esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Mechanical Properties of Highway
## 3 Experiment Results and Analysis
### The Effect of Freeze-Thaw Cycles on Mechanical Properties
The relationship curve between \(\varepsilon_{1}/(\sigma_{1}-\sigma_{3})\) and \(\varepsilon_{1}\) under different numbers of freezing and thawing cycles is shown in Figure 6.  
A linear fit was applied to each curve in Figure 6 to obtain the values of the test parameters a and b in the model, as shown in Table 4. The coefficient of determination R\({}^{2}\) was found to be greater than 0.98, indicating a high level of fit. The reciprocal of parameters a and b, which represent the initial tangent modulus and ultimate deviatoric stress, respectively, was plotted against the number of freeze-thaw cycles in Figure 7. From the Figure, it can be observed that after the first freeze-thaw cycle, parameter 1/a decreased by 11.0% and parameter 1/b decreased by 8.2%. After the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Freezing and Thawing Times (Time)** & **a** & **b** & **Adjusted R Square** \\ \hline
0 & 0.96465 & 2.74137 & 0.98142 \\
1 & 1.07915 & 2.98762 & 0.99929 \\
3 & 1.10352 & 3.09856 & 0.99643 \\
5 & 1.11787 & 3.16806 & 0.99731 \\
7 & 1.13136 & 3.2354 & 0.99144 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters under different freeze–thaw cycles. Figure 6: Curves of \(\varepsilon_{1}/(\sigma_{1}-\sigma_{3})-\varepsilon_{1}\) of different freeze–thaw cycles. third freeze-thaw cycle, parameter 1/a decreased by 2.0% and parameter 1/b decreased by 3.3%. Similarly, after the fifth freeze-thaw cycle, parameter 1/a decreased by 1.2% and parameter 1/b decreased by 1.9%. After the seventh freeze-thaw cycle, parameter 1/a decreased by 1.1% and parameter 1/b decreased by 1.8%. These results indicate that the initial tangent modulus gradually decreases with an increasing number of freeze-thaw cycles, while the reduction rate gradually slows down. Similarly, the ultimate deviatoric stress also gradually decreases, but the reduction rate slows down over time. According to Equation (9), the damage ratio \(\mathrm{R_{f}}\) of the soil is the product of the damage stress and the b value. The damage ratio \(\mathrm{R_{f}}\) under different numbers of freeze-thaw cycles was calculated using the test data, and the results are shown in Table 5.  
It can be observed from the calculations that the soil damage ratio \(\mathrm{R_{f}}\) gradually increases with an increasing number of freeze-thaw cycles, albeit at a relatively slow rate. After seven freeze-thaw cycles, the damage ratio \(\mathrm{R_{f}}\) of the soil increased by 6.4%. A larger damage ratio \(\mathrm{R_{f}}\) indicates that the strength of the soil is closer to its ultimate deviatoric stress. This suggests that as the number of freeze-thaw cycles increases, the strength of the thawed soil approaches its potential ultimate strength.
## 2 Materials and Methods
### Description of the Quantum Rotation Gate
#### 2.2.2 Quantum Rotation Gate  
In the DQOBSMA, the QRG strategy is introduced to update the position of some search individuals to enhance the exploitation of the algorithm. In the physical discipline of quantum computing, the quantum rotation gate is used as a state processing technique. Quantum bits are binary, and the position information generated by the swarm-based algorithm is floating-point data. In order to process the position information, the discrete data of the quantum bits need to be turned into the algorithm's continuous data. The information of each dimension of the search agent is rotated in couples and updated by a quantum rotation gate. The update process and adjustment operation of QRG are as follows. Equation (9) shows that the \(2\times 2\) matrix represents the quantum rotation gate.  
\[U(\theta_{i})=\left[\begin{array}{cc}\cos(\theta_{i})&-\sin(\theta_{i})\\ \sin(\theta_{i})&\cos(\theta_{i})\end{array}\right] \tag{9}\]  
The updating process is as follows:  
\[\left[\begin{array}{c}\alpha_{i}^{\prime}\\ \beta_{i}^{\prime}\end{array}\right] =U(\theta_{i})\left[\begin{array}{c}\alpha_{i}\\ \beta_{i}\end{array}\right]=\left[\begin{array}{cc}\cos(\theta_{i})&-\sin( \theta_{i})\\ \sin(\theta_{i})&\cos(\theta_{i})\end{array}\right]\left[\begin{array}{c} \alpha_{i}\\ \beta_{i}\end{array}\right] \tag{10}\]  
where\((\alpha_{i},\beta_{i})^{T}\) shows the state of the quantum bit of the \(i\)-th quantum bit of the chromosome before the update of the quantum rotation gate, and \(\left(\alpha_{i}^{\prime},\beta_{i}^{\prime}\right)^{T}\) indicates the state of the quantum bit after the update. \(\theta_{i}\) denotes the rotation angle of the \(i\)th quantum bit, the size and sign of which have been pre-set, and its adjustment strategy is shown in Table 1.  
Table 1 shows the rotation angle is labeled by \(\theta_{i}=\Delta\theta_{i}\cdot s(\alpha_{i},\beta_{i})\), where \(s(\alpha_{i},\beta_{i})\) denotes the rotation of the target direction. \(\Delta\theta_{i}\) represents the rotation angle of the \(i\)-th rotation, where the position state of the \(i\)-th search agent in the population is \(\alpha_{i}\), and the position state of the optimal search agent in the whole population is \(\beta_{i}\). By comparing the fitness values of the current target and the optimal target, the direction of the target with higher fitness is selected to rotate the individual, thereby expanding the search space. If \(f(x_{i})>best\_fitness\), then the algorithm evolves toward the current target. Conversely, let the quantum bit state vector transform towards the direction where the optimal individual exists [46]. Figure 1 shows the quantum bit state vector transformation process.
## 6 Results and Discussion
### Comparisons  
In order to compare our method to the GD method developed in [8] and the CMA-ES method, we used an area with the same properties as the map of the Campus of Laval University, Canada used in [8]. The same sensing range is defined, however, in our case, the sensing area is considered to be isotropic, while in [8] the sensing angle is between \(60^{\circ}\) and \(120^{\circ}\). This constraint shows no limitations since the isotropic sensing model plays the role of the directive sensing model. We defined two images. The first one represents a small map with dimensions of \(100\times 100\) pixels, named UL-A, and the second represents a larger map with dimensions of \(500\times 500\) pixels, named UL-B.  
Black pixels are points with a very low interest and they are assumed to have no interest. The white pixels are points with interest. For better results, we define \(\
u\) = 0.1 and \(\mu\) = 0.1. Figure 20 shows the optimal placement of sensor nodes on the first map, UL-A. 100% coverage is reached and the maximum interest is achieved. Figure 21 demonstrates that full connectivity is reached.  
On the second map, UL-B, the optimal deployment described in Figure 22 achieves 89.48% of the joint interest. The proposed topology ensures full connectivity as shown in Figure 23. Table 2 regroups the results of three methods, the proposed model, the GD algorithm developed in [8], and the CMA-ES algorithm used in [8].  
Figure 21: Illustration of the UL-A network connection graph.  
Figure 20: Optimal sensor deployment on area “UL-A”.  
Table 2 proves that the proposed algorithm provides competitive results especially on smaller maps, where the maximum coverage value is achieved with fewer sensor nodes. On larger maps, the algorithm provides good results since it offers approximately the same coverage, but with more nodes than is needed in the other methods. In both cases, the processing time of the proposed algorithm is far less than that of CMA-ES.
## 4 Wireless Body Area Networks (WBANs)
Adding sensor and path redundancy for solving node failure and network congestion problems cannot be a viable mechanism for a BASN seeking to minimize form factor and resource usage. WBANs also have a distinctly hierarchical nature. They capture large amount of data constantly and naturalistically that microprocessors should process to extract the needed information. Data processing must be hierarchical to exploit the asymmetry of resources, to maintain system efficiency, and ensure the availability of data, if necessary [20]. Figure 3: WBAN and its environment. The application of WBANs in a medical area consists of wearable and implantable sensor nodes that can sense biological information from the human body and transmit it over a short distance wirelessly to a control device worn on the body or placed in an accessible location. The sensor electronics must be miniaturized, low-power and detect medical signals such as electrocardiograms, photoplethysmograms, electroencephalography, pulse rate, pressure, and temperature. The gathered data from the control devices are then transmitted to remote destinations in a wireless body-area network for diagnostic and therapeutic purposes by including other wireless network for long-range transmission. Currently the monitoring devices that are used in medical centers are not completely wearable because their electronics are bulky and wires are used for connections to multiple sensors. As shown in Figure 1(a), the current application of a sensor network that used in some modern medical centers. A wireless control unit is used to collect information from sensors through wires and transmits it to a remote station for monitoring. The control unit is cumbersome and using wires is not recommended for the comfort of patients. As shown in Figure 1(b), the future medical sensor network requires miniaturized and wearable sensor nodes that can communicate with the receiving device wirelessly.
## 3 Test Methods
### Three Calculation Methods of Dielectric Constants
#### 3.2.1 The Coring Method  
The Coring method that is destructive can be used to calculate the dielectric constant of the asphalt concrete layer at the selected cored points and this can be considered as reference. For application of the Coring Method, the thickness of the asphalt concrete layer was measured from the cores. The electromagnetic wave reflection time was recorded from the GPR data detected.  
The five different points in the tested area were cored and the thickness of the samples were measured. The dielectric constants of asphalt concrete layers were calculated using Equation (1) [1]:  
\[\epsilon_{AC}=\frac{t^{2}c^{2}}{4H^{2}} \tag{1}\]  
where, \(\epsilon_{AC}\) denotes the dielectric constant, \(t\) (s) denotes the electromagnetic wave reflection time, \(c\) (m/s) denotes the velocity of light in vacuum, and \(H\) (m) denotes the thickness of asphalt concrete layer, respectively.  
The standard deviation and coefficient of the variation of the test results were calculated by Equations (2) and (3):  
\[Standard\ Deviation=\sqrt{\frac{\sum_{i=1}^{N}(\epsilon_{ACi}-\mu)^{2}}{N}} \tag{2}\]  
\[Coefficient\ of\ Variation=\frac{Standard\ Deviation}{\mu} \tag{3}\]  
where, \(\epsilon_{ACi}\) denotes the \(i\) time dielectric constant calculated by the method, \(\mu\) denotes the average dielectric constant value, and \(N\) denotes the test number.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & \begin{tabular}{c} **Minimum** \\ **Frequency** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Frequency** \\ \end{tabular} & \begin{tabular}{c} **Frequency** \\ **Step** \\ \end{tabular} & \
\begin{tabular}{c} **Dwell** \\ **Time** \\ \end{tabular} &
\begin{tabular}{c} **Sampling** \\ **Time** \\ \end{tabular} \\ \hline Value & 60 MHz & 2980 MHz & 20 MHz & 3.0 μs & 25 ns & 20.4 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: GPR parameters.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 5 CPS-WSS Monitoring Application Challenges and Future Directions
### Future Directions
#### Dynamic Water Quality and Leakage Model under Demand Uncertainty  
Conventional water quality sensors could not be utilized to determine the source and spread of contaminant in the system, as such, hydraulic modelling is required. However, due to the demand uncertainties, water quality hydraulic models in a cyber-physical water system must be robust and mathematically correct. As such, more research work should be conducted to develop a novel real-time dynamic hydraulic modelling system (for water quality and leakage) that considers demand uncertainties. Modelling real-world phenomenon under demand uncertainty is a hot research area in the hydraulic modelling domain. Models for determining the source and spread of contaminants should also be researched and integrated into the water quality models.
## 3 Sample Preparation Techniques and Development of Extraction Sorbents
### Liquid-Based Extraction
Recently, a new green extraction technique, termed as supramolecular solvent-based microextraction (SUPRAS), has received significant interest due to its inherent properties, particularly in mitigating the adverse effects of organic solvents to the environment [30]. SUPRAS corresponds to water-immiscible solvents consisting of amphiphile aggregates, which are generated via self-assembly of amphiphilic molecules. These solvents are excellent alternative solvents to the conventional solvents for the preconcentration of pesticides residues using microextraction techniques as they provide various interactions such as hydrogen bonding, ionic bonding and hydrophobic interaction [36; 37; 38]. A nanostructured supramolecular solvent-based LIME method for preconcentration of methyl parathion from water samples coupled with high-performance liquid chromatography (HPLC) was developed by [PERSON] et al. [30]. Under optimized conditions, the method showed low LOD (0.27 ng mL\({}^{-1}\)), which was probably due to its good enrichment factor (139). Besides that, the SUPRAS method exhibited excellent recovery, ranging from 92-109.9%, with excellent RSD (2.23-3.6%). The good analytical performance was due to its capability to extract polar and non-polar analytes simultaneously, as the SUPRAS has diverse regions of polarity as a result of its ordered structures. Besides that, this method has replaced the use of hazardous organic solvents, which are commonly used in conventional methods, with non-toxic amphiphilic compounds as extractant solvent. This makes the SUPRAS method more environmentally friendly. In conclusion, many improvements and developments have been carried out in LLE to overcome its drawbacks and limitations. However, the application of LLE-based techniques has significant limitations in terms of sensitivity by which sorbent-based techniques such as solid-phase extraction (SPE), solid-phase microextraction (SPME), and stir-bar sorptive extraction (SBSE) offer lower LOD in comparison to LLE-based techniques. This might probably be due to the combination of physical properties (sorbent porosity and surface area) and chemical properties (molecular interaction via functional moieties) of the sorbents that play crucial roles in enhancing the extraction efficiency of targeted analytes. Nonetheless, the sensitivity of solvent-based extraction techniques could be enhanced by exploring the potential of supramolecular solvents due to its amphiphilic properties. Besides that, SUPRAS is environmentally friendly (non-volatile and inflammable). These favorable characteristics make SUPRAS microextraction technique a promising alternative to the conventional liquid-based extraction techniques.
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 3 Privacy-Protecting Attributes Classification for the Storage of Cloud Data
### Key Management
#### 3.2.2 Length of the Keys  
The importance of key length cannot be overstated in cryptography. While advancements in computing technology enable more powerful processing capabilities, it is imperative to adapt cryptographic systems to employ longer and more robust key lengths to withstand evolving threats. Additionally, organizations and individuals must remain vigilant in implementing best practices for key management to mitigate the risk of key compromise and ensure the long-term security of their sensitive data. Cryptographic keys are critical components in securing sensitive data. Their length, combined with sound key management practices, forms the cornerstone of an effective privacy platform. By employing longer key lengths and following recommended key management guidelines, organizations can bolster the security of their cryptographic systems and safeguard sensitive information against unauthorized access [53]. Various key lengths are available depending on the methods used. Symmetric encryption algorithms like 3 DES and AES typically utilize key lengths ranging from 128 to 256 bits, while asymmetric encoding algorithms like RSA use key lengths ranging from 1024 to 4096 bits [54]. It is worth noting that the longer the key length, the more challenging and time-consuming the encryption or decryption process becomes [14].
# Recommender Systems in the Real Estate Market--A Survey
## 6 Evaluation and Benchmarking
### Evaluation Measures and Baselines  
There are several types of evaluation measures to assess the performance of RSs. While the relevance measures are frequently used for evaluation, there are other measures such as coverage, diversity, and serendipity which are also considered as additional performance measures [62] for RSs. Relevance measures such as precision, recall, and NDCG evaluate the RS in predicting the items that the user will interact with. Coverage measures how well the item catalog [63] or stakeholders [64] are covered in recommendation lists. Diversity measures can be applied, for instance, in news recommendations [65; 66] or music recommendations [67] to measure to what extent the recommendation lists contain diverse content. Serendipity metrics measure the novelty and unexpectedness of recommendation lists generated by RSs [68].  
There are different types of evaluation measures in the selected papers to reflect the performance of the proposed housing RS. The distribution of these measures is summarized in Figure 2. As is shown in this figure, precision and recall, which are classic information retrieval metrics, are the most popular measures in evaluating housing RSs. The first five measures in the figure (AUC, accuracy, precision, recall, and F1) evaluate the ability of the RS in predicting the relevant items for users. MRR (mean reciprocal rank), MAP (mean average precision), and NDCG (normalized discounted cumulative gain) are rank-sensitive measures, which means that they evaluate the ability of the proposed method in recommending relevant items in higher ranks. All the aforementioned measures are applicable in offline evaluation approaches. Moreover, conversion rate is an online evaluation measure which shows whether users find their desired items and therefore are satisfied with the recommendations.  
Figure 2: Evaluation measures in housing RSs.  
When it comes to baseline methods, the set of used baselines in the selected papers is quite diverse. In most of the papers, multiple versions of their proposed approaches are used as the competing methods in their benchmarking. The most frequently used baselines are KNN-based approaches (item-based or user-based) and popularity-based recommendation.
# A Two-Echelon Electric Vehicle Routing Problem with Time Windows and Battery Swapping Stations
## 4 Algorithms for the 2E-EVRPTW-BSS  
Considering the classical VRP is NP hard [36], its variant 2E-EVRPTW-BSS is undoubtedly an NP-hard problem as well. Hence, we adopt a metaheuristic to achieve the optimal solution within a reasonable time.  
The proposed metaheuristic is developed based on a VNS algorithm. [PERSON] and [PERSON] [43] first proposed the VNS algorithm. In their work, the systematic changes of the neighborhood were employed within the local search to get rid of the local optimal solutions, which can be used to successfully solve myriad optimization problems in reality [44; 45].  
In this study, the proposed algorithm begins with an initial solution \(S_{0}=(S_{1}^{0},S_{2}^{0})\), where \(S_{1}^{0}\) and \(S_{2}^{0}\) are the initial solutions of the first and second echelons, respectively. The shaking neighborhood structures (denoted as \(N_{k}\), \(k\) = 1,2,..., \(k_{\max}\) and \(k_{\max}\) is the number of shaking neighborhood structures) are first used to improve the efficiency in searching for solution space, followed by the local search neighborhood structures (denoted as the set \(N_{L}\), \(l\) = 1,2,..., \(l_{\max}\), and \(l_{\max}\) is the number of local search neighborhood structures). A solution \(S^{\prime}=(S_{1}^{\prime},S_{2}^{\prime})\) is first generated in a shaking neighborhood structure \(N_{k}\) in each iteration, then a local search procedure is applied to produce a better solution S\({}^{\prime}\) for improvement.  
The iterative process stops until the shaking neighborhoods have been exhausted or the given termination criterion is met--the maximum running time (\(T_{\max}\)) in this paper. The overall iterative structure of the proposed VNS is presented in Algorithm 1, and the details will be discussed in the following sub-sections.  
```
Input: initial solution \(S^{0}=(S^{0}_{1},S^{0}_{2})\).  A set of neighborhood structures \(N_{k}\), \(k=1,2,\ldots,k_{\max}\) for shaking phase. \(S^{*}\leftarrow S^{0}\) \(k,l=1\) Output: best feasible solution achieved \(S^{*}\). 1. While\(k\leq k_{\max}\) 2. Shaking: pick a random solution \(S^{\prime}_{2}\) from the \(k\)th neighborhood of \(S^{*}\). Construct \(S^{\prime}_{1}\) based on \(S^{\prime}_{2}\). Update the solution as \(S^{\prime}=(S^{\prime}_{1},S^{\prime}_{2})\) 3. While\(l\leq l\max\) 4. Local search \(S^{\prime\prime}_{2}\leftarrow\) the best solution from the \(l\)th neighborhood of \(S^{\prime}_{2}\). Construct \(S^{\prime\prime}_{1}\) based on \(S^{\prime\prime}_{2}\) Update the current solution as \(S^{\prime\prime}=(S^{\prime\prime}_{1},S^{\prime\prime}_{2})\). If \(f_{\text{reu}}(S^{\prime\prime})\leq f_{\text{reu}}(S^{\prime})\)then \(S^{\prime}\gets S^{\prime\prime}\) \(L=1\) else \(L=l+1\) 1. If \(f_{\text{reu}}(S^{\prime\prime})\leq f_{\text{reu}}(S^{*})\)then \(S^{*}\gets S^{\prime\prime}\) \(K=1\) 1. Else: \(K=k+1\) 1. If the termination condition is met, then return \(S^{*}\). End while
```  
**Algorithm 1. VNS.**