esa-sceva/satcom-chunk-collection huggingface dataset:
# AV-Teller: Browser Fingerprinting for Client-Side Security Software Identification
## 3 Strategies and Tactics for Profiling Antivirus Behavior
### Approaches for Constructing Antivirus Profiling Scenarios  
Antivirus profiling fundamentally entails the specification of detection conditions and scenario construction to evaluate antivirus responses within a browser environment. The detection conditions were defined through four primary approaches:  
* Exploration of Configuration Parameters: The diverse configuration parameters of antivirus solutions were systematically analyzed to assess their influence on detection mechanisms. For instance, comparative evaluation were conducted to analyze behavioral differences when real-time protection was toggled and to monitor system responses to altered advanced settings.
* Reverse Engineering: Reverse engineering techniques were employed to analyze the internal workings of antivirus software. This approach facilitated deeper comprehension into the algorithms used by detection engines under specific conditions and their interactions with web browsers. However, most antivirus software incorporates robust anti-reversing techniques, making the analysis both challenging and time-consuming. Particularly, mechanisms such as code obfuscation and anti-debugging significantly increased the effort required to understand the detection logic and operational processes.
* Analysis of Patent Information: Given that antivirus detection technologies are frequently safeguarded by patents, an examination of patent documentation provided critical insights into the theoretical foundations underlying specific detection methodologies. These insights were instrumental in identifying the distinctive detection mechanisms employed by various antivirus solutions and in formulating corresponding scenarios.
* Observation of Browser Interactions: Direct observation of interactions between web browsers and antivirus software enable the identification of key detection parameters. This involved monitoring behaviors, such as antivirus interference with loaded web pages.  
Figure 1 illustrates the four approaches described above employed to establish detection conditions for antivirus profiling: configuration parameter exploration, reverse engineering, patent analysis, and browser interaction observation. These approaches informed the development of three primary detection scenarios, forming the foundation for evaluating antivirus systems responses under specific conditions.
## 4 Analysis of Sound Radiation Characteristics for Reinforced Laminated Cylindrical Shell Structures
### Effects of Reinforcement Quantity  
This section explores the influence of the number of reinforcements on the radiated noise of laminated cylindrical shell structures. Based on the finite element model of the cylindrical shell established in the previous section, and with other parameters unchanged, calculations of radiated noise are conducted for laminated cylindrical shell structures with 4, 10, and 16 reinforcements, as shown in Figure 15.  
From the graph, it can be observed that at low frequencies, with an increase in the number of reinforcements, the overall sound power level curve shifts towards higher frequencies. Compared to the results with 4 and 10 circumferential rib reinforcements, the sound power level curve of the structure with 16 circumferential rib reinforcements shows fewer peaks in the low-frequency range, which is advantageous for reducing the structurally radiated noise. At high frequencies, a greater number of circumferential rib reinforcements result in stronger axial constraint on the cylindrical shell, leading to a smoother structural sound power level curve and fewer radiated peaks. In the frequency range of 600-1000 Hz, the sound power level curve for the structure with 16 circumferential rib reinforcements is the lowest, indicating the minimum structurally radiated noise. From the perspective of the  
Figure 15: Structural sound power level curves under different reinforcement quantities.  
overall sound power level, the total sound power levels for the structures with 4, 10, and 16 circumferential rib reinforcements are 90.1 dB, 80.82 dB, and 80.28 dB, respectively. It can be observed that with an increasing number of reinforcements, the structurally radiated noise decreases, effectively suppressing the structural sound radiation.
## 3 Results and Discussion
### Structure of Electro-Sprayed mPCM  
FT-IR spectra of PCL microparticles, \(n\)-hexadecane/PCL microparticles in EA (30/70, 50/50; 70/30), \(n\)-hexadecane/PCL microparticles in Chl, and \(n\)-eicosane/PCL microparticles in Chl are depicted in Figure 2. PCL microparticles spectrum shows a strong absorption band at 1750 cm\({}^{-1}\) corresponding to the stretching vibration of carbonyl group, and characteristic bands at 2915 to 2860 cm\({}^{-1}\) associated with the aliphatic C-H stretching vibrations. The characteristic bands at 1475 cm\({}^{-1}\), 1397 cm\({}^{-1}\), and 1361 cm\({}^{-1}\) are attributed to the bending vibration of the alkyl group, and those located at 1230 cm\({}^{-1}\), 1107 cm\({}^{-1}\), and 1042 cm\({}^{-1}\) corresponding to the stretching vibration of -CO-C- group. Stretching vibrations of -C-C- and -CO- are found at 1290, and 1160 cm\({}^{-1}\), respectively [47]. PCM spectra exhibit a strong absorption band at 2920-2852 cm\({}^{-1}\) associated with the aliphatic C-H stretching vibrations of the \(n\)-hexadecane or \(n\)-eicosane. The in-plane rocking vibration of the CH\({}_{2}\) groups is observed at 717 cm\({}^{-1}\), and C-H bending vibration in CH\({}_{2}\) is found at 1470 and 1370 cm\({}^{-1}\). All of the characteristic peaks for \(n\)-hexadecane, or \(n\)-eicosane, and PCL, are clearly distinguished in the spectra of the FT-IR spectra of the electro-sprayed micro-particle samples, which verifies that \(n\)-alkane are successfully entrapped by PCL.
## 3 Methodology
### Modelling
#### 3.4.2 Random Forest  
In [PERSON] [43], the concept of Random Forest was introduced as an ensemble of tree predictors, where each tree's outcome depends on a randomly sampled vector that is independently and identically distributed across all trees in the ensemble. Random Forest is an ensemble method used for both classification and regression tasks. It combines multiple Decision Trees to make predictions, and the final prediction is obtained by aggregating the predictions from all individual trees, employing a majority voting scheme for classification tasks.  
Similarly to the Decision Tree algorithm, the Random Forest model was implemented using the R programming language. The \(randomForest\) package was employed for this purpose, providing comprehensive tools for building Random Forest models to tackle both classification and regression problems [44].  
Consistent with the Decision Tree model, the splitting criterion, referred to as \(splitrule\) package, was one of the hyperparameters optimized in the Random Forest implementation. The values considered for the splitting criterion were Information Gain and the Gini index, as the Gain Ratio option was not available in this context, similar to the \(rpart\) package.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Range of values** \\ \hline \(split\) & Information Gain or Gini index \\ \(cp\) & 0.001 to 0.01, with increments of 0.001 \\ \(minbucket\) & 2 to 100, with increments of 1 (one) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Decision Tree model’s tuned hyperparameters and correspondent values.  
The number of trees (_nttree_) was another hyperparameter tuned in the model. This parameter determines the size of the ensemble and significantly impacts the model's predictive performance and generalization ability. However, increasing the number of trees excessively can lead to diminishing returns or higher computational costs. In this study, the tested values for _nttree_ ranged from 100 to 120, with increments of 1 (one).  
The feature selection process in this study utilized the Forest-RI approach, which involves random input selection for splitting at each node. Accordingly, hyperparameter tuning was performed for the variable _mtry_, which denotes the number of variables randomly sampled as candidates for each split. The values tested for _mtry_ ranged from 1 to 4, with increments of 1 (one).  
The minimum size of terminal nodes, represented as _nodesize_ in the _randomForest_ package, was the final hyperparameter tuned in the study. This parameter determines when to stop splitting a node based on the minimum number of examples required in any terminal leaf. It helps regulate the complexity of the trees and reduces the risk of overfitting the training data. During the tuning process, _nodesize_ values were tested from 2 to 20, with increments of 1 (one).  
Table 10 details the hyperparameters tuned in the Random Forest model, including the range of values tested for each parameter during the tuning process.
# Color Harmonization, Deharmonization and Balancing in Augmented Reality
## 4 Case Studies
### Results
#### 4.1.3 Painting  
In this case, we have considered the recoloring of the label background that contains the names of the characters in _The Last Super_ by [PERSON]. In this last case, it is expected that the textual content is stand out from the background in order to improve its legibility. Figure 10 shows how the real distribution covers a region of colors that goes from red to light green counterclockwise.  
The three strategies have been adopted to generate and recolor the virtual content. When the harmonic approach is used, templates orient themselves to cover most of the region of real distribution. CA ranges between a minimum value of 28.6% (template i-type) and a maximum value of 99% (template T). On the contrary, the disharmonic approach generates synthetic colors with high contrast with the real ones. The CA reaches the maximum value (9.2%) and the minimum value (0.2%) in the cases of template X and i, respectively. By using the latter approach, the CA reaches the minimum value in the case of I template, while the highest one is attained with the adoption of the X template. In Table 3, percentages of covered areas by the selected template are reported for each experiment.  
Figure 11 shows different ways to recolor the background of each label using a single color.  
The first approach generates very similar colors. In particular, shades of brown are proposed in the case of V, T, X templates. Instead, i and I templates produce shades of dark yellow. A light red color is obtained when L and Y templates are selected. The disharmonic approach returns colors that can be classified in three groups. The first one is related to the application of V and T templates that generate a color similar to green color. The second group, instead, is represented by shades of blue that are obtained when i and Y templates are used. The third group proposes shades of violet (templates L, I, and X). When the balance approach is adopted, shades of blue are generated and applied to the labels. High-resolution images about these visual examples can be found in the Supplementary Materials.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **i** & **V** & **T** & **L** & **I** & **Y** & **X** \\ \hline
**HARMONIC** & 28.6\% & 91.9\% & 99\% & 93\% & 29\% & 91.7\% & 93.2\% \\
**DISHARMONIC** & 0.2\% & 1.2\% & 0.5\% & 1.4\% & 3.3\% & 2.4\% & 9.2\% \\
**BALANCE** & - & - & - & 20\% & 12.6\% & 28\% & 45.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Covered areas for each experiment related to the painting.  
Figure 10: Color schemes and color palettes related to the painting.
# Adsorption of Hexavalent Chromium Ions Using Pine Sawdust Cellulose Fibres
## 3 Results and Discussion
### Kinetic Study for Adsorption of Chromium
Rather, it is determined by the likelihood of collisions between the relevant species and the speed at which chromium ions can bind to the reactive sites present on the surface of the adsorbent [69]. \begin{table}
\begin{tabular}{c c c c} \hline  & & **Linear** & **Nonlinear** \\ \hline
**Kineti Model** & **Parameters** & **Cr (VI)** & **Cr (VI)** \\ \hline Psuedo-first-order & q\({}_{e}\) (mg. g\({}^{-1}\)) exp. & 9.7772 & 7.7060 \\  & q\({}_{e}\) (mg. g\({}^{-1}\)) model & 1.5739 & 1.392 \\  & k\({}_{1}\) (min\({}^{-1}\)) & 0.0082 & 0.8532 \\  & R\({}^{2}\) & 0.9108 & 0.6257 \\ \hline Psuedo-second-order & q\({}_{e}\) (mg. g\({}^{-1}\)) model & 9.7847 & 1.650 \\  & k\({}_{2}\) (g mg\({}^{-1}\) min\({}^{-1}\)) & 0.1078 & 2.480 \\  & h (mg g\({}^{-1}\) min\({}^{-1}\)) & 10.3199 & 2688.1 \\  & R\({}^{2}\) & 0.9999 & \(-\)11.782 \\ \hline \end{tabular}
\end{table} 
Table 7: Kinetics parameters for the adsorption of hexavalent chromium ions on cellulose fibres (CF) at 299 K. \begin{table}
\begin{tabular}{c c c} \hline
**Kinetic Model** & **Parameters** & **Cr (VI)** \\ \hline Pseudo-first-order & SSE & \(4.55\times 10^{-3}\) \\  & RSME & \(2.55\times 10^{-2}\) \\  & \(\chi^{2}\) & \(6.50\times 10^{-4}\) \\  & R\({}^{2}\) & 0.9985 \\  & SNE & \(6.75\times 10^{-2}\) \\ \hline Pseudo-second-order & SSE & \(4.95\times 10^{-4}\) \\  & RSME & \(9.95\times 10^{-3}\) \\  & \(\chi^{2}\) & \(9.91\times 10^{-5}\) \\  & R\({}^{2}\) & 0.8854 \\  & SNE & \(2.22\times 10^{-2}\) \\ \hline \end{tabular}
\end{table} 
Table 8: Error analysis for kinetic models solved by linear regression method for hexavalent chromium ions. Error Analysis for Kinetics  
The results of the error function, as displayed in Table 8, suggest that the experimental data are best described by a pseudo-second-order kinetic model, which corroborates the Langmuir isotherm (as discussed in Section 3.5). The findings indicate that the attachment of chromium onto cellulose fibres (CF) is a chemisorption process. Furthermore, the pseudo-second-order model exhibits lower SSE, RMSE, and \(\chi^{2}\) values, indicating that it could be a better fit [70].
The CRO has been successfully applied to a number of areas, such as the analysis of time series [41], optimization of antennas for LTE and 5G services [42], selection of representative measuring points for temperature field reconstruction [43], data clustering [44], Wi-Fi channel assignment [45], materialized view selection in a data warehouse [46], and optimization of convolutional neural networks [47], among others. Looking at the application of CRO to the UA-FLP, it has been already used successfully in several variants. Firstly, [PERSON] et al. [48] put into practice a basic CRO algorithm applied to the UA-FLP, improving the solutions previously found in a well-known set of problems. This basic CRO was subsequently improved by means of an island evolution model [49] and a combined evolution model called a Substrate Layers model CRO (CRO-SL) [50]. Recently, a multi-objective interactive CRO algorithm has been developed to take into account the subjective preferences of an expert designer [10]. Nevertheless, to the best of our knowledge, the joint collaboration of several human designers has not been implemented yet over the UA-FLP. The aim of the multiple users' evaluation is to capture a diversity of criteria that enrich the design from several points of view derived from the designers' expert knowledge. Therefore, this paper deals with the collaborative incorporation of several expert designers' knowledge into the design of a plant layout. This is the first time that multiple users' collaboration has been applied to the problem of UA-FLP. The rest of the paper is organized as follows: Section 2 details the problem formulation. Section 3 describes the novel multi-user approach. Section 4 explains the set of experiments performed to validate the approach, presenting and analyzing the results obtained. Finally, in Section 5, the main conclusions and future lines of work are given.
## 4 Methods and Devices for Assessing Erythrocyte (RBC) Aggregation
### Image Analysis Method in a Flow Chamber
This was explained by non-uniform aggregate distributions in the parent vessel and the presence of a near-wall region lacking aggregates of certain sizes, which influenced aggregate partitioning during flow splitting. The findings highlight how aggregate size and distribution affect local blood viscosity and deepen the understanding of microvascular flow dynamics. Additionally, RBC aggregation was found to independently increase the width and roughness of the cell-depleted layer (CDL) and further widen the CDL when combined with flow partitioning at bifurcations. These insights contribute to the understanding of phenomena such as plasma skimming, hematocrit reduction (the Fahraeus effect), and viscosity reduction (the Fahraeus-Lindqvist effect) in microscale blood flow. [PERSON] et al. [47] studied the effects of pentoxifylline therapy on hemorheological parameters in patients with cerebrovascular disease (CVD) and peripheral arterial disease (PAD). After 4 weeks of daily pentoxifylline (1200 mg), improvements were observed in RBC deformability and a reduction in RBC aggregation was seen, indicating enhanced blood flow properties. In vitro experiments showed that pentoxifylline and other phosphodiesterase (PDE) inhibitors increased RBC deformability and decreased aggregation--suggesting these effects are linked to PDE inhibition. Among the tested drugs, vinpocetine showed the most significant aggregation reduction (by 50%; \(p\) < 0.05). Overall, the findings support the determination that modulating PDE activity in RBCs can improve microrheological properties and potentially enhance blood transport capacity in vascular disease patients.
## 4 Experimental Results and Analysis
### Visualization Analysis
#### 4.2.2 Link Traffic Flow Direction Characteristic Analysis  
To thoroughly understand directional characteristics of traffic flow, this study conducted a comparative analysis of traffic parameters for different flow directions on each link. By analyzing bidirectional average speeds, travel times, and flow distributions for each link, tidal traffic characteristics during morning peak periods can be accurately identified.  
The distribution of average speeds by direction for each link (Figure 12) clearly demonstrates directional speed disparities. On most links, reverse direction speeds (westbound and southbound) significantly exceed forward direction speeds, consistent with the dominant flow from residential areas to employment centers during morning peak periods. This  
Figure 11: Vehicle stop point density map.  
speed advantage is particularly pronounced on reverse direction links such as 8-1, 16-9, and 22-17, indicating relatively lower traffic pressure and underutilized road capacity in these directions.  
The distribution of average travel times by direction for each link (Figure 13) validates the tidal phenomenon from another perspective. Forward direction links generally exhibit longer travel times with greater variation ranges, reflecting congestion severity and instability. In contrast, reverse direction links demonstrate shorter and relatively stable travel times. This disparity provides justification for implementing asymmetric signal control strategies.  
Figure 12: Distribution of average speeds by direction for each link.  
Figure 13: Distribution of average travel times by direction for each link.  
The distribution of average flows by direction for each link (Figure 14) directly reflects directional imbalance in traffic demand. Forward flows significantly exceed reverse flows, with differences reaching 2-3 times on certain links. This flow distribution imbalance serves as an important basis for determining split allocations, with green times for each direction adjusted according to actual flow proportions.  
Based on flow direction characteristic analysis, signal control optimization should adopt the following strategies: First, implement asymmetric green wave control, providing wider bandwidth for dominant flow directions; second, dynamically adjust splits, allocating green times according to real-time flow proportions; third, consider traffic space optimization measures such as reversible lanes to improve road resource utilization efficiency.
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 10 AI Support for 5G Operations and Services
### Applied Artificial Intelligence Methods  
Machine and Deep Learning  
Statistical learning and symbolic reasoning have been developed largely by distinct research communities. Ericsson argues that hybrid approaches will be useful in intelligent 5G systems where robust learning of complex models is combined with symbolic logic that provides knowledge representation, reasoning and explanation facilities [114].  
Real-Time Analytics  
Real-time requirements entail that predictions, model updates and inferences from knowledge bases are based on live-streaming data. This has implications for system architecture - how to distribute the models and knowledge bases over the cloud, edge and devices; whether model training should be offline or online; how to represent and prepare data for fast consumption by algorithms, and more. The systems must support flexible, programmable data pipelines for the volume, velocity and variety of real-time data and algorithms capable of real-time decision making [114].  
Distributed Learning Algorithms  
There is a need to leverage the scale of distribution, make appropriate abstractions of local models and transfer the insight to other local models. Learning about global data patterns from multiple networked devices or nodes without access to the actual data is also possible. A recent approach is so called federated learning: learn local models based on local data patterns, send the local models to centralized cloud, average them and send back the average model to all devices [114].
# Validation of Lumbar Compressive Force Simulation in Forward Flexion Condition
## 2 Method
### CF Estimation  
#### 2.3.1 CF Simulator  
The CF simulator estimates the CF in two steps: inverse dynamic computation and lumbar muscular force computation. First, a virtual human model was reconstructed in the ergonomic assessment system DhaibaWorks, which was developed by the Japanese National Institute of Advanced Industrial Science and Technology (AIST) [25]. In this model, body segments are assumed to be coupled links. The intersegmental forces and joint torque of the body segments are computed by the inverse dynamic algorithm [26]. Second, the net joint torque at L5/S1 \(\mathbf{N}^{(15/S1)}\) exerted by all lumbar muscles, which can be regarded as being produced by a representative muscle [21] as shown in Figure 2a, was calculated as follows:  
Figure 1: Schematic of experimental setup for obtaining the ground reaction force and motions. Four force plates were used to measure the ground reaction force on both feet, and optical markers were placed all over the body and monitored by the motion-capture system. The external box with a mass of 20 kg was simulated in the compressive force (CF) simulator. The flexion angle \(\theta\) was considered as an indicator of the flexion level.  
\[\mathbf{N}^{(L5/S1)}=\mathbf{F}_{m}^{(L5/S1)}\times\mathbf{r}, \tag{1}\]  
where \(\mathbf{F}_{m}^{(L5/S1)}\) represents the representative muscular force (N). \(\mathbf{r}\) is the moment arm vector; its direction is from L5/S1 and perpendicular to the representative muscle line of action, and its length is set to 5.3 cm [18].  
In forward flexion motion (2D case), the representative muscle is the erector spinae, and the muscular force exerted by the erector spinae muscle maintains the lumbar stability at L5/S1. The mechanical equilibrium at L5/S1 is shown in Figure 2a. The magnitude of the CF, \(CF_{d}\) (N), and shear force, \(SF_{d}\) (N), can be expressed as follows:  
\[CF_{d}\equiv\|\mathbf{C}\mathbf{F}_{d}\|=\|\mathbf{F}^{(L5/S1)}\|\cos(\alpha)+\|\mathbf{F}_{m }^{(L5/S1)}\|, \tag{2}\]  
\[SF_{d}\equiv\|\mathbf{S}\mathbf{F}_{d}\|=\|\mathbf{F}^{(L5/S1)}\|\sin(\alpha), \tag{3}\]  
where \(\mathbf{F}^{(L5/S1)}\) (N) is the intersegmental force at joint L5/S1, and \(\alpha\) (rad) is the angle between \(\mathbf{F}^{(L5/S1)}\) and the axis \(pS1\) perpendicular to the sacrum surface through S1.