esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Verification of a Modular Reconfigurable Test Platform for Electric Tractors
## 4 Case Study
### Control Strategy and Simulink Model  
As mentioned in Section 3.2, it is easy to realize reconfigurable control; we need only design corresponding logical control strategies based on the series hybrid electric powertrain architecture in Simulink and implement those strategies following the steps in Figure 4. The Simulink model for this architecture for the test bench system, based on a modular design, is displayed in Figure 6. As can be seen in the figure, the model is divided into three logical sub-models: Load_Drive_Motor, Engine_Generator_Battery, and Torque-Speed Sensor.  
Figure 7a shows the logical control relationship between the drive motors and the load motors. The three orange blocks represent the logical models of the two load motors and the PTO motor, and the two green blocks represent the logical models of the two drive motors of motor 1 and motor 2. Similarly, Figure 7b shows the logical control relationship among the engine, the generator, and the battery pack. The yellow block is the logical model of the engine, the cyan block is the logical model of the generator, and the dark green block is the logical model of the battery pack. Lastly, Figure 7c shows the logical communication relationship between the six torque-speed sensors of the measurement system and the upper computer (laptop) through the CAN3 channel. It should be noted that five of the six torque-speed sensors are currently used, as depicted in Figure 5, while the remaining one also has been reserved as a development interface to provide a test platform for research on different architectures (such as the dual-motor coupling structure) of electric tractors.  
In addition, due to its modular design, the designed Simulink model can also be used to study BET with a single-motor drive structure (without PTO) or a dual-motor drive independent structure.
# Tunable Effect of the Calcination of the Silanol Groups of KIT-6 and SBA-15 Mesoporous Materials
## 3 Results and Discussion
### Effect of the Calcination Temperature on the Silanol Groups
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & **Signal** & **\(\boldsymbol{\delta}\) (ppm)** & **Intensity** & **Area (\%)** \\ \hline \multirow{3}{*}{KM4} & Q\({}^{2}\) & \(-91.2\) & \(1103\) & \(11.8\) \\  & Q\({}^{3}\) & \(-100.7\) & \(3928\) & \(77.3\) \\  & Q\({}^{4}\) & \(-111.0\) & \(956\) & \(11.0\) \\ \hline \multirow{3}{*}{KM5} & Q\({}^{2}\) & \(-91.2\) & \(1329\) & \(15.8\) \\  & Q\({}^{3}\) & \(-100.7\) & \(3528\) & \(69.0\) \\  & Q\({}^{4}\) & \(-111.0\) & \(1086\) & \(15.2\) \\ \hline \multirow{3}{*}{KM6} & Q\({}^{2}\) & \(-91.6\) & \(113\) & \(14.0\) \\  & Q\({}^{3}\) & \(-100.3\) & \(295\) & \(69.9\) \\ \cline{1-1}  & Q\({}^{4}\) & \(-108.7\) & \(76\) & \(16.0\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._The literature reports a tendency to increase the Q\({}^{3}\) and, especially, Q\({}^{4}\) signal, as the calcination temperature increases [(69; 78)]. Molecular sieves such as SBA-15 and KIT-6 have more siloxane groups than vicinal silanols. Thus, the Q\({}^{4}\) signal should be more intense than the Q\({}^{3}\) signal, even in qualitative analyses. However, due to the porosity of these samples and because there is no pretreatment needed for NMR analysis, moisture retained in the pores affects the Q\({}^{3}\) and Q\({}^{4}\) signal intensities; thus, the Q\({}^{3}\) intensity is dominant [(78)]. A manner for minimizing this effect is a relative analysis the peaks. Table 6 presents the ratios of the percentage area integration of Q\({}^{2}\):Q\({}^{4}\), Q\({}^{3}\):Q\({}^{4}\), and Q\({}^{2}\):Q\({}^{3}\) just for the calcinated samples. According to results presented in the Table 6 for SBA-15 and KIT-6, the ratio Q\({}^{2}\):Q\({}^{4}\) decreases as the temperature increases, indicating that the geminal silanols are converted in siloxane as the temperature increases. Besides that, in 400 \({}^{\circ}\)C, the most accentuated decrease in this ratio begins, indicating that the dehydroxylation had started and that this is the crucial moment for preserving the geminal silanols. This is in accordance with the results obtained from N\({}_{2}\) sorption and XRD because silanol condensation shrinks the structure and decreases the total pore volume. This result indicates that calcination at 300 \({}^{\circ}\)C is more beneficial than that at 400 \({}^{\circ}\)C in terms of silanol preservation. Nonetheless, the N\({}_{2}\) sorption results show that the pore volume is small upon calcination at 300 \({}^{\circ}\)C. The ratio Q\({}^{3}\):Q\({}^{4}\) decreases too as the temperature increases; however, it is most accentuated at the most extreme treatment (500 \({}^{\circ}\)C during 10 h). In this way, this result indicates a tendency to convert the vicinal silanol in siloxane, as expected. The ratio Q\({}^{2}\):Q\({}^{3}\) is practically unchanged.
Thus, AE characteristic parameters can be used to monitor the cracking and failure process of RC concrete on a microscopic cracking level. However, few studies focus on the mechanical properties and the cracking process of an RC beam affected by steel corrosion on a microscopic cracking level by AE technology; this topic should be analyzed through experimental and advanced analytical methods in sufficient detail. In this paper, a bending test and AE technology were utilized simultaneously. The parameters, including ultimate flexural loads and failure modes, as well as rise time/peak amplitude (R/A), ringing counts/duration (AF), and improved b (Ib) values (statistical parameters calculated from AE signals) were obtained for indicating the transformation of corroded RC beams' crack modes, as well as failure modes, under the effect of steel corrosion, and further investigating the effect of steel corrosion on the flexural characteristics of the RC beam on a microscopic cracking level.
# A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
###### Abstract  
Wetlands, situated at the vital intersection of terrestrial and aquatic ecosystems, are pivotal in preserving global biodiversity and maintaining environmental equilibrium. The escalating trend of global urbanization necessitates the utilization of high-resolution satellite imagery for accurate wetland delineation, which is essential for establishing efficacious conservation strategies. This study focuses on the wetlands of Macau, characterized by distinctive coastal and urban features. A noteworthy enhancement in this study is the integration of the Coordinate Attention mechanism with the RegNet model, forming the CA-RegNet. This combined model demonstrates superior performance, outdoing previous Macau wetlands segmentation studies that used ResNet, evidenced by an approximate rise of 2.7% in overall accuracy (OA), 4.0% in the Kappa coefficient, 1.9% in the mAcc, and 0.5% in the mIoU. Visual evaluations of the segmentation results reinforce the competence of the CA-RegNet model in precisely demarcating coastal wetlands and Saiwan Lake, thereby overcoming the former constraints of ResNet and underscoring the robustness and innovation of this study.  
CA-RegNet model; deep learning; Macau Wetlands; image segmentation; remote sensing Article  
A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images. April. 2022, 13, 12178. [[https://doi.org/10.3390/appl32212178](https://doi.org/10.3390/appl32212178)]([https://doi.org/10.3390/appl32212178](https://doi.org/10.3390/appl32212178))
# Structure and Optical Properties of Transparent Cobalt-Doped ZnO Thin Layers
## 3 Results and Discussion
### Optical Measurements
#### 3.2.3 Optical Zone  
There is agreement between the obtained values of the thickness of the layer, both annealed and non-annealed, and those calculated by the Swanpoel method. Figure 5a shows the absorption spectrum calculated by Equation (9). Annealing of the ZnO:Co layer leads to a decrease in the value of the absorption coefficient. Figure 5b show the plot \((\alpha\cdot\mathit{h}v)^{2}\) versus \(\mathit{h}v\) for thin ZnO:Co layers. The plot is linear over a wide range of photon energies, indicating a simple type of transition. The intersections of the extrapolation of these sections (straight lines) with the energy axis reflect the width of the optical band gap.  
Figure 4: (**a**) Parameters of the optical transmission spectrum for the calculation of the refractive index \(n\) and the dependence of the refractive index on the wavelength for the ZnO:Co layer annealed at 400 \({}^{\circ}\)C, (**b**) Refractive index n (black line) and extinction coefficient k (blue line).  
The optical absorption edge was analyzed using Equation (11), described by the directly allowed transition [33]:  
\[\alpha h\
u=B\big{(}h\
u-E_{g}\big{)}^{1/2} \tag{11}\]  
where \(B\)--band edge sharpness is a constant and it was obtained from the slope of the plot in the range of interband absorption [48]; \(E_{g}\)--optical energy gap.  
We point out that the increase in the degree of crystallinity of the layer after annealing leads to a change in the shape of the fundamental absorption edge.  
The calculated values of the optical band gap are, respectively, 2.63 eV (for as grown), 3.11 eV (annealed at 300 \({}^{\circ}\)C), 3.15 eV (annealed at 400 \({}^{\circ}\)C), and 3.28 eV (annealed at 500 \({}^{\circ}\)C). The obtained values of the optical band gap of ZnO:Co (Figure 5b) are smaller than for pure ZnO (about 3.37 eV), which is consistent with observations made in the works [41; 60]. The decrease in the value of the optical band gap can be resulted from active transitions involving 3d levels of Co\({}^{2+}\) ions and intense sp-d interactions between itinerant '\(sp^{\prime}\) carriers and localized '\(d\)' dopant electrons [41].  
Table 3 shows that the value of the ZnO:Co optical band gap depends on the cobalt content, as well as the annealing temperature in post-growth.
# Maintaining Secure Level on Symmetric Encryption under Quantum Attack
## 2 Literature Review
### Advanced Encryption Standard  
This algorithm is a NIST-approved symmetric encryption block encryption method, also known as the Rijndael algorithm. In 1997, NIST announced that it would choose a better algorithm to replace the DES algorithm that was widely used at that time. The biggest disadvantage of DES is that the key is only 56 bits, which is relatively small, and it is more and more vulnerable to attack. In 2001, the Rijndael algorithm was selected as the AES [4], because the Rijndael encryption method can support a wider range of block and key lengths.  
AES is encrypted and decrypted in units of blocks, each block is fixed at 128 bits, and the key has three lengths of 128 bits, 192 bits, and 256 bits to choose from. According to the key length, the encryption rounds are also divided into 10, 12, and 14 rounds. The AES encryption workflow of each round includes the following four steps: AddRoundKey, SubBytes, ShiftRows, and MixColumns.  
AddRoundKey: Each byte in the matrix is XORed with the round key, and each sub-key is generated by the key generation scheme.  
SubBytes: Each byte is replaced with the corresponding byte using a lookup table through a non-linear replacement function.  
ShiftRows: A circular shift is applied to each row in the matrix.  
MixColumns: In order to fully mix the operations of each column in the matrix, this step uses a linear transformation to mix the four bytes within each row. The MixColumns step in the last encryption loop is omitted and replaced with another AddRoundKey.  
After 10 to 14 rounds of repeated calculations, the complexity of the brute force cracking method can reach up to \(2^{256}\) times, which ensures the security strength of the AES on traditional computers.
## 1 Introduction
In the context of perturbation, lower SampEn values, indicating greater reproducibility of movement patterns, suggest a reduced level of perturbation, whereas higher SampEn values, indicating less reproducibility, suggest a greater level of perturbation [28]. While introducing task constraints inevitably perturbs performers [29], the specific nature of this perturbation depends on the dynamic interplay between the task, athlete and environment. Therefore, careful assessment is necessary to fully understand the effects of task constraints on MV. In this context, the development of a large vibrating SB has enabled the incorporation of vibration/surface constraints into sliding tasks [30]. Traditionally, whole-body vibration (WBV) exercise has been employed in resistance training, but its application as an unstable perturbation during displacement sliding tasks has been limited due to the absence of suitable platforms. However, WBV has been combined with unstable surfaces or shoes during squatting exercises and has been evaluated in terms of muscle activation [17], reaction time [18] and MV [19]. These studies have demonstrated that the interaction of task constraints can significantly impact MV, highlighting the need to consider the specific athlete-task-environment combination when evaluating the effects of constraint perturbation. Recent studies have demonstrated that WBV training enhances MV [31]. This enhancement of MV is considered a crucial component of adaptability to the environment, which is essential for enhancing athletic performance [32]. Similarly, task constraints introduced by adding external implements, such as balls, have been shown to lead to increased MV, resulting in alterations in the coordination patterns of the system [33]. However, the investigation of MV in ice hockey remains limited. Therefore, this study aimed to identify differences in acceleration and MV when incorporating WBV and an implement (stick) during an SB skating exercise on a slide vibration board (SVB). We hypothesised that the combined effects of vibration stimuli and the stick as task constraints would enhance the variability structure of the players' body acceleration, which could be detected using a non-linear approach.
## 3 ZTF Data Processing Overview
### Retrieving the Catalog File from IRSA Remote Resource  
The primary objective of the ZTF project is to identify and classify astronomical objects such as variable stars and supernovae in real time. This project also aims to organize and query a catalog that contains other celestial objects for more in-depth analysis. The output of astrometric calibration (ZTF CCD-quadrant images) is in FITS format. Each image in the FITS file includes a header data unit (HDU) and image data. HDU is observational metadata, which is the corresponding information linked to an image.  
We have established a local repository for the purpose of the study. The IRSA remote server is used to obtain the table header information, imagery data, and key-value pairs for the tables. The download process was automated using a Python script that creates SQL dynamically, then optimized and saved in a PostgreSQL database on a local LINUX server with 16 GB memory and 5 TB disk space [42]. An SQL script assisted in optimizing and creating new tables in the local database with entity relations (key and value). The downloaded raw data are reorganized into a relational database structure (shown in Figure 3). These database tables are Nights, Exposures, Procimages, CCD, Filters, Fields, Host Fields, and Host Galaxy.  
* Nights: Nights contain the date or time of the images taken, with the unique index _nid_ and alternative key--nightdate.
* Fields: Fields database table stores images according to the X and Y coordinate and assigned identification ID. In this table, _fieldid_ is a unique index and _field_ is an alternative key.  
Figure 3: ER Model of the Database with Relations and Attributes.  
* Exposures: Exposure tables contain information from both right database table and fields database table. Exposure tables also contain detailed information on CCD, such as CCD ID, exposure time, image type, etc. The _expid_ is a unique index and _obsdate_ is an alternative key.
* Procimages: Procimages contains processed-images metadata, i.e., image file names. The unique index is processed-images number _pid_.
* Filters: Filters includes a record for each camera filter that was used to acquire the exposures. The unique index is _fid_.
* CCDS: CCD (16 charge-coupled devices) contains the camera details numbered CCDID 1,... 16. The unique index is _ccdid_.
* Host_galaxy: Host galaxy consists of the names of the galaxies.
# Design of Zoom Optical System from Visible to NIR-II for Vivo Fluorescence Imaging Device
## 4 Results  
### The Layout and Aberration Diagrams of Optical System  
The layout and aberration diagrams of the optical system with different focal lengths after optimization are shown in Figure 5. Compared with the original patent, the number of lenses decreases, the aperture is reduced, the wavelength is increased, and the total optical length is changed from fixed to floating. The final layout is similar to the initial layout obtained from the calculation above. The first fixed group does not move, taking the first surface as the reference; however, as the focal length increases, groups 2 and 4 move away from the first surface, and group 3 moves closer to the first surface. d\({}_{23}\) decreases, while d\({}_{12}\), d\({}_{34}\), and d\({}_{4f}\) all become larger.  
The modulation transfer function (MTF), spot diagrams, and distortion diagrams are important indicators for evaluating the imaging quality of an optical system.  
When the focal length of the zoom optical system is 25 mm, 100 mm, and 150 mm, the MTF of the optical system is greater than 0.3 at the Nyquist frequency, and it can even be greater than 0.4 at the mid-focus position, which meets the design requirements.  
The size of the spot in the spot diagrams can accurately determine the diffusion of the imaging rays of the optical system. The smaller the radius of the scattered spot is, the better the imaging quality of the optical system is. The root means square radius RMS is smaller than the CCD image element size of 5 um, which can be clearly imaged.  
As for optical system distortion, the short focal length part is close to 3%, the medium focal length part is 1.76%, and the long focal length part is 0.66%.
# Explainable AI in Education: Techniques and Qualitative Assessment
## 2 Theoretical Background
### Explainability Metrics
#### 2.6.2 Stability Metrics  
Secondly, we assessed the stability metric, which is a critical metric for assessing the reliability of ML models' explanations, particularly for intricate models like ANNs. The stability of the explanations is evaluated when slight perturbations are made to the input data. For instance, if a model's forecast explanation for a given instance changes dramatically when the input features are minimally altered, it may be unstable and so less interpretable [3].  
To measure the degree of stability, we simply compute the consistency of post hoc explanations under small perturbations of the input instance. For each iteration, an instance is selected (e.g., the first test instance), and its original explanation is generated using the post hoc method. The instance is then perturbed by adding random noise, resulting in multiple slightly altered versions. Post hoc explanations are produced for each perturbed instance, and the feature importance weights are extracted. Stability is assessed by calculating the cosine similarity between the original explanation's feature weights and those of each perturbed explanation, ensuring consistency by aligning the feature sets. The average cosine similarity across all perturbations serves as the stability score for that iteration. This process is repeated for 50 iterations and 5 folds, with the final stability score being the mean stability score across all folds, indicating the robustness of the post hoc method's explanations.
## 3 Experimental Validation
Moreover, it can be seen that that a good agreement was found between the simulated outlet pressure ripples and the  
Figure 9: Steady-state flow characteristics regarding different operating speeds (500–2000 rpm) and outlet pressures (5–20 MPa): (**a**) outlet flowrate; and (**b**) volumetric efficiency. experimental results regarding different operating conditions, noting that the accuracy of the pressure sensor was within 0.5% (full scale) and the nonlinearity of the pressure sensor was within 0.5%. Figure 11 depicts the comparison of the frequency spectra of the simulated and experimental outlet pressure ripples at 500 rpm, 20 MPa. As observed, the primary frequency concerning the simulated results yielded 108.33 Hz with an amplitude of 0.253 MPa, and the primary frequency concerning the experimental results yielded 108.33 Hz with an amplitude of 0.250 MPa, suggesting a good match of the frequency spectra. Aside from that, the primary frequency of the simulated and experimental results was in accordance with the analytical value given by Equation (21). \[f=\frac{z_{1}\cdot n}{60}=\frac{13\times 500}{60}=108.33\,\mathrm{Hz} \tag{21}\]  
Judging from the analysis above, a good match was found between the simulated and experimental results regarding the steady-state outlet flowrate and the outlet pressure ripples, therefore justifying the capability of the proposed model regarding the analysis of the pump's outlet flow characteristics. Figure 10: _Cont._
## 2 Community Detection across Space and Its Variation across Time  
Our method improves the traditional K-Medoids method based on traffic flow volume and network structure properties. Firstly, we partition the study region into equally sized square cells, and then model each cell as a node and the connectivity between each pair of cells as a link. Based on the network traffic flow, we propose that the similarity of nodes can be calculated based on the attraction degree and structure similarity. In this way, community clustering can be implemented. Finally, we use the graph matching technique to calculate the similarity between the community structures within different time periods. Therefore, our method can be considered a spatio-temporal analysis tool.