esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods  
### Sample Collection  
A total of 58 core samples were obtained from 4 wells in the study area (Figure 2). These samples represent the Triassic Montney Formation source rock. About 15 g from each sample was pyrolyzed using the source rock analyzer (SAR) instrument to obtain S\({}_{1}\), S\({}_{2}\), S\({}_{3}\), Tmax, and TOC (Table 1). Figure 1: Ideal stratigraphic sequence of the Montney Formation modified from [37]. Figure 2: Map showing the location of the studied wells, Montney Formation, NE BC. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{\
\begin{tabular}{c} **Well Location** \\ \end{tabular} } & **Well,** & **Depth (m)** & **TOC (wt.\%)** & **mg HC/g** & **mg HC/g** & **mg CO/g** & **Tmax’ C** & **mg HC/g** & **mg CO/g** & **PI** & **GP** \\  & & & **rock** & **rock** & **rock** & **rock** & **TOC** & **TOC** & & **(S**1+ S2)** \\ \hline \multirow{9}{*}{
\begin{tabular}{c} **Well Location** \\ \end{tabular} } & 2386.05 & 1.4 & 0.63 & 0.56 & 0.3 & 453 & 39.91 & 21.38 & 0.53 & 1.19 \\ \cline{2-11}  & 2396.05 & 0.65 & 0.32 & 0.23 & 0.33 & 447 & 35.38 & 50.77 & 0.58 & 0.55 \\ \cline{2-11}  & 2406.28 & 1.45 & 0.54 & 0.6 & 0.52 & 458 & 41.41 & 35.89 & 0.47 & 1.14 \\ \cline{2-11}  & 2416.44 & 1.84 & 0.86 & 0.99 & 0.38 & 473 & 53.78 & 20.64 & 0.46 & 1.85 \\ \cline{2-11}  & 2426.55 & 2.47 & 1.11 & 1.38 & 0.29 & 486 & 55.85 & 11.74 & 0.45 & 2.49 \\ \cline{2-11}  & 2436.75 & 1.79 & 0.96 & 0.9 & 0.33 & 476 & 50.36 & 18.47 & 0.52 & 1.86 \\ \cline{2-11}  & 2446.92 & 1.47 & 0.8 & 0.79 & 0.31 & 473 & 53.85 & 21.13 & 0.5 & 1.59 \\ \cline{2-11}  & 2456.19 & 2.06 & 1.14 & 1.02 & 0.27 & 482 & 49.44 & 13.09 & 0.53 & 2.16 \\ \cline{2-11}  & 2466.25 & 1.19 & 0.78 & 0.51 & 0.35 & 465 & 42.79 & 29.36 & 0.6 & 1.29 \\ \cline{2-11}  & 2476.55 & 1.56 & 0.73 & 0.61 & 0.34 & 460 & 39.15 & 21.82 & 0.54 & 1.34 \\ \cline{2-11}  & 2486.85 & 0.5 & 0.49 & 0.33 & 0.38 & 450 & 67.48 & 77.71 & 0.6 & 0.82 \\ \cline{2-11}  & 2496.9 & 1.71 & 1.16 & 0.9 & 0.3 & 464 & 52.79 & 17.6 & 0.56 & 2.06 \\ \cline{2-11}  & 2506.1 & 1.3 & 0.96 & 0.99 & 0.26 & 455 & 45.45 & 20.03 & 0.62 & 1.55 \\ \cline{2-11}  & 2516.35 & 1.55 & 0.75 & 0.45 & 0.3 & 450 & 28.99 & 19.33 & 0.63 & 1.2 \\ \cline{2-11}  & 2526.61 & 0.93 & 0.48 & 0.29 & 0.35 & 446 & 31.12 & 37.55 & 0.62 & 0.77 \\ \cline{2-11}  & 2536.2 & 1.42 & 0.71 & 0.43 & 0.27 & 450 & 30.3 & 19.03 & 0.62 & 1.14 \\ \cline{2-11}  & 2546.71 & 1.23 & 0.65 & 0.39 & 0.35 & 447 & 31.71 & 28.46 & 0.63 & 1.04 \\ \cline{2-11}  & 2556.62 & 1.19 & 0.49 & 0.3 & 0.29 & 450 & 25.13 & 24.29 & 0.62 & 0.79 \\ \cline{2-11}  & 2566.2 & 1.37 & 0.59 & 0.39 & 0.32 & 454 & 28.51 & 23.39 & 0.6 & 0.98 \\ \cline{2-11}  & 2576.15 & 1.47 & 0.45 & 0.33 & 0.24 & 460 & 22.43 & 16.32 & 0.58 & 0.78 \\ \cline{2-11}  & 2586.34 & 1.81 & 0.64 & 0.45 & 0.37 & 461 & 24.9 & 20.48 & 0.59 & 1.09 \\ \hline \multicolumn{11}{l}{2592.35} & 1.85 & 0.36 & 0.24 & 0.27 & 460 & 13.01 & 14.63 & 0.6 & 0.6 \\ \end{tabular}
\end{table} 
Table 1: SRA data set shows the different pyrolysis parameters for 58 core samples from 4 Montney Formation wells.
# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
## 3 System Design
### Preliminaries  
The network topology is represented as a connected graph, \(G(V,E)\), where the nodes in V correspond to network elements such as routers, and edges \(\ell\) in E are defined as communication links between two network elements. Each link \(\ell\) in E has two non-negative attributes: latency and cost, denoted as \(D(\ell)\) and \(C(\ell)\), respectively. \(D(\ell)\) represents the delay experienced by communication between the two nodes connected by the edge, measured in milliseconds, while \(C(\ell)\) represents the cost required for communication between the two nodes. In this paper, the formula for calculating communication link costs is as follows:  
\[C(\ell)=\frac{K}{\text{Bandwidth}(\ell)} \tag{1}\]  
In this context, \(K\) denotes the fixed reference bandwidth, set at 3000, while Bandwidth(\(\ell\)) signifies the maximum interface bandwidth of the link \(\ell\), measured in Mbps.  
Within a network, nodes maintain two distinct types of routing tables: the Least Delay Table (LDT) and the Least Cost Table (LCT). To determine the shortest paths, [PERSON]'s algorithm is utilized, with delay serving as the metric for LDT and cost acting as the metric for LCT. Owing to the volatile nature of network conditions, resulting in frequent fluctuations in link delay information, the delay metric is based on average link delay statistics collected within a predetermined time window. To minimize the necessity for frequent LDT recalculations, this table undergoes refreshing at longer intervals, specifically set at once every 30 min in this study. This ensures that computational costs remain manageable, as observed in other routing protocols such as OSPF. Although the resulting least-delay routing information may not be entirely precise due to dynamic changes in link delays, it can function as a reference for routing decisions.  
Each entry in the routing table includes the following information: the network address (NA) of the destination, path delay, path cost, Gateway (GW), two-hop gateway (GW2), GW Delay, and GW2 Delay. As shown in Figure 1, GW is the next-hop address from Source (S) to Destination, GW2 is the second-hop address from S to Destination (if it exists), and GW Delay and GW2 Delay represent the delay when S reaches nodes GW and GW2, respectively, in the route calculation process.  
The network cost of multicast tree \(T\) is defined as follows:  
\[\textit{TreeCost}(T)=\sum_{\ell\in T}C(\ell) \tag{2}\]  
Define _P(T,i)_ as the set of links from the root of tree \(T\) to receiver \(i\) along the internal paths of the tree. The multicast transmission delay of receiver \(i\) is defined as follows:  
\[\textit{TransmissionDelay}(T,i)=\sum_{\ell\in P(T,i)}D(\ell) \tag{3}\]  
Figure 1: The information recorded in the routing table.
## 1 Introduction
[PERSON] proposed two different modifications of HS for the optimization of truss structures [19]. The sequential HS algorithm was proposed to find the shape optimization of structural systems, including frequency constraints [20]. For the control of the structures, HS-based methodologies are also perfectly applied for the placement of dampers [21], tuned mass dampers [22; 23], and base isolators [24]. In structural health monitoring, [PERSON] et al. proposed and improved HS for optimal sensor placement to crane structures [25]. For the optimization of double- and triple-layer grid structures, a modified gravitational HS algorithm was used with the efficient member grouping strategy [26]. [PERSON] et al. proposed a method combining HS and Artificial Neural Networks for the aim of assessing the reliability of structures with the implicit limit state function [27]. By using the three-parameter Vlaslov Elastic Foundation model, soil-structure interaction was also considered in the optimum design of braced steel frame structures using HS and TLBO [28]. [PERSON] investigated the optimum design of laminated composite structures via HS [29]. The modification of HS for the structure of the algorithm is presented in a review study conducted by [PERSON] and [PERSON] [30]. The main criticized factor of TPO/MA is the use of several iterations, while FEM can find the results directly for linear systems. Although FEM also needs the iteration methods for non-linear systems, the number of iterations is the most important doubt for TPO/MA. The duration of analysis is generally short for TPO/MA, but the area of using algorithms with a fast convergence ability is still a needed and important area. Due to that, hybrid algorithms are needed to develop for sensitive and fast analysis. A novel hybrid approach was proposed to find comparative results that can be found with less iterations than other algorithms. New generation metaheuristic algorithms are easy to apply to engineering problems directly and the number of parameters is generally low, and these parameters are not critically effective on solutions. Due to that, Harmony Search (HS) and the Flower Pollination Algorithm (FPA), with a Levy distribution different from a classical random generation between 0 and 1, are hybridized instead of using classical algorithms, such as Genetic Algorithms and PSO. The hybrid Harmony Search (HHS) approach combined with FPA is an effective tool for this goal according to numerical experiments proposed in the study.
## 3 Results and Discussion
### SEM Observations  
#### 3.6.1 Study of Fabric Surface  
The morphological nature of these denim fabrics was examined by scanning electron microscopy on the desized and bleached samples. Figures 4a and 5a show SEM images of desized cotton fabric and kapok/cotton fabric, respectively, the fibers of which are arranged in parallel in this image. Laser treatment was to use the heat produced by the laser machine to etch the dye onto the fabric surface directly. This would damage some of the fibers, but the arrangement of those fibers is not changed (Figure 4b, Figure 5b). It can be seen from Figures 4 and 5 that the surface arrangement of the fabrics after conventional bleaching treatment are not as neat as that of the desizing denim.  
Figure 4: Scanning electron microscopy (SEM) pictures of different cotton samples (\(\times\)50): (**a**) Desized, (**b**) laser bleaching, (**c**) enzyme bleaching, (**d**) sodium hypochlorite (NaClO) bleaching, (**e**) potassium permanganate (KMnO\({}_{4}\)) bleaching, (**f**) hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) bleaching.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{8}{c}{**Crease Recovery Angle (\({}^{\circ}\))**} \\ \cline{2-9}  & & **Desized** & **Laser** & **Enzyme** & **NaClO** & **KMnO\({}_{4}\)** & **H\({}_{2}\)O\({}_{2}\)** \\ \hline \multirow{2}{*}{Fabric 1} & warp & 140.2 & 129.9 & 116.7 & 104.8 & 86.5 & 106.7 \\  & weft & 171.4 & 166.7 & 150.1 & 171.6 & 133.0 & 168.3 \\ \hline \multirow{2}{*}{Fabric 2} & warp & 129.1 & 120.8 & 116.1 & 109.3 & 103.8 & 113.2 \\  & weft & 187.3 & 153.3 & 132.8 & 164.5 & 129.6 & 151.1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Crease recovery angle of denim fabrics with different bleaching types.  
This is because the fabric needs to be continuously stirred with a glass rod in the process of conventional bleaching, and some fibers would be distorted and damaged, resulting in the disorder of the fiber arrangement in the denim fabrics.
## 1 Introduction
For small-sized and medium-sized EWLs, shovel force is mainly predicted and analyzed through empirical-formula-based methods. In comparison to small-sized and medium-sized EWLs, large-tonnage EWLs have significant differences in power matching, transmission, tire traction, and other technologies. Empirical-formula-based methods cannot meet the precision requirements of the designs of large-tonnage EWLs. It is necessary to research shovel force under typical conditions for large-tonnage EWLs to increase in operational efficiency. This article contains the following innovative points and contributions. 1. Combining DEM and BP NNLA: A method for accurately predicting shovel force in large-tonnage EWLs is proposed by combining DEM and BP NNLA. The innovation of this method lies in the combination of DEM simulation and BP NNLA learning to enhance the accuracy of the predictions of shovel force in EWLs. 2. Calibration of Material Parameters: Through BP NNLA, the material parameters are calibrated to enhance the reliability of the model. This step involves the systematic analysis and calibration of factors such as material density, Poisson's ratio, shear modulus, etc., making the model more closely aligned with real-world conditions. 3. Accurate Prediction of Shovel Force for Large-Tonnage EWLs: Through simulation methods, an accurate prediction of shovel force for EWLs of various tonnages has been successfully achieved. Compared to traditional empirical formulas, this method improves prediction accuracy, particularly for large-tonnage EWLs, resulting in more reliable forecasting results. 4. Power-Matching Optimization: With the accurate prediction of shovel force, the power matching of EWLs has been optimized, thereby enhancing operational efficiency. This paper will revolve around two experimental frameworks. Firstly, accurately predicting the shovel force of large-tonnage EWLs, using the DEM and NNLA. Secondly, optimizing the matching and control of EWLs through experiments under different operational conditions.
# Secure Control of Linear Controllers Using Fully Homomorphic Encryption
## 4 Control System with Encrypted Controller
### Encrypted Controller Using BFV Encryption Scheme
Then, the ciphertext of signals is output to the actuator to decrypt and control the axis to accelerate or decelerate its rotation. The cycle repeats itself until the completion of the transmission of the table. In scheme 1, the controller input and parameters are ciphertext since the encrypted controller allows homomorphic additions and homomorphic multiplications. As shown in the Figure 4, in the whole control process, from the sensor sampling data and encrypting until the actuator decrypts the ciphertext, the data in the transmission channel and encrypted controller are all ciphertext, which greatly improves the security compared with the previous partially homomorphic encrypted controller. The table is generated in advance, so that the ciphertext can be obtained only by looking up the table according to the plaintext index, saving the time of encryption. However, we know that BFV encryption is a random encryption scheme, and the precomputation causes the search table to obtain the same ciphertext from a plaintext \(m\), that is to say, from random encryption to deterministic encryption. This process reduces the security of the scheme. Therefore, we propose two approaches below to remedy this deficiency.
# Enhancing Reliability Analysis with Multisource Data:
## 1 Introduction
### Background  
Bridges are generally designed for an expected service life of 75 years and a reliability index of 3.5 for inventory level performance [13] and 2.3-2.5 for operating level performance [14]. However, due to uncertainties, a reliability index is expected to range between 2.0 and 4.0 [8]. Additionally, uncertainties exist in the variables measured in WIM systems such as a vehicle weight measurement in a dynamic weight sensor and a dynamic amplification factor (1.25 or greater) due to moving vehicles. Therefore, in this study, a minimum threshold RI of 2.0 is selected. This is equivalent to observing a 1-2 in 100 chance of observing a structurally deficient bridge (or the probability of failure ranging between 0.01 and 0.02). A load rating is an important indicator for assessing the performance of bridge components such as deck, superstructure, and substructure. The load carrying capacity decreases over time due to the deterioration of bridge components as a result of natural forces, such as environmental, climatic, and hydraulic impacts. Additionally, heavy vehicular movements on bridge structures have a significant impact on bridge deterioration. Such bridge deterioration should be closely monitored by state highway agencies in conjunction with live loads for load rating. However, prioritization of bridges for monitoring based on risk remains a challenging task. This study proposes a data-informed risk quantification method using vehicle weight information obtained in WIM systems.
## 2 Materials and Methods
### Fixed-Bed (Dynamic) Experiments  
For dynamic breakthrough experiments, small-scale columns were completely packed with resin and capped at both ends with Teflon frits, resulting in a total bed volume (BV) of 5 mL wet settled resin. The columns were agitated during packing to promote homogeneous distribution of resin particle size throughout the bed. To ensure efficient mass transfer between solution and resin and to reduce the risk of 'channelling' [18] a reverse-flow setup was employed, whereby the PLS was introduced at the bottom of the vertical column. For elution studies, a smaller BV of 1.4 mL of wet settled resin was used to minimize the concentration of eluent peaks and required dilution for analysis. PLS were pumped using either a 'Heidolph Hei-Flow Value 01' pump with 'SP Quick' pump head, or a 'BioRad Econo Gradient Pump'. Verification of solution flow rates was achieved by pumping deionised water through each packed column for a set time and using the mass of water collected to calculate volumetric flow in bed volumes per hour (BV/h). Effluent solutions were collected using a 'BioRad Model 2110' fraction collector set to advance at specified time intervals and diluted using either 1% HNO\({}_{3}\) for ICP-OES or AAS analysis, or deionized water for ion chromatography (IC) analysis. IC analysis was performed using a Metrohm '833 Basic IC Plus' fitted with a 'Metrosep A Supp 5' column (PVA-quaternary ammonium), a carbonate eluent (4.5 mM Na\({}_{2}\)CO\({}_{3}\), 803 uM NaHCO\({}_{3}\)), and a 0.1 M H\({}_{2}\)SO\({}_{4}\) regenerant.
## 4 Experiments and Results
### Experimental Results
#### 4.2.1 Training Policy Evaluation  
To verify the DRL training policy, the average reward was implemented over different MPRs and real traffic volumes. The training policy became better as the average reward became higher. Figure 7 expresses the average reward with different MPRs and real traffic volumes. As seen in this figure, the reward achieved a higher value as the MPRs and traffic volumes increased. In particular, the fully autonomous condition outperformed the others. This means that the fully autonomous condition obtained the highest value of reward of 43,087.05 with a traffic volume of 1000 veh/h. Moreover, the fully autonomous condition increased the average reward 4.7 times compared to the 20% MPR. Therefore, the efficiency of the leading AVs was clearer at a higher MPR and traffic volume.
## 4 Experimental Validation
### Mechanical Behavior of Concrete Overlays with Bended Rebars
Figure 13: Detail of mechanical connection between concretes (**a**). Arrangement of the bent rebars in the bottom concrete layer (**b**). = 5.0 MPa, and slump of 7 to 10 cm in the Abrams cone. Quality control tests of CF-5.0 showed a \(f_{\text{ct,fl}}\) = 5.0 MPa and \(f_{\text{c}}\) = 35.0 MPa were reached after 7 days. As shown in Figure 14a,b, pouring, compacting, and finishing of the CF-5.0 is done using manual methods. Both radial and concentric slab joints were cut 6 to 12 h after placing the CF-5.0 overlay and subsequently sealed with materials resistant to diesel fuel. Radial joints were sealed with nitrile-butadiene rubber profile while concentric ones were filled with hot mastic asphalt. Figure 15 shows the finished whitetopping of the roundabouts with open traffic. The time between construction and opening to traffic was between 7 and 15 days to ensure that at least 80% of \(f_{\text{c},t}\) = 5.0 MPa was achieved. Average depths measured with the sand patch test (UNE-EN 13036-1:2010) were larger than 1.10 mm, indicating that surface finishing was suitable even for speeds higher than 100 km/h. Two years after construction, a visual survey was conducted to determine the existence of cracks and surface faults. The slabs corresponding to zones with full-depth reconstruction and bent rebars in the interface (OL) did not present cracks (see Figure 16a). Meanwhile, cracks appeared in some of the concrete slabs of the WT, which corresponded to about 10% of the total number of slabs of the pavement of the four roundabouts (see Figure 16b,c). Moreover, as Figure 16d shows, the transition between WT and OL was sometimes clearly observed at simple sight, since cracks in WT were not prolonged to the slabs of the OL solution. The presence of cracks might be explained by the fact that the actual stiffness of the asphalt in the zones would be lower than that assumed in the overlay design. A more extensive campaign of load plate tests could help to determine the mechanical parameters of the asphalt more accurately. Nevertheless, as the stiffness of the asphalt reduces, the concrete overlay of the WT should be thicker, thereby increasing the global cost of the pavement restoration. Considering the above, it can be stated that the structural performance of the OL solution was excellent and superior to that of the WT solution directly on the asphalt. Consequently, the use of  
Figure 14: Compaction of the overlay (**a**) and surface finishing (**b**). Figure 15: The final appearance of the roundabouts. steel bended rebars at the interface of bi-layer concrete pavement is a feasible alternative for the reconstruction pavements subjected to the high flux of heavyweight vehicles.
## 4 Illustrative Example
### Noise Impact Analysis  
The method is based on the FEM, but the FEM inevitably has errors. In order to analyze whether the algorithm is sensitive to noise, only the \(SNR\) value in conditions 1-2 is changed to 12 dB, and the other conditions remain unchanged. The two recognition results are as follows.  
As shown in Figure 16, when the \(SNR\) decreases, a few first recognition results do not meet the above conclusions (The part marked with red circles). In addition, the \(IR_{Z}\) in Figure 17 also decreases, indicating that noise impacts the recognition rate. After changing the \(SNR\) value in conditions 1-2, its recognition effect can be calculated and fit.  
Figure 16: Distribution of first recognition results (\(SNR\) = 12): **(a)** Condition 1; **(b)** Condition 2.  
Figure 17: Distribution of second recognition results (\(SNR\) = 12): **(a)** Condition 1; **(b)** Condition 2.  
As shown in Figure 18, there is an \"S\" curve relationship between \(SNR\) and \(IR_{\rm Z}\). The higher the \(SNR\), the higher the \(IR_{\rm Z}\). When the \(SNR\) is greater than 14 dB, the recognition effect is better and has good robustness. The smaller the \(SNR\), the stronger the interference of the noise, and the recognition effect will gradually decrease.