esa-sceva/satcom-chunk-collection huggingface dataset:
# Modular Bus Unit Scheduling for an Autonomous Transit System under Range and Charging Constraints
## 4 Computational Results
### Case Setup  
Three in-service bus lines in Zhengzhou, China, with the same starting point and similar endpoints, were set as scheduling test scenarios, as shown in Figure 4. The close geographical locations among the lines make the application of scheduling schemes with multiple mixed trips more realistic and consistent with the research context of this study.  
According to the research associated with the modular bus [20; 21; 22; 23], we tune the parameters associated with the modular bus, including \(c_{m}=\$10\) per vehicle, \(c_{t}=\$3.43\) per hour, \(c_{s}=\$5.72\) per hour, \(c_{p}=\$3\), \(c_{w}=\$1.72\) per hour, \(Q=30\) kWh, \(\mu=0.7\), \(M=15\) passengers, \(U=8\), \(t_{a}=3\) min, \(t_{b}=30\) min, \(t_{c}=10\) min.  
The durations \(w_{i}-v_{i}\) for different node types are set as follows: (1) \(i=o\), \(w_{i}-v_{i}=0\); (2) \(i\in K\), \(w_{i}-v_{i}\) is determined by the actual operation time of the bus lines; (3) \(i\in D\), \(w_{i}-v_{i}=30\) min; (4) \(i\in F\), \(w_{i}-v_{i}=20\) min; (5) \(i=s\), \(w_{i}-v_{i}=\infty\).  
Correspondingly, the current operating strategy of one electric bus (EB) covering one trip is used as the control group, and the relevant parameters are set as follows [28; 29; 30; 31]. \(c_{m}(\text{EB})=\$97\) per vehicle, \(c_{t}(\text{EB})=\$12.34\) per hour, \(c_{s}(\text{EB})=\$20.56\) per hour, \(c_{p}(\text{EB})=\$252\), \(c_{w}(\text{EB})=\$6.17\) per hour, \(Q(\text{EB})=250\) kWh, \(\mu(\text{EB})=0.7\). Note that the meaning of the above parameters is the same as the definition in modular bus, with \"(EB)\" added to highlight the parameters associated with the electric bus.  
Figure 4: Test scenario of three in-service bus lines.
# Recognition of Driving Behavior in Electric Vehicle's Li-Ion Battery Aging
## 3 The Recognition of Driving Behavior
### Conventional Driving Behavior Recognition
#### 3.2.1 Hidden Markov Model (HMM)  
For low-level or fragmented driving behaviors, the HMM and its derivative approaches showed a simple answer. Using CAN data, the HMM can predict simple driving intents (including left and right turns, stops, lane changes, etc.) with the best performance of 90% accuracy in less than 2 s [9]. Lane change using image detection can be simply accomplished to assess the behavior when a camera is used [10]. The simple HMM is also strengthened by the derived models (hierarchical HMM/multi-layer) to reflect some of the high-level actions [11]. The hierarchical HMM can describe the connections between HMMs in multi-layers [12]. Therefore, multiple HMMs can identify a wide variety of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Access** & **No. of Features** & **Sampling Rate** & **Battery Info.** & **Labels** \\ \hline
[94] & HCRL & 54 & 1 s & - & Driver \\
[95] & VED & 22 & 1 s & SOC, A, V & Trip \\
[96] & Battery and Heat in R.D.C. & 28 & 0.1 s & A, V, C & - \\
[89] & Nissan Leaf & 31 & 0.25 s & SOC, SOH, A, V, C & Driver and Trip \\ \hline \hline \end{tabular}  
* A: high voltage battery current: V: voltage; C: temperature.  
\end{table}
Table 4: The public CAN bus data for driving behavior recognition.  
behaviors. The hierarchical hidden Markov model (HHMM) then calculates the end result after each HMM has described the low-level operations [13]. The disadvantage of the HMM is that further embedding is necessary because the input data do not come straight from the CAN bus. Additionally, it needs to take time sequences into account. [PERSON] et al. [14] suggested that the auto-regressive hidden Markov model (AR-HMM) take the order of the input samples into account and provide the HMM with the sequence information. However, feature extraction, model structure, and temporal segmentation still highly influence the outcomes.
## 3 Results
### Problem Statement
#### 3.1.2 Local Measurements with Variable Lift-Off  
Demonstration of the material identification through the amplitude angular dependence was done through measurements at a fixed lift-off [12,23], i.e., with all objects being the same distance from the primary rf coil. Figure 4 shows angular dependencies of the rf signal amplitude (a) and phase (b) recorded over the copper plates with lift-off values of 3 mm, 10 mm, 20 mm, and 30 mm. Without prior knowledge of the measurement conditions, i.e., in this case lift-off values, the results of the rf signal amplitude measurements might be interpreted as objects with different compositions.
# Designing Parallel Adaptive Laplacian Smoothing for Improving Tetrahedral Mesh Quality on the GPU
## 2 Background
### Laplacian Mesh Smoothing
#### 2.1.3 Two Forms of Smoothing Iteration  
In Laplacian mesh smoothing, there are two forms to select the coordinates of neighboring nodes when iteratively calculating the smoothing coordinate of a vertex: Form A and Form B. For example, when updating the smoothing coordinates of vertices in iteration pass \((q+1)\), Form A completely select the old coordinates of neighboring nodes calculated in the previous iteration pass \(q\), while Form B need not only the old coordinates of neighboring nodes calculated in the previous iteration pass \(q\) but also the new coordinates of neighboring nodes calculated in the current iteration pass \((q+1)\). It is clear that Form A is a special case of Form B when the number of neighboring nodes derived from iteration pass \((q+1)\) is 0.  
Form A:  
\[\overline{x_{i}^{q+1}}=\frac{1}{N}\sum_{j=1}^{N}x_{j}^{q} \tag{1}\]  
Form B:  
\[\overline{x_{i}^{q+1}}=\frac{1}{N}\left(\sum_{j=1}^{N_{q}}x_{j}^{q}+\sum_{k=1} ^{N_{q+1}}x_{k}^{q+1}\right),\ \left\{\begin{array}{c}0\leq N_{q}\leq N\\ 0\leq N_{q+1}\leq N\\ N_{q}+N_{q+1}=N\end{array}\right. \tag{2}\]  
where \(N\) is the total quantity of neighboring nodes of computing node; \(\overline{x_{i}^{q+1}}\) is the new location calculated in the iteration \((q+1)\); \(N_{q}\) is the number of neighbors derived from the iteration \(q\) ; \(N_{q+1}\) is the number of neighbors derived from the iteration \((q+1)\) ; \(\overline{x_{j}^{q}}\) is the old location calculated in the iteration \(q\) for \(N_{q}\) nodes; \(x_{k}^{q+1}\) is the old location calculated in the iteration \((q+1)\) for \(N_{q+1}\) nodes. Form A is a special case of Form B where \(N_{q+1}=1\)  
Figure 2: An illustration for comparing the smoothed positions of a sample node in the concave area of a tetrahedral mesh when using ordinary Laplacian mesh smoothing and adaptive Laplacian mesh smoothing. (**a**) Input mesh; (**b**) mesh after ordinary Laplacian smoothing; (**c**) mesh after adaptive Laplacian smoothing.
## 3 Results and Discussion
### Characterization of CAL
Overall, the enhancement of the particle size of CAL with increasing aging temperature can be clearly observed in Figure 2. SEM images of CaAl-NO\({}_{3-}\) LDH samples synthesized at different temperatures ((a) 60 \({}^{\circ}\)C, (b) 70 \({}^{\circ}\)C, (c) 80 \({}^{\circ}\)C, (d) 100 \({}^{\circ}\)C, and (e) 120 \({}^{\circ}\)C). As compared with the CAL sample synthesized at 60 \({}^{\circ}\)C, CAL-120 had a sharper particle edge and more regular morphology, indicating a higher crystallinity of CAL-120 compared to CAL-60. The CAL synthesized at 60 \({}^{\circ}\)C and 70 \({}^{\circ}\)C showed similar particle size and crystallinity, which was consistent with the intensity and sharpness of diffraction peaks increasing with the elevation of the aging temperature, as can be seen in Figure 1. XRD patterns (a) and FT-IR spectra (b) of CaAl-NO\({}_{3-}\) layered double hydroxides (LDHs) prepared at different temperatures (samples were indicated as CAL-x, where x = aging temperature, 60-120 \({}^{\circ}\)C). To further determine the effect of aging temperature on particle size of CAL, particle size distribution of CaAl-NO3- LDHs prepared at various aging temperatures were given in Figure 3. Particle size distribution obtained by dynamic laser scattering (DLS) analysis of the Ca-Al LDH (CAL) samples prepared at different aging temperatures. The average particle size of CAL obtained at 60, 70, 80, 100 and 120 \({}^{\circ}\)C was 6.148, 11.580, 11.829, 14.278 and 22.932 \(\upmu\)m, respectively. This variation tendency of particle size was consistent with the above SEM images observation results. The aging temperature has an important influence on the synthesis of LDHs, especially on the development of crystalline grains. Crystal growth, agglomeration or lamination occurred simultaneously in the heat treatment of CAL [29]. Metal hydroxide precursors formed at the initial stage of CAL synthesis can be used as seeds of CAL crystals. Increasing aging temperature can not only promote the formation of these seeds, but also increase the recombination rate of microcrystals in solution. Moreover, the crystal growing rule of CAL can be illustrated by [PERSON]'s aging mechanism, that is, aging temperature enhancement will promote the dissolution of smaller particles and then promote the growth of particles with a larger size [29; 30]. Subsequently, these particles were stacked continuously to form larger CAL particles with more stacked layers and larger lateral dimensions. Figure 1: XRD patterns (**a**) and FT-IR spectra (**b**) of CaAl-NO3– layered double hydroxides (LDHs) prepared at different temperatures (samples were indicated as CAL-x, where x = aging temperature, 60–120 \({}^{\circ}\)C). Figure 3: Particle size distribution obtained by dynamic laser scattering (DLS) analysis of the Ca-Al LDH (CAL) samples prepared at different aging temperatures. Figure 2: SEM images of CaAl-NO\({}_{3}\)– LDH samples synthesized at different temperatures ((**a**) 60 \({}^{\circ}\)C, (**b**) 70 \({}^{\circ}\)C, (**c**) 80 \({}^{\circ}\)C, (**d**) 100 \({}^{\circ}\)C, and (**e**) 120 \({}^{\circ}\)C).
## 2 The Definition of the CNC Parameters and Dynamic Errors
### The Dynamic Errors' Chain Corresponding to the Trajectory Generation  
Dynamic errors signify the deviation between the path of the NC code and the actual path. They contain the error from the NC code after interpolation in the controller that is a command error. In Figure 1, the command error (\(\varepsilon_{1}\)) is the deviation between the original command from the NC code (\(S_{g}\)) and the position command (\(S_{c}\)) through the interpolator, expressed as:  
\[\text{Command error}=\varepsilon_{1}=S_{g}-S_{c}. \tag{2}\]  
The command error represents the influence of the CNC parameters in the tool-path planning.  
Contour error (\(\varepsilon_{2}\)) is another dynamic error, caused by the dynamic characteristics of the servo loop and feed drive system. The corresponding contour error is the deviation between the position command (\(S_{c}\)) and actual position (\(S_{a}\)) expressed as:  
\[\text{Contour error}=\varepsilon_{2}=S_{c}-S_{a} \tag{3}\]  
where \(S_{c}\) and \(S_{a}\) are position command and actual position, respectively. Numerous studies [4; 11; 17] used the contour error to analyze the performance of machine tools. The phenomenon of servo delay causes position error, called tracking error, on a single axis, and is named as contour error if the path was simultaneously moving by multi-axis. The dynamic errors (\(\varepsilon_{3}\)) include \(\varepsilon_{1}\) and \(\varepsilon_{2}\), introduced as:  
\[\text{Dynamic errors}=\varepsilon_{3}=\varepsilon_{1}+\varepsilon_{2}=S_{ g}-S_{a} \tag{4}\]  
The \(\varepsilon_{1}\) was caused by the interpolator in tool-path planning, while the \(\varepsilon_{2}\) was caused by the closed loop feed drive system of the machine. Figure 5 shows the dynamic errors chain with \(\varepsilon_{1}\) and \(\varepsilon_{2}\) for the corner path and the circular path, respectively. In the following description, we focused on the development of the HS/HP/HQ mode by observing the dynamic errors during the axis movement.  
Figure 4: The principle of the tool-path planning in interpolator.  
The tool-path planning purpose is to control the path velocity and slow down the velocity change of each axis when the machine moves along the three-dimensional path. When the velocity of each axis changes drastically, the generation of acceleration causes the machine to be stressed and thus generates errors and vibrations.
## 4 Discussion  
### Band-To-Band Registration of Planet Dove Satellite Images  
The deliberately inexpensive sensor designs and commercial off-the-shelf components of small satellites worsen the consistency of acquired data quality in comparison with that of traditional large satellites [42]. Measurements over slightly mismatched areas from different spectral bands lead to much less accurate science data products when they are used together in heterogeneous areas [84]. Because the imager of PS2 had a split-frame visible (VIS) + NIR filter [41] and the RGB bands were recorded in the same frame based on the Bayer array [83], there was no misregistration between RGB bands. On the other hand, misregistration between RGB and NIR bands recorded in different frames was seen in many images (Figure 3c,d) of wide areas with contamination by small clouds. This band-to-band misregistration suggests that the registration was matched to the drifting cloud that could not be removed because of the difference in observation timing of approximately 0.5 s between the RGB bands and the NIR band. Images with misregistration between some RGB bands were taken by the PS2.SD sensors. Because PS2.SD has a four-band frame imager with a butcher-block filter providing blue, green, red, and NIR stripes [43], the observation timing differs slightly among all four bands. Therefore, misregistration may occur in all bands, even between RGB bands.
## 1 Introduction
Additionally, to achieve better control performance of the AGC, artificial intelligence with deep Q-network (DQN) assistance is introduced to optimize the PI parameters of the AGC. For the receiving end, an adaptive DC voltage droop control method is proposed. This method allows the droop coefficient to adapt to changes in DC power and voltage, thereby reasonably distributing unbalanced power and improving the responsiveness of power adjustments at the converter station.
# Using Data Mining in Educational Administration: A Case Study on Improving School Attendance
## 2 Literature Review  
### Educational Data Mining  
The definition of EDM in [11] accurately surmises the approach of using what was once commercial data mining techniques to improving outcomes in education, including government-sponsored education. EDM, according to [11], \"seeks to analyse educational data repositories to better understand learners and learning and to develop computational approaches that combine theory and data to transform practice to benefit learners\". Similar definitions for EDM were provided in [12; 13] where EDM is defined as a knowledge extraction process where valuable insights are obtained from data originating from an educational setting. In this regard, EDM may be compared to commercial techniques like Market Basket Analysis (MBA), which is in essence, a technique that leverages data analytics on customer transaction data to enhance customer engagement, and transaction intensity within the retail sector [6; 14; 15; 16].  
The popular MBA techniques of Clustering and Association Rule Mining (ARM) have been widely used in EDM in a variety of contexts. [PERSON], in [14], [PERSON] et al., in [15], and [PERSON], in [13], noted that ARM has been very useful in educational applications such as in finding mistakes that are commonly made together by students, making recommendations to students on e-learning course choices, and finding associations in behavioural patterns of students. Similarly in [17], ARM was used to find factors that influenced student performance in courses, with the study concluding that student performance directly correlated to attention in class (including attendance), completing assignments, and good note-taking.  
Clustering has also been widely used in education with success. In their review of clustering within EDM, [PERSON] et al., in [18], discussed the various educational contexts in which clustering was used including using K-Means clustering to improve learning by grouping students with similar learning styles and clustering brain scans of students who showed similar responses to learning into groups and targeting each group differently to improve learning. Similarly, clustering was also used to understand student behaviour in online learning environments by comparing sequential student data and leveraging a clustering algorithm to group like-minded students [19]. It should be noted that while clustering does have its place, it needs to be done carefully within the government schooling sector as it may be perceived by some parents as unfairly \"targeting\" groups of pupils, which is generally not the case [20].
## 2 Materials and Methods
### Electrochemical Measurements  
The electrochemical tests were performed using an Autolab PGSTAT302N (Metrohm) controlled with NOVA 1.8 software, and a three-electrode cell including an Ag/AgCl (3.0 M KCl) reference electrode, a Pt wire auxiliary electrode, and one of the cork-graphite sensors (GrRAC) as the working electrode (geometrical area of approximately 0.45 mm\({}^{2}\)). Differential pulse anodic stripping voltammetry (DPASV) measurements were performed with different concentrations of Pb(II) ions in acetate buffer solutions (pH 4.5), 0.5 M NaNO\({}_{3}\), and 0.5 M H\({}_{2}\)SO\({}_{4}\). The accumulation of Pb(II) ions on the surface of the composite sensor was achieved by applying a potential of \(-\)1.2 V (vs. Ag/AgCl) for different preconcentration times (40, 70, 100, 130, and 160 s), during which the stirring conditions were kept constant for 30, 60, 90, 120, and 150 s; the remaining 10 s were considered as an equilibration time, without stirring. Subsequently, the anodic stripping scan was performed at 50 mV s\({}^{-1}\), with a modulation amplitude of +0.05 V, and a modulation time of 0.04 s. Square-wave anodic stripping voltammetry (SWASV) measurements were performed in 0.5 M H\({}_{2}\)SO\({}_{4}\). In this case, a preconcentration potential of \(-\)1.2 V was applied to the working electrode for 120 s under continuous magnetic stirring, with a scanning frequency of 80 Hz, an amplitude of 50 mV, and a step potential of 5 mV. All electrochemical studies were conducted without deaerating and performed at room temperature (25 \(\pm\) 2 \({}^{\circ}\)C). Each measurement was performed in triplicate, and obtained data were subjected to statistical analysis and reported as mean \(\pm\) standard deviation (SD). For the determination of Pb(II) in different water matrices (tap water, groundwater, and produced water), the water samples were spiked with a known quantity of a standard solution of Pb(II) and the determination of Pb(II) was performed using the standard addition method.
## 2 Equations and Methods
### Boundary Condition  
In the simulation of oil and gas reservoir production, the frequently used boundary conditions include the constant pressure boundary, constant pressure gradient boundary, constant flow rate boundary, and symmetrical boundary. Generally, the pressure gradient boundary condition is used for the outer boundary. The symmetrical boundary condition is used at the symmetrical location. The constant pressure or constant flow boundary condition is used at the wellbore location. In this paper, the zero-pressure gradient boundary condition is adopted for the outer boundary, the symmetric boundary condition is used for the symmetric position, and the constant pressure boundary condition is used for the borehole wall. For the numerical calculation of pressure in Equation (10), because it is a 7-diagonal large sparse matrix equation system, it can be solved using an iterative method. The commonly used iterative methods are Jacobin iteration, Gauss-Seidel iteration, SOR iteration, etc. In this paper, we used the Gauss-Seidel iteration to ensure the stability and convergence of calculation when adopting the static pressure boundary condition.
## 4 Implementing L2S-M in a Cloud-Native Platform for Intra-Site Connectivity: K8s Case
### Virtual Network Management Flow
#### 4.2.4 Virtual Network Deletion  
Finally, once a user wants to create a virtual network, as seen in Figure 7, the user will instruct K8s through kubectl to delete the resource inside the cluster. Similarly to the creation of networks, L2S-M picks up the K8s event and removes the virtual network entry from its database. This action will only be possible if all pods have been detached from the network; otherwise the operator will throw an error and prevent the deletion of the virtual network.
## 1 Introduction  
Currently, much attention is paid to the development of functional foods for healthy eating. The main task of functional food is to provide a positive physical effect on the human body and thereby strengthen its health. One of the priority directives, solving the nutrition problems of the population, is the use of local wild raw materials. Natural compounds of plant origins have a very active effect on the enzyme systems of detoxification of the body, contribute to the neutralization and excretion of many toxicants of an endo- and exogenous nature, and contribute to the normalization of the internal environment of the body and increasing the efficiency of its adaptive mechanisms [1; 2; 3]. One such type of biologically valuable raw material available in the Republic of Kazakhstan is sea buckthorn.  
Sea buckthorn (_Hippocampae rhamnides_) holds a special place among berry crops due to its chemical composition. The presence of water-soluble and fat-soluble biologically active substances significantly affects the functional properties of the finished product [4; 5]. From sea buckthorn juice, a nutritious beverage with a high content of suspended solids and a very high content of vitamin C and carotenes is obtained [6; 7].  
Different juice production methods, such as diffusion, extraction, and pressing, are employed to obtain this juice. The pressing method is one of the main and most common methods. During the pressing process, the cellular structure is disrupted, which varies from fruit to fruit. As a result, the processing costs, processing time, and production costs are reduced. To study the process of pressing (screw pressing) in the production of various food products, including juices, many scientists have conducted research on screw presses [8; 9; 10; 11; 12; 13; 14].  
The scientific literature provides limited data on the performance of screw presses for processing sea buckthorn and even less information on designs featuring a working body with a self-regulating pressure mechanism for uniform pressure distribution during raw material pressing. This gap leads to significant power losses due to low juice yields and the inability to apply optimal pressure for the effective separation of the liquid fraction.  
Considering this, there is a need for a universal press that is suitable for juice extraction under low-power production conditions to accelerate the juice separation process from wild sea buckthorn without using chemical methods. Utilizing the improved experimental press equipment eliminates the necessity for manual effort, intensifies the juicing process, and increases the efficiency of the equipment.  
The purpose of this research is to enhance the efficiency of juice extraction from sea buckthorn (_Hippocampae rhamnides_) fruits by developing and evaluating an upgraded screw press equipped with a self-regulating pressure adjustment mechanism.