esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Comprehensive Analysis Based on the FAEWM
### Determination of Weights Based on the Factor Analysis Method
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Delay**} & \multirow{2}{*}{**Travel Time**} & \multirow{2}{*}{**Number of Conflicts**} & \multirow{2}{*}{**TTC**} & \multirow{2}{*}{**PET**} & \multirow{2}{*}{**CSI**} & \multirow{2}{*}{**CO**} & \multirow{2}{*}{**Fuel**} \\  & & & & & & & & & & \\ \hline \multirow{2}{*}{Scheme 1} & Ingredient 1 & 0.243 & 0.233 & 0.245 & \(-\)0.116 & \(-\)0.117 & 0.196 & 0.217 & 0.217 \\  & Ingredient 2 & \(-\)0.074 & \(-\)0.0055 & \(-\)0.009 & 0.568 & 0.521 & \(-\)0.01 & \(-\)0.014 & \(-\)0.014 \\  & Ingredient 3 & 0.076 & 0.067 & 0.073 & 0.079 & \(-\)0.127 & 1.075 & 0.059 & 0.059 \\ \hline \multirow{2}{*}{Scheme 2} & Ingredient 1 & 0.218 & 0.209 & 0.224 & \(-\)0.086 & \(-\)0.054 & 0.181 & \(-\)1.198 & 1.628 \\  & Ingredient 2 & \(-\)0.044 & \(-\)0.023 & \(-\)0.072 & 0.588 & 0.531 & \(-\)0.079 & 0.244 & \(-\)0.275 \\  & Ingredient 3 & 0.062 & 0.043 & 0.073 & 0.036 & \(-\)0.179 & 1.065 & \(-\)0.066 & 0.815 \\ \hline \multirow{2}{*}{Scheme 3} & Ingredient 1 & 0.248 & 0.242 & 0.236 & \(-\)0.219 & \(-\)0.054 & 0.177 & 0.479 & \(-\)0.061 \\  & Ingredient 2 & \(-\)0.133 & \(-\)0.109 & \(-\)0.09 & 0.719 & 0.474 & \(-\)0.035 & 0.321 & \(-\)0.355 \\  & Ingredient 3 & 0.035 & 0.049 & 0.055 & \(-\)0.029 & \(-\)0.029 & 1.06 & 0.188 & \(-\)0.07 \\ \hline \multirow{2}{*}{Scheme 4} & Ingredient 1 & 0.522 & 0.892 & 0.571 & \(-\)0.239 & \(-\)0.205 & 0.546 & 0.173 & 0.173 \\  & Ingredient 2 & \(-\)0.384 & \(-\)0.162 & 0.188 & 0.891 & 0.333 & \(-\)0.034 & \(-\)0.014 & \(-\)0.014 \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Ingredient matrix. where \(F_{1}\), \(F_{2}\), and \(F_{3}\) denote the main ingredient s; \(a_{i}\), \(b_{i}\), and \(c_{i}\) denote the score coefficients of each original variable in the main ingredients; and \(x_{i}\) denotes the value of the original variable after standardization. In summary, by using factor analysis, the eight original variables selected were downscaled to three common factors, \(F_{1}\), \(F_{2}\), and \(F_{3}\), with \(F_{1}\) being the \"efficiency-environment factor\", \(F_{2}\) being the \"accident frequency prediction factor\", and \(F_{3}\) being the \"accident severity factor\".
## 7 Risk Analysis
### Emergency Response Service
#### 7.3.3 Elevation of Privilege  
1. **Component**  
_Security risk_: Considering the huge number of ports that are typically opened on a server and the multiple exploitation shown for performing privilege escalation this threat is particularly relevant since an attacker might be able to change the server configuration in order to deny access to the emergency services.  
_Security measure_: A typical countermeasure could be the IDS and firewall, both aiming at reducing the possible attacks. Moreover, considering that privilege escalation is usually associated with database systems, a simple denying port service could be enough.
## 4 Discussion
### The Variability of the Friction between the Human Skin and the Helmet Inner Liner  
Human skin has been shown to exhibit a complex and highly variable friction behaviour. [PERSON] et al. obtained an average friction coefficient between the interior of the helmet (nylon fabric) and the human skin of 0.683 [23]. However, other experiments using human cadaver heads estimated substantially lower values for the coefficient of friction between the interior of the helmet (polyester fabric, a common material used in the interior liner of helmets) and the scalp (0.29 \(\pm\) 0.07) [28]. In a tactile perception study unrelated to helmets, [PERSON] et al. reported a COF about 0.7 between polyester fabric and the polar forearm of in vivo volunteers [29]. This value could be even higher in the case of the head, as other experimental studies have revealed that friction coefficients at the polar forearm were lower (COF = 0.26) than those measured on the forehead (COF = 0.34) [30]. The variation of friction coefficients measured for the human skin has been suggested to depend on skin hydration [31], with moisture increasing the value of the COF between the skin and the fabric up to a maximum and then decreasing it, when there is excess of water in the interface [31; 32]. As sweating is very common among helmeted riders specially in warmer weathers, this study is essential to understand how the performance of helmets can be affected by changes in the COF.  
Although the influence of the COF had been addressed already in [23], to our knowledge, this is the first time that an experimental study combines the effect of varying the tangential component of the impact velocity and the COF at the same time. Our data shows  
that the combination of different magnitudes of \(\rm V_{T}\) and of COF influences differently the helmet performance.
# The Fusion of MRI and CT in the Planning of Bracotherapy for Cancer of the Uterine Cervix
## 1 Introduction  
Tumors of the uterine cervix are among the most common carcinomas in women and cause fatalities in many cases [1; 2]. While excision is performed for tumors in early stages, intracerebral brachytherapy is an indispensable part of curative treatment in locally advanced tumors [3]. Although the tumor is more recognizable in an MRI than in a CT, the practical application of MRIs in planning brachytherapy is still difficult [4; 5; 6; 7; 8] for hospitals not equipped for treatment planning on MRI-only. Even in compliance with the tried and tested recommendations of the Gyn-GEC-ESTRO working group for 3D image-based anatomy [9] and for useful MRI-sequences [10], discrepancies between different modalities of imaging are challenging for Radiation oncologists. Additionally, missing the target ofBrachytherapy can easily be disastrous for the patient. Technical solutions are not always reliable. The present study examines the technical possibilities and practical applications of merging CT and MRI in the planning of brachytherapy.
# Research on an Equivalent Algorithm for Predicting Gas Content in Deep Coal Seams
## 2 The Equivalent Layers of Coal Seam Gas Emission  
The theory and prediction methodology of the gas emission equivalent layer has undergone rigorous testing and optimization for over a decade. This predictive approach has advanced from manual execution to intelligent augmentation, bolstered by its dynamic correction algorithm [23; 24; 25; 26; 27; 28; 29; 30; 31]. It consistently maintains a monthly prediction accuracy of over 90% for gas emissions emanating from mining faces.  
The equivalent layer theory and its corresponding prediction methodologies encompass five fundamental components: the equivalent lithology, the diagram for the division of equivalent layers, the basic emission quantity of gases within the equivalent layers, the grouping of factors influencing gas emissions, and the algorithmic prediction for the quantity of gas emissions within the equivalent layers. This framework ensures a systematic and comprehensive approach to the analysis and prediction of gas emissions.
# Device-Free Indoor Location Estimation System Using Commodity Wireless LANs
## 3 DF Indoor Location Estimation  
The DF indoor location estimation system was developed using the WiFi sounder described above. The receiving antennas were arranged linearly at approximately 6.3 cm intervals (half-wavelength of the minimum frequency 2.403 GHz). The bandwidth-broadened channel impulse responses were obtained by concatenating the CSIs taken at six consecutive WiFi channels to extend the bandwidth to approximately 68 MHz (delay resolution: approximately 15 ns, distance resolution: approximately 4.5 m).
## 2 Material Properties and Calculation Model  
### Main Material Parameters  
The temperature changes rapidly around the attachment area of a composite reinforced panel subjected to lightning strike. Related studies show that the local temperature may reach 10,000 \({}^{\circ}\)C and the high temperature will cause the variation of material properties, so the material parameters show obvious temperature-dependence in this case [12]. The influence of material properties that vary with temperature is considered in order to improve the accuracy and reliability of calculation results. The material type adopted in this research is an IM600/133 composite material. Mechanical, electric, and thermal properties of the IM600/133 composite material at different temperatures are given in Tables 1-4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Temperature/\({}^{\circ}\)C** & \(E_{x}\)/**GPa** & \(E_{y}\)/**GPa** & \(E_{z}\)/GPa** & \(\mu_{xy}\) & \(\mu_{yz}\) & \(\mu_{xz}\) & \(G_{xy}\)/**GPa** & \(G_{yz}\)/**GPa** & \(G_{xz}\)/**GPa** \\ \hline
25 & 137 & 8.2 & 8.2 & & & & 4.36 & 3 & 4.36 \\
200 & 137 & 6.56 & 6.56 & & & & 3.488 & 2.4 & 3.488 \\
260 & 137 & 0.082 & 0.082 & 0.02 & 0.34 & 0.02 & 0.03488 & 0.024 & 0.03488 \\
600 & 137 & 0.0041 & 0.0041 & & & & 0.001744 & 0.0012 & 0.001744 \\
3316 & 137 & 0.0041 & 0.0041 & & & & 0.001744 & 0.0012 & 0.001744 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of IM600/133 composite material.
## 2 Equation of Motion and Its Solution Considering Structural Nonlinearity
\(N_{m}\) is the number of the term of input decomposition which is also derived from the order of the decomposition. The orthogonal polynomial \(\mathbf{T}_{m}\) and order of the decomposition \(N_{m}\) can affect the accuracy of input approximation. The order of input decomposition \(N_{m}\) is closely related to the input history length and concerned bandwidth of the external excitation in the frequency domain. The orthogonal polynomial \(\mathbf{T}^{i}{}_{m}\) can be determined with different kinds of decomposition methods. In this paper the Chebyshev orthogonal polynomial is utilized for the decomposition and can be written as follows. \[ \begin{split}& T_{1}=\frac{1}{\sqrt{\pi}}\\ & T_{2}=\sqrt{\frac{2}{\pi}}(\frac{2t}{T}-1)\\ & T_{n+1}(t)=2(\frac{2t}{T}-1)T_{n}(t)-T_{n-1}(t)\ (n=3,\ldots,N_{m}-1) \end{split}  \tag{16}\]  
where \(N_{m}\) is the order of decomposition. Based on the Chebyshev polynomial standard orthogonal polynomial decomposition, Equation (1) can be rewritten as follows:  
\[\mathbf{M}\mathbf{\hat{x}}+\mathbf{C}\mathbf{\hat{x}}+\mathbf{R}(\mathbf{x}) \approx\mathbf{L}\sum_{i=1}^{N_{\mathrm{f}}}\sum_{m=1}^{N_{m}}w_{m}{}^{i}T^{i} {}_{m} \tag{17}\]  
where \(N_{\mathrm{f}}\) is the number of the external excitation. The external excitation is approximated by the Chebyshev polynomial. To re-construct the external force accurately, the order of the Chebyshev polynomial should be determined and the polynomial parameter \(w_{m}{}^{i}\) is required to be identified accurately. In the force identification problem, the state space equation of the nonlinear structural system with unknown input can then be rewritten as  
\[ \begin{bmatrix}\mathbf{X}_{k}\\ \mathbf{W}_{k}\end{bmatrix} = \begin{bmatrix}F(\mathbf{X}_{k-1},\mathbf{W}_{k-1}) \\ \mathbf{W}_{k-1}\end{bmatrix} + \begin{bmatrix}\mathbf{w}_{k-1}\\ \eta_{k-1}\end{bmatrix}  \tag{18}\]  
where \(\eta_{k-1}\) is a part of process noise. The coefficients of Chebyshev basis augmented in the state variable can be identified by the UKF introduced in references [17; 19; 22; 23; 24]. When the parameters of a nonlinear structural system must be identified, the unknown parameters can be augmented to the state variable of the state space as shown in Equation (18). In this paper, the accurate identification of the external force is the focus and the observability for the force identification can relate to reference [17].
## 3 Improved DBSCAN Algorithm Based on Sparrow Search Algorithm
### Iterative Process of Parameter Optimization  
By simulating the foraging behavior of sparrows, the parameters Eps and MinPts of the DBSCAN algorithm were optimized. These parameters \((Eps,\ MinPts)\) are represented as the position of a single sparrow. In each iteration, determine the coordinates \((Eps*,\ MinPts*)\) of the optimal solution and set the range of these coordinates according to the method outlined in Section 3.2.1.  
The Sparrow Search Algorithm simulates behaviors such as foraging and evading predators. Discoverers are responsible for finding food for the entire group and providing information about the food's location and the surrounding area. Followers continuously monitor the behavior of the discoverers and immediately follow and snatch the food once the discoverers locate it. Additionally, sparrows can flexibly switch roles between discoverers and predators. Sparrows in the center of the group sometimes move closer to nearby sparrows to reduce their exposure to danger.  
Observers are responsible for monitoring the surroundings of the foraging area. When a predator is detected, observers immediately issue a warning. If the warning signal exceeds the alert threshold, the entire group will quickly move to a new foraging site under the guidance of the discoverers. Taking the dataset SampleD as an example, the DBSCAN parameters Eps and MinPts are optimized through the Sparrow Search Algorithm. The number of randomly generated sparrow individuals is set to 500, and the number of iterations is 10. The evolution of the fitness function over successive iterations is illustrated in Figure 2. Following the fourth iteration, the fitness value of the best solution converges and remains stable at 0.701. At this point, the corresponding optimal parameters for Eps and MinPts are 2.8014 and 79, respectively. Therefore, the SSA-DBSCAN algorithm is capable of adaptively tuning DBSCAN parameters by leveraging the underlying distribution characteristics of the dataset.  
Figure 2: Iterative change graph of optimal fitness function value (taking the dataset SampleD as an example).
## 4 Experiment Setup
### Dataset  
The data collection process was performed on a local Wi-Fi network, and the set of traffic traces comprised FTP, video streaming, low-latency and mix of these. The traces were collected over a 50 Mbps Internet connection, and the throughput measurements of the active traffic in both directions were recorded and stored. In total, over 350,000 samples of throughput values (more than 35 h of applications usage) were collected.  
For the experiments, we designed two types of scenario: basic and complex. The basic scenarios, detailed in Table 2, involved pairwise combinations of traffic types, while the complex scenarios, described in Table 3, involved three or more types of traffic co-existing simultaneously. Each scenario was meticulously selected to ensure a comprehensive evaluation of our model's performance under various real-world traffic conditions.The selection of such traffic mixes is similar to the approach used in the Low Latency DOCSIS study, which also employed a mix of different traffic types to evaluate latency performance and network behavior under varying conditions [47].  
In the basic scenarios where (Table 2), we tested combinations such as the following:  
* FTP + Video Streaming (A + B);
* FTP + Low-Latency (A + C);
* Video Streaming + Low-Latency (B + C);
* Repeated instances of the same traffic type (A + A, B + B, C + C).  
For the complex scenarios (Table 3), we included combinations such as the following:  
* Three instances of the same traffic type (3A, 3B, 3C);
* Two instances of one type combined with one instance of another (2A + B, A + 2B, 2A + C, A + 2C, 2B + C, B + 2C);
* One instance of each traffic type (A + B + C);
* Multiple instances of mixed traffic types (2A + B + C, A + 2B + C, A + B + 2C).  
This comprehensive scenario design enabled a robust analysis of low-latency Internet traffic, ensuring the reliability and accuracy of our classification methodology.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Traffic Scenario** & **Low-Latency** \\ \hline A + B & NO \\ A + C & YES \\ B + C & YES \\ A + A & NO \\ B + B & NO \\ C + C & YES \\ \hline \hline \end{tabular} A = File Transfer, B = Video Streaming, C = Low-Latency.  
\end{table}
Table 2: Basic traffic scenarios.
## 1 Introduction
The rapid heating and cooling speeds cause the columnar grains to grow, leading to anisotropic properties being found in all types of AM parts. It is widely known that equiaxed grains possess excellent mechanical properties, which efficiently prevent crack expansion due to the increased density of grain boundaries. This grain growth can significantly impact the reliability and quality of critical components under complex conditions. Rolling is an impactful method for converting dendritic structures into equiaxed grains and is frequently used in welding processes. Numerous AM studies, including those by [PERSON] et al. [14], have reported that rolling not only mitigates distortions and residual stresses, but also refines grains to enhance mechanical properties. They studied the effect of applying rolling pressures from 25 kN to 75 kN at room temperature and the shape of the roller on the geometry and mechanical properties of the deposited product. [PERSON] et al. [15] applied a hot rolling method with simultaneous deposition. The experiments were carried out at lower pressures, and the focus of the study was on finding the optimum distance between the roller and the welding torch. [PERSON] et al. [16] studied composite pressing methods that pressed both the plane and the sides. [PERSON] et al. [17] combined the hot forging method with an additive process and introduced a presser that applied pressure through a cylindrical hammer simultaneously with the WAAM additive process. Previous studies have discussed the effects of cold and hot rolling. However, cold rolling requires a significant waiting time for the weld bead to cool down to room temperature, and it requires a high pressure. On the other hand, hot rolling can be applied simultaneously during deposition, improving mechanical properties with less pressure compared to cold rolling. Additionally, hot rolling results in a lower anisotropy than cold rolling. In previous studies, this effect has been examined by applying pressure to a single metal after its deposition. In contrast, this study analyzed the outcomes when depositing and then pressurizing two distinct metals. The application of rolling can effectively reduce the distortion, residual stress, porosities, and anisotropy in mechanical properties. Furthermore, the application of pressure during the rolling process refines coarse columnar grains into fine equiaxed grains. These benefits have the potential to significantly mitigate a variety of defects that may develop in Inconel625-SS308L bimetallic structures produced via WAAM. Therefore, this study focused on the influence of variable-pressure rolling on a wire arc additively manufactured Inconel625-SS308L bimetallic structure. The results section contains an analysis of the relationship between the applied pressure and the resulting mechanical properties.
# A Concise Review of Silver Diamine Fluoride on Oral Biofilm
## 2 Materials and Methods  
Two independent reviewers ([PERSON] and [PERSON]) conducted a literature search using the four most commonly used databases: Medline, PubMed, Embase, and Scopus. Only papers published in English were included. The keywords used were (silver diamine fluoride OR silver diamine fluoride OR silver fluoride OR AgF AND biofilm OR plaque). No publication date limitation was established during the search. The latest search was conducted on 30 October 2020. The inclusion criteria were as follows: (1) papers investigating biofilm; (2) SDF solution at any concentration adopted as an intervention. The exclusion criteria were (1) studies with no information concerning biofilm and (2) reviews, case reports, and conference papers. In addition, a manual search of the reference lists of the selected papers and review articles was conducted. The two reviewers independently screened the papers and identified the relevant studies. When a disagreement occurred, another researcher ([PERSON]) was consulted to achieve a consensus. The results were summarized into three tables classified by the aim of the studies: Table 1 is for the studies investigating the effect of SDF on biofilm formed via microbiota, Table 2 is for the studies on the diversity and microbial change resulting from the application of SDF, and Table 3 is for the studies focused on the effect of SDF on fungus. Information regarding the following aspects of the background of the included studies was extracted and is presented in the summary tables: (1) authors and year; (2) materials studied;(3) setting of the study; (4) block (or sample) adopted in the study; (5) microbiota studied; (6) assessing method for the biofilm; (7) duration of the study; and (8) findings of the study.
## 4 Concurrency Enhancement to Gherkin and Tool Support
### The Proposed Enhancement and Examples Resolved  
Figure 4 shows the proposed enhancement in a class diagram. A feature contains one or more scenarios. A scenario is composed of one or more _sequential groups_ that are executed in the order of appearance. Each sequential group has exactly one _lead step_, which can be a Given step, a When step, or a Then step. The lead step is followed by zero or more _concurrent steps_ that take place simultaneously with the lead step. A concurrent step can be an And step or a But step. _A sequential group passes if and only if all of its constituent steps pass; it fails if any of its constituent step fails._ For brevity of reference, a sequential group beginning with a Then step is called a Then group, etc.  
With the enhancement, the scheduled sprinkling scenario of Figure 2 can now be interpreted correctly. The three steps of the Then group (lines 5-7) are executed concurrently. Thus, clogs in sprinkler A and sprinkler C are correctly detected with line 5 and line 7 failing, and sprinkler B passes the check at line 6.  
In the lift safety scenario of Figure 3, the three steps in the Then group are executed concurrently (lines 4-6). Thus, even if the lift fails to stop (line 4), the checks of warning and request cancellation are still performed as required. More importantly, the check for doors opening in the Then step (line 7) is performed _only if_ the lift indeed stops (line 4).  
Note that a failure at line 5 and line 6 causes the Then group to fail, consequently preventing the check at line 7 even if line 4 passes. A remedy to this problem calls for the supporting tool to be capable of continuing execution in case either one of the concurrent steps at line 5 and line 6 fails; see Section 4.2.