esa-sceva/satcom-chunk-collection huggingface dataset:
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 2 Bridge Scenario
### Exfiltration  
The exfiltration scenario is illustrated in Figure 3. Malicious software actively scans the targeted network for pertinent content and subsequently utilizes a designated mechanism to transmit it to a predetermined destination.  
* Data transmission: Malware utilizes the CPU and other hardware to manipulate power consumption fluctuations in devices, encoding and transmitting data through the power line. The attacker can schedule a specific time for malware transmission  
Figure 3: Schematic diagram of the air-gap channel for data exfiltration, where malware on the air-gap workstation modulates binary information onto power line transfer device power consumption and is intercepted by a nearby smart plug.  
Figure 2: Scenes of data infiltrating and exfiltrating through smart plugs.  
or send it after receiving instructions from the attacker, which involves bidirectional communication.
* Data reception: The signal transmitted by the air-gapped device can be received by the smart plug that provides power to it. Subsequently, the received signal will be recorded and forwarded to the receiver for decoding. The hardware responsible for receiving signals may either be deployed by internal personnel or consist of IoT devices that have already been remotely controlled.
## 2 Comparison of Penetration Experiment and Simulation
The material parameters of wood mainly include density, elastic modulus, shear modulus, tensile strength, compressive strength and shear strength, and other parameters are shown in Table 2.  
In Table 2, \(\rho\) is the density of wood, \(E_{//}\) is the parallel modulus of elasticity, \(E_{\perp}\) is the vertical modulus of elasticity, \(\sigma_{st||}\) is the parallel tensile yield stress, \(\sigma_{sc//}\) is the parallel compression yield stress, \(\sigma_{st\perp}\) is the vertical tensile yield stress, \(\sigma_{sc\perp}\) is the vertical compression yield stress, and \(\mu\) is Poisson's ratio. From the photos of the wood target plate, it can be seen that the cutting plane is perpendicular to the radial direction, which is consistent with the state of most fragments in the actual situation. Based on this, a model with the same parameters as the experiment was established in the simulation, and the residual velocity was taken as the main parameter for comparison, as shown in Table 3.  
From the comparison of simulation and experimental parameters in Table 3, it can be seen that the material model of wood has a high degree of explanation for residual velocity and failure mode, wherein the maximum error of residual velocity is 22.18% and the minimum error is 0.14%. The failure mode in the simulation is the same as that in the experiment. A little wood flies out on the back of the target plate, as shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\rho\)/(g\(\cdot\)cm\({}^{-}\)3) & \(E_{//}\)/GPa & \(E_{\perp}\)/GPa & \(\sigma_{st//}\)/GPa & \(\sigma_{sc//}\)/GPa & \(\sigma_{st\perp}\)/GPa & \(\sigma_{sc\perp}\)/GPa & \(\mu\) \\ \hline
0.473 & 11.35 & 0.2468 & 0.0852 & 0.0212 & 0.00205 & 0.00408 & 0.157 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Wood material parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Fragment** & **Initial** & **Experimental Residual** & **Simulation Residual** & **Difference Ratio/\%** \\  & **Diameter/mm** & **Speed/(m\(\cdot\)s\({}^{-1}\))** & **Speed/(m\(\cdot\)s\({}^{-1}\))** & **Speed/(m\(\cdot\)s\({}^{-1}\))** & **Speed/(m\(\cdot\)s\({}^{-1}\))** \\ \hline
1 & 6 & 985.65 & 85.36 & 66.43 & 22.18 \\
2 & 6 & 1151.6 & 223.24 & 245.7 & 10.06 \\
3 & 6 & 85
## 2 Literature Survey
(2016) proposed the double-level-LEACH (DL-LEACH). The DL-LEACH algorithm predicts the node's remaining energy, the distance from the BS, and the adjacent nodes, to select the group leader. The DL-LEACH algorithm extends the NLT and solves the problems of other LEACH methods. The use of DL-LEACH does not affect the network lifetime [31]. Difference machine learning and soft computing and the deep-learning algorithm are proposed in [32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46]. An effective, reliable data transmission and prediction-based data transfer has been processed using soft computing and a regression algorithm [57; 58; 59; 60; 61; 62; 63; 64; 65]. Using network partitioning and an evolutionary protocol to select the best CH, location information, and residual power decomposed by WSN can improve power consumption. CH optimization is a complex non-deterministic polynomial (NP) problem in QoS. Such problems will be solvedusing polynomial-time general-approximation algorithms. Local optima are a problem for metaheuristic problems, and this study used a hybrid technique to address the local optima for CH selection. Efficient WSN protocols that use cross-layer techniques are required to enhance system stability and performance. However, the aforementioned methods strongly emphasize node clustering, routing, and low-power cluster-head selection. As a result, it is essential to create a CL protocol that can use a novel hybrid algorithm for CH selection, eliminate hotspots by using different clusters and apply a suitable optimization algorithm to perform routing. Due to density, farm area, long-distance communication, high latency/latency, and packet loss, these approaches are challenging for IoT-driven WSNs. WSN applications require low data-transfer latency, low latency, and energy efficiency. This work encourages us to suggest an IMSF solution to deal with energy asymmetry in WSN applications by considering different layers' requirements when choosing CHs and forming stable clusters. Energy efficiency and the solution to the energy-asymmetry issue can both be attained by choosing the best CH node. Table 1 Comparative analysis of existing algorithm and its limitations. The main contribution proposed is to improve the overall QoS performance of WSNs using an optimization-based hybrid routing and clustering algorithm.
# Study on the Effect of Vibrating Process on the Compactness of Slipform Concrete
## 4 Optimal Vibrating Parameter Equation and Paving Speed Equation  
### Equation of Optimal Vibrating Parameters  
This study determined the optimum vibrating time \(T\) at different vibrating frequencies according to the compressive strength, segregation rate \(SC\) value, and macroporosity \(p\) value of slipform concrete (Table 5).  
For airport road surface slipform concrete, the strength and the macroporosity requirements of a large thickness (\(\geq\)42 cm) should be met. The comprehensive analysis showed that when the vibrating frequencies were 6000 r/min, 8000 r/min, and 10,000 r/min, the best vibrating time were 70-90 s, 60-80 s, and 50-70 s, respectively (Table 2). The ball test measured the time \(t\) of the ball rising at different vibration frequencies and vibration coefficient \(K\) value (Table 6).  
Figure 11: Vibration frequency of 10,000 r/min under the macroscopic pore distribution of concrete.  
Figure 12: Macroscopic porosity of concrete under slip molds with different vibrating parameters.  
The comprehensive analysis of the compressive strength of slipform concrete, segregation rate \(SC\), and macroporosity \(p\) value, the optimum range of the vibration coefficient \(K\) value was determined to be 1.60-2.00.  
The time \(T\) and height h of the ball rising in the small ball test had a mathematical relationship with the vibration viscosity coefficient \(\eta\). Therefore, the public \(t\) was substituted with the vibration viscosity coefficient \(\eta\), and the equation of the vibrating time length \(T\) and the vibration viscosity coefficient \(\eta\) were further deduced (Equation (3)).  
\[T=K\frac{H\eta}{78.2747(\rho_{C}-0.0858)}, \tag{3}\]  
where \(\rho_{C}\) is the volumetric weight of concrete (g/cm\({}^{3}\)), \(H\) is the rise height of the small ball \(\geq\) 42 cm, \(\eta\) is the vibration viscosity factor (Ns/m\({}^{2}\)) at 10 cm from the bounding bar, and \(T\) is the required vibration time of concrete (s) at the corresponding vibration frequency.
# Low-Frequency Square Kilometer Array Pattern Optimization via Convex Programming
## 2 The Proposed Approach
The optimization problem is then posed as follows:  
Find \(\{I_{1},I_{2},\cdots,I_{N}\}\) such to:  
\[\max_{I_{1},I_{2},\cdots,I_{N}}\text{Re}(\mathbf{E}(\theta_{T})) \tag{2}\]  
subject to:  
\[\text{Im}(\mathbf{E}(\theta_{T}))=0 \tag{3}\]  
\[\left|E(\theta)\right|^{2}\leq UB(\theta)\forall\theta\
eq\theta_{T} \tag{4}\]  
where \(UB(\theta)\) is an arbitrary upper-bound function enforced in the sidelobes' region, while \(\text{Re}(\cdot)\) and \(\text{Im}(\cdot)\) denote the real and imaginary parts, respectively. Note that the objective function (2) is designed to maximize reception in the target direction, while the constraint (3) enforces a purely real field component at that point. Instead, the constraint (4) limits the sidelobe levels to remain within the defined mask \(UB(\theta)\). Given that (4) is a positive semi-definite quadratic form, constraints derived from its angular discretization are convex. Additionally, both the real and imaginary part of \(\mathbf{E}(\theta_{T})\) are linear with respect to the unknown weights \(I_{1},\ldots,I_{N}\), ensuring that the feasible region defined by the constraints remains convex. Consequently, the intersection of these constraints defines a convex set in the solution space. Therefore, the problem qualifies as a CP optimization and, hence, the identification of the global optimum is guaranteed even when employing fast local-optimization techniques [12]. This feature makes the proposed approach particularly well-suited for maximizing antenna performance, especially in the context of SKA-Low, where the very large number of elements in each station makes computational efficiency a critical factor. Figure 3: SKA-Low Configuration: (**a**) Cartoon of an aerial view of SKA-Low station; (**b**) Schematic of the antenna layout illustrating the Vogel distribution, from [8]. Credits: ICRAR/INAF. Furthermore, the proposed technique can be applied (and guarantees the global optimality of the corresponding solution) regardless of the operating frequency and the array layout geometry. It also enables simultaneous optimization of receiving patterns at multiple frequencies, further expanding the applicability and relevance of the design procedure and associated tools.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 8 Submerged HF Membrane Integrity and Failure
### Excessive Fiber Movement  
The advantage of submerged membranes is that the hydrostatic pressure generated eliminates the need for the membrane modules to be pressurized. For such configurations, air-scouring or bubbling is employed to provide a shear force along the membrane surface to help alleviate the fouling phenomenon [14]. A higher shear force on the membrane surface results in a more efficient removal of foulants. In addition, specifically for bubbly flow around hollow fibers, another mechanism at play is the back and forth movement of the fibers induced by the bubbles, which causes a transverse vibration for loose fibers that leads to enhanced secondary mixing [15]. However, although the bubble-induced shear and fiber movements were able to reduce the fouling rate by up to 10-fold less, the excessive membrane movement due to a higher shear force can also lead to fiber breakage [15]. This phenomenon, coupled with the degradation of the membrane fibers due to ageing, could lead to a higher occurrence of fiber failure. Excessive fiber movement is also constrained if the fiber looseness is limited, for which the practical limits are typically 1-5% [3].
## 1 Introduction
After circular FFT operation, correlation results contain two peaks of the same magnitude and specified relative position. The following steps consist of two levels. In the first level, the max-value tactic is adopted and the positions of the two highest peaks are preserved. The second level verifies the result in the first level. The probability that noise values on any certain position has largest magnitude is low, and therefore the false alarm probability is reduced. Multi-peak verification and max-value detection bring better acquisition performance than the conventional methods. Numerical results show that DC-XFAST has better performance in certain false alarm probabilities compared with zero-padding, XFAST and Dual-Folding method. The rest of this paper is organized as follows: the second part will be detailed description of DC-XFAST, including system model and observation window of the method. In the third part, the performance is analyzed, including detection probabilities, false alarm probabilities and mean acquisition time, theoretical results are also presented. Numerical results are provided in the fourth part. The discussion of the application is in the fifth part and the conclusion is presented in the sixth.
###### Abstract  
Direct-injection technology applied in hydrogen internal combustion engines can effectively prevent backfire, thereby improving the engine performance. Nonetheless, optimizing the injection strategy is highly intricate, requiring a comprehensive understanding of the hydrogen-air mixture formation process inside the cylinder. In this study, a simulation of hydrogen-air mixture formation was systematically conducted in a hydrogen direct-injection internal combustion engine using three-dimensional computational fluid dynamics (CFD) software. Under rated conditions, the influence of the nozzle hole number, injection direction, injection timing, and combustion chamber geometry on the mixture formation was analyzed from the perspectives of flow state and mass transfer. The results indicate that more nozzle holes would lead to more significant non-uniformity of the mixture, mainly due to the Coanda effect. The normalized standard deviation (NSD) of a six-hole nozzle design is 0.3495, which is higher than the NSD of all the single-hole nozzle conditions. By changing the hydrogen injection timing from \(-144\)\({}^{\circ}\)CA to \(-136\)\({}^{\circ}\)CA, the non-uniformity coefficient of the mixture is little affected, while notable differences in the distribution of the mixture are observed. The appropriate injection directions and optimized combustion chamber geometries could also help to effectively organize the in-cylinder flow, significantly improving the uniformity of the in-cylinder mixture and reducing the likelihood of abnormal combustion events.  
hydrogen internal combustion engine; direct injection; mixture formation; numerical simulation +
Footnote †: journal: applied sciences
## 3 Mobile Communications Background
### The 5G Test Environments  
The ITU-RM.2412-0 report [54] defines a test environment as a combination of use cases and geographical environments. In the report, a set of five test environments were identified for the IMT-2020 and listed as follows.  
1. Indoor Hotspot-eMBB: This is an enclosed location with a high number of pedestrian users, such as offices and shopping malls.
2. Dense Urban-eMBB: This is a heavily populated urban area with high traffic loads, with a focus on drivers and pedestrians.
3. Rural-eMBB: This describes a rural location that focuses on providing more extensive and continuous coverage for pedestrians and vehicle users.
4. Urban Macro-mMTC: This is an urban macro settlement consisting of many machine-type devices which are connected for continuous coverage.
5. Urban Macro-URLLC: The provision of ultra-reliable and low-latency communication is a goal of the macro-urban environment, and a macro-urban location is used to achieve it.  
Figure 3: The 5G Standardization Timeline.  
Rural-eMBB, Dense Urban-eMBB, and Indoor Hotspot-eMBB test environments are also suitable for enhanced broadband use cases. In contrast, the Urban Macro-URLLC and Urban Macro-mMTC test environments are suitable for ultra-reliable and low-latency communication and massive machine-type communication.
# BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning
## 2 System Model
The task queue is recorded as the set \(\mathbb{Q}^{\text{T}}=\left\{q_{1}^{t},q_{2}^{t},\cdots,q_{\zeta}^{t}\right\}\), where \(\zeta\) is the maximum capacity of the task queue \(\zeta\in\mathbb{N}^{+}\), and the element \(q_{i}^{t}\) is defined as a quadruple:  
\[q_{i}^{t}\triangleq\left\langle size_{i_{t}},\mathbf{r}_{i}^{sat},t_{i}^{ start},t_{i}^{end}\right\rangle \tag{2}\]  
where \(size_{i}\) is the short-message task size. \(\mathbf{r}_{i}^{sat}\) is the response matrix, which records the short-message satellites that can respond to the task. For example, when \(n=3\), \(\mathbf{r}_{i}^{sat}=[0,1,1]\), which means that \(SAT_{2}\) and \(SAT_{3}\) can respond to \(q_{i}^{t}\). \(t_{i}^{start}\) records the time when the task enters the queue. \(t_{i}^{end}\) records the time that the task is processed. The task queue is updated after the end of each snapshot, i.e., \(s\) time slices. The unprocessed tasks in the previous snapshot are copied to the head of the queue, and new  
Figure 2: Short-message satellite communication system resource allocation model. tasks continue to be received in the current snapshot, i.e., \(\mathbf{Q}_{t+1}^{\mathrm{T}}\leftarrow\mathbf{Q}_{t}^{\mathrm{T}}-task^{r}+task^{ new}\). \(task^{r}\) indicates that the task has been responded to, and \(task^{new}\) is the new task.
## 2 Methods
### Handling of Outliers in ESS Data  
The presence of outliers in the Endothelial Shear Stress (ESS) data can potentially distort the visualization and interpretation of the ESS distribution. To ensure the accuracy and reliability of the analysis, a systematic approach was adopted to identify and handle these outliers. The mean and standard deviation of the ESS values were first calculated. An outlier was then defined as a data point that deviated by more than three standard deviations from the mean ESS value. This threshold was chosen based on the empirical rule, which states that for a normal distribution, about 99.7% of the data falls within three standard deviations of the mean. The outliers identified by this method were then excluded from the subsequent analysis. This approach helped to mitigate the impact of extreme ESS values that could be due to measurement errors or other anomalies in the CFD simulation. By handling these outliers, a more accurate and representative visualization of the ESS distribution in the aorta was achieved.
# On the Influence of Data Imbalance on Supervised Gaussian Mixture Models
## 1 Introduction  
### Motivation  
Gaussian mixture models (GMMs) are probabilistic models widely employed in clustering and density estimation tasks which provide a flexible framework for modeling complex data distributions [1; 2; 3]. These models represent data as a finite mixture of Gaussian distributions with unknown parameters, where the latter are often estimated through the Expectation-Maximization (EM) algorithm [4; 5].  
In addition to the aforementioned unsupervised tasks, GMMs also find application in classification tasks. Supervised Gaussian mixture models (S GMMs) proved to be effective in this domain due to their ability to represent complex data patterns. However, these models are sensitive to class imbalance, where the number of samples in one or more classes significantly differs from the others, leading to biased parameter estimation, diminished generalization, and reduced accuracy [6; 7]. This scenario is especially relevant in domains such as fraud detection, rare event prediction, disease diagnosis, and many other classification and pattern recognition problems. Despite these drawbacks, data imbalance in GMMs has received little attention in the literature [8; 9].
###### Abstract  
Developing new therapeutic drugs to prevent ischemia/reperfusion (I/R)-induced renal injuries is highly pursued. Liposomal encapsulation of spironolactone (SP) as a mineralocorticoid antagonist increases dissolution rate, bioavailability and prevents the drug from degradation. In this context, this work develops a new formulation of oil-in-water type microemulsions to enhance the bioavailability of SP. The size of the SP-loaded microemulsion was about 6.0 nm by dynamic light scattering analysis. Briefly, we investigated the effects of nano-encapsulated SP (NESP) on renal oxidative stress, biochemical markers and histopathological changes in a rat model of renal I/R injury. Forty eight male Wistar rats were divided into six groups. Two groups served as control and injury model (I/R). Two groups received \"conventional\" SP administration (20 mg/kg) and NESP (20 mg/kg), respectively, for two days. The remaining two groups received SP (20 mg/kg) and NESP (20 mg/kg) two days before induction of I/R. At the end of the experiments, serum and kidneys of rats underwent biochemical, molecular and histological examinations. Our results showed that I/R induces renal oxidative stress, abnormal histological features and altered levels of renal biomarkers. Administration of SP in healthy animals did not cause any significant changes in the measured biochemical and histological parameters compared to the control group. However, SP administration in the I/R group caused some corrections in renal injury, although it could not completely restore I/R-induced renal oxidative stress and kidney damage. On the contrary, NESP administration restored kidney oxidative injury via decreasing renal lipid peroxidation and enhancing glutathione, superoxide dismutase and catalase in kidneys of the I/R group. The deviated serum levels of urea, creatinine, total proteins and uric acid were also normalized by NESP administration. Furthermore, NESP protected against renal abnormal histology features induced by I/R. Therefore, NESP has beneficial effects in preventing kidney damage and renal oxidative stress in a rat model of I/R, which deserves further evaluations in the future.  
spironolactone; polymeric biomaterials; nanomaterials; neuroprotective; oxidative stress; neurotoxicity; nanocapsule; rat model +
Footnote †: journal: Applied Sci. 2021, 11, 1264. [[https://doi.org/10.3390/app11031264](https://doi.org/10.3390/app11031264)]([https://doi.org/10.3390/app11031264](https://doi.org/10.3390/app11031264))
# Technology Behavior Model--Beyond Your Sight with Extended Reality in Surgery
## 6 Implications
### Managerial Implication  
In this study, for the first time, the use and perception of extended smart glasses for medical specialists is presented. The medical expert's view is more authoritative in the use of clinical smart surgery. It is also the first time that the human body structure image composition is proposed, and the physical and virtual environment is unified by the existing extended reality technology combined with the visualized detection instrument images to build a three-dimensional image [54; 55]. Secondly, the smart glasses system adds device identification and operation rules, which can effectively remind the operator of the correct usage. Again, the self-contained high-definition camera, not only in limited to recording and accessing images, but also has enhanced human eye vision, which can replace the use of microscopes. Finally, it adds eyeglass tracking to the existing interaction technology. Although this technology is still in the early stages of development, it can better assist medical experts in completing surgical operations by combining mature voice and gesture technology. These new changes above provide medical experts with the convenience of completing surgery and effectively improve the success rate and efficiency of surgery.