mean, standard deviations (\(\pm\)SD), 95% confidence intervals, \(F\)-statistics, and \(p\) values of the one-way ANOVA for all variables are depicted in Table 1. Significant differences between under-15, under-17, and under-20 players were found for body mass, height, leg length, CMJ height, standing long jump height, single-leg lateral jump from the left and right leg, skate SIM time, off-ice sprinting time (0-6 m and 0-30 m), off-ice inline skating time (0-30 m), on-ice skating time (0-6 m and 0-30 m), and SOSPT time.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Under-15**} & \multicolumn{3}{c}{**Under-17**} & \multicolumn{3}{c}{**Under-20**} & & & \\ \cline{2-13}
**Variable** & \multicolumn{3}{c}{**Mean \(\pm\) SD**} & \multicolumn{3}{c}{**Mean \(\pm\) SD**} & \multicolumn{3}{c}{**Mean \(\pm\) SD**} & \multicolumn{3}{c}{**Mean \(\pm\) SD**} & \multicolumn{3}{c}{**\(F\)**} & \multicolumn{3}{c}{_df_} & \multicolumn{1}{c}{\(p\)**} & \(\eta^{2}\) \\ \cline{2-13}  & \multicolumn{3}{c}{**95\% CI**} & \multicolumn{3}{c}{**95\% CI**} & \multicolumn{3}{c}{**95\% CI**} & \multicolumn{3}{c}{**95\% CI**} & & & \\ \hline Body mass [kg] & \(\begin{array}{c}56.2\\ 50.0\end{array}\) & \(\begin{array}{c}\pm\\ -\\ -\end{array}\) & \(\begin{array}{c}10.3\\ 62.4\end{array}\) & \(\begin{array}{c}67.6\\ 63.0\end{array}\) & \(\begin{array}  
Pearson Product-Moment correlation analysis revealed a significant correlation (\(p<0.001\)) between SOSPT time and CMJ height (\(R=-0.72\)), standing long jump height (\(R=-0.81\)), single-leg lateral jump from the left (\(R=-0.52\)) and right leg (\(R=-0.55\)), Skate SIM (\(R=0.60\)), off-ice sprinting time 0-6 m (\(R=0.49\)) and 0-30 m (\(R=0.84\)), off-ice inline skating time 0-6 m (\(R=0.55\)) and 0-30 m (\(R=0.84\)), as well as on-ice skating time 0-6 m (\(R=0.37\)) and 0-30 m (\(R=0.83\)). Linear regression plots (and calculated \(R^{2}\)) separated by age-groups of the SOSPT time and CMJ height, standing long jump height, single-leg (right and left) lateral jump distance, skate SIM, off-ice sprinting, off-ice skating, and on-ice skating (0-6 m and 0-30 m) for under-15, under-17, and under-20 players are shown in Figures 2 and 3. Calculating the relationship (Pearson Product-Moment correlation) between on-ice and off-ice sprint tests, we found significant correlations (\(p<0.001\)) between (0-30 m) off-ice sprints and off-ice skating (\(R=0.75\)), as well as on-ice skating (\(R=0.83\)) and between (0-30 m) off-ice and on-ice skating (\(R=0.84\)) time.  
Figure 2: Linear regression analysis scatter-plot of the specific overall skating performance test (SOSPT) and (**A**) counter-movement jump height (CMJ), (**B**) standing long jump distance (long jump test), (**C**) total time on the lateral skate simulation test (Skate SIM), (**D**) lateral single-leg jump left leg, and (**E**) lateral single-leg jump right leg separated by age groups into under-20, under-17, and under-15 players. (\(p<0.001\)***:\(p<0.01\)**:\(p<0.05\)*).