esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of the Weighted Multi-Facility Location Problem Using MS Excel
## 2 Materials and Methods
### Metaheuristic Algorithm  
The metaheuristic method is used in order to compare the performance of MS Excel when solving the continuous multi-variable optimization problem. As this metaheuristic method, the simulated annealing-based algorithm is adapted for the MFLP-W problem. This choice was based on our previous experiences in using this method for position optimization problems. The algorithm was implemented by the authors in C++ programming language under the Visual Studio IDE. The concept of this method is inspired by annealing in metallurgy. See [2] for more information.  
The basic steps of this method in pseudocode are as follows:  
1. Generate random solution.
2. Set temperature to its maximum limit.
3. Repeat points 4 to 6 for a predefined number of times.
4. Transform current solution.
5. Replace the current solution by the transformed solution with the probability given by the Metropolis criterion.
6. Save the best solution if found.
7. Cool the temperature.
8. When the temperature is not below its lower limit, repeat the whole process in point 3.
9. Terminate the algorithm and return the best solution found.  
The transformation of the current solution in point 4 is based on changing one randomly selected variable using a random number generator with normal distribution. The size of the change depends on the current value of temperature. The higher the temperature, the bigger the change; this principle allows for the extensive search in the state space at the beginning of the optimization and small tuning near its end. This transformed solution replaces the original with the probability given by the Metropolis criterion in Equation (4). If the transformed solution is better than the original, it is always replaced; otherwise, it depends on the temperature and the difference in solution qualities.  
\[p\big{(}D\to D^{\prime}\big{)}=\begin{array}{c}1\\ e^{-\frac{D^{\prime}-D}{T}}\end{array}\quad\text{for }D^{\prime}\leq D\\ \text{otherwise}\end{array} \tag{4}\]  
where \(p(D\to D^{\prime})\) is the probability that the current solution is replaced by the transformed solution, \(D\) is the objective function of the current (original) solution, \(D^{\prime}\) is the objective function of the transformed solution, \(T\) is the current value of temperature.  
The cooling schedule in point 7 is performed according to Equation (5), where \(\alpha\) is the cooling coefficient (\(0<\alpha<1\)).  
\[T=T\cdot\alpha \tag{5}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Max time & Unlimited \\ Iterations & Unlimited \\ Constraint precisions & 0.000001 \\ Convergence & 0.0001 \\ Population size & 100 \\ Random seed & 0 \\ Mutation rate & 0.075 \\ Maximum time without improvement & 30 s \\ Max subproblems & Unlimited \\ Maximum feasible solutions & Unlimited \\ Integer optimality & 1\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used for optimization in the Excel-Solver.
## 2 Analysis of Cooling Vests and Literature Review
### Testing and Evaluation of Comfort
#### 2.2.2 Physiological Analysis of Comfort  
During tasks such as climbing ladders and maneuvering hoses [25], firefighters engage their muscles, and their basal metabolism, peripheral vasodilation, blood circulation, and skin temperature increase because of the temperature and humidity of the clothing microclimate, which imposes considerable thermal stress on the body. In general, firefighters can only work in environments with temperatures of \(<\) 65 \({}^{\circ}\)C for long periods of time. When the ambient temperature reaches 120 \({}^{\circ}\)C, firefighters' activity is limited to approximately 45 s. Firefighters can only remain in an ambient temperature of 475 \({}^{\circ}\)C for 2 s [26]. Firefighter uniforms with low air permeability limit heat dissipation and cause heat to accumulate between the layers of clothing [27]. High-temperature environments and the heat produced by the human metabolism produce extreme thermal environments. Without proper cooling, such environments can disrupt the balance between the production and dissipation of heat from the body [28], which can result in burns that affect the internal organs in addition to general discomfort [29]. This condition can eventually cause disordered body temperature regulation, dizziness, heat stroke, seizure, shortness of breath, increased heartbeat, impaired reflexes and comprehension, and even death [30].  
Heat stress is the main factor affecting firefighters' work efficiency and has therefore been widely studied. Cooling vests contain devices that reduce the temperature of the clothing microclimate within firefighter uniforms, accelerate heat dissipation, alleviate heat stress, increase comfort, and reduce body temperature.
# Using Machine Learning for Web Page Classification in Search Engine Optimization
## 7 Discussion  
In previous research by other authors, there have been many attempts to identify relevant factors that affect ranking on search engines. In our research, machine learning classifiers and experts' knowledge were used to identify these factors and predict the on-page SEO quality of a web page. The built classification models showed accuracy between 54.69% and 69.67% in predicting the on-page SEO quality (degree of adjustments to the SEO guidelines).  
Ranking factors identified using experts' knowledge and machine learning in this study confirmed previous research--having keywords in page title, meta tag, H1 tag, and text body of the web page is important in the SEO process. By further investigating variables of pages in \"high SEO\" class, we can identify keyword frequencies used by pages  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **J48** & **NaiveBayes** & **KNN** & **SVM** \\ \hline NaiveBayes & 0.0377 & - & - & - \\ KNN & 0.9692 & 0.0048 & - & - \\ SVM & 0.9986 & 0.0808 & 0.8896 & - \\ LogReg & 0.8267 & 0.3925 & 0.4357 & 0.9371 \\ \hline \end{tabular}
\end{table}
Table 10: Comparison of differences between pairs of classification models by Nemenyi test (\(p\)-values).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Baseline** & **J48** & **Knn** & **SVM** & **LogReg** & **N.Bayes** \\ \hline
1 & 0.48 & 0.59 & 0.73 & 0.63 & 0.63 & 0.49 \\
2 & & 0.70 & 0.73 & 0.62 & 0.59 & 0.53 \\
3 & & 0.68 & 0.67 & 0.57 & 0.68 & 0.68 \\
4 & & 0.62 & 0.68 & 0.64 & 0.65 & 0.53 \\
5 & & 0.75 & 0.78 & 0.69 & 0.62 & 0.50 \\
6 & & 0.71 & 0.75 & 0.75 & 0.67 & 0.43 \\
7 & & 0.65 & 0.63 & 0.58 & 0.58 & 0.54 \\
8 & & 0.58 & 0.65 & 0.68 & 0.68 & 0.63 \\
9 & & 0.75 & 0.63 & 0.74 & 0.60 & 0.61 \\
10 & & 0.73 & 0.70 & 0.72 & 0.58 & 0.52 \\ Average & 0.48 & 0.68 & 0.69 & 0.66 & 0.63 & 0.55 \\ W & & 55 & 55 & 55 & 55 & 52 \\ P & & 0.001953 & 0.001953 & 0.001953 & 0.001953 & 0.009766 \\ \hline \end{tabular}
\end{table}
Table 9: Results of the Wilcox signed-rank test of 10-folds’ cross validation for comparison of used classification algorithms with baseline classification in the majority class.  
in this class: For title, meta tag, and H1 this is 1-3 and for body text it is 2-4 keywords. The average length of page title was eight words, meta description was 10, and H1 was six words. Previous studies report similar results [3; 4; 5; 22]. This shows that by using expert knowledge to classify pages we can also find preferred values of ranking factors.  
Following the same principle, by exploring variables of pages in \"low SEO\" category, we could find patterns that are undesirable in SEO process. However, since our data set is small, and most of the pages in the \"low SEO\" class had zero values in independent variables, we believe that, for making such research, further variables should be introduced or the size of data set must be increased.
## 2 Methodology
The experimental analysis for obtaining the curves was carried out using the CIU triaxial test, under controlled conditions that simulate the field behavior of silt-like mine tailings [28]. These tests were selected due to their relevance in evaluating the phenomenon of static liquefaction, as such conditions represent states in which excess pore pressure can rapidly accumulate in mine tailings storage facilities, potentially leading to a sudden loss of shear strength. Standard cylindrical soil specimens were used, with dimensions of 38 mm in diameter and 76 mm in height, a dry density of 16.5 \(\mathrm{kN}/\mathrm{m}^{3}\), and an estimated void ratio of e = 0.8. The specimens were subjected to prior isotropic consolidation to ensure full saturation of the material [29]. The tests were conducted under three different confining pressures: 100, 200, and 400 kPa, up to an axial strain of approximately 20%. These confining pressures were selected to represent a range of field-relevant conditions. Figure 3 presents the stress-strain response curves (a), pore pressure-strain curves (b), and p\({}^{*}\)-q stress path diagrams (c). \begin{table}
\begin{tabular}{c c c} \hline
**Property** & **Value** & **Test Procedure** \\ \hline Specific Gravity, Gs & 2,97 & ASTM D854-14 [23] \\ Specific particle sizes (\(\upmu\)m) & 3, 15, 65, & ASTM D2487-17e1 [24] \\ (D10, D30, D50, D60) & 112 & ASTM D2487-17e1 [24] \\ Coefficient of curvature and uniformity & 37.3, 0.7 & ASTM D2487-17e1 [24] \\ Cut, Cc & 37.3, 0.7 & ASTM D2487-17e1 [24] \\ \(\mathrm{Atterberg\ limits}\) (liquid limit, plastic limit and plasticity index) & 18, 15, 3 & ASTM D4318-17e1 [25] \\ LL, PL, PI & 20.3 & ASTM 2012 D698-12; D1557-12 [26; 27] \\ Unified Soil Classification System & ML & ASTM D2487-17E1 [24] \\ \hline \end{tabular} \({}^{*}\)Standard Proctor Test.  
\end{table} 
Table 1: Physical Properties of the Tailings. Figure 2: Particle size distribution of the tested material [22]. The specimens were reconstituted using the moist tamping method. To optimize specimen saturation, CO\({}_{2}\) was applied through back pressure from the base of the sample (upward flow) for 30 min. Subsequently, and still using upward flow through back pressure, the sample was saturated at a pressure of 5 kPa with distilled and demineralized water with neutral pH. The water flow was stopped once a water volume equivalent to four times the void volume of the specimens had been reached. Table 2 shows the conditions of each specimen and the applied pressures in the triaxial tests.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 4 Applications and Target Industry
### Healthcare  
The healthcare industry is one of the most-heavily regulated industries in the world, and using FRSs to share data can help reduce the burden of compliance [154; 155]. In particular, FRSs can allow various healthcare providers share patient data, medical records, and other relevant information while maintaining the privacy of individual data. Researchers have studied the potential of FRSs for healthcare data sharing [156; 157]. The consensus is that FRSs can provide a secure, efficient, and cost-effective way to share sensitive patient data between organizations. Studies have also shown that FRSs can reduce the cost of healthcare data storage, improve the quality of care, and reduce the burden of compliance with data privacy regulations [158; 159]. FRSs can benefit healthcare in a number of ways. For one, they can allow for more secure and efficient data sharing between healthcare providers. By using FRSs, organizations can share data without sacrificing the privacy of individual data [160]. Moreover, FRSs can reduce the cost of healthcare data storage and improve the quality of care by allowing a more comprehensive view of each patient [161]. Finally, FRSs can reduce the burden of compliance with data privacy regulations, as data can be stored in a secure and encrypted manner [162].
## 4 Experimental Results
### Comparison of YOLO Models  
For the analysis of the stamp detection process, the models YOLOv8s, YOLOv9s, YOLOv10s, and YOLOv11s were used. These architectures have different numbers of parameters, layers, sizes, and FLOPS, as shown in Table 3. A gradual reduction in these characteristics is observed with the progression of versions, except for the number of layers in YOLOv9s. In this case, the significant increase in layers, compared to the other versions, directly impacts the computational cost and model complexity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Total Images** & **Train** & **Validation** & **Test** \\ \hline
732 & 512 & 110 & 110 \\
100\% & 70\% & 15\% & 15\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Organization of data from the adapted dataset.  
Figure 8: Scanned document with extracted annotation: (a) Image from the StaVer dataset. (b) Mask representing the location of the object in the image. (c) Annotated image.  
The hyperparameters used for training the detection models include an input image size of 640 \(\times\) 640, an initial learning rate of 0.01, a weight decay of 0.0005, a momentum parameter of 0.937, and a batch size set to 16. Patience was set to 50 for early stopping in case there was no improvement in metrics during the last 50 epochs of training, with the number of epochs limited to 500. The best weights were saved for evaluation and inference on the test set.  
The training performance is shown in Table 4. It can be seen that the YOLOv9s required six times more training time than YOLOv10s and twelve times more than YOLOv8s and YOLOv11s.
## 4 Experimental Results  
### The Grid Point Construction Results in LRBF  
The process of solving the maximum influence distance L using Gaussian basis function integration is as follows: (1) the initial value of \(d\) and the step size are set; (2) the value of F(\(d\)) and the value of \(F(d+\Delta d)\) are calculated; (3) \(F(d+\Delta d)-F(d)\) and \(d=d+\Delta d\) are calculated, if \(F(d+\Delta d)-F(d)<0.001\), and the value of \(d\) is outputted; otherwise, Steps (2)-(3) are repeated. The final \(d\) is the maximum influence distance \(L\) (Figure 10).  
In the LRBF method, the regular grid points are generated within the study area first. Based on the original EMR sampling data, the optimal shape parameters of the Gaussian basis function are calculated using PSO as \(\varepsilon=0.2159\), and the Gaussian basis function expression is \(e^{-0.2159^{2}x^{2}}\). The Gaussian basis function is integrated using Equation (5), setting \(d\) = 0 and the step size \(\Delta d=1\). When the difference in the integration values between adjacent steps is less than 0.001, the maximum influence distance \(L\) = 14 m is obtained, and the Gaussian basis function and the integral function image are shown (Figure 11a). The interval of the grid points is set as \(L/2\), which is 7 m, and the result of the LRBF grid opinion construction is shown (Figure 11b).
# An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering +
## 2 Related Work
### Contribution  
1. We developed a novel framework that requires little to no parameter setting consisting of a simple swarm intelligence technique BBPSO and MOO technique MOEA/D with a solution update based on K-means for data clustering. To the best of our knowledge, this approach has not been explored.
2. We ran extensive investigations to evaluate its performance. We tested on 13 datasets with different characteristics and evaluated using 5 different popular metrics. We compared our results with other work to show how the proposed technique performs relatively. We conducted other analysis such as analysis of the convergence plot to assess the stability of the proposed technique and visual examination of biplots of the clustering found by the proposed technique.
## 4 Case Study: Sustainability Assessment
### Sustainability Assessment Methodology
#### 4.1.1 Environmental Life Cycle Assessment (e-LCA) Method  
In the environmental life cycle assessment (e-LCA), the reference method applied was based on ISO 14040:2006 [51] and ISO 14044:2006 [52]. The environmental impact categories assessed were the following:  
* GWP100: Emission of greenhouse gases (expressed as the sum of global warming potential, GWP, 100 years, in carbon dioxide equivalents, CO\({}_{2}\) equivalent).
* AP: Acidifying potential (expressed as sulfur dioxide equivalents, SO\({}_{2}\) eq.).
* EP: Emission of substances to water contributing to oxygen depletion, \"europhication\" (expressed as phosphate equivalents, PO\({}_{4}\)\({}^{-}\) eq.).
* POFP: Emission of gases contributing to the creation of ground-level ozone, \"photochemical oxygen creation potential\" (expressed as ethylene equivalents, C\({}_{2}\)H\({}_{4}\) eq.).
* ODP: Emission of ozone-depleting gases (expressed as the depletion potential of the stratospheric ozone layer, ODP kg CFC11 eq.).
* ADP: Depletion of abiotic resources--elements (expressed as abiotic depletion potential (ADP--elements) for non fossil resources, kg Sb eq.).
* ADP-FF: Depletion of abiotic resources--fossil fuels (expressed as abiotic depletion potential (ADP--fossil fuels) for fossil resources, MJ, net calorific value).  
The characterization models and factors used to calculate each environmental impact category are based on CML-IA (baseline 2013) methodology. Likewise, another two additional environmental impact categories were also assessed, namely, CED--cumulative energy demand (M) and WU--water resources used (m\({}^{3}\)).  
The specific software, database, and impact assessment methodology used in this study for LCA was Simapro v.9.2.0.1 software, Ecoinvent v.3.4 database and the environmental impact categories assessed were those defined in the product category rules of the EPD international system for highways, streets, and roads (International EPD System, 2018).
# The Security Perspectives of Vehicular Networks: A Taxonomical Analysis of Attacks and Solutions
## 4 Security Solutions
### Identity-Based Solutions for VANETs  
Identity-based security solution was first introduced by [PERSON] in the year 1984, but implemented practically later in 2001 by [PERSON] and [PERSON] [90]. In identity (ID) based cryptography solutions, certificates are not used for verification of public keys as these are used in Public Key Infrastructure (PKI). In this system, identity information of an entity is used for generating the public keys. This information is publicly available like name, IP address, email address, etc. This was first used with bilinear pairings on elliptic curves. ID-Based Cryptographic (IBC) solutions are further divided into Identity-Based Encryption (IBE) and Identity Based Signatures (IBS). Some of the important solutions are mentioned here. An ID-based blind signature and ring signature are used in a solution [91]. It uses bilinear pairings for less computational overheads. Signatures are also used with Gap Diffie Hellman (GDH) groups [92]. Bilinear pairings are constructed for Computational Diffie-Hellman problem (CDHP). Small signature size increases the efficiency. Other ID-based ring signatures are used in [93, 94]. Pairing computations are used with variable group size. The signature size is less, and therefore, computational complexity is less. Security frameworks based on cryptographic parameters are shown in [95, 96]. No extra memory is used to map the pseudonymity. As VANETs are connected with IoE, security solutions are also important for grids. Such a solution is shown in [97]. It uses the Identity-based Key Infrastructure for Grid (IKIG) and uses gentry-Silverberg full HIBE and HIBS schemes. The least cost of computation and communication are the advantages of this solution. Privacy preservation with an ID-based scheme is shown in [98]. It observes a 92% improvement in various factors and also ensures less computation complexity. A solution using Elliptic Curve Cryptography (ECC) is shown in [99]. It uses general one-way hash functions in the process. It provides reduced single signature \(cost=0.4438\) ms and batch signature \(cost=0.442+0.0018n\) ms. A proficient message verification scheme is shown in [100]. It claims for less computational cost as compared to the traditional verification schemes. VANET-based Privacy-Preserving Communication Scheme (VPPCS) is shown in [101]. It uses ECC and ID-based encryption and ensures content and contextual privacy. A summarization of these solutions are listed in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} Implementation efficiency \\ needs to be increased \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: List of ID-based solutions for VANETs
# Foot Anatomical Structural Variations Increase the Risk of Falls in Older Adults
## 3 Results
### Logistic Regression Model  
A logistic regression model has been proposed to study the influence of the variables sex, age, BMI, presence of HAV and tailor's bunion in falls and labeled \"falls\" as a dependent variable. In addition, it was observed that for each unit in which BMI increased, independently, the risk of falls increased by 10% (OR = 1.10, CI = 1.01-1.21, \(p\) = 0.038). Moreover, it was stratified by gender. In women with HAV, the risk of falls increased 3.07 times compared to those who did not have HAV (OR = 3.07, CI = 1.15-8.68, \(p\) = 0.029) (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **N = 164** & \begin{tabular}{c} **Non-Fallers** \\ **N = 81** \\ \end{tabular} & \
\begin{tabular}{c} **Fallers** \\ **N = 83** \\ \end{tabular} & **OR** & \(p\)**. Ratio** & \(p\)**. Overall** \\ \hline FPI & 5.0 [3.0; 9.0] & 5.0 [3.0; 10.0] & 5.0 [2.5; 8.0] & 0.95 [0.88; 1.03] & 0.188 & 0.181 \\ FPI & & & & & & 0.045 \\ More & 32 (19.5\%) & 22 (68.8\%) & 10 (31.3\%) & Ref. & Ref. & \\ pronated & 46 (28.1\%) & 17 (36.7\%) & 29 (63.0\%) & 3.66 [1.42; 9.97] & 0.007 & \\ Normal & 68 (41.5\%) & 32 (47.1\%) & 36 (52.9\%) & 2.44 [1.02; 6.17] & 0.046 & \\ Supinated & 18 (11.0\%) & 10 (55.6\%) & 8 (44.4\%) & 1.74 [0.51; 5.93] & 0.372 & \\ \hline \hline \end{tabular}
\begin{tabular}{c} FPI = Foot Posture Index. \\ \end{tabular}
\end{table}
Table 2: Relationship of the general FPI and the foot anatomical structural pathologies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dependent: Falls** & \multicolumn{2}{c}{**No**} & \multicolumn{2}{c}{**Yes**} & \multicolumn{2}{c}{**OR (Multivariate)**} \\ \hline Gender & Men with HAV & 38 (59.4\%) & 26 (40.6\%) & - \\  & Men with HAV & 15 (60.0\%) & 10 (40.0\%) & 1.07 [0.33-3.54, \(p\) = 0.907] \\  & Men with tailor’s & 9 (47.4\%) & 40 (52.6\%) & 2.38 [0.71-85.2, \(p\) = 0.167] \\  & Women with HAV & 43 (43.4\%) & 56 (56.6\%) & 1.52 [0.75; 3.08, \(p\) = 0.246] \\  & Women with HAV & 26 (36.1\%) & 46 (63.9\%) & 3.07(1.15-8.68, \(p\) = 0.029) \\  & Women with tailor’s bunion & 15 (35.7\%) & 27 (64.3\%) & 1.34 [0.52-3.45, \(p\) = 0.541] \\ \hline Years & Younger than 70 & 44 (49.4\%) & 45 (50.6\%) & - \\  & Older than & 37 (49.3\%) & 38 (50.7\%) & 0.70 [0.35; 1.37, \(p\) = 0.299] \\ \hline BMI & Mean of BMI & 26.2 (3.5\%) & 27.2 (4.3\%) & 1.10 [1.01; 1.21, \(p\) = 0.038] \\ \hline HAV & No & 40 (59.7\%) & 27 (40.3\%) & - \\  & Yes & 41 (42.3\%) & 56 (57.7\%) & 1.88 [0.91; 3.94, \(p\) = 0.090] \\ \hline Tailor’s bunion & No & 57 (55.3\%) & 46 (44.7\%) & - \\  & Yes & 24 (39.3\%) & 37 (60.7\%) & 1.69 [0.83; 3.47, \(p\) = 0.151] \\ \hline Sport & Never & 65 (46.1\%) & 76 (53.9\%) & - \\  & Frequent & 16 (69.6\%) & 7 (30.4\%) & 0.45 [0.15; 1.19, \(p\) = 0.117] \\ \hline \hline \end{tabular}
\begin{tabular}{c} HAV = Hallux Abducts Valgus; Never = 0 days/week; Frequent = 2 or 3 days/week. \\ \end{tabular}
\end{table}
Table 3: General multivariate.