esa-sceva/satcom-chunk-collection huggingface dataset:
## Case Study
### The Influence of System Scale and the Number of Control Variables on the Computation Time
#### 4.2.1 Analysis of the Influence of System Scale on the Computation Time  
Firstly, test the impact of the system scale on the computation time with three systems, containing the standard distribution system of IEEE 33 nodes, PG & E 69 nodes and a practical 292 distribution system with 292 nodes. The three testing systems all contain one on-load tap changer (OLTC) and three shunt capacitor compensation nodes, and the specific configuration information of capacitors are shown in Table 5. The single group capacity of capacitors is 50 kvar, and tap of OLTC is 1 \(\pm\) 8 \(\times\) 1.25%.  
The proposed EWOSM, GA method and SQP method are used for reactive power optimization respectively on the three testing systems, and the comparison of computation time is shown in Figure 9.  
As shown in Figure 9 that while the number of control variables is the same, the computation time of EWOSM and GA method increases with the increasing of the system scale, but the computation time of SQP method has little relevance to the system scale. The reason is that power load of all nodes need to be matched in the load distribution matching. Therefore, the computation time of EWOSM is positive correlated to the number of system nodes, but it is still much shorter than GA method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**IEEE 33 Nodes System**} & \multicolumn{2}{c}{**PG\&E 69 Nodes System**} & \multicolumn{2}{c}{**292 Nodes System**} \\ \cline{2-7}  & **Connected Bus** & **Capacity/Kvar** & **Connected Bus** & **Capacity/Kvar** & **Connected Bus** & **Capacity/Kvar** \\ \hline Capacitors C1 & 13 & 500 & 35 & 500 & 29 & 1200 \\ Capacitors C2 & 23 & 500 & 45 & 1200 & 157 & 600 \\ Capacitors C3 & 29 & 1000 & 61 & 500 & 277 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The configuration of capacitors in three different scale distribution networks.
## 3 Results and Discussion
### Communication Overhead
#### 3.2.4 Break-Glass Approach  
In lightweight break-glass, the ECM generates a key measuring 0.256 kB for both public and private key processes. In multi-authority MCP-ABE-BG, break-glass access is not implemented. However, in the proposed method, the break-glass key size is reduced to 0.198 kB due to the bloom filter operation. This shows that the proposed MCP-ABE-BG requires minimum storage size compared to the multi-authority multi-ABE and lightweight break-glass approach. Therefore, the proposed method MCP-ABE-BG is computationally effective and requires less space for secure data transmission in the smart healthcare environment.
# Dynamic Analysis of Cavitation Tip Vortex of Pump-Jet Propeller Based on DES
## 2 Numerical Method and Computational Setup  
### Governing Equation  
In this study, the homogeneous mixed flow model is used for the numerical simulation. The continuity equation and momentum equation based on the homogeneous mixed flow model are as follows:  
\[\frac{\partial\rho_{m}}{\partial t}+\frac{\partial(\rho_{m}u_{j})}{\partial x_ {j}}=\ 0 \tag{1}\]  
\[\frac{\partial}{\partial t}(\rho_{m}u_{i})+\frac{\partial(\rho_{m}u_{i}u_{j})} {\partial x_{j}}=\ -\frac{\partial p}{\partial x_{i}}+\frac{\partial}{\partial x_{j}} \bigg{[}(\mu_{m}+\mu_{t})\bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac {\partial u_{j}}{\partial x_{i}}\bigg{)}\bigg{]} \tag{2}\]  
where \(\rho_{m}\) is the density of the mixture, which is equal to \(\alpha_{v}\rho_{v}+(1-\alpha_{v})\rho_{t}\), and \(\rho_{t}\) is the density of the liquid phase, \(\rho_{v}\) is the vapor phase density; \(\alpha_{v}\) is the volume fraction of the vapor phase, \(u_{i}\) and \(u_{j}\) are the velocity component in the \(i\) direction and the \(j\) direction, respectively, \(\mu_{m}\) is the dynamic viscosity of the mixed phase which is equal to \(\mu_{1}(1-\alpha_{v})+\mu_{v}\alpha_{v}\), and \(\mu_{t}\) is turbulent viscosity.
## 4 Possible Corrective Actions  
In order to guarantee the safety of the devices and to protect the final user, it is recommended to plan the execution of specific tests and laboratory experiments. A complete test panel should include:  
1. An evaluation of the biocompatibility not only of the stock material, but especially of the finished product. This kind of analysis involves several tests summed up in the regulation ISO 10993:2021 and should be performed on the finished product. It is fundamental to trace every single phase of the production process in order to understand the various contaminants that enter in contact with the prosthesis. Once the test has given positive results for a specific device, its whole process is intended to be safe from the biocompatibility point of view. The result of the analysis is considered acceptable as long as anything in the process is altered, causing a modification in the contamination chain.
2. A microbiological (bioburden) and sterility test according to the ISO 11737 to evaluate the efficiency of the sterilization and of the cleaning phase. Chemical characterization test according to the ISO 17025.
3. Resistance tests on melted material samples: tensile test to evaluate the \(\sigma\)-\(\varepsilon\) curve, rotating fatigue test to understand the performance of the material when stressed with an alternated symmetrical cycle.
4. Finite element analysis (FEA) structural characterization of the final geometry of the prosthesis, according to a defined stressing conditions protocol that simulates the actual working conditions of the prosthesis, in order to investigate the static resistance and the efficiency of bone fixation. The magnitude of the loads applied should be then increased by a security factor (Figure 4).
5. In case of articulations involving cycled loads of great intensity or of prosthesis in which critical geometrical features are stressed, a fatigue test on a final component should be arranged. This test should be performed with a setup representing the worst-case loading condition. Indeed, a dedicated ISO for CMF implants does not exist yet, but a proper setup should be created taking as guidelines the ISO 14801:2007 or ISO 16428:2005.
6. Porosity analysis through metallographic cuts, ultrasound technology or magnetic resonance, to understand the homogeneity of the internal metallographic structure and to investigate the surface porosity as well.
7. Wear test in aggressive environment replicating the actual working conditions of the prosthesis, together with a count of the residual particulate released by the contact surfaces after the test in order to give evidence of the absence or presence of fretting phenomena. In this case, ISO 17853:2011 should be taken as reference.
## 5 Discussion
### Opportunities and Future Directions  
In view of all discussions previously made, we summarize a set of promising opportunities and future directions for African countries:  
* Monitoring of waterways, the water levels and the quality to avoid or manage floods during the rainy seasons and safeguard the biodiversity in waterways.
* Accurate forecasting of drought and rainfall due to climate change, to enable making appropriate decisions.
* Collection of temperature data, which is useful to predict periods of peak temperatures currently experienced in West Africa, which are almost unbearable without air conditioning.
* Monitoring or tracking of animals in their natural environment or in the livestock sector using IoT. The animals in the herd may be monitored to observe their behavior, state of health and reactions to potential threat.
* Crop and soil type prediction using emerging technologies to identify the compatibility between crops and soils and the season suitable for the crop production. In other words, it means the use of digital technologies to make the variable-rate technology (VRT) more effective in these countries. The variable-rate technology enables growers to vary the rate of crop inputs such as fertilizer, soil amendments, irrigation and agricultural chemicals.
* Greenhouse farming using emerging technology such as IoT, Artificial Intelligence, robotics and data analytics. Agricultural greenhouses offer an ideal solution for improving crop productivity, especially in Africa, where climatic conditions can be extreme. Its main advantages are protection against pests and diseases, optimized use of water resources and year-round production.
* Improvement of the fishing system using precision fishing for small- and large-scale fishing. Precision fishing consists of the use of advanced tools and technologies to optimize fishing operations and management. Fishermen can optimize fish production while fishing in a more sustainable way.
* Development of rice-fish farming systems to jointly improve the rice and fish production. It is based on a mutually beneficial relationship between rice and fish in the same agroecosystem, most commonly with freshwater fish.  
These directions are directly useful for research and development companies, educators, researchers and NGOs in West Africa.
# Fast Screening Algorithm for Satellites Based on Multi-Constellation System
###### Abstract  
This paper proposes a fast satellite screening algorithm aimed at the problem of balancing between positioning accuracy and system computing efficiency in a multi-constellation system environment under the Global Navigation Satellite System (GNSS). The algorithm constructs an observation model based on a positioning error for the larger number of satellites under a multi-constellation. The space region is divided based on elevation and azimuth angles to implement the screening algorithm for the solution of the point to be determined. An analysis of the experimental data shows that the average GDOP value of this scheme is 1.835, and the position error of the point to be determined is controlled within 2.5 m when the cut-off altitude angle is 5\({}^{\circ}\) and the screening ratio is more than 70%.  
satellite screening; elevation angle; GDOP value +
Footnote †: journal: Electronics
# An Improved Target Network Model for Rail Surface Defect Detection
## 4 Experimental Results and Analysis
### Comparative Experiments  
Based on the experimental findings, we computed precision and recall values at various thresholds, connecting these data points to construct a precision-recall (PR) curve, illustrated in Figure 7a. The curve that is closer to the upper right-hand corner shows that as the recall rate increases, the decrease in precision is less pronounced, indicating a better overall performance of the model.  
The confusion matrix, which reflects the relationship between the true class of the sample data and the class predicted by the classifier, is a common way to evaluate the performance of a classifier. The horizontal axis of the confusion matrix represents predicted values, while the vertical axis represents true values and \"background\" represents the background class. The diagonal line in the graph shows the number of correctly identified samples, with darker colors indicating higher numbers. Visualizing test results through the confusion matrix not only allows for the calculation of correct recognition numbers for each class, but also provides insights into the distribution of misclassifications. The confusion matrix detected by YOLOv7 and the proposed method on the same dataset are shown in Figure 7b. The TP rates for Joint, Squats, and Ssquats are 100%, 97%, and 86%, respectively. The corresponding FP rates are notably low, at 0%, 3%, and 14%, respectively. While occasional instances of FN may arise due to the prevalence of shadows and the influence of intricate environmental conditions, potentially affecting the model's effect, the overall classification of rail defects demonstrates precision and comprehensiveness.
###### Abstract  
This research focused on the contents of the five most bio-toxic heavy metals, As, Cd, Hg, Cr, and Pb of 26 municipal solid waste (MSW) samples from the Eastern Guangdong Area. To investigate the apportion of the heavy metal source, Pearson correlation and principal component analysis (PCA) were introduced as major approaches. The health risks posed to MSW workers exposed to heavy metals in MSW were assessed using a Monte Carlo simulation combined with the US Environmental Protection Agency Health Risk Assessment Model. The As, Cd, Hg, Cr, and Pb contents of the east Guangdong MSW were (\(0.76\pm 0.75\)), (\(2.14\pm 4.44\)), (\(0.11\pm 0.14\)), (\(55.42\pm 31.88\)), and (\(30.67\pm 20.58\)) mg/kg, respectively. Hg, Cr, and Pb were potentially derived from glass, textile, food waste, and white plastic, while As and Cd were mainly derived from soil and food waste in the MSW. The non-carcinogenic risks of heavy metal in MSW exposure to MSW workers could be ignored. However, the heavy metals in MSW might pose carcinogenic risks, with the probabilities for male and female workers being 35% and 45%, respectively. The non-carcinogenic and carcinogenic risk indices were slightly higher for female workers under the same exposure situations.  
MSW; heavy metals; source apportionment; MSW workers; health risk +
Footnote †: journal: Applied Sciences
# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 3 Method
### Curvature Filter Attention  
In the image segmentation tasks, local and global features are both important. In order to efficiently integrate the global information, TSP is used to extract the feature information from three planes and successfully compresses the global information into three two-dimensional feature maps. Subsequently, EGA is utilized to further filter these feature maps. However, to achieve precise edge extraction, we propose an efficient local information extraction module.  
Curvature can accurately reflect the geometric shape information of the image edges, which is particularly important for image segmentation. Generally, high-curvature regions correspond to edges and details in the image, while low-curvature regions correspond to flat areas. During the filtering process, the pixel changes in the high-curvature regions are relatively larger, while the low-curvature regions are smoother. By precisely calculating the curvature of image edges, subtle changes in the edges are captured. This capability allows curvature to identify object boundaries more accurately, thereby achieving precise segmentation of different regions.  
However, the lack of concrete curvature parameters have limited the widespread application of curvature, we make a series of improvements to curvature and designed the CFA to better extract local features from medical images, the structure of CFA as shown in Figure 3. This method can more effectively highlight the edges of objects, providing the possibility of using curvature to enhance edge features. To solve the problem of curvature parameter selection, we set a learnable matrix as the initial curvature value. During the learning process, this matrix gradually adapts to the rough shape of the tumor. Subsequently, we embed the curvature into a convolution operation by weighting the convolution. Finally, the three feature maps are converted into the same three-dimensional space through expand operation, which allows the feature from the three dimensions to complement each other, thus further enhancing the representation of the tumor region. Finally, in order to combine global and local information, the feature maps obtained from the three planes are integrated and the input and output of the ECA are made to be of the same size, achieving a plug-and-play feature.
## 3 Parametric Analysis  
### Control Algorithm Parameter \(\alpha\)  
The parametric analyses were carried out by Simulink software. The influences of control algorithm parameter \(\alpha\), rotary inertia ratio, mass ratio, suspended structural length ratio and excitation frequency are discussed in this part. The parameters mass ratio and rotary inertia ratio are determined as:  
\[r_{m}=\frac{m_{a}}{m+m_{a}},\ r_{l}=\frac{m_{a}r^{2}}{(m+m_{a})l^{2}}\]  
The parametric analysis curves of the LQR control algorithm parameter \(\alpha\), rotary inertia ratio and mass ratio are shown in Figure 3. In the figures above, it can be found that: (a) When the rotary inertia ratio and mass ratio are different, the control effect curves are like steps. (b) When the \(\alpha\) value  
Figure 2: Effect of LQR parameters: (**a**) curves of \(\alpha\); (**b**) curves of \(\beta\).  
is less than \(10^{-8}\), the control effect is very bad. As the \(\alpha\) value continues to decrease, the system loses the control effect. (c) When the value is greater than \(10^{-8}\), the control effect becomes better as it increases. When the \(\alpha\) value reaches \(10^{-2}\), the control effect remains unchanged as it continues to increase. Figure 3a shows that when \(\alpha\) is within the value range, the control effect becomes better as the rotary inertia ratio increases. Figure 3b shows that when \(\alpha\) is between the \(10^{-8}\) and \(10^{-2}\) interval, the control effect is more sensitive to \(\alpha\). The control effect becomes better as the mass ratio decreases. When \(\alpha\) is greater than \(10^{-2}\), the optimal control effect is no longer related to the mass ratio. The system with a different mass ratio has the same control effect.
# A Precise Modeling Method for PMSM Servo System of Spacecraft
###### Abstract  
Permanent magnet synchronous motor (PMSM) servo system is the control and execution mechanism of spacecraft, and its precise modeling is of great practical significance for improving the performance of the spacecraft. A comprehensive model considering elasticity, backlash, and friction was established based on the structural composition and transmission characteristics of spacecraft. Firstly, an elastic-dead zone model was established based on the transmission characteristics, with two mass elastic and gear backlash dead zones as the main features. Then, an improved LuGre friction model was established that considers the effects of vibration and environmental temperature, and the three models were organically combined. Finally, a method for identifying nonlinear parameters of the system using sinusoidal sweep and the least squares method was proposed, and the accuracy of the constructed model was verified. The experiment shows that the error of the simulation results is consistent with the experimental results. This method can precisely obtain the servo system model, providing theoretical support for control method research.  
spacecraft; permanent magnet synchronous motor (PMSM); servo system; precise modeling +
Footnote †: journal: Applied sciences
# Control-Flow Integrity: Attacks and Protections
## 2 Memory Errors
### Integer Overflow or Integer Wraparound  
An integer overflow or wraparound occurs when the outcome of an arithmetic operation is too large to store in the associated representation [28]. This is related to the way numbers are represented internally. For example, a 32 bit unsigned integer can represent up to \(2^{32}\) values. If the output of an arithmetic operation is larger than the maximum (results in a negative value), the result is truncated. This memory vulnerability is listed as CWE-190 by MITRE for strict overflows and CWE-191 when the error is an underflow.  
Many languages consider that integer arithmetic is by default modular and so it is not a bug [29]. If x is unsigned int then (x+c) shall be considered interpreted as ((x+c) mod \(2^{32}\)), which never causes an exception. An example of a program that causes integer overflows is shown in Listing 4.  
```
intmain(intargc,char*argv[]){ charbuf[50]; inti=atoi(argv[1]); unsignedshorts=i; if(s>10){ return; } memcpy(buf,argv[2],i); }
```  
Listing 4: Integer Overflow Vulnerability.  
The fault is caused when the programmer does not take into account this behavior and the overflow or overwrapped variable is used to access an array or data structure out of its bounds. On the other hand, some strongly typed programming languages (for example Ada) emit code that checks for overflows and raises the corresponding exception. Although it may seem that this solution produces robust and reliable software, it is not always the case. The first launch of the Ariane rocket by ESA was a failure due to an unnecessary integer overflow exception. The exception occurred in one of the tasks (Ada tasks) (threads) that were no longer necessary, but that was left active to avoid problems stopping individual tasks on a concurrent system. Unfortunately, the exception got propagated to the whole module, which contained more necessary tasks (the fly control), and resulted in a fatal loss of control [30].
# High Density RF-DC Plasma Nitriding under Optimized Conditions by Plasma-Diagnosis
###### Abstract  
This paper is concerned with plasma diagnosis on a N\({}_{2}\)-H\({}_{2}\) gas mixture to determine the optimum parameters for the nitriding process. Plasma parameters such as pressure, RF-voltage, and DC-bias were varied for optimization. The active species such as N\({}_{2}^{+}\) and NH were identified in plasma diagnosis. In the N\({}_{2}\)-H\({}_{2}\) gas mixture, hydrogen imposed a great influence on plasma generation. The small addition of a hydrogen molecule into the gas mixture resulted in the highest yield of N\({}_{2}^{+}\) ions and NH radicals; the optimum hydrogen content was 20% in the mixture. The austenitic stainless-steel type ASI304 was nitrided at 673 K and 623 K to experimentally demonstrate that hydrogen gas content optimization is necessary to improve the surface hardness and to describe low temperature nitriding under high nitrogen flux at the surface.  
plasma diagnosis; nitrogen-hydrogen gas mixture; hydrogen gas content; ASI304 stainless steels; surface hardness; nitrogen supersaturation; inner nitriding behavior +
Footnote †: journal: Applied  
In the present study, the RF (Radiofrequency)-DC (Direct Current) plasma nitriding system is utilized to investigate the effect of the hydrogen-to-nitrogen molar ratio to the activated species in plasmas. First, OES is employed to perform a plasma diagnosis on the effect of plasma processing parameters onto plasma activation, with and without the use of the hollow cathode device. Secondly, the hydrogen-to-nitrogen molar ratio is controlled to describe the variation in NH-radical intensity in OES. The AISI304 stainless steel specimen is nitrided at 673 K for 14.4 ks to demonstrate that the highest surface hardness is obtained under the optimum hydrogen-to-nitrogen molar ratio. Finally, the AISI304 specimen is nitrided at 623 K for 14.4 ks with the use of a hollow cathode to prove that this optimization is necessary to perform effective nitriding of stainless steels at lower holding temperatures.