esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A  
The basic theory of the unified parametric model addressed in this paper considers the PSU made up of one or more three-port mechanisms (TPMs), which consists of one active PG and up to three OGs (Figure A1). \(X\), \(Y\), \(Z\) indicate a general way to refer to the PG branches (ring gear, sun gear, and carrier); \(x,y,z\) indicate a general way to refer to the PSU main port (\(in\), \(out\), \(i\), \(o\)).  
The nomenclature adopted in this model distinguishes between torques and powers assessed by considering the PSU ideal or real. In the latter case, torques and powers symbols are overlined. Nonetheless, this distinction affects only the dependent variables, which are supposed to counterbalance the PSU mechanical powerletters indicate dimensional power flows, while the dimensionless ones are indicated by lowercase letters without or with an apex, depending on whether they are normalized to the input power (\(P_{in}\)) or to the opposite of the output power (\(-P_{out}\)), respectively. The former normalization is exploited to address power-split operation, while the latter is used in FEV analysis.  
The total mechanical power losses (\(\overline{p}_{L}^{\prime}\) in FEV operation) are the sum of the losses occurring in OGs and PGs, which can be derived as follows:  
\[\left.\overline{p}_{L}^{\prime}\right|_{OG}=-\frac{\left.\overline{P}_{Loss} \right|_{OG}}{P_{out}}\approx-\left|(1-\eta_{X/x})\ p_{x}^{\prime}\right| \tag{1}\]  
\[\left.\overline{p}_{L}^{\prime}\right|_{PG}=-\frac{\left.\overline{P}_{Loss} \right|_{PG}}{P_{out}}\approx-\left|\left(1-\eta^{Z}\right)\left(\frac{ \phi_{x/y}^{z}-\psi_{X/Y}^{Z}}{1-\psi_{X/Y}^{Z}}\right)p_{x}^{\prime}\right| \tag{2}\]  
In Equation (1) \(\eta_{X/x}\) is the OG efficiency, while in Equations (1) and (2) \(p_{x}^{\prime}=-P_{x}/P_{out}\) is the normalized power ratio of the \(x\)-th shaft. In Equation (2) \(\eta^{Z}\) is the efficiency of the PG evaluated when its branch \(Z\) is still, while \(\psi_{X/Y}^{Z}\) is the speed ratio between branches \(X\) and \(Y\) when \(Z\) is still. Therefore, both \(\eta^{Z}\) and \(\psi_{X/Y}^{Z}\) are constant parameters depending on the PG Willis' ratio \(\Psi\) and its basic fixed-carrier efficiency \(\eta_{0}\)[36]. \(\phi_{x/y}^{z}\) is the characteristic function, crucial tool for both analysis and design purpose [33; 36], which is a function of \(\tau\) ruled by nodal ratios:  
\[\phi_{x/y}^{z}=-\frac{\left.\tau_{x}/\tau_{y}\right|_{\tau_{z=0}}}{\tau_{x}/ \tau_{y}}=\frac{\left.\tau_{\mu_{Z}}-\tau_{\mu_{X}}\right.}{\tau_{\mu_{Z}}- \tau_{\mu_{Y}}}\frac{\tau-\tau_{\mu_{Y}}}{\tau-\tau_{\mu_{X}}} \tag{3}\]  
It is worth noting that in FEV operation \(\tau\rightarrow\infty\); this can be modeled in numeric software by considering a value of \(\tau\) high enough (e.g., \(10^{5}\)). Moreover, the characteristic functions rule the ideal power ratio between two TPM shafts:  
\[\phi_{x/y}^{z}=-\frac{P_{y}}{P_{x}}=-\frac{p_{y}^{\prime}}{p_{x}^{\prime}} \tag{4}\]
# Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning
## 2 Related Work  
Static and dynamic malware analysis techniques have been applied for detection and classification problems. The evolution of ML and DL techniques has opened many horizons for the analysis and prediction of malware analysis using malware binary or hex files. These files have been converted into images because DL has been extensively used on images or grid-like data. In 2011, [PERSON] et al. [14] identified the texture features in the malware image file. These files have been obtained by interpreting the byte code of the portable executable (PE) binary file to the grey level of the pixel value in the image. In [14], texture features have been extracted from the malware image using wavelet decomposition and provided to the K-nearest neighbour classifier. This technique achieved 98% accuracy on the benchmark malware dataset consisting of 25 malware families. In [15], the artificial neural network has been trained on the malware texture features and SVM is used for classification. A lightweight malware classification technique for the Internet-of-Things (IoTs) has been reported in [16]. Lightweight CNN has been employed on channel binaries collected from the IoTs network. This approach reached an accuracy of 94.0% for DDoS malware. Android systems are not far away from malware attacks. A malware detection framework has been proposed for the android environment using neural networks [17]. A hierarchical ensemble neural network is proposed to exploit trace detection in Intel processors [18]. These traces have been converted into images data for training deep CNN. [PERSON] et al. [19] extracted malicious op-code sequences and converted them to images. Virtual data of the malware attacks have been generated using generative adversarial networks [20]. It helped in achieving the capability of zero-day malware attack detection and reported an accuracy of 95.74%. [PERSON] et al. [21] proposed a deep learning approach using LSTM-CNN and achieved an accuracy of approximately 98% along with improved time efficiency. However, generally, the reported techniques lack to tackle the following aspects:  
1. Most of the reported works have been evaluated in terms of accuracy on the validation dataset. However, precision and recall measures are considered as better performance metrics than accuracy for the imbalance dataset. Moreover, evaluation of these performance metrics on the test dataset is needed for the robustness of the detection model.
2. Previous techniques largely misclassified malware variants like Autorun.K, Swizzor.gen.II, Wintrim.BX, and Yuner.A that feature minor interclass differences. In this study, we have addressed the above-mentioned research gaps. In this regard, we proposed a feature boosting based DBFS-MC framework for the identification of exceptional malware families and evaluated the results on stringent unseen MalImg dataset in terms of accuracy, precision, recall, and F-score.
## 3 Overall System
### Visual Recognition System
#### 3.1.1 Image Preprocessing System  
The image preprocessing system mainly uses a Depth Range Filter to address challenges in complex construction environments. In practice, rebar grids are usually multi-layered, but only the top layer requires tying. Relying solely on RGB images makes it difficult to isolate the top layer from lower layers, often leading to false detections in intersection recognition. Furthermore, construction sites contain various irrelevant objects, such as other materials and tools, which add background noise and hinder accurate rebar localization. By applying a Depth Range Filter, the system effectively filters out layers and background clutter, improving the precision of rebar intersection detection.  
Since our camera is installed perpendicular to the rebar grid, the distance between the camera and the rebar at different positions on the top layer is approximately the same. Based on this configuration, we introduce a Depth Range Filter based on RGB-D images. The function of the filter is illustrated in Figure 3.  
The function of the filter is to remove pixels from the depth image that fall within a specific asymmetric depth range. Specifically, a fixed depth value \(D_{0}\) is first set, and then deviations \(\Delta_{1}\) are \(\Delta_{2}\) defined for the lower and upper bounds, respectively. These  
Figure 3: Depth Range Filter for rebar layer separation.  
parameters determine the filtering range, which is used to construct a masking function. The mathematical expression for this function is given by Equation (1).  
\[M(u,v)=\begin{cases}1,&\text{if}D_{0}-\Delta_{1}\leq D(u,v)\leq D_{0}+\Delta_{2} \\ 0,&\text{otherwise}\end{cases} \tag{1}\]  
The function of the masking function is as follows: when the depth value D(u,v) lies within the range [\(D_{0}-\Delta_{1}\), \(D_{0}+\Delta_{2}\)], M(u,v) = 1, this indicates that the pixel is retained; otherwise, M(u,v) = 0, indicating that the pixel is filtered out.  
The filtered depth image D\({}^{\prime}\)(u,v) is obtained by applying the masking function to the original depth image, as shown in Equation (2).  
\[D^{\prime}(u,v)=D(u,v)\times M(u,v) \tag{2}\]
## 6 Conclusions  
In the case of voltage feedforward control, the frequency-coupled admittance models of phase-domain PR control and dq-domain PI control were established using the harmonic linearization method. The stability of the GCI system under these two current control methods was compared and analyzed based on the equivalent positive and negative sequence impedance and the traditional Nyquist criterion, yielding the following conclusions:  
1. Regardless of whether voltage feedforward control is considered, adopting PI control and PR current control, considering the frequency coupling characteristics, will establish a more accurate frequency-coupled admittance model.
2. With voltage feedforward control, the admittance characteristics of GCI systems controlled by PI and PR are similar. Without voltage feedforward control, the frequency-coupled admittance models of GCI systems under PI and PR control differ significantly in the baseband.
3. To enhance the stability of the GCI system, the bandwidths of the phase-locked loop and the voltage outer loop should be minimized, provided that dynamic performance requirements are met.
4. In conditions of high penetration, voltage feedforward control diminishes the stability of GCI systems under both PI and PR control, increasing the likelihood of instability. Moreover, if a GCI system controlled by PI shows insufficient stability, employing PR control can marginally enhance stability without altering the mainline parameters and control settings.  
Methodology, J.Y.; formal analysis, W.Q.; writing--original draft, Z.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the graduate student innovation project of North China University of Water Resources and Electric Power, grant number NCWUYC-202315046.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Stents in Congenital Heart Disease: State of the Art and Future Scenarios
## 2 Stent Design
### Fabrication Method  
Stents can be made from sheets, wires, ribbons, or tubes, although the majority of balloon-expandable and self-expandable cardiovascular stents are made from metal wire or tubes. The choice of the fabrication method is strongly influenced by the form of the raw material employed, including machining tubular forms and machining and rolling sheets, as well as welding, braiding, knitting, and coiling wires to form the tubular structure of stents [23]. Probably the most common manufacturing process involves laser cutting from tubes, which also allows for great precision in creating complex patterns on very small tubes (from 0.5 mm diameters) [24] (Figure 1).  
Balloon-expandable and self-expandable stents are usually laser-cut from small tubes in a crimped or near-crimped form; following the cutting phase, deburring and surface polishing are mandatory. At this point, balloon-expandable stents are ready to be loaded on the catheter. On the contrary, self-expandable stents need to reach their final expanded configuration before they can be constrained through the sheath in the delivery system. This is achieved through a process of shape-setting, which involves using progressively larger mandrels and heat treatments to eliminate any plastic deformations caused by the enlargement. Additionally, heat treatment is a crucial step for such stents as it determines their mechanical properties [25].  
Recently, additive manufacturing technologies have been studied as a potential way of stent manufacturing [26]. These stents can revolutionize the field of personalized medicine, with the realization of custom-made devices. In 2020, the FDA approved the use of 3D-printed silicone stents to help patients with serious breathing disorders by keeping their airways open. As for cardiovascular applications with metal stents, there are advances in the realization of functional stents that, however, should be improved to guarantee the same performances as the traditionally manufactured ones [27].
## 6 Results and Discussion  
In this section, we discuss the best-performing architectures and smallest models for each dataset as observed from the results in Tables 11-16. We observed that in most of the cases, the fast gate architectures and the basic RNN architecture required the least memory. So, we found memory savings via these fast gates over the best performers. We noticed that fast gates deviated from the best performers. We report the performance deviation (F1 score deviation) when the fast gates were chosen for inference on the edge. All the metrics are reported after training and compression was carried out on Tensorflow 1.15. The hyperparameters that suit one RNN cell type mostly suit all other types as well. The same set of choices is not applicable to a different dataset. For example, Figure 11 shows the impact of validation accuracy for different batch sizes on the FastRNN architecture on the DSA19 dataset. Table 7 shows the choice of optimizers for the datasets used. The number of hidden units has a direct impact on model size. This is shown inTable 14. Other hyperparameters must be tuned for each dataset separately. Table 8 shows this analysis for the FastGRNN architecture with 16 hidden units on the DSA19 dataset. Also, the regularization and compression techniques are closely related. For example, with respect to the DSA19 dataset, with 16 hidden units per RNN cell, regularization and compression techniques adversely affected both performance and model size. Only hard thresholding and spare retraining did not prove efficient. Only Low-Rank Parameterization showed slightly improved performance and memory savings. A combination of hard thresholding, low-rank parameterization, and sparse retraining showed memory-optimized models and performance improvement. Furthermore, we observed that whenever there were imbalances in the class distribution (Opportunity dataset), increasing the rank of the weight matrices improved performance but adversely affected model size. The Opportunity dataset is unbalanced and has more null cases, which resulted in lower F1 scores compared to those of the others. For the Opportunity dataset, GRU16, GRU32, and LSTM16 performed well. Although FastGRNN8 was the smallest, at 3.92 KBytes, its F1 score was very low (0.29) (see figures in red in Table 12). Therefore, we took the next one, i.e., FastGRNN32, as the best model size of 8.05 KBytes. LSTM16 was 1.26\(\times\) bulkier and GRU32 was 2.09\(\times\) bulkier than FastGRNN8. Performance deviation was about 5% when we considered fast gates. GRU's F1 score was the highest for this dataset. On the Pamap2 dataset, UGRNN16 provided the highest performance of 81% but an F1 score of 0.77 and was heavier than FastGRNN by 2.03\(\times\). The F1 score of GRU32 was the highest on this dataset. UGRNN16 was 2.03\(\times\) bulkier than FastGRNN8, and GRU32 was 3.86\(\times\) bulkier than FastGRNN8. FastGRNN deviated from the best performers by around 13 to 16%.
## Appendix B Healthcare and Educational Data--Systems or Databases
### Healthcare Data Systems/Databases  
* Systems or Databases--Cadastro do Cartao Nacional de Saude (CADSUS--Brazil), Medicare and Medicaid (US), Systeme National des Donnees de Sante (SNDS--France), NHS Spine (UK), Ayushman Bharat Databases and National Health Stack (India).
* Common Attributes Present--Unique ID, patient ID, name, parent's name, spouse's name, date of birth, medical history, treatments, medications, hospital visits, diagnostic results, vaccination status, address, contact information (ex. email or phone number).
## 4 Simulations and Results Analysis
### Fishhook Maneuver  
The fishhook maneuver was performed to verify the system performance and robustness of the designed controller. The initial speed of the vehicle was taken as 108 km/h, and the road friction coefficient was set as 0.85. Because the front wheel angle is commonly proportional to the steering  
Figure 21: The sinusoidal responses of the front wheel angle: (**a**) sideslip angle response; (**b**) yaw rate response.  
Figure 20: The sinusoidal input of the front wheel angle.  
wheel angle controlled by the driver, it was taken as the input signal, as shown in Figure 22. The simulation results for sideslip angle and yaw rate responses are displayed in Figure 23.  
Figure 23: The fishhook maneuver simulation results: (**a**) sideslip angle response; (**b**) yaw rate response.  
Figure 22: The front wheel angle under the fishhook maneuver.  
In Figure 23a, we can see that the sideslip angle of the 4 WS vehicle with the proportional controller had overshoot, and that of the Ho\(\circ\) controller showed fluctuations and responses that were slower than that of the H2/He\(\circ\) controller. As can be seen in Figure 23b, the 4 WS vehicle with the H2/H\(\circ\) controller was able to track ideal yaw rate well, while those of the Ho\(\circ\) controller and the proportional controller had yaw rate tracking errors. In addition, the 4 WS vehicle equipped with H2/H\(\circ\) controller had faster response in terms of yaw rate. We can safely come to the conclusion that the 4 WS system with the H2/H\(\circ\) controller showed a better system performance than those of the Ho\(\circ\) controller and the proportional controller.  
To compare the robustness performance of the proposed three controllers, the effects of side wind disturbance and yaw rate sensor noise were taken into account. The side wind, whose speed was set as 18 m/s (_Level_\(8:17.2\) m/s \(\leq V_{wind}\leq 20.7\) m/s), was used to simulate external interferences, as shown in Figure 24. The fishhook maneuver simulation under disturbances was conducted, and the simulation results are shown in Figure 25. As can be seen in Figure 25, under the disturbances in a 7-8 s period, the sideslip angle and the yaw rate of the 4 WS vehicle equipped with the H2/H\(\circ\) controller and the Ho\(\circ\) controller were slightly disturbed, while that of the proportional controller showed vibrations, and the sensitivities \(\lambda_{sen}\) of the sideslip angle and the yaw rate to the disturbances were greater than 0.08. It was concluded that the H2/H\(\circ\) controller and the Ho\(\circ\) controller are more robust than the proportional controller.  
Figure 24: The disturbances inputs: (**a**) side wind; (**b**) yaw rate sensor noise.
# A Framework to Quantify the Quality of Source Code Obfuscation
## 2 Background
### Source Code Obfuscation
#### 2.1.3 Control Flow Obfuscation  
Control flow obfuscation complicates a program's logical flow while preserving its functionality. Techniques include:  
* [noitemsep,topsep=0 pt]
* Bogus Control Flows [28]: inserts dummy code affecting the control flow graph.
* Opaque Predicates [42]: creates conditional statements that insert garbage code.
* Control Flow Flattening [55]: transforms the program structure into a single, complex switch-case statement.  
These methods obscure the execution path, making it difficult for observers to trace and comprehend the program's logic, thereby protecting intellectual property and defending against security vulnerabilities.  
Table 1 summarizes the obfuscation techniques discussed. Our experiments employed characteristic obfuscation techniques from each category to achieve diverse obfuscation quality.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Categories**} & **Obfuscation** & **Description** \\  & **Techniques** & \\ \hline \multirow{4}{*}{Layout Obfuscation} & \multirow{2}{*}{Scramble Identifiers} & Mangles symbols such as function names and \\  & & variable names \\ \cline{2-3}  & & Changes the format of source code by deleting or adding \\ \cline{2-3}  & Change Formatting & white space, newline characters, etc. \\ \cline{2-3}  & Remove Comments & Deletes comments written by programmers \\ \hline \multirow{4}{*}{Data Obfuscation} & \multirow{2}{*}{Data Encoding} & Transforms strings, values, and similar elements to \\  & & obscure recognition \\ \cline{1-1} \cline{2-3}  & Instruction & Complicates the structure of instruction calculation \\ \cline{1-1} \cline{2-3}  & Substitution & expressions such as add, sub, etc. \\ \cline{1-1} \cline{2-3}  & Mixed Boolean & Uses formulas that combine Boolean algebra and \\ \cline{1-1} \cline{2-3}  & Arithmetic & arithmetic operations \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of source code obfuscation techniques by category.
## 4 Results and Discussion
### Operational Efficiency, Cost Reduction, and Material Recovery  
The integration of MCDA and AI has led to significant improvements in operational efficiency, cost reduction, and material recovery in RSCSW:  
* Operational efficiency: The application of AI-based models has enabled the automation of decision-making processes, resulting in greater efficiency. For example, [PERSON] et al. [45] demonstrated that the use of graph neural networks (GNNs) combined with TOPSIS improved ranking metrics by 5.78%, highlighting AI's potential to enhance decision making in complex scenarios. [PERSON] et al. [55] applied multi-objective programming with the NSGA-III algorithm to optimize reverse logistics networks, demonstrating the ability to robustly minimize costs and environmental impacts.
* Cost reduction: The use of Mixed-Integer Linear Programming (MILP) and genetic algorithms has proven effective in minimizing operational costs. [PERSON] et al. [60] proposed a p-median optimization model to determine the optimal locations for recycling centers in Shanghai, achieving a total cost reduction of CNY 144 million. Similarly, [PERSON] et al. [59] developed a robust MILP model to handle uncertainties and carbon emission constraints, reducing costs in global reverse supply chains.
* Material recovery: The integration of MCDA and AI has facilitated more efficient material recovery processes. [PERSON] et al. [62] proposed an industrial symbiosis model that optimized the recycling and exchange of waste materials, achieving 90% material recovery efficiency. [PERSON] et al. [63] explored the economic and environmental benefits of distributed plastic recycling for 3D printing, demonstrating the potential for material recovery in localized supply chains.  
By performing an analytical review and contrasting points, it is possible to find the following:  
* Scalability concerns: While AI-based methods optimize logistics, [PERSON] et al. [14] argue that the circular economy transition faces scalability challenges due to infrastructure gaps and regulatory fragmentation. This suggests that technological improvements alone are insufficient without broader systemic changes.
* Regulatory inconsistencies: [PERSON] et al. [59] demonstrated significant cost reductions using MILP models; however, regulatory inconsistencies often delay the implementation of optimization models, as pointed out by OECD [28]. This highlights the importance of policy alignment alongside technological advancements.
* Social and ethical implications: While AI improves decision making, it may also displace low-skilled labor in waste management sectors, raising ethical concerns about job losses and the digital divide [61].