esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### BER Performance  
Figures 6-10 depict the BER versus link distance for a range of aperture sizes, FoVs, and different water types. Overall, it can be seen that the BER performance was quite comparable in terms of water types and aperture sizes. The general loss of performance seen in all water types is the result of scattered photons, which transits from low scattering numbers at smaller link distance, to higher scattering numbers at longer link distance. This is an interesting phenomenon, as frequently scattered photons remain close to the optical beam axis, they have a greater probability of intersecting the receiver aperture. However, these photons are heavily absorbed; therefore, they are ultimately rejected upon reception. Furthermore, the results not only show the performance degradation due to temporal dispersion, they also illustrate the loss of performance due to absorption.  
The BER performance is improved by the increase of aperture size in harbor, coastal, and ocean water. It is worth noting that the pattern of improvement by increasing apertures is the same for all water types. As the turbidity level increases, so does scattering, which causes severe temporal dispersion. However, as the aperture size increases, BER decreases. In ocean and coastal water, scattering is apt to disperse light over a bigger cross-sectional area due to longer path lengths; thus, scattered photons are less expected to cross the receiver aperture. However, in harbor water, the higher order of scattering events tend to distribute light over a smaller area due to smaller path lengths; therefore, they have a greater chance of crossing the receiver. These results also indicate that the change of FoV is almost negligible. Thus, a wider aperture helps to improve performance, even though minimal, but it shows a connection which is yet to be optimized considering the optimal distance, and optical diffusivity. Greater scattering events not only cause temporal dispersion, they also degrade the photon's energy as a result of absorption as compared to lesser scattering events which cause less absorption.  
Figure 6: Effect of increasing transmission distance and aperture on the BER performance of harbor, ocean, and coastal waters for 1\({}^{\circ}\) receiver FoV.  
Figure 8: Effect of increasing transmission distance and aperture on the BER performance of harbor, ocean, and coastal waters for 45\({}^{\circ}\) receiver FoV.  
Figure 7: Effect of increasing transmission distance and aperture on the BER performance of harbor, ocean, and coastal waters for 18\({}^{\circ}\) receiver FoV.  
Figure 9: Effect of increasing transmission distance and aperture on the BER performance of harbor, ocean, and coastal waters for 90\({}^{\circ}\) receiver FoV.
# Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention Mechanism
## 3 Results  
### Contrastive Learning Succulent Image Classification Model  
To further validate the advantages of the lightweight GoogLeNet model, this paper trains the succulent dataset on the Aexnet, VGG16, GoogLeNet, and lightweight GoogLeNet network models, respectively. The validation accuracy curves and loss curves of these network models in the training phase are shown in Figure 6.  
From Figure 6, we can see that there are obvious differences in the performance of the four models in terms of training loss and validation accuracy: AlexNet has a faster decline in training loss and a stable validation accuracy of about 90%, but the complexity of the model is low and there is limited room for improvement in the accuracy; VGG16 has a smooth decline in training loss and a gradual improvement in validation accuracy, but there are large fluctuations in the later stage, indicating that it is prone to overfitting and has a weaker generalization ability. The validation accuracy of GoogLeNet improves faster in the early stage, but there are large fluctuations in the later stage, which shows the overfitting phenomenon, and the model's performance on the validation set is not stable enough. In contrast, the lightweight GoogLeNet performs well, the training loss decreases rapidly, the validation accuracy increases steadily and finally exceeds 90%, and the fluctuation in the validation accuracy is small, which indicates that the model has good generalization ability. Introducing the lightweight module reduces the complexity of the model, solves the overfitting problem of the original GoogLeNet, and maintains high accuracy despite the limited resources. Overall, the lightweight GoogLeNet has the best overall performance among these four models, solving the overfitting problem and achieving excellent performance on the validation set.  
Figure 6: Validation accuracy and loss curves for each network model.
# Mechanical Properties of Cement Concrete with Waste Rubber Powder
## 3 Results and Discussion
### Noise Reduction  
Figure 23 illustrates that the addition of waste rubber powder resulted in a reduction in the decibel level of cement concrete upon impact by rubber balls. Furthermore, an incremental increase in waste rubber powder content corresponded to a more pronounced decrease in concrete decibel levels post impact. Remarkably, concrete containing 10-mesh waste rubber powder exhibited the lowest decibel level post impact, while those with 20-mesh, 30-mesh, and 60-mesh waste rubber powder demonstrated similar decibel levels.
# Probabilistic Model Checking GitHub Repositories for Software Project Analysis
## 3 GitHub Model Checking
### State Structure  
To create the DTMC model for development actions, it is necessary to define each state. In this paper, a state represents individual development actions and includes information about the actors involved. Each state is composed of a triple of participants, locations, and actions. The components of a state are as follows:  
* participant: {p1,..., pn}
* location: {master, branch}
* action: {create, commit, pullRequest, accepted, merge, rejected}  
**Participant** refers to individuals who have attempted to contribute to the project, and for anonymity, they are assigned as p1 to pn in the order they appear in the project. Thus, p1 represents the first person who initiated the project. **Location** indicates the location of the action performed, where the default branch is labeled as \"master\", and all other branches are labeled as \"branch\". **Action** represents essential activities for project development on GitHub. In this context, \"rejected\" and \"accepted\" are added to indicate the acceptance status of the pull requests submitted by participants. Each state is represented by a combination of <participant_location_action>, which can be interpreted as <participant> performing <action> on <location>. Examples of states include:  
* <p1_branch_create> denotes p1 creating a branch.
* <p2_master_commit> represents p2 committing to the master branch.
* <p3_branch_pullRequest> indicates p3 initiating a pull request for a branch.
* <p4_branch_rejected> signifies that the pull request submitted by p4 has been rejected.  
In addition, the state marked \"week\" is used to distinguish the interval of one week. Here, a weekly analysis is assumed, but the period can be changed in various ways depending on the purpose.
# SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements
## 4 Sea Ice Extent Mapping and Validation  
### SVM Sea Ice and Water Discrimination Algorithm  
In this study, the support vector machine (SVM) classifier will be used to discriminate between sea ice and water. The SVM classifier is a supervised learning algorithm that converts the input parameter vector into a high-dimensional space and finds the optimal linear classification surface in this new high-dimensional space to solve nonlinear problems. An increasing number of studies are being published using the SVM method to solve problems relating to remote sensing image classification [30; 33; 34].  
In order to train the SVM classifier, the NSIDC SIC data from NSIDC are used as a priori knowledge of sea water and ice. If the SIC is greater than or equal to 15%, the corresponding pixels of ice maps are identified as sea ice, while if the SIC is less than 15%, the corresponding pixels of ice maps are identified as sea water. The training dataset was collected during the year 2020, and we randomly picked 20,000 samples for both water and ice per month. A set of characteristic parameters were derived from multiple scatterometer data containing a priori information on sea ice and open water (as shown in Table 2), where the sample dataset D can be expressed as follows:  
\[D=\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{i},y_{i})\},\ x_{i}\in R^{n},y_{i} \in\{-1,1\} \tag{2}\]  
where \(x_{i}\) represents the input samples, which consist of features for classification, and \(y_{i}\) is the class label of \(x_{i}\). In this study, there are only two categories of sea ice and seawater, i.e., \(y_{i}\) is set to \(+1\) and \(-1\). SVM is used to find an optimal classification hyperplane in high-dimensional space, denoted by Equation (3):  
\[\omega^{T}*x+b=0 \tag{3}\]  
At this point, the maximum classification interval algorithm can be formulated as Equation (4):  
\[\left\{\begin{matrix}min\frac{1}{2}\|\omega\|^{2}\\ y_{i}\left(\omega^{T}*x_{i}+b\right)\geq 1\end{matrix}\right. \tag{4}\]  
Equation (4) can be solved using the Lagrange multiplier method. That is, by first adding Lagrange multipliers \(\alpha_{i}\geq 0\) to each constraint, the Lagrange function for this problem can be rewritten as Equation (5):  
\[L(\omega,b,\alpha)=\frac{1}{2}\|\omega\|^{2}+\sum_{i=1}^{n}\alpha_{i}\Big{(}1- y_{i}\Big{(}\omega^{T}*x_{i}+b\Big{)}\Big{)} \tag{5}\]  
The optimal solution \(\omega^{*}\) for \(\omega\) and the optimal solution \(b^{*}\) for \(b\) are obtained using the sequential minimum optimization algorithm once, for any sample, the output of the classification has been determined by the decision function. The nonlinear change in the transformation from a low-dimensional vector to a high-latitude space is achieved by defining appropriate inner product functions, i.e., kernel functions. In this study, the Gaussian kernel function is chosen. Figure 3 shows the flowchart of sea ice monitoring using the SVM classifier with multisource scatterometer measurements used in this study.
## 5 System Engineering Analyses
### Link Budget  
A link budget is an analysis tool that is used to establish whether or not a communication link meets such mission requirements by considering data and factors such as transmitted signal power, frequency, data rate, and link bandwidth. During this calculation procedure, the gain and loss of the Radio Frequency (RF) signal from the modulation at the transmitter to the demodulation at the receiver are considered. The nanosatellite link can be affected by different factors, such as ionospheric and atmospheric attenuation, which generates influence on the polarization of the wave, losses that result from the misalignment of the antennas and losses of the free space depending on the tilt range since it is crucial for the geometric analysis of tracking. In the same way, the noise of the system that is directly related to the temperature and the noise of the different elements in a decisive way in the calculations are taken into consideration.
# Study on Components Determination and Performance Evaluation of LS Pre-Maintenance Agent
## 4 Road Performance Test Plan
### Restore Performance Test  
The restore effect of the LS pre-maintenance agent is determined by the recovery degree of the aged asphalt. Ordinary road petroleum asphalt was used as the matrix asphalt, based on the \(\ast\)Highway Engineering Asphalt and Asphalt Mixture Test Rules() (JTG E20-2011) [22], the sample was prepared and assayed according to the softening point, 25 \({}^{\circ}\)C penetration, and 15 \({}^{\circ}\)C ductility, then the asphalt was aged for 3 h by RTFOT and equal amounts of LS pre-maintenance agent and Rhinophalt agent were added. In the current maintenance project, the adding amount of the reducing agent is about 5-10% of the asphalt mass [24]. The selected reducing agent amount is 5, 7, and 9%, and the equivalent to the LS pre-maintenance agent is 25, 35, and 45% of the asphalt mass. Melted state aging asphalt  
\begin{table}
\begin{tabular}{c c c c} \hline
**Indexes** & **25 \({}^{\circ}\)C Penetration/0.1 mm** & **Softening Point/\({}^{\circ}\)C** & **5 \({}^{\circ}\)C Ductility/cm** \\ \hline SBRI-C & 77.3 & 53.5 & 54.7 \\ SBR + 3\% LS agent & 77.9 & 52.8 & 44.1 \\ SBR + 5\% LS agent & 79.3 & 51.4 & 42.9 \\ SBR + 7\% LS agent & 80.1 & 50.9 & 42.1 \\ Aged SBRI-C & 54.7 & 59.1 & 34 \\ Aged SBR + 3\% LS agent & 56.3 & 56.9 & 26.1 \\ Aged SBR + 5\% LS agent & 60.1 & 53.3 & 24.9 \\ Aged SBR + 7\% LS agent & 60.9 & 53.2 & 23.8 \\ \hline \end{tabular}
\end{table}
Table 6: Asphalt indexes results.  
is added into LS and Rhinophilat agent and for stirred 15 min. The data is compared to evaluate the restorative effects.
# A Distributed Real-Time Monitoring Scheme for Air Pressure Stream Data Based on Kafka
## 4 Experimental Results and Analysis
### Transfer Data  
To monitor air pressure, sensors continuously gather real-time air pressure data and transmit it to producers via the UDP communication protocol. The producers are responsible for converting these data into a structured message, which includes key information including the sensor ID, pressure value, battery level, and transmission signal strength. Once the producer creates the message, it is sent to the cluster or message queue. Consumers can subscribe to the transmission topics as needed to receive the required pressure  
Figure 6: Web page data feedback.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Content** & **Specification** \\ \hline CPU & Intel® Core TM i9-9900K 3.6 GHz \\ Memory & 32 GB \\ SSD & 500 GB \\ OS & 2.12-2.4.1 \\ Apache Kafka & Windows 10 Version 22H2 (OS build 19045) \\ Apache ZooKeeper & 3.5.7 \\ Java & 1.80\_321 \\ ActiveMQ & 5.16.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hardware and software environment.  
data. The data structures transmitted by Kafka and ActiveMQ for the performance comparison are in an easily parsed and processable JSON format (Table 4). Additionally, to consider transmission efficiency and data integrity, the size of each message is limited to 40 bytes.
## 4 Experiment Validation
### Case 1: Bearing Fault Diagnosis Based on CWRU Bearing Datasets
#### 4.1.2 Model Parameters and Results Analysis  
The data weighted by the SAM are input into DWSC for DWC and PWC, as well as the subsequent convolution, BN and pooling, Sigmoid, and classification. The model parameters are given in Table 2. The results of training and testing of the proposed model on CWRU datasets are shown in Figure 8. Figure 8a,b show changes in the accuracy and loss of training and testing datasets with epoch, respectively. As can be seen in Figure 8, when the epoch is about 15 times, the model basically reaches convergence, and the change of accuracy and loss tends to be basically stable. The highest fault diagnosis accuracy rate is 100%. Figure 8c,d are the visualization results by t-SNE, Figure 8c is the visualization graph of the training result, and Figure 8d is the visualization graph of the test result. It can be seen that diverse fault types are almost perfectly separated in Figure 8c,d. Figure 8e,f are the confusion matrix of the training and test results. It can be observed that for the nine fault types, the proposed model can identify and classify them perfectly, and the recognition accuracy of each type of fault is 100%.
## 4 Results and Discussion
### Performance of System Throughput vs. Average Offered Traffic Load  
The results in Figure 6a-c show the performance of system throughput behaviour with increasing average offered traffic load while considering SNR = 8 dB and Num\({}_{\text{STA}}\) = 4 under the effects of different traffic models (Weibull, Pareto, and fBM). This experiment examined the performance of the proposed adaptive aggregation algorithm over the FIFO aggregation algorithm and Adaptive FA Con. approach in terms of the trade-off between maximizing the system throughput and minimizing the system delay.  
According to the results shown in Figure 6a-c, the proposed approach achieved the maximum performance when the offered frame size was increased in all traffic models. For instance, the proposed approach achieved the maximum performance of 635 Mbps using the Weibull traffic model and 603 Mbps using the fBM traffic model. In the case of the Pareto traffic model, the proposed approach achieved a maximum system throughput performance of 308 Mbps, which is the worst performance as compared to the results achieved by the Weibull and fBM traffic models. This result illustrated that the proposed approach was better at coping with the Weibull and fBM traffic models than the Pareto traffic model. This was because the traffic pattern among streams in the Pareto traffic was highly bursty, thus the FIFO FA (baseline approach) outperformed with a lower cost of queue delay for the optimal system throughput performance. However, the FIFO FA (baseline approach) achieved the worst performance of \(550\) Mbps in the Weibull traffic model and 308 Mbps in the Pareto traffic model due to the nature of traffic in those models. Therefore, according to these results, our proposed approach was significant and could efficiently maximize the system throughput of the WLAN in the downlink MU-MIMO channel by minimizing the cost of increasing delay. However, according to the results shown in Figure 6a,b, the Adaptive FA Conv. approach always achieved the maximum performance by employing a dynamic adaptive aggregation selection strategy while focusing on optimizing the throughput. However, it provided a suboptimal solution at the expense of maximum delay even though it achieved maximum throughput. In general, this experiment illustrated that the traffic load in different traffic models could affect the performance of a WLAN to realize an optimal trade-off between maximizing throughput and minimizing delay.
## 4 Analysis of Experimental Results  
### Static Test Results  
Using the prototype and a high-precision static testing system, it is possible to measure the static precision of the pressure measurement catheter, with measurement indices including non-linearity, hysteresis, and repeatability.  
The static testing process is as follows: The pressure sensor is adjusted to the working state and placed into the pressure chamber connected to relevant circuits. After closing the chamber door, the overall airtightness of the pressure chamber is checked, and the automatic testing program is started. The static testing system begins by introducing gas into the chamber with the pressure starting at 104 kPa. The sensor output voltage is recorded each time the internal pressure changes by 2 kPa. The pressurization stops when the chamber pressure reaches 114 kPa and then begins to release pressure, again recording at every 2 kPa change, stopping once it reaches 104 kPa again. This process constitutes one complete cycle, and data from three such cycles are needed to calculate the sensor's static indices. The system is equipped with software that automates this process and automatically records the sensor output voltage at each pressure point, with its curve shown in Figure 10a. Based on the data, the system's linearity, hysteresis, and repeatability can be calculated.  
Figure 9: Physical photo of the dynamically varying pressure testing system.  
Figure 10b shows the positive motion data corresponding to each pressure point output, and Figure 10c shows the negative motion data. After calculations, the non-linearity of the pressure measurement catheter is found to be 38.0 Pa, repeatability is 42.4 Pa, and hysteresis is 25.8 Pa. The combined values yield a mercury column error of 49.9 Pa within the high-precision static testing system, demonstrating the system's high static pressure measurement precision.
## 2 Study Area, Data and Methodology
### Land Use Data  
The land use data in this paper are based on scientific research data from Guangzhou University. These land use data are supervised by support vector machine (SVM) from Landsat 5 Thematic Mapper (TM) images in 2004 and 2010 and Landsat 8 Operational Land Imager (OLI) images in 2016. Land use was reclassified into 8 types, and the classification standard according to \"the Contents and Indicators of Basic Geographic Monitoring (GQJC03-2017)\" issued by the State Bureau of Surveying and Mapping Geographic Information of China is shown in Table 1. After partial correction of the SVM-supervised classification results, the accuracy of random sampling of land use patches was better than 90%.
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 5 Results and Testing
### Image Encryption Result
#### 5.2.3 Histogram Analysis of the Encrypted Image  
Three color photos were selected, and their histograms were computed. Figure 8 shows the three photos' color histograms before and after encryption. The encrypted image's histogram is unit-form and substantially different from the originals. Consequently, no trace of the original image may be used in statistical assaults.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Image** & **Proposed** & **Ref. [33]** & **Ref. [35]** & **Ref. [36]** & **Ref. [37]** & **Ref. [38]** & **Ref. [39]** & **Ref. [40]** & **Ref. [41]** & **Ref. [42]** & **Ref. [43]** \\ \hline House & 37.23 & 33.9025 & - & - & - & 33.45 & - & - & - & - & - \\  & & & & & & & 35.4426 & & & & \\ \hline Baboon & 34.46 & 33.8000 & 33.563 & 33.392 & 33.4846 & 33.48 & 0.3318 &
\begin{tabular}{c} (Chach20) \\ 33.71 \\ \end{tabular} & 33.41 & 33.5531 & 33.43754 \\ \hline Peppers & 31.98 & 32.9625 & 33.45786 & - & 33.5301 & 33.5 & 0.3336 & - & 33.34 & 33.4862 & 33.45324 \\ \hline \hline \end{tabular}
\end{table}
Table 4: UACI Results in Comparison to Research Studies.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Name** & **Proposed** & **Ref. [33]** & **Ref. [44]** & **Ref. [45]** & **Ref. [46]** & **Ref. [38]** & **Ref. [39]** & **Ref. [40]** & **Ref. [41]** & **Ref. [42]** & **Ref. [43]** \\ \hline Peppers & 99.6392 & 99.7328 & 99.62209 & – & 99.6078 & 99.6392 & 0.9956 & \
\begin{tabular}{c} Hitzl-Zele \\ map and \\ Tinkerbell \\ \end{tabular} & 99.58 & 99.6218 & 99.64142 \\ \hline \hline \end{tabular}
\begin{tabular}{c} With values typically around 99.63%, the NPCR analysis in the table demonstrates the robustness of the suggested technique in fending off differential attacks. It displays sensitivity to even slight changes in the original image. The somewhat lower NPCR values (99.5450 for House and Baboon) in Ref. [33] and Ref. However, using chaotic maps and ChaCha20 encryption, Ref. [40] obtains higher values (99.803 and 99.6, respectively), indicating robust security features. However, for some references, like Ref. [38], Ref. [99], and Ref [41], the differences among distinct images may indicate an inconsistent encryption effectiveness. The suggested approach maintains a constant NPCR across all evaluated photos, supporting the dependability and efficiency of safe image encryption.  
\end{table}
Table 3: _Cont._Figure 8: _Cont_.  
An image's calculated histogram is shown in Figure 8, which depicts the many pixels in each indicator or degree measure on the indexed color image. When image pixels are long, the histogram contains the information required to perform image normalization, ensuring sufficient dissimilarity. The normalization method can change in these histograms. Normalization extended the pixel levels' measure domain to its maximum extent to increase the image dissimilarity.