esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases
## 2 Motivation and Related Work  
GAs are used in numerous and diverse fields when optimal solutions are needed in reasonable amounts of time. In the case of software testing, GAs are used to search for appropriate test cases. Software testing evaluates the quality of software to satisfy user requirements or to prove the correctness of software. Testers wish to generate the smallest number of test cases that cover as many statements as possible and adequately test a given test criterion. For example, applying a GA for test case generation corresponds with du-path coverage to execute all paths where variables are defined and used [8]. GAs have been applied to evaluate test cases to satisfy mutation testing [9] and used to generate test cases in accordance with branch coverage [10; 11; 12; 13].  
In recent years, researchers have adjusted GAs to increase efficiency for application to new complex systems. For instance, researchers have focused on improving the selection operator to find optimal solutions to the multicast routing problem [1], performing a local search with the best offspring in a generation to quickly converge on the global optimum [7], improving the population initialization and crossover operator to segment magnetic resonance images [14], applying the Gaussian function with crossover and mutation to reduce computation time [15], and solving the traveling salesman problem by combining a GA and two local optimization strategies [16].  
MSGAs improve upon traditional GAs by employing a selection operator with two types of chromosomes having different mutation probabilities. MSGAs successfully find optimal solutions and obtain global-minimum solutions. Although MSGAs have been previously applied in network systems, this is the first time that an MSGA is being used in software testing. In this study, we investigated the performance of an MSGA in software testing by considering the configuration of the important parameters of population size, crossover operator, and mutation operator. Subsequently, the MSGA performance in terms of test case generation and time was compared to that obtained using a traditional GA and an HGA. HGA [7] is a GA modified by adding a local search procedure after the processes of GA. The local search is only executed on the best offspring in current population. The HGA improves accuracy and efficiency to find a solution. Specifically, performance comparison investigates: Does the improvement of the selection operator in the MSGA affect in generating test cases when algorithms were assigned the same parameters?
## 1 Introduction
The pile motion characteristic is analyzed according to the numerical results. The pore pressure responses from published experimental results are used as validation sources to test the proposed model. The simulated pore pressure results are also analyzed from the aspects of evolution and distribution. The structure of this paper is as follows. In Section 2, a brief description of a published experiment on vibratory pile driving [8] is presented. Section 3 presents the establishment of the numerical model in FLAC3D. Section 4 shows the influence of the density scaling method on numerical stability. In Section 5, the results of the proposed model are validated and discussed. In Section 6, value differences between the numerical results and the measured values [8] are discussed. The conclusion is presented in Section 7. Figure 1: The complicated construction environment for pile installation.
## 4 Results and Comparative Analysis
### Discrete biodegradability models  
The results from the tested discrete models are presented in Table 3. The methods of Logistics Regression, Logistic-KV (using Log (KV) only), MBR and K-Means had the best accuracy for the low end, with 3.23% error. For the high end, Logistic Regression, Two-Step, and K-means-KV achieved the best result, with a 3.13% error. For the whole set, Logistic Regression, Logistics with KV, and K-Means-KV had the best prediction with a 4.76% error. Overall, mismatch errors ranged from 4.76% to 12.70%. However, it is worth noting that the powerful method of DT did not achieve the highest performance for all the sets with 9.68%, 6.25% and 7.94% errors for the low, high, and overall sets, respectively.  
[PERSON] _et al._'s findings that Log (KV) is the most significant predictor suggested the use of Log (KV) as the sole predictor. With this single variable, DT uncovered an important result given by the classification Rules below:  
1. If Log (KV) \(\geq\) 1.9 (KV\(\geq\) 96), then Biodegradability is Low (\(<\)50%),
2. If (Log (KV) \(<\) 1.9, (KV \(<\) 96) then Biodegradability is High (\(\geq\)50%).  
This simple Rule derived from C5.0 DT provides an efficient classification approach that achieves the same best performance level obtained by more powerful methods for the high-end and overall data sets with a 3.13% and 4.76% error respectively, while providing the second lowest error of 6.25% for the low-end data set.  
Furthermore, most modeling techniques with this single variable approach generated prediction classification accuracies similar to models using the three original predictors. K-Means and Logistics Regression provided the best performing classification results using this variable only. The results are reported in Table 3 under Logistics-KV and K-Means-KV, which were the best performers with 3.23%, 3.13%, and 4.76% errors for the low, high and the overall sets respectively.
# A Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv8
## 2 Introduction of YOLOv8 Detection Network  
YOLOv8 is a deep neural network based on a single-stage target detection algorithm proposed by Ultralytics, which is the latest version of the current YOLO series; its network architecture mainly consists of two parts: a backbone and a head. The backbone is based on the DarkNet53 network, and it completes three downsampling operations of the input image through stacked residual modules and generates three feature maps with different scales and resolutions for input to the first half of head. The first half of head structure uses a feature pyramid network and path aggregation network for feature fusion in order to extract richer target information. The second half of the head part uses a decoupled head structure consisting of classifiers and detectors to classify and regress the position of the feature map. The specific network structure of YOLOv8 is shown in Figure 1.
# Cooperative Jamming-Based Physical-Layer Group Secret and Private Key Generation
## 3 Group Secret and Private Key Generation Scheme  
\(G_{1}\) is considered to be the reference channel, and the relay enables all the legitimate members to obtain the observations of the reference channel. The proposed CJ-based group SK and PK generation scheme consists of two stages: channel measurement and key agreement.
# Phase Noise Cancellation in Coherent Communication Systems Using a Radio Frequency Pilot Tone
## 8 Conclusions  
In this work, an orthogonally polarized RF pilot tone is investigated to suppress the LPN and EEPN in long-haul coherent optical fiber communication systems. A 28-Gsym/s QPSK optical transmission system is numerically implemented, where the CPR is performed using the one-tap NLMS and DPD methods, respectively. Our results demonstrate that the LPN in the communication system can be completely eliminated and the EEPN can also be effectively suppressed, when the RF pilot tone scheme is applied prior to the CPR.  
Theoretical analyses and numerical simulations were implemented by [PERSON] The paper was mainly written by [PERSON], [PERSON] wrote some parts and edited the manuscript. [PERSON] and [PERSON] made modifications. All authors reviewed and discussed the paper.  
This work is in parts supported by the EU H2020 RISE Grant DAWN4 IoE (778305) and the UK EPSRC Program Grant TRANSNET (EP/R035342/1).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Double-Notch Filters  
A two-notch directional filter with parameter values shown in Table 1 was implemented in the same way by increasing the number of coupling resonators in each vertical branch from one to two. The magnitude of \(\kappa_{c}\) determines the frequency locations of the notches in \(S_{21}\) according to Equation (25). However, smaller values of \(\kappa_{c}\) also increase propagation losses through the coupling resonators, reducing notch depth and increasing losses in the bandpass channel. Figure 8a compares theoretical and experimental results for \(\kappa_{c}=0.3\). Agreement between simulation and experimental results is again excellent. Attenuation in \(S_{21}\) is the same as for the single-notch filter, implying that two signals can be extracted simultaneously without hindering performance.  
Figure 8: Experimental (solid) and theoretical (dashed) frequency dependence of S-parameters for double passband MI filter with resistive narrowband terminations with \(\omega_{0}=13.56\) MHz, \(Q=180\), \(Z_{0M}=50\)\(\Omega\), and \(\kappa_{c}=0.3\). (**a**) No improved rejection; (**b**) improved rejection.  
Figure 7: Experimental (solid) and theoretical (dashed) frequency dependence of scattering parameters for MI filter with resistive narrowband terminations (2) with \(\omega_{0}=13.56\) MHz, \(Q=200\), and \(Z_{0M}=50\)\(\Omega\). (**a**) \(\kappa_{c}=0.28\) and \(\lambda=1\); (**b**) infinite rejection with \(\kappa_{c}=0.25\) and \(\lambda=0.79\).
## 2 Materials and Methods
### Dataset
#### 2.2.3 Sensitivity Experiments
To investigate the influence of using GDAS reanalysis data versus GFS reanalysis data as the initial field, a comparative experiment was conducted using GFS reanalysis data as the initial field. This comparative experiment, combined with exp2 from Group A, constituted a sensitivity experiment examining different initial fields. The simulated results from these two experiments were then compared against the observed data, and the statistical results are shown in Table 3.  
According to the data in Table 3, it can be observed that the simulation results of exp1 and exp2 were relatively good, and similar performance was shown by both experiments in terms of the correlation coefficient and error indicators. This suggests that accurate simulation results can be obtained using both GDAS and GFS reanalysis data as the initial field. However, slightly better simulation results were achieved when GDAS reanalysis data was used as the initial field compared to GFS reanalysis data. This difference can be attributed to the higher spatial resolution of GDAS reanalysis data, which is 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\), whereas GFS reanalysis data has a spatial resolution of 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\). Therefore, GDAS reanalysis data was used as the initial field in the subsequent research. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Boundary Layer Scheme** & **Near-Surface** & **Correlation Coefficient** & **RMSE (m/s)** & **Bias (m/s)** & **MAPE** \\ \hline exp1 & YSU & MM5 & 0.92 & 1.95 & 1.57 & 0.21 \\ exp2 & MRF & MM5 & 0.92 & 1.61 & 1.21 & 0.18 \\ exp3 & MYJ & MO & 0.89 & 2.13 & 1.73 & 0.23 \\ exp4 & BL & MM5 & 0.70 & 3.15 & 2.52 & 0.38 \\ exp5 & MYNN3 & MYNN & 0.70 & 3.55 & 2.86 & 0.45 \\ exp6 & MYNN2.5 & MYNN & 0.11 & 3.38 & 2.61 & 0.40 \\ exp7 & QNSE & QNSE & 0.59 & 5.68 & 5.00 & 0.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of boundary layer and near-surface sensitivity experiments. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Initial Field** &
\begin{tabular}{c} **Correlation** \\ **Coefficient** \\ \end{tabular} & **RMSE (m/s)** & **Bias (m/s)** & **MAPE (m/s)** \\ \hline exp1 & GDAS & 0.93 & 1.57 & 1.24 & 0.17 \\ exp2 & GFS & 0.92 & 1.61 & 1.21 & 0.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Initial field sensitivity experimental results.
# Real-Time Seismic Data from the Bottom Sea
## 2 Standalone Broadband Ocean Bottom System
### The Mooring and Inductive Communication System  
Prior to the deployment two different models were proposed and the dynamics of the systems were studied numerically in order to design optimal materials, cables, buoys, and connections in these critical marine climate conditions. The final deployment of this system is envisioned for the Alboran Sea (Western Mediterranean), which is an active seismic area at a depth of 250-300 m, where simulations have been done for this location.  
A study of meteorological conditions at the Carboneras fault has been done to get the main values for the wind, waves, and current in two cases: normal (or more usual) meteorological conditions as well as in extreme conditions. The OrcaFlex software under academic license N1594 has been used to simulate the dynamics of two different models of the seismometers mooring under these realistic weather conditions.
# On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression
###### Abstract  
Reversible Colour Transforms (RCTs) in conjunction with Bi-level Burrows-Wheeler Compression Algorithm (BBWCA) allows for high-level lossless image compression as demonstrated in this study. The RCTs transformation results in exceedingly coordinated image information among the neighbouring pixels as compared to the RGB colour space. This aids the Burrows-Wheeler Transform (BWT) based compression scheme and achieves compression ratios of high degree at the subsequent steps of the program. Validation has been done by comparing the proposed scheme across a range of benchmarks schemes and the performance of the proposed scheme is above par the other schemes. The proposed compression outperforms the techniques exclusively developed for 2-D electrocardiogram (EEG), RASTER map and Color Filter Array (CFA) image compression. The proposed system shows no dependency over parameters like image size, its type or the medium in which it is captured. A comprehensive analysis of the proposed scheme concludes that it achieves a significant increase in compression and depicts comparable complexity similar to the various benchmark schemes.  
Keywords:Burrows-Wheeler Compression Algorithm (BWCA); colour image compression; raster map; Colour Filter Array (CFA); EEG compression; Reversible Colour Transform (RCT) +
Footnote †: journal: Applied sciences
# You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection
## 2 You Only Hear Once (YOHO)
### Models for Comparison  
In this sub-section, we present two additional models, which are slight deviations from the YOHO architecture--CNN and CRNN. The motivation behind these models is to investigate which aspects of YOHO are actually advantageous. The feature-extraction for all these models is the same, which make them directly comparable. The CNN model aims to create a segmentation-by-classification version of the YOHO architecture. As you can see in Table 1, some Conv2D-dw layers adopt a stride of [2; 2]. These strides were set to [1; 1] instead of [2; 2] and max-pooling was adopted to reduce the frequency dimension by half. This way, the time resolution of the network does not reduce through its depth. Note that using a stride of [1; 2] would have produced a similar effect of maintaining the time resolution and reducing the frequency resolution. However, TensorFlow currently does not support rectangular strides for depthwise convolutions and hence, we adopted max-pooling. The number of parameters in the CNN was 3.9 million, which is the same as the network for YOHO.  
In the CRNN model, the first 13 layers were identical to the YOHO network. We skipped the convolutional layers where the number of filters became larger than 256 because the network became too large to fit into the RAM. Following the convolutional layers, we had two B-GRU layers with 80 units each. The number of parameters for the CRNN was 1.3 million, which is less than the YOHO network. Increasing the number of convolutional layers only worsened the performance of the CRNN. Therefore, it was optimal to have a CRNN with fewer parameters.  
The output shape for the CNN and CRNN was 801 \(\times\) 2, performing binary classification for music and speech at each time step. We compared the performance of YOHO with these two additional models on the in-house test set for music-speech detection. We also compared the inference times of these models. A summary of the architectures for comparison can be found in Table 3.
# Numerical Simulation Analysis of Concrete-Filled Circular CFRP-Steel Middle Long Columns
## 2 Axial Compression Test
### Experimental Results  
Based on the aforementioned testing methodology, the experimental data are presented in Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen Number** & **Specimen 1** & **Specimen 2** & **Specimen 3** & **Average Value** \\ \hline CM1-3.5 & 1749.52 & 1531.46 & 1829.58 & 1703.52 \\ CM1-3.5-5 & 1872.89 & 1983.06 & 1842.50 & 1899.48 \\ CM1-3.5-6 & 1865.24 & 2081.87 & 1976.51 & 1974.54 \\ CM0-2.5 & 1203.93 & 982.53 & 1118.01 & 1101.49 \\ CM1-2.5 & 1467.63 & 1610.47 & 1630.88 & 1569.66 \\ CM2-2.5 & 1891.82 & 1792.94 & 1912.34 & 1865.70 \\ CM3-2.5 & 1905.14 & 1947.03 & 2373.78 & 2075.32 \\ CS1-4.25 & 2036.36 & 1945.98 & 2042.38 & 2008.24 \\ CM1-4.25 & 1945.41 & 1783.45 & 1856.06 & 1861.64 \\ CL1-4.25 & 1660.86 & 1518.78 & 1719.65 & 1633.10 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results (unit: kN).  
Figure 1: Diagrams of test device: (**a**) Photographs of the experimental setup. (**b**) Schematic diagram of the experimental setup.
# Routing Protocols for Delay Tolerant Networks:
## 3 Performance Evaluation
### Evaluation Using Real-World Mobility Traces
#### 3.3.3 Advanced Protocols  
In Figure 7, we first observe that in real traces, all of the protocols behave similarly in terms of delivery ratio, when message generation time is sufficiently high. This means that the advanced techniques used in the protocols are comparable for this metric, if the frequency of new messages injected into the network is not too high. However, the delay of RAPID and MaxProp is remarkably lower, and this is also confirmed on synthetic traces. The price to pay for such better performance is overhead, but it is worth noting that MaxProp with 100 KB of buffer outperforms the other protocols in terms of overhead and at the same time provides excellent performance in terms of delivery delay and delivery ratio. The reason for this is that the size of the buffer is sufficient to \"permanently\" store all of the relevant messages. As a consequence, such messages do not have to be retransmitted, and this results in a lower overhead.  
We also observe that the behavior of the protocols on synthetic traces is similar to the one observed in real ones, with one significant difference: the meeting opportunities. As an example, SCAR on real traces performs reasonably well in terms of delivery ratio, irrespective of the message generation time, while it achieves the worst performance on synthetic traces. We understand that, even if SCAR implements FW and QM, it limits the number of copies of a message in the network. As a consequence, only the nodes (four in our settings) having a copy of the message can deliver it to the destination, and thus, this is the protocol that is more influenced by the meeting opportunities, which are lower in the synthetic traces. This is also confirmed by the fact that SCAR, apart from MaxProp 100 KB, is the protocol with the lower overhead.  
Once again, the observations on the operation of the protocols based on the indoor case are comparable to the ones obtained for the outdoor case. In the outdoor case, the performance of the three protocols is comparable, with MaxProp achieving a slightly higher delivery rate compared to the other two protocols. As before, the delay of RAPID and MaxProp is lower than the other protocols. Similarly, the overheads of the protocols are similar to the indoor case.
## 4 Implementation of Joint Plans
### Joint Signal Processing Flow
The constant false alarm rate (CFAR) detection algorithm, widely adopted in practical radar signal processing, regulates the threshold to ensure a constant false alarm rate throughout the detection process. Specifically, CFAR can use the noise, jamming, or ground clutter around the target echo signal to estimate the power of clutter signals in space. Following variations in clutter signal power, it filters out noise signals and detects target signals by setting an appropriate threshold. However, in scenarios where jamming is present in the reference cells, CFAR tends to overestimate noise power. This results in a higher CFAR detection threshold than the actual target signal power, creating a masking effect on the target. At the same time, the elevated CFAR detection threshold diminishes the power of the jamming signal below the detection threshold, rendering the jamming signal undetectable by the opposing radar. In situations involving multiple false targets, the power of jamming signals from false targets significantly surpasses the noise power. When the power of these jamming signals from false targets remains constant, the CFAR detector on the corresponding side detects multiple false targets. Consequently, the CFAR detection threshold experiences an increase under such circumstances.
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 4 Results and Discussion
### Levenshtein Distance  
The minimal number of single-character modifications required to turn one sentence into another is measured by the Equation \(d\) (_i,j_). When comparing anticipated and real sentences, it is useful to utilize the function d(i,j) as the difference between them. The purpose of the function is to compare the anticipated and real sentences for similarity. When comparing expected and actual sentence similarity, the following equation is helpful.  
\[\text{d(i,j)}=\begin{cases}\begin{aligned} \text{i} &\text{if j}=0,\\ \text{j}&\text{if j}=0,\\ \text{min}\{\begin{aligned} \text{d(i-1,j)}+1,\\ \text{d(i-1,j-1)}+1,\\ \text{d(i-1,j-1)}+\text{cost(i,j)}\end{aligned}\end{aligned}\end{cases} \tag{15}\]  
Equation (15) calculates the Levenshtein Distance, the least edit operations needed to change a string. It involves d (i,j), indicating the Levenshtein Distance between string A's first i and j characters and cost (i,j), reflecting the cost of changing A[i] into B[j]. The distance is the length of the opposite string, if empty, and the minimum of insertion, deletion, and substitution.  
Figure 9 shows the Levenshtein Distance, also known as edit distance, used in error correction, DNA sequence alignment, and natural language processing. Differences between strings are determined by the fewest operations needed to transform them. Figure 9  
Figure 8: Error Rate.  
indicates that the Levenshtein Distance compares HGE-ACN, DDSTGCNN, MSeqGraph, and BAE under different conditions. All models increase error rates when conditions are challenging, although HGE-ACN frequently maintains accuracy. This shows that HGE-ACN can endure noisy, dynamic environments.