esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Shortcomings of Previous Research  
Generally, while the references provided offer valuable insights into the optimal planning of battery swapping stations, the following acknowledges the potential shortcomings or limitations that can be expressed about these research studies:  
1. Limited scope: Some studies may have a narrow focus, addressing specific aspects of optimal planning such as sizing, siting, or allocation, without considering the broader context of dynamic power distribution network reconfiguration or comprehensive technical aspects of the power grid like recent modern research that was performed in [4; 5; 10; 21].
2. Simplifying assumptions: Many studies rely on simplifying assumptions or models, which may not fully capture the complexity of real-world scenarios. For example, assumptions about user behavior, load variability, or renewable energy integration could oversimplify the planning process [2; 6; 21].
3. Lack of real-world validation: Some research may lack validation through real-world data or case studies, limiting the applicability and reliability of the proposed methodologies in practical settings [22; 24].
4. Limited consideration of interactions: The interactions between battery swapping stations and other grid components, such as renewable energy sources, conventional generation, and grid infrastructure, may not be adequately addressed in some studies, leading to suboptimal planning decisions [1; 4; 19].  
Neglecting operational challenges: While optimal planning is crucial, operational challenges such as maintenance, grid congestion, cybersecurity, and regulatory constraints are equally important but may receive less attention in the literature [12; 15].
6. Limited consideration of stakeholder perspectives: The perspectives and preferences of stakeholders such as utilities, EV owners, policy makers, and local communities may not be adequately incorporated into the planning process, potentially leading to suboptimal solutions [24].
7. Single-objective optimization: Some studies may focus solely on optimizing a single objective, such as cost minimization or grid reliability, without considering trade-offs or multiple conflicting objectives in the planning process [1; 3; 22].  
Addressing these shortcomings could enhance the robustness, applicability, and effectiveness of research on the optimal planning of battery swapping stations in dynamic power distribution networks.
## 3 Results
### SMCI Model Adaptations
#### 3.2.3 Feature Selection  
No significant changes were made in the PCA parameters for the adapted model, and the PCA was refit with a 95% explained variance threshold. Before the PCA, there were 144 features and after the PCA there were 16 features.
## 3 Intelligent Integrated Navigation Q-Learning FEKF Algorithm
### Navigation Information Fusion with the Q-Learning
These are divided into three state types, including corner regions (such as \(\mathbf{S}^{(11)}\), \(\mathbf{S}^{(m1)}\), \(\mathbf{S}^{(1m)}\), and \(\mathbf{S}^{(mm)}\)), edge regions (such as \(\mathbf{S}^{(11)}\), \(\mathbf{S}^{(1)}\), \(\mathbf{S}^{(m)}\), and \(\mathbf{S}^{(m)}\)), \(i\
eq 1\), \(m-1\), \(j\
eq 1\), \(n-1\)) and the central region (\(\mathbf{S}^{(ij)}\), \(i\
eq 1\), \(m-1\) and \(j\
eq 1\), \(n-1\)). The process of state transition defines the actions \(\mathbf{A}\) set with up direction (\(\uparrow\)), down direction (\(\downarrow\)), left direction (\(\leftarrow\)), and right direction (\(\rightarrow\)), which is represented by \(A=\{0\uparrow,1\downarrow,2\leftarrow,3\rightarrow\}\). The actions that could be selected for the corner regions (top left, bottom left, top right, bottom right) are \(A_{corner\_tl}=\{1,3\}\), \(A_{corner\_bl}=\{0,3\}\), \(A_{corner\_tr}=\{1,2\}\), and \(A_{corner\_br}=\{0,2\}\); the edge regions (up, down, left, right) are \(A_{edge\_u}=\{1,2,3\}\), \(A_{edge\_d}=\{0,2,3\}\), \(A_{edge\_l}=\{0,1,3\}\), and \(A_{edge\_r}=\{0,1,2\}\); and the central region is \(A_{center}=\{0,1,2,3\}\).
# Shape Optimum Design by Basis Vector Method Considering Partial Shape Dependence
## 7 Discussion
### Remeshing Problem of Analysis Model  
During the process of shape optimization, with the change of shape, the mesh of the analysis model will change greatly. The aspect ratio of some meshes, or the inner angle of the element edge, will be increased so the mesh quality will have significantly deteriorated, so it may not be able to continue the analysis in the process of shape optimization.  
To solve this problem, when each basic shape change pattern is determined and the corresponding basis vector is created, the mesh quality corresponding to each basis vector can be checked using the following Equation (40):  
\[\alpha=\alpha_{0}+w_{i}(\alpha_{i}-\alpha_{0})i=1,2,,,n \tag{40}\]  
Here \(\alpha_{0}\) and \(\alpha_{i}\) are the initial shape and the basis vector corresponding to the basic shape change pattern, respectively, \(w_{i}\) is the weight coefficient corresponding to the \(i\)th basic shape change pattern, and \(n\) is the number of basis vectors.  
A concrete method of checking the analysis mesh will be described using the simple example shown in Figure 16a-c which are structural shapes and analysis meshes obtained by setting weighting coefficients \(w_{i}\) to \(w_{i}=0\), a maximum value (\(w_{i}=1\)), and a minimum value (\(w_{i}=-1\)), respectively. Also, the weighting coefficient \(w_{i}\) may be considered as the adoption rate of the \(i\)th basic shape change pattern, but \(w_{i}=1\) and \(w_{i}=-1\) mean that the respective basic shape change pattern is adopted.  
Here, only the analysis mesh of Figure 16b,c with the largest shape change is checked with respect to the analysis mesh of the original shape and, if there is no problem in the quality of the analysis mesh of these two cases, all of the analysis meshes of the structural shapes formed in the optimization process linearly change between Figure 16b,c, so there is no problem in the quality of the analysis mesh.  
When there is a problem with the quality of the analytical grid in Figure 16b,c, if the analysis fails, the maximum or minimum value of the weighting coefficient can be adjusted appropriately in the direction close to 0, or the basic shape change mode \(i\) is modified, and the system vector can be recreated according to the situation.
# RISC-Based 10K+ Core Finite Difference Method Accelerator for CFD
## 3 Design and Implementation of Multi-Core Accelerator Architecture
### FMesh, the Bridge of FAcc Communicatio
#### 3.2.2 Package and Flits Design Scheme  
This work defines five types of flits, categorized as head-body flits (Type 1-4) and body-only flits (Type 5), as detailed in Table 2. These flits orchestrate the transmission of instruction programs and computational data to the IM and GPR of each FCore. Each data package requires a minimum of two flits, with transmission governed by four distinct scenarios.  
* Type 1 followed by one or more Type 5 flits.
* Type 2 followed by one or more Type 5 flits.
* Type 3 followed by one or more Type 5 flits.
* Type 4 followed by one or more Type 5 flits.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Label Code** & **Description** & **Flit Component** \\ \hline
1 & 000 & Data transmission of & Head–Body \\  & & point-to-point & Head–Body \\ \hline
2 & 001 & Instruction transmission of & Head–Body \\  & & point-to-point & Head–Body \\ \hline
3 & 010 & Data broadcast & Head–Body \\ \hline
4 & 011 & Instruction broadcast & Head–Body \\ \hline
5 & 1 & Data or Instruction & Body \\ \hline \hline \end{tabular}
\end{table}
Table 2: The type of flit.  
Figure 7: The structure of FRouter.  
The channel width is designed as 33 bits, with Figure 8 illustrating the fit encoding scheme. The two most significant bits (MSBs) dictate the transmission mode:  
* MSBs = 00, unicast to a specific FCore at coordinates (X_destination, Y_destination).
* MSBs = 01, region-based broadcast to all FCores with X \(\leq\) X_destination_max and Y \(\leq\) Y_destination_max.
* MSB = 1, direct transmission of data or instructions to targeted FCores.  
Notably, the IM operates on a 16-bit architecture. During instruction transmission, a single 33-bit flit encapsulates two 16-bit instructions, optimizing bandwidth utilization while maintaining compatibility with the FCore's instruction pipeline.
## 7 Research Challenges, Open Issues, and Future Directions
### Generation of Class Labels in Real Time  
Labels are class affiliations that are specified with every feature set. During training, labels are supplied; however, at test time, labels are hypothesized. Selecting proper class labels in a real-time environment is vital, since picking erroneous labels would lead to mass catastrophe. Older technology using machine learning is still experiencing this difficulty, and exact measurements are needed to solve such problems in an extremely important environment.
# Nuclear Analysis of High-Power LIEBE Molten Target at CERN for the Production of Radioisotopes
## 1 Introduction
To compliment the literature and provide new knowledge regarding the LIEBE target interaction with proton beam, this paper presents a numerical study of the interaction and resulting neutron and gamma radiation. Presented simulations were performed by a series of proton transport calculations using the Monte Carlo N-Particle (MCNP) code [5]. The latest LIEBE target model was created in high-fidelity geometry and FENDL-3.1 (Fusion Evaluated Nuclear Data Library) cross-section data library was used. Simulations produced neutron and gamma fluxes and dose rate maps of the operating LIEBE target. The paper is organized as follows: Section 2 explains the general methodology of MCNP code. In the Section 3, the process of converting the LIEBE CAD model, provided by CERN, to an MCNP input is described. In Section 4, obtained results of the proton transport calculations and the distribution of neutron and gamma dose rates are presented and discussed.
###### Abstract  
A compact laser collimation system is presented for the simultaneous measurement of five-degree-of-freedom motion errors. The optical configuration of the proposed system is designed, and the principle of the measurement of five-degree-of-freedom errors is described in detail. The resolution of the roll and the horizontal straightness is doubled compared with other laser collimation methods. A common optical path compensation method is provided to detect light drift in real time and compensate for straightness and angle errors. An experimental setup is constructed, and a series of experiments are performed to verify the feasibility and stability of the system. Compared with commercial instruments, the pitch and yaw residuals are \(\pm\)2.5\({}^{\prime\prime}\) and \(\pm\)3.5\({}^{\prime\prime}\) without correction, and the residuals are \(\pm\)1.9\({}^{\prime\prime}\) and \(\pm\)2.8\({}^{\prime\prime}\) after correction, respectively. The comparison deviations of the horizontal straightness and vertical straightness changed from \(\pm\)4.8 \(\upmu\)m to \(\pm\)2.8 \(\upmu\)m and \(\pm\)5.9 \(\upmu\)m to \(\pm\)3.6 \(\upmu\)m, respectively. The comparison deviation of the roll is \(\pm\)4.3\({}^{\prime\prime}\). The experimental results show that the data of the five-degree-of-freedom measurement system obtained are largely the same as the measurement data of commercial instruments. The common optical path compensation can effectively improve the measurement accuracy of the system.  
5 DOF motion errors; laser collimation; common optical path
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 3 Pre-Processing  
Feature standardization is commonly used to speed up the convergence of gradient descent based algorithms [8]. This process changes the feature distribution to have zero mean and unit variance. In order to compensate for the large dynamic range in environmental sound recordings, logarithmic scaling is commonly applied to spectrogram based features. Other low-level audio signal pre-processing methods include dereverberation and low-pass filtering [32].  
Both ASC and AED face the challenge that foreground sound events in acoustic scenes are often overshadowed by background noises. [PERSON] et al. used per-channel energy normalization (PCEN) [33] to reduce stationary noise and to enhance transient sound events in environmental audio recordings [34]. This algorithm performs an adaptive, band-wise normalization and decorrelates the frequency bands. [PERSON] et al. enhanced edge-like structures in Mel spectrograms using two edge detection methods from image processing based on the difference of Gaussians (DoG) and Sobel filtering [35]. The background drift of the Mel spectrogram is removed using median filtering. Similarly, [PERSON] et al. used background subtraction and applied median filtering over time [7] to remove irrelevant noise components from the acoustic scene background and the recording device.  
Several filtering approaches are used as pre-processing for ASC algorithms. For example, [PERSON] et al. applied a nearest neighbor filter based on the repeating pattern extraction technique (REPET) algorithm [36] and replaced the most similar spectrogram frames by their median prior to the classification [37]. This allowed emphasizing repetitive sound events in acoustic scenes such as from sirens or horns. As another commonly used filtering approach, harmonic-percussive source separation (HPSS) decomposes the spectrogram into horizontal and vertical components and provides additional feature representations for ASC [7; 32; 38]. While most of the discussed pre-processing techniques have been proposed just very recently, logarithmic magnitude scaling is the only well-established method, which is consistently used among the best performing ASC algorithms.
# Supervised Learning-Based Image Classification for the Detection of Late Blight in Potato Crops
## 3 Materials and Methods
### Mobile System Design  
As illustrated in Figure 2, the distributed run-time system for the plant disease detector is organized with parts executing on mobile devices at the user side and on centralized servers at the cloud side. The layer in the left describes the Frontend server where the deep-learning model used in the system (i.e., CNN for Supervised learning) and the Intermediate Representation (IR) model run on the mobile device. The layer on the right illustrates the Backend focused user interface, which is developed as an Android app to enable system users to interact with the system conveniently.  
Figure 1: The flowchart of the methodology.  
The detection of potato late blight was divided into three stages: Data adquisition, implementation of linear kernel, and performance validation stage. In the training stages, firstly, we need to create data sets for training, preprocess the data sets for training and detection, then extract the features of contrast dissimilarity, homogeneity, energy, correlation, and ASM from the existing data sets. After extracting the features, we need to train CNN and SVM classifiers to obtain the final classifier.  
In the linear kernel stage, firstly input the detection image; then, extract the characteristics of contrast, dissimilarity, homogeinity from the input image; then, detect whether the potato leaves suffer from late blight through the trained classifier; finally, obtain the detection results.  
For the implementation and development of this work, the Python programming language was used, and the most used libraries for processing, feature extraction, classification, and visualization of results were OpenCV, Scikit-learn, TensorFlow, and Matplotlib. The first stage included the acquisition of the images of the potato leaves, then a pre-processing is performed on each of the images of the data set. This process includes the elimination of noise, scaling, and transformation of the color space so that the image would be cleaner, and in this way, the segmentation techniques will be applied to later extract the characteristics of the image. Finally, the SVM supervised learning models were trained using linear kernel and CNN with architecture created from scratch based on the AlexNet model to detect whether or not the leaf image is infected with late blight, and the validation process of the models was carried out and obtained to evaluate the performance of each classifier.
## 5 Numerical Experiment
### Small Size Problem
#### 5.2.3 Comparison of Results of CPLEX and the Genetic Algorithm  
To evaluate the performance of a genetic algorithm, the results derived from CPLEX and the genetic algorithm are compared. The derived results, and the calculation time are shown in Table 5. It is shown that the genetic algorithm has the same results as CPLEX in all cases. Moreover, the computation time of Case 1 that has six vehicles, and eight orders is even faster when using the genetic algorithm than CPLEX. It is confirmed that the computation time of CPLEX can take very long in certain cases, and if there are six vehicles and the number of orders exceeds eight, it is not possible to derive a solution within 30,000 s. However, in the case of genetic algorithms, there is no significant change in computation time even if the number of orders is increased.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Situation** & **Vehicle** & **Round** & **Travel Schedule: Node (Type of Goods)** & **Satisfaction** & **Total Complete Time** \\ \hline Situation 1 & & 1 st & \(0\to 1(1)\to 4(1)\to 4(2)\to 6(1)\) & & \\
1 UAV & UAV1 & 2 nd & \(0\to 7(1)\to 0\) & 4.46 & 395 \\
1 UGV & & 2 nd & \(0\to 15(1)\to 13(1)\to 13(2)\to\) & & \\
8 orders & UGV1 & 1 st & \(0\to 15(1)\to 13(1)\to 13(2)\to\) & & \\  & UAV1 & 1 st & \(0\to 7(1)\to 0\) & & \\ Situation 2 & UAV2 & 1 st & \(0\to 4(1)\to 4(2)\to 0\) & & \\
3 UAVs & UAVs & 1 st & \(0\to 1(1)\to 6(1)\to 0\) & & \\
3 UGVs & UGV1 & 1 st & \(0\to 13(2)\to 0\) & 5.98 & 140 \\
8 orders & UGV2 & 1 st & \(0\to 13(1)\to 0\) & & \\  & UGV3 & 1 st & \(0\to 15(1)\to 0\) & & \\ Situation 3 & UAV1 & 1 st & \(0\to 15(1)\to 0\) & & \\
2 UAVs & UAV2 & 1 st & \(0\to 1(1)\to 4(1)\to 4(2)\to 0\) & 5.74 & 170 \\
2 UGVs & UAV3 & 1 st & \(0\to 7(1)\to 6(1)\to 0\) & & \\
8 orders & UGV1 & 1 st & \(0\to 13(1)\to 13(2)\to 0\) & & \\  & UAV1 & 1 st & \(0\to 7(1)\to 0\) & & \\ Situation 4 & UAV2 & 1 st & \(0\to 1(1)\to 4(2)\to 0\) & & \\
3 UAVs & UAV3 & 1 st & \(0\to 4(1)\to 0\) & & \\
3 UGVs & UGV1 & 1 st & \(0\to 15(1)\to 0\) & 5.24 & 140 \\
7 orders & UGV2 & 1 st & \(0\to 13(2)\to 0\) & & \\  & UGV3 & 1 st & \(0\to 13(1)\to 0\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The result of small size problem (Case 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & \multicolumn{3}{c}{**CPLEX**} & \multicolumn{3}{c}{**Genetic Algorithm**} \\ \cline{2-5} **(Number of Vehicles, Number of Orders)** & **Result (Satisfaction)** & **CPU Time (Second)** & **Result (Satisfaction)** & **CPU Time (Second)** \\ \hline Case 1 (2,8) & 5049 & 338.24 & 5049 & 596.11 \\ Case 1 (6,8) & 6803 & 17,156.01 & 6803 & 561.31 \\ Case 1 (4,8) & 6492 & 2835.86 & 6492 & 564.10 \\ Case 1 (6,7) & 6134 & 2427.79 & 6134 & 580.27 \\ Case 2 (2,8) & 4.46 & 31.16 & 4.46 & 561.63 \\ Case 2 (6,8) & 5.98 & 328.55 & 5.98 & 577.01 \\ Case 2 (4,8) & 5.74 & 92.80 & 5.74 & 572.07 \\ Case 2 (6,7) & 5.24 & 66.96 & 5.24 & 576.88 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of CPLEX and the genetic algorithm.
## 4 Conclusions  
The objective of this study was to develop and validate a simulation model of methanol synthesis from a syngas obtained through pyrolysis oil gasification. For this purpose, Aspen Plus simulation software was used. The model was validated using experimental data from literature and the results show a good agreement between simulations and experimental data for gasification, Rectisol, and methanol synthesis processes. The validated model was then used to analyze the influence of operating conditions on key parameters.  
More specifically, the influence of the gasification temperature and moisture content in pyrolysis oil on syngas composition and syngas SN value was analyzed. Afterwards, Rectisol operation condition and methanol synthesis operation condition on methanol yield were studied. When gasification temperature increased at 1000 \({}^{\circ}\)C, the concentration of CO and H\({}_{2}\) increased which results in the highest syngas SN value (0.85). The moisture content in pyrolysis oil should be kept at 30 wt.%, the SN reaches a maximum of 0.865. As the moisture content continues to increase, the syngas SN value is maintained at 0.865. The Rectisol process was applied to absorb acid syngas and simultaneously adjust syngas SN value; Rectisol unit was set at \(-20\)\({}^{\circ}\)C and 40 bar. Then, syngas SN value reached a maximum of 2.07; the methanol yields also reached a maximum of 8.04 kmol. The methanol production is favored at high pressure and relatively low temperature. In this context, 50 bar and 250 \({}^{\circ}\)C were selected as the optimal operating conditions for the methanol synthesis.  
Finally, although the methanol yield of pyrolysis oil is relatively low compared to other feedstocks (e.g., biomass and natural gas), the high volumetric density of pyrolysis oil makes it less expensive to transport and store; this advantage gives biomass the opportunity to be used on a large-scale plant. Besides that, pyrolysis oil is a cleaner bioresource than its original biomass, syngas produced from pyrolysis oil contains less impurities, making the pyrolysis oil more suitable to methanol production process. This article briefly provides guidance for the future pyrolysis oil industrial application to produce methanol.  
Conceptualization, Z.Z. and B.D.; methodology, Z.Z. and B.D.; software, Z.Z. and B.D.; validation, Z.Z.; formal analysis, Z.Z. and B.D.; investigation, P.M.; resources, B.D. and P.M.; data curation, Z.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, B.D. and O.R.; visualization, Z.Z.; supervision, P.M.; project administration, P.M.; funding acquisition, P.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors are grateful to [PERSON] (Universite du Quebec a Trois-Rivieres) for funding this research.  
The author declares no conflict of interest