esa-sceva/satcom-chunk-collection huggingface dataset:
# Probabilistic Model Checking GitHub Repositories for Software Project Analysis
## 4 Experimental Results
### Repository Characteristics
#### 4.1.2 Activity per Week  
When collecting data from GitHub, we segmented our analysis into one-week intervals. Let us delve into the significance of this one-week interval.  
\[P=?[X\ \text{1''-}week^{\prime\prime}] \tag{2}\]  
Query (2) is used to calculate the probability of performing actions each week. Through the \(X\) operator, it calculates the probability of the next step not being in the _week_ state, thus indicating the probability of continued activity in the following week. Hence, the higher this probability, the more it demonstrates the sustainability of development activities. The results are shown in Table 4. Model checking results indicate that Redline and Hearth had a history of weekly activity throughout their projects. In contrast, kafkaVision has the lowest percentage, indicating periods of inactivity during the project's progression.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Repository** & **Probability** \\ \hline DeSolver & 83.33\% \\ kafkaVision & 66.67\% \\ Redline & 100.00\% \\ Sherlogs & 91.67\% \\ Hearth & 100.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Result of Query 2: Activity sustainability in repository.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Create** & **Commit** & **Pull Request** & **Accepted** & **Merge** & **Rejected** \\ \hline DeSolver & 0.0962 & 0.5224 & 0.1122 & 0.0994 & 0.0994 & 0.0128 \\ kafkaVision & 0.1266 & 0.4120 & 0.1524 & 0.1395 & 0.1395 & 0.0107 \\ Redline & 0.0962 & 0.3471 & 0.1856 & 0.1684 & 0.1684 & 0.0172 \\ Sherlogs & 0.0909 & 0.5584 & 0.1136 & 0.0877 & 0.0877 & 0.0227 \\ Hearth & 0.1106 & 0.3230 & 0.1814 & 0.1814 & 0.1814 & 0.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Result of Query 1: Action rate in the long run.  
\[R\{\text{``}r\_steps^{\prime}\}=?[X\ F\ \text{``}week^{\prime}] \tag{3}\]  
\[R\{\text{``}r\_pullRequest^{\prime}\}=?[X\ F\ \text{``}week^{\prime}] \tag{4}\]  
Having explored the probability of weekly activity, let us now inquire about the extent of activity each week. Query (3) employs the transition reward, \(r\_steps\), to calculate the average cumulative action count per week. Using the \(X\) and \(F\) operators, it determines the weekly activity until reaching \"week\" in the future. When applied to five models, the average is found to be 39.63, indicating an average of approximately 40 actions performed per week. Query (4) is a modification of (3), using the transition reward \(r\_pullRequest\). This query calculates how many pull requests were made on average each week.  
When computed for five models, the average is found to be 6.35. Figure 4 shows the model checking results for (3) and (4), organized by repository. This reveals that DeSolver exhibited relatively lower activity levels, while Redline showed the highest activity levels. The rate of pull requests to weekly activity averaged around 10-20% overall.
# Generative Adversarial Networks in Business and Social Science
## 5 Why Use GANs in Business Economics?  
Given that the main aim of GANs involves the automatic generation of data, we ask when and how can they be used in Economics. These networks can be utilised to expand the size of the working set, since data availability in most economic problems is often scarce yet crucial. This helps researchers to develop mechanisms that are essential for making economic, business, and commercial decisions, which is pivotal for the proper functioning of the sector.  
On the other hand, anomaly detection is highly sought-after in this field for the detection of credit card fraud, insurance fraud, and banking operations. In these problems, there is often an imbalance issue, with only a small amount of data in the relevant class compared to abundant data in the other class. In this scenario, GANs can be conditioned to generate data for the minority class since it is well-known that models perform better when data are balanced.  
Another issue that can be addressed using GANs is that of time-series data, which can simulate temporal series and stochastic processes for risk management, financial projections, stock prediction, monetary policy formulation, price evolution, capital modelling, solvency projection, reserve management, asset and liability management, and mortality projections, among others.  
Another significant issue that can be addressed by GANs is privacy preservation, given that most business data and other information may be secret, confidential, and/or sensitive. For instance, a company such as a private hospital could withhold its real patient database by generating a set of synthetic data with the same characteristics as the real patients, even though these patients do not exist. This method preserves privacy while allowing the use of data to create models that improve the practices of this and other hospitals.  
Another problem is that of missing data, which affects the field of business economics and poses a challenge when applying analytical methods that are often unprepared for missing datasets.  
One of the key advantages GANs offer over conventional methods in solving these aforementioned issues involves their operation through supervised learning, which enhances the performance of the models.
## 6 Conclusions  
The main research findings of this paper are as follows:  
1. By comparing the detection outcomes of YOLO models using different activation functions, the optimal activation function suited to the target scenario was identified. An evaluation of the effects of weight decay, data augmentation, and the Dropout method on the YOLO model was conducted, thereby determining that the SiLU activation function exhibits the highest compatibility with this model; more robust data augmentation techniques significantly enhance the model's generalization capabilities.
2. Innovatively integrating a background subtraction algorithm into the improved YOLOv5 model, as described in (1), bitwise operations between the foreground mask and the original image are performed, resulting in the preservation of only the moving foreground targets. This addresses the issue of higher priority for moving targets in the target scene. A more suitable neural network dynamic detection algorithm for complex railway slope scenarios was proposed by combining the background subtraction algorithm with YOLOv5, and the dimensions of moving target information were enriched through monocular vision technology.  
In summary, this paper first proposes a target detection algorithm for slope rockfalls with strong specificity through improvements to the YOLOv5 model and constructs a proprietary dataset for specific scenarios. By incorporating the background subtraction algorithm into the YOLOv5 model to address the issue of filtering moving rockfalls in complex slope scenarios, high-risk moving target detection information is obtained, and size measurement risk assessments are conducted. This forms a comprehensive neural network dynamic detection model capable of capturing dimensional information about rockfalls.  
[PERSON] and [PERSON] led the research program, and [PERSON] and [PERSON] designed the simulations. The manuscript was written by [PERSON] and [PERSON]; [PERSON] and [PERSON] reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the National Natural Science Foundation of China (Grant No. 52078493); the Natural Science Foundation of Hunan Province (Grant No. 2022 JJ30700); the Natural Science Foundation for Excellent Young Scholars of Hunan (Grant No. 202 JJ20057); the Science and Technology Plan Project of Changsha (Grant No. kq2305006), and the Innovation Driven Program of Central South University (Grant No. 2023 CXQD033). These financial supports are gratefully acknowledged.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# An Overview of Terahertz Imaging with Resonant Tunneling Diodes
## 3 RTD THz Technology
### RTD THz Source  
The first RTD THz source operating at 420 GHz with 0.2 uW output power was demonstrated by a research group at Massachusetts Institute of Technology in 1989 [37], and this was followed by a 712 GHz (0.3 uW) source in 1991 [38]. There was a lull in RTD research until in 2010 when research group at Tokyo University of Technology reported a 1 THz (7 uW) source [39]. The nano-sized (0.39 um\({}^{2}\)) RTD device was integrated with a slot antenna as shown in Figure 4a [40]. The InP chip was mounted on silicon hemispherical lens to collimate the THz beam. By using this approach, 1.98 THz with 40 nW output power was achieved in [41]. In the sub-THz range, 1 mW at 0.26 THz was reported in [31] where a single RTD was integrated with a microstrip resonator (see Figure 4b).  
Figure 3: Simulated RTD output power versus frequency and load conductance. Reprinted with permission from Ref. [30].  
RTD arrays have also been developed for the purpose of power; combining 0.61 mW at 620 GHz was reported in [32] when two RTD-slot antenna element arrays were employed. The researchers of [42] reported 0.73 mW at 1 THz when 89 RTD-slot-dipole antenna element arrays were integrated. A dipole antenna was placed over the slot resonator and coupled the radiation power away from the high permittivity substrate. The array was operated in pulsed mode and multiple spectrum peaks were observed due to lack of synchronization between all elements.
## 3 Network and VoD Content Modeling
### VoD Content Catalog Modeling
#### 3.2.2 VoD Content Characteristics  
A video item is described by _i)_ its popularity, _ii)_ its duration and _iii)_ its size. The popularity is defined as the rank of the video in the content catalog; i.e., item #1 is the most popular, while the last item, item #20000, is the least popular. The duration of VoD content follows a power-law distribution in which short videos are more common than large ones, and the duration ranges between 1200 and 8400 s. Moreover, we assume that each VoD item can be streamed at three different bit-rates, i.e., 3 Mbps, 6 Mbps and 12 Mbps, and that a VoD item is stored in its best format [20,21]. The size of a VoD content is simply the product of its duration and best bit-rate possible. In addition, we consider the chunk-nature of video content, where each item is made up of a number of small video-chunks [22]. Each chunk has a fixed duration of 1.5 s, and the number of video-chunks in a video item can be determined by dividing its duration by the chunk duration.
## 3 Results
### Evaluation
#### 3.2.1 Existing PCEs  
A sample set of real-life postcoordinated expressions was determined, as described in Section 2.5. Automatically checking these 41 PCEs for Concept Model adherence resulted in 34 cases of a response containing errors. The subsequent manual analysis revealed 6 PCEs with actual Concept Model violations, 26 PCEs with minor syntax issues caused by reasonable restrictions of the Ontoserver and 2 PCEs that were falsely identified as incorrect due to an error in SNOMED CT's Concept Model. The latter two sets were considered acceptable, finally resulting in 35 correct and 6 incorrect PCEs.  
For the correct PCEs, a two-step process was developed to verify that WASP enables guided replication of them. In the first step, the usage context of the respective PCE is  
Figure 7: A screenshot of the user interface showing the Template creation process. Here, the Template presented in Section 2.3 is recreated with the same Attributes, Attribute value ranges, cardinalities and Role groups. Only for the “Procedure site”, a more specific value range is chosen, limiting possible Attribute values to the subhierarchy of “Bone structures”.  
identified, so that a Template--appropriate for the general use case--can be generated within WASP. This Template is then applied as the basis for the second step, in which the original PCE is recreated using the \"Create PCE based on Template\" function. This process was conducted with 10 randomly chosen PCEs. For all of these, both the Template and PCE could be created without issues. The original and the replicated PCE were compared pairwise and evaluated as equivalent.  
To further test WASP's capability to prevent erroneous input, an attempt was made to recreate all 6 incorrect PCEs directly using the \"Create PCE based on Concept Model\" function. Hereby, two different kinds of violations occurred that correctly prevented the formulation of the respective PCE: Unauthorized Attribute used and maximum cardinality exceeded. In the first case, the PCE contains an Attribute that is not sanctioned for the Focus concept's domain. Here, WASP prevents the entry by only showing the allowed Attributes in a list to choose from. In the second case, the PCE contains the same Attribute more times than allowed. Similar to before, WASP no longer offers an Attribute if the maximum cardinality is reached. Thus, none of the incorrect PCEs could be created within the user interface.
## 3 Performance Evaluation
### Derivation of \(F_{\gamma_{\text{SS}_{i}\text{SS}_{j}}}(x)\) and \(\Phi_{i}\)  
From Equation (7), we can formulate \(F_{\gamma_{\text{SS}_{i}\text{SS}_{j}}}(x)\) as  
\[\begin{array}{ll}F_{\gamma_{\text{SS}_{i}\text{SS}_{j}}}(x)&=\text{Pr}\Big{(} Y_{i}(Z_{i,\text{Sum}})^{2}<x\Big{)}=\text{Pr}\bigg{(}Y_{i}<\frac{x}{\left(Z_{i, \text{Sum}}\right)^{2}}\bigg{)}\\ &=\int_{0}^{+\infty}F_{Y_{i}}\Big{(}\frac{x}{x}\Big{)}f_{(Z_{\text{Sum}})^{2} }(z)dz.\end{array} \tag{15}\]Substituting Equations (11) and (13) into Equation (15), which yields  
\[F_{\gamma_{\text{SS}_{i}\text{SS}_{j}}}(x)=1-\frac{1}{2\Gamma(\vartheta)(\phi)^{ \vartheta}}\int_{0}^{+\infty}\exp\Bigl{(}-\frac{\kappa_{i}x}{z}\Bigr{)}\Bigl{[} 1-\exp\Bigl{(}-\frac{\mu_{i}z}{x}\Bigr{)}\Bigr{]}z\frac{\vartheta}{2}^{-1}\exp \biggl{(}-\frac{\sqrt{z}}{\phi}\biggr{)}dz. \tag{16}\]  
By inter-changing variable \(t=\sqrt{z}/\phi\), we can rewrite (16) as  
\[F_{\gamma_{\text{SS}_{i}\text{SS}_{j}}}(x)=1-\frac{1}{\Gamma(\theta)}\int_{0}^ {+\infty}t^{\vartheta-1}\exp(-t)\exp\biggl{(}-\frac{\kappa_{i}x}{\phi^{2}t^{2 }}\biggr{)}\biggl{[}1-\exp\biggl{(}-\frac{\mu_{i}\phi^{2}t^{2}}{x}\biggr{)} \biggr{]}dt. \tag{17}\]  
The integral in Equation (17) can be computed by using computer tools.  
Next, we define \(\Phi_{i}\) as the probability that \(C_{\text{SS}_{i}\text{SS}_{j}}\) is below a pre-determined data rate \(C_{\text{th}}\). Indeed, \(\Phi_{i}\) can be calculated as  
\[\Phi_{i}=\Pr\Bigl{(}C_{\text{SS}_{i}\text{SS}_{j}}<C_{\text{th}}\Bigr{)}=F_{ \gamma_{\text{SS}_{i}\text{SS}_{j}}}(\gamma_{\text{th}}), \tag{18}\]  
where \(\gamma_{\text{th}}=2^{C_{\text{th}}/(1-a)}-1\).  
We note that the probability that one packet \(p_{i}\) is correctly relayed from \(\text{SS}_{i}\) to \(\text{SS}_{j}\) and is computed as \(\overline{\Phi_{i}}=1-\Phi_{i}\).
# Power Profiling of Smart Grid Users Using Dynamic Time Warping +
## 6 Potential Privacy Issues with Power Profiling
### Behavioral Insights and Privacy Risks  
Power usage analysis at different times provides behavioral insights into user routines. Such analysis can infer when residents wake up, leave for work, return home, and go to sleep [72; 73]. A spike in energy use in the evening may indicate cooking activities, while increased power consumption during holidays could suggest social events or gatherings. Additionally, appliance-specific data (e.g., heating, air conditioning, or entertainment systems) can provide clues about personal preferences, income levels, and lifestyles [74].  
In our analysis, refrigerator consumption patterns enabled us to distinguish between weekday and weekend power consumption behaviors. This capability highlights how power profiling can facilitate user surveillance and tracking, potentially exposing habits such as mealtimes, showering routines, or sleep schedules [75]. To mitigate these risks, our approach can be improved by shifting focus from individual appliance usage to aggregated consumption trends. Furthermore, differential privacy techniques [76] and secure multiparty computation [77] can be integrated to prevent unauthorized access to sensitive user profiles. Anonymization techniques can also be applied to ensure data privacy while retaining its utility for energy management.
# Secure Physical Layer Network Coding versus Secure Network Coding +
## 4 Network with Three Source Nodes
### Secure NC
#### 4.1.2 Security (S4)  
To make a code satisfy Security (S4), we modify the above protocol as follows. The modified protocol uses the channels between the intermediate (untrusted) nodes and the  
Figure 4: Network with three sources.  
destination node twice. In addition, it employs the channels between the source nodes and the intermediate (untrusted) nodes only once. Source node \(S_{i}\) sends the scrambled variable \(L_{i}\) to the intermediate (untrusted) node \(I_{i\oplus 1}\) via the edge \(e_{i}\). Each source node \(S_{i}\) prepares the scrambled variable \(L_{i}\) and sends the variable \(M_{i}\oplus(-L_{i})\) to the intermediate (untrusted) node \(I_{i\oplus(-1)}\) via the edge \(e_{3\oplus i}\). Here, \(i\oplus 1\) and \(i\oplus(-1)\) are regarded as elements of \(\mathbb{Z}_{3}\). Each intermediate (untrusted) node sends both received variables to the destination node by using the channel twice. Then, the destination node \(D\) can recover the messages as:  
\[M_{1} =(M_{1}\oplus(-L_{1}))\oplus L_{1},\ M_{2}=(M_{2}\oplus(-L_{2})) \oplus L_{2}, \tag{19}\] \[M_{3} =(M_{3}\oplus(-L_{3}))\oplus L_{3} \tag{20}\]  
because the node \(D\) obtains information \(L_{1}\), \(L_{2}\), \(L_{3}\), \(M_{1}\oplus(-L_{1})\), \(M_{2}\oplus(-L_{2})\), and \(M_{3}\oplus(-L_{3})\). The information at the intermediate (untrusted) node \(I_{i}\) is the pair of \(L_{i+1}\) and \(M_{i-1}\oplus(-L_{i-1})\), which is independent of anyone of \(M_{1}\), \(M_{2}\), and \(M_{3}\). Hence, this code guarantees Security (S4) as well as Security (S3).
## 4 Experimental Verification  
### Experimental Setup  
The platform depicted in Figure 8 comprises a host computer, a target computer, an FSM, a PCI-6221 data acquisition and control card, and its E00 high-voltage driver. The experiment employs the FSM (S330.2 SL) manufactured by PI Corporation for parameter identification, model validation, and tracking performance testing. The LabVIEW real-time operating system manages real-time control and the sampling process, with the PCI-6221 handling the 16-bit analog-to-digital conversion. The DA output control signal of PCI-6221 is amplified by the E00 high-voltage driver to drive the FSM, with a fixed gain of 20 for E00. Additionally, a resistance strain gauge sensor (SGS) is connected to the PZT actuators of the FSM for angle measurement; the SGS signals are then sampled through the first AD channel of PCI-6221 post-amplification by E00.  
Figure 7: Schematic diagram of traditional composite control method.
# Application of Split Coordinate Channel Attention Embedding U2 Net in Salient Object Detection
## 4 Results and Discussion
### Ablation Studies  
This paper conducts ablation experiments on the two proposed enhanced strategies to verify the impact of SCCA and C2 loss on detection performance, respectively. The model that incorporates both improvement strategies achieves optimal performance across three metrics, albeit with a slight increase in prediction time. The results of ablation studies are shown in Figure 10 and Table 2. The MAE of U2 Net+ SCCA+ C2 Loss is significantly smaller than that of the original model, without any additional improvement strategies added. Moreover, the F\({}_{1}\)-score is higher than that of the model with only a single improvement  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Attention**} & \multirow{2}{*}{**Size**} & \multicolumn{3}{c}{**DUTS**} & \multicolumn{3}{c}{**HKU-IS**} \\ \cline{3-8}  & & **Times** & **MAE** & **F\({}_{1}\)-Score** & **Times** & **MAE** & **F\({}_{1}\)-Score** \\ \hline Encoder & 1.29 & **0.387** & **0.071** & 0.801 & **0.020** & **0.032** & **0.932** \\ CA & 0.58 & 0.555 & 0.072 & **0.802** & 0.054 & 0.033 & 0.927 \\ SCCA & **0.57** & **0.424** & **0.067** & **0.806** & 0.054 & **0.028** & **0.938** \\ CBAM & 0.6 & 0.609 & 0.074 & 0.801 & 0.089 & 0.032 & 0.930 \\ SENet & **0.5** & **0.436** & 0.071 & **0.802** & **0.021** & **0.031** & **0.931** \\ Triplet & 0.88 & 0.544 & 0.071 & 0.801 & **0.034** & 0.034 & 0.924 \\ GCB & **0.57** & 0.582 & **0.070** & 0.800 & 0.038 & 0.036 & 0.919 \\ SKNet & 6.65 & 1.086 & 0.840 & 0.792 & 0.072 & 0.040 & 0.894 \\ NLB & 35.01 & 1.568 & 0.180 & 0.768 & 1.002 & 0.048 & 0.882 \\ APNB & 35.01 & 1.653 & 0.160 & 0.770 & 1.004 & 0.048 & 0.884 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detection results of the compared attention mechanisms.  
Figure 9: Detection results of the compared attention mechanisms.  
strategy added. Therefore, the ablation experiment demonstrates that the two enhancement strategies outlined in this paper lead to improvements in the model's performance to varying degrees.
## 1 Introduction
In fact, pectin and hemicellululose are the main sources of surface polarity, which promotes hydrophilic behaviour and leads to moisture absorption. An effective treatment requires an accurate choice of enzymes and their contents. The leading enzymes used in the fibre modification are pectinases, used to hydrolyse pectin; hemicellulases (xylanases), able to destroy the hemicellulosic material; laccases, able to degrade lignin; and cellulases, responsible for the degradation of \(\beta\)-1,4-glucosidic bonds in crystalline and amorphous cellulose [27]. In some studies, enzymes are combined with chelating agents, such as EDTA (ethylene diamine tetra-acetic acid) or EDTAMPA (ethylene diamine tetra methylene phosphonic acid), whose role is to make pectin easily attacked by enzymes by extracting calcium ions from pectin in the cell walls of the plant [28; 29]. Different studies in literature have investigated the effect of enzymatic treatments on the mechanical behaviour of composite materials. [PERSON] et al. [30] addressed the effects of an enzymatic treatment with fungamix on the properties of abaca fibres and their polypropylene-based composites. The enzymatic treatment caused a decrease in the fibre roughness and pronounced fibrillation, while the tensile and flexural tests highlighted an increase of 5%-45% for the tensile strength and of 10%-35% for the flexural strength after the fibre modification. Moreover, the moisture absorption of the composites was reduced by 20%-45%. [PERSON] et al. [20] tested different commercially available enzymes--namely xylanase, polygalacturonase, laccase and xylanase + cellulase--on hemp and flax fibres embedded in a polypropylene matrix. Among all treatments, the one based on polygalacturonase was the most effective in decreasing the amount of hemicellulose and pectin, thus providing composite materials with a better water resistance and thermal stability compared to the untreated ones while preserving their mechanical properties. These available studies suggest that enzymatic treatments show potential to shape the interfacial adhesion in composites reinforced with natural fibres. In this framework, this study is one of the first that addresses the influence of commercially available and inexpensive enzyme preparations without the presence of any chemicals on the morphological, thermal and mechanical properties of flax yarnsintended for composite applications. In addition, this study reports an assessment, by the single-fibre fragmentation test (SFFT), of the degree of fibre/matrix adhesion with epoxy and vinylester matrices.