esa-sceva/satcom-chunk-collection huggingface dataset:
# Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based Secured Internet of Drone Things (SIoDT)
## 4 Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based SIoDTs (BDTC-SIoDTs)
### Dynamic Trust Estimation
#### 4.1.1 Trust for Vehicle to CH  
Trust calculation during the process of data transfer between the vehicles and CH is initiated. The presented trust model utilizes the shortest trust value calculation between the vehicles and CH by considering the attributes such as packet forwarding ratio, received ratio, trust value set, and the weight factor. The mathematical expression for the shortest trust calculation between the vehicle and CH is shown below:  
\[T_{short}(V,CH)=\sum_{i=1}^{n}W_{i}(V,CH)\times T_{i}(V,CH)\times F_{i}(V,CH) \times R_{i}(V,CH) \tag{2}\]  
In Equation (2), n denotes the number of trust attributes, \(W_{i}(V,CH)\) denotes the weight of metric \(i\) by the vehicle to the CH, \(T_{i}(V,CH)\) denotes the trust value set by the vehicle to the CH, \(F_{i}(V,CH)\) denotes the packet forwarding ratio by the vehicle to the CH, and \(R_{i}(V,CH)\) denotes the packet received ratio by the vehicle to the CH. Using this equation, the CH measures the trustworthiness of the vehicle. Here, the calculation for the packet forwarding ratio and the received ratio is given below:  
\[F_{i}(V,CH)=\frac{SF_{i}+USF_{i}}{SF_{i}-USF_{i}} \tag{3}\]  
\[R_{i}(V,CH)=\frac{SR_{i}+USR_{i}}{SR_{i}-USR_{i}} \tag{4}\]  
In Equations (3) and (4), \(SF_{i}\)_and_\(SR_{i}\) denote the successful forwarding and receiving rate of the message, and \(USF_{i}\)_and_\(USR_{i}\) denote the unsuccessful forwarding and receiving  
Figure 4: Schematic Structure of the Proposed BDTC-SIoDTs.  
rate of message. To measure the aggregated shortest trust value of each vehicle that transfers the data to the CH, the number of neighbor's \(s\) are considered. The mathematical expression for the calculation of aggregated shortest trust value is shown below:  
\[AT_{short}(V,CH)=\frac{1}{s}\times\sum_{v=1}^{s}T_{short}(V,CH) \tag{5}\]  
Then, the total trust measure based on the final weight factor is given below:  
\[DT_{short}(V,CH)=((\alpha*T_{short}(V,CH))+(\beta*\ AT_{short}(V,CH))) \tag{6}\]  
In Equation (6), the terms \(\alpha\) and \(\beta\) are the constant values used for the experimental purpose, and it satisfies the condition \(\alpha+\beta=1\).
# Multi-Task Learning for Medical Image Inpainting Based on Organ Boundary Awareness
## 3 Proposed Method  
### Network Architecture  
There have been several methods for boundary detection, such as sketch generation using GAN [27; 28]. A contour generation algorithm is used to output contour drawings of arbitrary input images [27]. An application for face photo-sketch synthesis based on the composition-aided GAN is introduced by [28]. The proposed network consists of three GANs for edge reconstruction, organ boundary prediction and image inpainting. Our multi-task learning model is built on an adversarial framework, where the three discriminators feedback the discrimination results to the generator as well as the discriminator. Figure 1 shows the detailed architecture of our model. After encoding the input image, three decoder networks predict the edge map, the organ boundary, and the completed image simultaneously. These results are fed into the discriminator networks, whose feedbacks are directed to the generators. The generator network is a modified autoencoder with one shared encoding and three decoding parts. The Dilated Residual Network (DRN) block of an upgraded ResNet block [4] is constructed by replacing the first convolutional layer with the dilated convolutional layer. Dilated convolutions are used with a dilationfactor of two instead of original convolutions in the residual layers to effectively expand the receptive field without losing resolution in subsequent layers [29; 30; 31]. Figure 2 shows the detailed architecture of the DRN block. In the training process, the decoding part is usually difficult to generate feature maps with enough detailed information. Therefore, we employ a super-resolution module (SRM) inside the decoding parts for helping the network learning feature efficiently and produce feature maps with more details. SRM is a modification of the fast super-resolution convolutional neural network (FSRCNN) [32], which makes our model faster with better-reconstructed image quality. Our model is based on the pix2 pix GAN [33]. The proposed network takes images from one domain as input and outputs the corresponding image in the other domain, rather than a fixed-size vector. Unlike the initially proposed architecture which classifies a whole image as real or fake, the pix2 pix GAN-based model tries to classify patches of an image as real or fake. Therefore, the output is a matrix of values instead of a single value.  
Figure 1: The overall architecture of our multi-task framework. The network is built on an adversarial framework. It leverages the edge and organ boundary knowledge with multi-task learning (simultaneous image, edge and organ boundary generation).
## 3 Design of Serial Bus Network System  
### Design of Experimental Setup  
We designed a serial bus network system on a PCB composed of unit network systems with 16 sensor platform LSIs. In this study, we used the PCB to construct the experimental setup, because the design and production of PCBs are well established and easy to access, which can provide a stable evaluation environment. Due to this reason, the PCB is convenient to investigate the behavior of the sensor platform LSIs on the serial bus line as the first trial. According to Ref. [23], the design method of this PCB-based network system can be partially applied to the development of the network system based on flexible circuits.  
Figure 8 shows the photograph and schematic of the unit network system made on a PCB. On this board, 16 sensor platform LSIs were mounted on an 80 cm long bus line in backbone topology with short stubs. The package of the sensor platform LSI is Quad Flat No-leads (QFN) package with 64 terminals. There were two power lines (1.2 V and 3.3 V) on the PCB. Fifteen capacitors of 1 uF were added to the 3.3 V power line for power stabilization. Eight switches were used to adjust the physical address of a sensor platform LSI. To measure the signal waveform sent by each sensor platform LSI near the corresponding bus pin, a small Test Point (TP) was mounted on the junction point at which the stub connecting the sensor platform LSI meets the backbone bus line.  
The serial bus network system with 48 sensor platform LSIs was constructed using three unit network systems shown in Figure 9. To connect the PCBs, we designed a board-to-board connector, through which the signal and ground lines of two PCBs were connected. The bus line cross section of the connector is the same as the unit network system to reduce the discontinuity of the bus line characteristic impedance. For the bus line termination, we designed a terminal board, on which a terminal resistor was soldered. Five DC power supplies were used to supply 1.2 V and 3.3 V powers for three PCBs. A bus communication speed of 1 MHz was selected in this study because the maximum available communication speed was unknown for a long serial bus line with a lot of sensor platform LSIs connected.  
Figure 8: Unit network system with 16 sensor platform LSIs for building larger network system: (a) photograph; (b) schematic diagram.  
For the bus line design, we used Advanced Design System (ADS) simulation software by Keysight Technologies, Inc. (Santa Rosa, CA, USA) to analyze the signal integrity of the sensor platform LSI. The Input/output Buffer Information Specification (IBIS) provided by TSMC was used to simulate the operation of the LSI. To measure bus signal waveforms, we used an active probe with an input capacitance less than 1 pF (TAP1500, Tektronix, Inc., Beaverton, OR, USA).
# Generative Artificial Intelligence-Enabled Facility Layout Design Paradigm
## 2 Evolution of Facility Layout Design Paradigms
### Facility Layout Design 1.0: Experience-Based Methods  
The first stage of facility layout design (FLD 1.0) emerged during the early stages of industrialization, particularly in Industry 1.0. At this point, manufacturing was characterized by rudimentary production techniques, limited automation, and manual labor. The methods in this stage were largely experience-based and rule-of-thumb approaches. These methodologies were grounded in practical knowledge, drawing heavily on the personal experience and intuition of managers and planners rather than on formal mathematical models or data-driven approaches. The focus was primarily function-oriented, with the primary aim being the placement of machines, tools, and workstations in a manner that facilitated basic production tasks, often with little regard for optimizing efficiency or minimizing costs. Given the relatively simple and static nature of early manufacturing processes, these methods were largely sufficient to address the layout challenges of the time. However, the approaches lacked precision and scalability, and their reliance on human judgment limited their ability to handle more complex production environments as industries began to expand.
## 3 Results and Discussion
### Created VR Application for Rehabilitation
#### 3.1.2 Components Providing Proper Rehabilitation  
The crates appear in one of four different, bright colors. The color of the box determines the color of the object to be placed in them. In this way, the game provides a visually relevant stimulus at the border of the patient's visual area. Visual scanning is then directed to the non-neglected side. In addition to training visual scanning, upper limb activation is included by having to move the object to the box. The application has four levels of  
Figure 3: VR application: (**a**) choosing side, (**b**) objects in different colors, (**c**) target boxes, (**d**) game statistics.  
difficulty, which differ in the location of the boxes. Levels 1-3 differ in the location of the crate. With each successive level, the crate is advanced farther and farther to the neglected side, allowing the difficulty level to be adjusted to the patient's range of vision. In addition, level 4 features two boxes in random locations. In addition to motor exercises, this level also develops cognitive functions, making it an ideal complement to therapy. During the game, an object is dropped into a box in the same color, all items go to their original place on the shelf, the box changes its color, and points are added (varying amounts depending on the level). However, when the moved item does not have the right color, none of the above-mentioned events will happen, and the patient must keep trying. To ensure that the application provides continuous training, a reset button has been implemented in the game, which restores objects to their original places. This allows the game to continue if an object is dropped in such a way that the patient is unable to pick it up.
## 3 Results and Discussion
### Microscopic Observations
#### 3.2.6 Energy Storage and Dissipation  
The energy dissipation mechanism provides an important way to explain the micro-mechanical mechanisms in granular materials. To study the effect of fiber orientation on reinforcement, it is necessary to study the evolution of individual energy components in the sand-fiber system under direct shear load. Figure 17 shows the evolution of four energy components, namely bond strain energy, friction energy, strain energy, and kinetic energy during the shear process under normal stress of 200 KPa. Fiber deformation leads to the storage of strain energy in the parallel bonds that join the fiber balls to one another. As shown in Figure 17a, the effect of bond strain energy storage in the fibers gradually appears with the development of direct shear, which also means that the mechanical properties of fibers have been developed gradually. In addition, the bond strain energy stored in fibers having an orientation of \(60^{\circ}\) is the largest, which means that the mechanical properties of fibers having an orientation of \(60^{\circ}\) can be mobilized to the greatest extent. Figure 17b indicates that the friction energies increase monotonically with the increase of horizontal displacement. Furthermore, the friction energy dissipation in the sample with fibers having an orientation of \(60^{\circ}\) is higher than that in samples with other orientations. The stored strain energy in the FRS material increases first and then remains stable during the shearing process (see Figure 17c). The stored strain energy in the sample with fibers having an orientation of \(60^{\circ}\) is the largest. The enhanced capacity of the assembly to store strain energy could also be a factor in enhancing its shear strength. Because of the low loading rate, the kinetic energy is very small and almost unchanged during the shearing process (see Figure 17d).  
The energy dissipation analysis can explain the mechanisms of optimizing the reinforcement performance in the presence of different fiber orientations. Numerical results show that friction energy and strain energy play a significant role in energy storage and dissipation. The high reinforcement effects of fibers having an orientation of \(60^{\circ}\) could be attributed to an increase in the energy storage/dissipation potential, which enables the particle system to transfer and bear loads in a relatively stable state.  
Figure 16: Tensile force contact percentage in fibers.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Path Establishment (Route Discovery)  
According to the way of identifying or discovering possible routes, the routing protocol can be proactive, reactive or hybrid. Proactive protocols generate a routing table at each node containing all the routes before they are really needed using routing information, which is periodically updated. Reactive protocols do not generate routing tables and compute routes only if needed or on an on-demand basis. Finally, hybrid schemes combine the advantages of proactivity and reactivity. In the following, we detail those three classes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Protocol**} & \multicolumn{2}{c}{**Network Architecture**} & \multirow{2}{*}{**Data Aggregation**} & \multirow{2}{*}{**Scalability**} & \multirow{2}{*}{**Power Usage**} \\ \cline{2-2} \cline{4-5}  & **Data-Centric** & & & & \\ \hline
**ACQUIRE** & ✓ & & & Low & Low \\ \hline
**RUGGED** & ✓ & & N A & Low & N A \\ \hline
**Min-Hop** & & ✓ & N A & Medium & N A \\ \hline
**PEW** & & ✓ & N A & Medium & N A \\ \hline \hline \end{tabular}
\end{table}
Table 4: Network architecture-based protocols.
# An Automated Behaviour-Based Clustering of IoT Botnets
## 2 IoT Botnet Behaviour
### Capturing IoT Botnet Behaviour  
The behaviour of the IoT botnet samples can be captured by executing them inside a sandbox. A sandbox is a controlled environment comprised of one or more virtual machines (VMs) equipped with tools for execution tracing and network traffic capturing. The sandbox records the behaviour of a botnet sample during its execution on one of the VMs. However, the botnet behaviour capturing through sandbox execution may be challenged by the following factors:  
1. **Anti-sandbox Techniques**. The sandbox execution of botnet samples may be challenged by anti-sandbox techniques. A sample equipped with sandbox detection capability may halt its execution or hide its true behaviour if it detects that it is being run inside a sandbox [22]. A botnet sample may be able to detect and disrupt execution tracing and traffic capturing tools to prevent the capturing of its behaviour [9]. Another anti-sandbox technique that may be used by IoT botnets is delayed execution. For instance, a botnet sample can be configured to stay idle for two minutes after it is executed to avoid discovery of the C2 server. To ensure a successful execution of the botnet samples, the execution duration should not be too short, and the sandbox should detect, avoid, and report the use of anti-sandbox techniques.  
2. **IoT Botnets Heterogeneity**. A botnet may infect IoT devices with different CPU architectures or with the same CPU architecture. In our recent study of IoT botnets [8], we identified that approximately one third of the analysed IoT botnets infected only a single CPU architecture. To be able to capture the behaviour of the botnets infecting a single CPU architecture, the sandbox should support the CPU architectures which are the most targeted by IoT botnets. Furthermore, a botnet sample may require software tools and libraries that are expected to be available on the vulnerable devices [10]. If the sandbox lacks some of the required software, the sample may fail to execute or may execute partially. Therefore, to increase the chances for a successful sample execution, the sandbox should provide the software tools and libraries that may be required by IoT botnets.
3. **Time of Sample Execution**. It is important the botnet to be active when the sample is executed to ensure that the sample can exhibit all its functionalities, including the C2 communication. Moreover, some IoT botnet infection attacks may include a stage-two payload. In such case, the MD server hosting the stage-two payload must be available during the sample execution for the infection to complete. If a sample is executed long after it was captured, there is a high probability that the C2 and MD servers have been suspended or their configuration has been changed [23]. Thus, the botnet samples should be executed in the sandbox as soon as they are captured or discovered.
## 3 Results
### Reachable Workspace Results  
To analysis the reachable workspace RSA, the motion paths for subjects were collected in the 3-dimensional rehabilitation training virtual game. The 3D surface is divided into four quadrants: area1, area2, area3 and area4. Figure 9 shows the intuitive graphical visualization of the 3D reachable workspace for different post-stroke patients.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Verables** & **Results** \\ \hline Age, years a & 61.3 (12.9) \\ Male: Female & 19:16 \\ Time since stroke, weeks & 8.3 (0.8–24.7) \\ Infarct: Haemorrhage & 15:20 \\ Left: Right side & 14:21 \\ FMA score in the affected arm a & 32.3 (14.7) \\ \hline \hline \end{tabular}  
* Mean (SD); FMA, Fugl–Meyer assessment.  
\end{table}
Table 1: The detailed clinical information about patients.  
Figure 8: Rehabilitation scene in Nanjing Tongren hospital.  
In Figure 9a, representing patient1 with an FMA score of 96, the RSA distribution between each quadrant is as uniform as in healthy subjects. The arm of patient 2 (Figure 9b) mainly moves in the sagittal plane and the lateral coronal plane, but the force on the medial side is small. For patient 3 the accessibility at the top of the quadrant is slightly reduced (Figure 9c), and the hand could not reach the I quadrant. Patient 4 (Figure 9d) has almost no force to lift his arm to the upper quadrant. Due to muscle weakness, the hand of patient 5 (Figure 9e) only moves within the ipsilateral quadrant of the lower side, that is to say, the affected limb is mainly active in the IV quadrant.  
As can be seen from Figure 9, as the FMA score decreases, the RSA reduces, especially in the I, II, and III quadrant, and the trajectory is wild and bumpy in the intuitive view. Thus, with the increase of upper limb injuries, the smoothness of the trajectory of the upper limb reachable workspace is reduced,  
Figure 9: 3D reachable workspace of post-stroke patients: (**a**) patient 1, right hand, FMA score is 65; (**b**) patient 2, right hand, FMA score is 51; (**c**) patient 3, right hand, FMA score is 37; (**d**) patient 4, right hand, FMA score is 22; (**e**) patient 5, left hand, FMA score is 10.  
the movement quality is decreased and the movement becomes clumsy. The RSA of the patients' arm could be obtained to facilitate the understanding of the patient's condition.
## 5 Performance Evaluation
### Comparison Algorithms
#### 5.3.2 Comparison Methods: ARIMA Model  
The essence of the ARIMA algorithm [28] is to identify trends, seasonal and periodic patterns in the data and then extract the rule information from the data layer by layer, so that the data are free of patterns or noise.  
The ARIMA model is used as the comparison algorithm to conduct a comparative experimental study on the time series model and the algorithm. ARIMA model can be divided into three parts, AR (Auto Regressive model), MA (Moving Average model) and I (Integrated), and their formulas are  
\[AR:Y_{r}=\mu+\varepsilon_{r}+\sum_{i=1}^{p}\gamma_{i}Y_{r-i} \tag{23}\]  
\[MA:Y_{r}=\mu+\varepsilon_{r}+\sum_{i=1}^{q}\theta_{i}\varepsilon_{r-i} \tag{24}\]  
Here, \(Y_{r}\) is the current value, \(Y_{r-i}\) is the historical value, \(\mu\) is the constant term, \(\gamma_{i}\) is the autocorrelation coefficient, and \(\theta_{i}\) is the error term. The AR formula is used to describe the relationship between the current value and the historical value and uses historical time data of variables to predict, while the \(MA\) formula is concerned with the accumulation of the error terms in the autoregressive model. I is the difference method, and ARIMA is an ARMA model after the difference method to ensure the stability of the value.  
The ARIMA model contains three parameters, i.e., \(p\), \(d\), and \(q\). \(p\) represents the lag number of the time series data itself used in the prediction model. \(d\) represents the order of difference required for time series data to be stable. \(q\) denotes the lag number of prediction error used in the prediction model.
# Code Smell Detection Using Ensemble Machine Learning Algorithms
### Research Questions  
This study has the following research questions.  
RQ1. Which ensemble and deep learning algorithm is better /best for detecting the code smells?  
Motivation. [PERSON], [PERSON] et al. [22] applied machine learning and stacking ensemble algorithms, and [PERSON] et al. [24] applied deep learning for code smell detection. They found that the stacking ensemble and deep learning algorithms obtained better performance accuracy than the MLTs. For that reason, we examine the impact of other ensemble learning and deep learning algorithms to detect code smells.  
RQ2. Does a set of metrics chosen by the Chi-square FSA improve the performance of code smell detection?  
Motivation. [PERSON] et al. [25; 26], [PERSON]. [27], and [PERSON] et al. [23] presented the impact of different FSAs on the performance measurements. They found that using the FSA improved the accuracy, although these authors did not examine the effect of various subsets of metrics on the algorithms' performance. Therefore,in this study, the Chi-square FSA is applied to improve the algorithms' performance and identify which subset of metrics plays a better role in the code smell detection procedure.  
* Does the SMOTE class balancing technique improve the performance of code smell detection?  
Motivation. [PERSON] et al. [28] applied a random sampling method to solve the class imbalance issue. They found an improvement in the results with the applied random sampling method. [PERSON] et al. [29] applied an ADIODE method to identify the code smell with class labels. They found good results. It motivated us to apply the SMOTE method to find the impact of the class imbalance problem in our study.  
The outline of the paper is as follows: Section 2 describes the literature review. Section 3 describes the used datasets and research framework. Section 4 depicts the implementation work. Section 5 discusses the result analysis and threats to validity. Section 6 concludes the study.