esa-sceva/satcom-chunk-collection huggingface dataset:
# Pose Measurement Method Based on Machine Vision and Novel Directional Target
## 4 Results
### Camera Calibration Simulation Experiment  
Based on the 3 ds Max platform, a set of simulation experimental systems is designed; its main function is to use the camera imaging principle without considering the lens aberration, image noise, mechanical errors generated during installation and mechanical errors in the plane movement to be measured, according to the input pose transformation value to be transformed from the zero-pose image to the image to be measured, to replace the actual painting, and by this paper's method for camera. The camera is self-calibrated according to the method of this paper, and 20 images are simulated to prove the feasibility of the method of this paper by calculating the reprojection error value of each feature point in each image. In the simulation platform, the physical dimensions of the newly set target are 220 mm \(\times\) 220 mm.  
To verify the accuracy difference between the new directional target and the traditional checkerboard target in calibrating the camera parameters, and considering the potential ambiguity of the checkerboard target after a 180-degree rotation, a simulated checkerboard target with dimensions of \(22\times 21\) specifications is generated. The physical size is 230 mm \(\times\) 220 mm. Subsequently, 20 images are captured for camera calibration usingthe aforementioned simulation system, and the calibration results are calculated by the MATLAB Camera Calibration Toolbox.  
The reprojection error values of each feature point in the 20 maps are expressed as Euclidean distances in pixels. There are 462 traditional checkerboard target feature points and 441 new directional target feature points. The experimental results are shown in Figure 5. The green line represents the reprojection error value corresponding to each point in the 20 images of the traditional chessboard target, and the dark green line is the average of the 20 error values; The blue line represents the reprojection error value corresponding to each point in the 20 images of the new target without fitting and intersecting points, and the dark blue line is the average of the 20 error values; The red line represents the reprojection error value corresponding to each point in the 20 images of the new target after fitting and intersecting points, and the dark red line is the average of the 20 error values.  
From the results shown in Figure 5, it can be seen that the reprojection error of the traditional checkerboard target is still larger than that of the new directional target without fitting the intersection points; after the intersection points are fitted to the feature points, the value of the reprojection error is further reduced, and the difference in the reprojection error between individual points is also further reduced.
# A P2P Scheme for Debating and Voting with Unconditional Flexibility
## 2 Preliminaries
### Blind Signature  
A blind signature is similar to a digital signature except that it allows an entity (a user) to obtain another entity (usually the authority that checks the identity of the user) to sign a message without revealing its content. Blind signature [33] is used to authenticate the user without disclosing the content of a ballot. Hence, the authority, whose function is to verify the eligibility of a user, will not know who is voting. The security level of blind signature has been analyzed with satisfactory results [34].  
To ensure the secrecy of their message, users generate a random number \(r\) in a blinding function and send it to the authority. For example, if the authority \(A\) has a pair of RSA private and public keys denoted by \(\{K_{A}^{-1},\,K_{A}\}\), the blind function for \(m\) (where \(m\) is the hash of a message \(M\)), \(Blind(m,r,K_{A})\), can be defined as:  
\[Blind(m,r,K_{A})\equiv m\cdot r^{K_{A}}\equiv m^{\prime}\ \ (mod\ \ n_{A}) \tag{1}\]  
This (1) and the following equations represent operations in modular arithmetic. The authority receives this blinded message and cannot disclose it without \(r\). Next, the authority signs the blinded message with its private key \(K_{A}^{-1}\) and retrieves it to the user:  
\[Sign(m^{\prime},K_{A}^{-1})\equiv(m^{\prime})^{K_{A}^{-1}}\equiv(m\cdot r^{K_ {A}})^{K_{A}^{-1}}\equiv\]  
\[m^{K_{A}^{-1}}\cdot r\ \ (mod\ \ n_{A}) \tag{2}\]  
Finally, the user removes the blinding factor \(r\) by multiplying all by \(r^{-1}\) and obtains the message signed by the authority:  
\[Sign(m^{\prime},K_{A}^{-1})\cdot r^{-1}\equiv m^{K_{A}^{-1}}\cdot r\cdot r^{ -1}\equiv m^{K_{A}^{-1}}\equiv\]  
\[Sign(m,K_{A}^{-1})\ \ (mod\ \ n_{A}) \tag{3}\]  
In the method presented here, every user contacts with the other users, revealing his/her identity by means of a digital certificate. The other users check that user against a list and make just a blind signature per user (\(n-1\) signatures where \(n\) is the total number of users), playing the role of authorities. Blinded messages can be stored but signers will not be able to disclose them. Therefore, privacy is guaranteed. Although RSA cryptography has been used in this example, other cryptographic systems capable of implementing blinding and signing functions may also be valid.
# Influence of Chinstrap Stiffness on Cerebrospinal Fluid Dynamics and Brain Stress in Helmet Impacts
## 4 Discussion
Their exclusion may lead to an overestimation of the stresses transmitted to the brain, as the model does not capture the potential for energy attenuation and shear resistance provided by these layers. In particular, the meninges can play a critical role in modulating the transmission of both linear and rotational forces, and their absence may bias the results toward higher predicted intracranial stresses. Furthermore, the model is limited to the analysis of linear impact dynamics and does not incorporate rotational kinematics or the resulting shear stresses. Rotational acceleration is a well-established mechanism of traumatic brain injury, particularly in the context of concussion and diffuse axonal injury, as it induces shear strains within neural tissues that are not adequately represented by linear impact models. The absence of rotational dynamics in the simulation may therefore underestimate the risk of injury associated with certain helmet and chinstrap configurations, especially those that permit greater helmet displacement or instability. This simplification could bias the findings by failing to account for the full spectrum of injury mechanisms relevant to real-world impacts, where both linear and rotational forces are typically present. These limitations suggest that the results should be interpreted with caution, as the exclusion of anatomical layers and rotational dynamics may affect the generalizability of the findings. Future studies should aim to incorporate more anatomically complete models and simulate both linear and rotational impact scenarios to provide a more comprehensive assessment of helmet and chinstrap performance. Nevertheless, it should be noted that the present study is comparative in nature, with chinstrap stiffness as the sole parameter varied across simulations. As such, while the anatomical simplifications and exclusion of rotational dynamics may influence the absolute values of stress and energy transmission reported, these factors are held constant for all chinstrap configurations. Therefore, the relative differences observed between chinstrap stiffness conditions remain valid within the context of the model.
# An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching Strategy
## 2 Preliminaries
### NSGA-II Algorithm  
[PERSON] et al. proposed the fast non-dominated sorting genetic algorithm with elite strategy (NSGA-II) [5] in 2000, which specifically shows its advantages from the following three points: (1) It provides a hierarchy-based non-dominated sorting method to select the optimal solution by hierarchical sorting. Compared with the original method, the operation speed is significantly improved, unnecessary calculations are reduced, and the speed of problem solving is greatly improved. (2) The parent population is fused with the offspring population to retain the better individuals by selecting the optimal solution through an elite strategy. (3) A crowding distance comparison operator is given to solve the original problem that requires a specified shared radius, and makes the optimal solution evenly distributed [9] in space.  
In order to determine the degree of superiority of each solution at the identical ranking level in the results of algorithm, a crowding distance is assigned to all solutions. The basic idea is to spread out the already solved Pareto optimal solutions from the search domain to the greatest extent possible, and this process is also a performance indicator to ensure diversity among the solutions. The crowded distance comparison operator can be used as a solution for each stage of the selection algorithm by ranking individuals in terms of crowded distance comparison and taking the top N individuals to drive the Pareto optimal front in an evenly distributed state.  
The specific implementation process of NSGA-II algorithm is as follows:  
Step 1. Population initialization. The population size is N, and the Gen = 1 is set as the number of evolutionary generations;  
Step 2. Based on the execution of selection, crossover and mutation operators, determine whether the first generation of offspring population has been formed. If it has been generated, make the evolutionary generation Gen = 2. If not, perform the non-dominant sorting operation on the initial population and continue to perform the selection, crossover, and variation operations to produce a population of first-generation offspring with evolutionary generation Gen = 2;  
Step 3. Generate a new population by integrating parent and offspring populations;  
Step 4. First determine whether a new parent population has been formed, and if not, the objective function of individuals in the new population is calculated, and a new parent population is generated by performing operations such as fast non-dominance sorting, calculating crowding [11], and elite strategy. Otherwise, go to step 5;  
Step 5. Generate the offspring population by performing selection, crossover, and variation operators on the generated parent population;  
Step 6. First determine whether Gen is equal to the maximum evolutionary generation; if not, then make the evolutionary generation Gen = Gen + 1 and return to step 3. Otherwise, the algorithm runs to the end.
# Cybersecurity Risk Assessment: A Systematic Mapping Review, Proposal, and Validation
## 4 Analyze and Discuss the Results
### Cybersecurity Risk Assessment Tools (Reference Models and Applications)
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tool** & **Type of Risks** & **Focus Area** \\ \hline ADAMANT & Any & General purpose \\ AWWA & Any & Water infrastructure \\ CSAT & Any & Information systems \\ CSET & Any & General purpose \\ CSRAS & Any & Banks \\ MVS visualization platform & Any & General purpose \\ OSCAD-Ciras tool & Any & Critical infrastructure \\ OSINT & Any & Critical infrastructure \\ Patch management & Any & General purpose \\ RACOMAT & Any & General purpose \\ RL-BAGS & Any & Critical infrastructure \\ Risk watch & Any & General purpose \\ Teng algorithm & Any & General purpose \\ TISRIM & Any & Telecommunications \\ Tritilanunt, Suratose tool & Any & Health \\ Mobile risk calculator & Any, except human risks & General purpose \\ Eye retina & Technical risk & General purpose \\ GFLanguard & Technical risk & General purpose \\ N circle & Technical risk & General purpose \\ Nessus & Technical risk & General purpose \\ Nikto & Technical risk & General purpose \\ OpenVAS & Technical risk & General purpose \\ Quiys guard & Technical risk & General purpose \\ SAINT & Technical risk & General purpose \\ Security system analyzer & Technical risk & General purpose \\ CYRVM & Network risk & General purpose \\ MST risk analysis & Network risk & General purpose \\ [PERSON]. Hu model & Network risk & General purpose \\ SGIS toolbox & Network risk & Energy distribution \\ Asset assessment methodological tool & Web application development risk & General purpose \\ CRISM tool & Software vulnerabilities and network & General purpose \\ Data protection impact assessment tool & Risks in the cloud & General purpose \\ Quantitative network security risk situation model & Multimedia communication traffic risk & General purpose \\ [PERSON]. [PERSON] model & Cyber-attack risks & General purpose \\ Rimsha tool & Software risk & Gas industry \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Tools related to the type of risk and focus area. quantitative calculations. (2) Models such as NIST CSF or OWASP are usually used for qualitative calculations. (3) An important group of tools does not use a reference risk assessment model. (4) Most tools are focused on any type of risk and/or are general purpose. (5) Another regressive group comprises tools that consider technical risks such as denial-of-service, man-in-the-middle, or password attacks.
## 3 Results
### Effect of Compactness on Disintegration Characteristics
#### 3.3.2 Untreated Waste Soil  
Figure 11 presents the variation in the residual weight and disintegration ratio of silty clay with the disintegration time under two different compactness. The disintegration could be completed in a relatively short period of time, which is mainly divided into two phases, namely, slow disintegration and accelerated disintegration [30]. The first phase was the water absorption of the soil sample with a disintegration ratio of less than 5%. There were basically no cracks appeared on the specimen surface, and only a small amount of soil fell off, resulting in a small inclination of the disintegration curve. The soil samples with 100% compactness took 30 s longer than those with 90% compactness, indicating that higher compactness enhances the water stability of the soil mass during the initial disintegration stage [39]. Then, cracks developed internally and penetrated through the specimen, and a rapid increase was observed in the disintegration ratio, which was demonstrated by the sudden surge in the disintegration ratio of the soil sample with 90% compactness around 30 s. Afterward, there was an almost linear change with time. This is different from the disintegration characteristics of other soils that have been reported, where the disintegration rate increases sharply with disintegration time [34; 36] or increases first and then decreases [32; 34].  
Compared with the untreated soil with 90% compactness, the disintegration time of the soil with 100% compactness took about 180 s to contribute to a total increase of 20%. As the soil with lower compactness has larger pores, water easily penetrates into the pores and increases the internal air pressure of the soil sample. Higher compactness makes the pores among the soil samples smaller, and the rate of water intrusion into the soil and the increase in air pressure slows down, resulting in a longer disintegration time. Therefore, the compactness of the silty clay as subgrade filler in projects should be increased as much as possible, and interception and drainage measures are also required.
## 2 State-of-the-Art
### Deep Learning  
Neural networks differ from analytical approaches in one fundamental aspect. Analytical approaches use a deterministic mathematical model to represent the transformation equation from input to output. On the other hand, neural networks are modeled by an adaptive mathematical equation. This equation consists of an input vector \(X\), a weight vector \(W\) and a bias \(b\)[16]:  
\[k_{neuron}=f(W^{T}\cdot X+b) \tag{8}\]  
Equation (8) represents a neuron \(k\), which exhibits a nonlinear activation function \(f\). This again produces a nonlinear transformation of the input signal. Weights are parameters updated by error propagation, also known as backpropagation. Neurons are usually grouped into layers, which can be distinguished into input, hidden and output layers. The connection model between layers can vary. So, comprehensible task processing can be achieved. The most common layer topologies are perceptron layers as well as convolutional layers. This last type is particularly efficient for tasks regarding image processing [19; 33; 34]. The type of neural network is determined by its architecture. This refers to the size, type and connection model that exists between the layers. The implementation of convolutional layers in neural networks has become a crucial factor for image processing tasks in recent years [19; 35]. An example of this, is the Unet in Figure 4, top. Later, the Dense Unet has shown improvement (Figure 4, overall) by replacing convolutional layers with denseblocks [19]. Here, a denseblock consists of a set of convolutional layers concatenated using a skip-layer strategy (see Figure 4, bottom). Similar to a convolutional layer, dense blocks have their own hyperparameters, such as the growth rate \(k\) and the number of repetitions \(l\). The growth rate \(k\) refers to the feature layers that are calculated from the previous step. Then, \(l\) indicates how many times \(k\) feature layers will be concatenated.  
Figure 4: Dense Unet architecture (**top**) used for limited angle tomographic image reconstruction. Example of denseblock structure (**bottom**), with hyperparameters \(k=8\) and \(l=4\).  
With the use of dense blocks in every convolutional step, there are more connections between every neuron in the step. Thus, the overfitting can be reduced [36]. The disadvantage is a larger number of hidden layers, which requires more computing power and increases the training time.
# High Cellulose Purity by Acid Hydrolysis Pretreatment on Kenaf Outer Bast
###### Abstract  
Acid hydrolysis treatment of kenaf outer basf fiber can produce pure cellulose content and hydrolyzed hemicellulose to monosaccharides. The effects of various reaction temperatures (110-130 \({}^{\circ}\)C), acid concentrations of sulfuric acid (0.25-1.00 N), and reaction times (60-120 min) were investigated as the optimum condition to gain pure cellulose content. A 1H NMR spectroscopy was used to analyze the carbohydrate content in the reaction of acid hydrolysis treatment. The results showed that optimum conditions for acid hydrolysis refer to two treatment prospects. First, a higher reaction temperature of 130 \({}^{\circ}\)C was necessary to increase the reaction for the hydrolyzes of hemicellulose--the high yield content produced by 0.25 N sulfuric acid with a short reaction time of 60 min. to improve the purity of cellulose, provided by the high sulfuric acid solution of 1.00 N for 120 min. Hemicellulose was hydrolyzed at almost 100% based on the two optimal conditions. The analysis revealed that a high temperature of acid hydrolysis was the primary treatment to hydrolyze hemicellulose to increase high pure cellulose from the kenaf outer basf fiber.  
k 0000-0002-  
production [13; 14; 15; 16]. Cellulose nanocrystals could be applied in food processing, bioplastics, ceramics, pharmaceutical, and cosmetic sectors as value-added products and reinforcing fillers in bio-composite production fields [13; 14]. In addition, cellulose nanofiber can be a good candidate for replacement for petrochemical-based materials and can surpass fossil-based materials such as electronics, biomedical applications, etc. [15; 16].  
In order to produce high-purity cellulose, lignin and hemicellulose must be effectively removed [17; 18]. Chemical pulping and bleaching are required to remove lignin and hemicellulose [3]. The hemicellulose removal process is primarily [19] divided into essential treatments in which hemicellulose is dissolved using an appropriate base concentration and acid treatment [19; 20]. Hemicellulose is hydrolyzed faster than cellulose in acidic conditions [20]. Dissolving pulp-grade cellulose used as a raw material for fine chemicals requires a purity of 90.0% or higher [6].  
The process of selectively removing hemicellulose using sulfuric acid or other acids has been studied for producing pulp for dissolution. Hydrolyzed hemicellulose is a pre-treatment process prior to enzymatic saccharification [21; 22; 23]. Xylan has the fastest decomposition rate in acid hydrolysis [24]. Glucomannan decomposes faster than cellulose [25]. Due to the difference in decomposition rate, it is possible to retain hemicellulose while leaving most cellulose. Therefore, this study explored the possibility of the selective removal of hemicellulose in dilute acid hydrolysis conditions to produce high-purity cellulose, a raw material for fine chemicals from kenaf cobs, from which lignin was removed.
###### Abstract
In this work, instead, the Citymob4 Roadmaps (C4R) proposed in [15; 16] has been considered, with realistic behaviors, because mobility traces are generated according to roads structures, extracted from concrete environments. By analyzing user mobility, a prediction technique is proposed for NSIS-MIP flows, in order to evaluate the cells that a user with an active flow will probably visit, achieving a higher system utilization. The proposed idea is based on two important statistics: the Cell Stay Time (CST) probability distribution and the Hand-off Directions Probabilities values (HDP). The main contributions of this work are:  
1. Extension of MRSVP protocol with additional features inherited by NISIS, in order to dynamically manage NSIS-MIP and NSIS-MDP classes also during on-going calls;
2. Proposal of a utility-based rate adaptation scheme with the application of utility functions for the management of Best Effort (BE) associated to NSIS-MDP class and video traffic associated to NSIS-MIP class;
3. Proposal of a Hand-off Direction and CST based reservation schemes in order to meet adaptive QoS requirements during users' movements. In-advance bandwidth reservations is proposed in order to reduce the QoS degradation and call dropping probability;
4. Static and Dynamic (threshold-based) reservation prediction schemes proposal considering real mobility traces;
5. Validation of the considered scheme through realistic mobility patterns. This paper is organized as follows: Section 2 gives an overview of the related work concerning the proposed reservation techniques and studies in wireless environment. Section 3 describes the signaling protocols adopted to make the reservation and the supported QoS classes. The considered mobility generation scheme has been briefly introduced in Section 4; the utility functions adopted in the call admission control and bandwidth management are presented in Section 5; the proposed static and dynamic bandwidth reservation schemes are presented in Section 6. In Sections 7 and 8 simulation results and conclusions are, respectively, summarized.