esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Discriminant Analysis Algorithms
#### 2.6.2 Discriminant Analysis of PCA-SVM  
A support vector machine (SVM) classifier is a supervised learning algorithm that tends to perform better after data normalization [35; 36]. SVM has a high accuracy rate for the classification of linear data, and can be utilized to deal with high dimensional nonlinear problems, such as discriminant classification of nonlinear data [37; 38]. Various types of kernel functions are usually applied in an SVM classifier, such as the radial basis function (RBF) kernel function, linear kernel function, and polynomial kernel function. In this work, a type of RBF kernel function was applied for multi-category classification purposes [38]. The visible and near-infrared transmittance spectra for each sample included 1500 variables. On this basis, all the spectral data for measured samples were combined directly and the features were calculated. This method includes the most information, which should improve accuracy [39]. However, the increased amount of data to be delivered and calculated will decrease the computational rate of the SVM classifier. Therefore, before SVM discriminant analysis, the principal components were extracted in the visible and near-infrared spectra using principal component analysis (PCA) for the dimensional reduction of the spectral data. As shown in Figure 4, the total contribution of the first six principal components accounted for 100%, indicating these components can represent the entire spectral data. Thus, compared with SVM alone, PCA-SVM is a better choice as a classifier because it reduces the computational requirements.  
Figure 4: Contribution rate distribution of principal components (PCs).
## 3 Methodology
### Data Preprocessing, Feature Selection, and Feature Scaling
#### 3.2.1 Recursive Feature Elimination (RFE)  
To reduce dimensionality and retain only the most relevant predictors, RFE was applied with an RF classifier as the base estimator. RFE works by recursively training the model and removing the least important features until the desired number of features is reached (10 features retained after RFE).  
**RFE mathematical process:**  
Let:  
\(X\in\mathbb{R}^{\ n\ xd}\) be the feature matrix (with \(n\) samples and \(d\) features)  
\(y\in\mathbb{R}^{\ n}\) be the corresponding labels.  
RFE proceeds as follows:  
Fit the model on \(X\) and \(y\).  
Compute feature importance FI\({}_{j}\) for each feature \(j\in\{1,\ldots,d\}\).  
For Random Forest: FI\({}_{j}=\sum\) Gini decrease \({}_{j}\) across all trees.  
Remove the feature with the smallest FI\({}_{j}\).  
Repeat until only \(k\) features remain.  
This iterative reduction selects the top k features that most significantly contribute to prediction accuracy.
## 4 Comparison Results
### Computational Efficiency  
We measured the training and inverse projection time of the three studied decision-map techniques on an Intel Core i7-12700 CPU machine with 32 GB of RAM and an NVIDIA GeForce RTX 3070 GPU with 8 GB of RAM. For this, we used the synthetic blob dataset with varying dimensions (10-500) and numbers of samples (250-5000). In particular, we evaluated DV, which requires a pre-trained classifier, with logistic regression, random forest, neural network, and SVM classifiers. It is important to note that DV's training time varied based on the classifier used (as detailed below).  
Figure 9 shows the training times for the compared methods (that is, the time needed to construct the functions \(P\) and \(P^{-1}\)). When the number of samples \(N\) and dimensions \(n\) were both small (\(N<1000\), \(n<50\)), the training time of DV (with logistic regression)  
Figure 8: **(Left) Distance to decision boundary \(B\). (Right) Distance to the nearest training data \(N\). Projections of the training data are shown as white dots.**  
was comparable to that of the SDBM and DBM. For larger \(n\) and/or \(N\), compared to the real-world datasets (Table 1), the DBM and SDBM showed comparable training times. DV, however, exhibited substantially higher training times. The SDBM remained the fastest method, with average training times of less than 10 s across all tested scenarios. The DBM's training time was affected more by the number of samples \(N\) and far less by the number of dimensions \(n\). DV's training time, when using SVM, increased drastically for larger \(n\) and/or \(N\)--training DV with SVM for \(n=500\) and \(N=5000\) took over 2.7 h. This steep increase made SVM-based DV experiments unfeasible on our real-world datasets.  
Figure 10 shows the time required to create the decision maps (given trained direct and inverse projections \(P\) and \(P^{-1}\)) for various numbers of dimensions \(n\), samples \(N\), and grid resolution \(I\). As for training, DV took the longest and was heavily influenced by all these parameters, even failing to handle resolutions over \(I=150\) pixels squared. Conversely, both the DBM and SDBM achieved relatively high and consistent performance, roughly linear in the number of pixels in the map and the number of samples \(N\). In summary, we conclude that the DBM and SDBM are practical methods for creating decision maps for real-world datasets, whereas DV is not.  
Figure 10: Times taken to create decision maps for the DBM, the SDBM, and DV.  
Figure 9: Times taken to train \(P\) and \(P^{-1}\) pairs. (**Left**) Training times of the DBM, the SDBM, and DV with logistic regression. (**Right**) Training times of DV with 4 different classifiers.
## 5 Conclusions  
This work presents a systematic mapping study to identify the most commonly used techniques for overcoming non-IID data problems and communication challenges in FL. A total of 193 articles that met our inclusion and exclusion criteria were selected using a systematic mapping study. We categorized these articles into three groups based on the problem they aimed to solve: articles that addressed the non-IID data problem, articles that aimed to provide communication-efficient solutions in FL, and articles that provided solutions to both challenges. To answer RQ1, we analyzed the selected articles that aimed to overcome the non-IID data challenge; we concluded that label distribution skew, specifically the quantity label imbalance, where clients had some missing labels, was most commonly used. To answer RQ2, we analyzed the selected articles and identified the most commonly used techniques for overcoming non-IID data problems; we found that enhancing the aggregation method and clustering are the two most commonly used techniques. For RQ3, we analyzed the selected articles and identified the most commonly used techniques for providing communication-efficient solutions in FL; we found that quantization and sparsification are the two most commonly used techniques. Some techniques can be used to provide solutions to both challenges, such as clustering, two-level aggregation, and client selection. For RQ4, we extracted the most commonly used learning models in the selected articles and found that the supervised learning model CNN is the most used. For RQ5, we extracted the most commonly used dataset in the selected articles, and we found that the image datasets Cifar-10 and MNIST datasets are the most commonly used datasets to evaluate the proposed work in the selected studies. **Author Contributions:** Conceptualization, B.A., F.A.K. and S.M. ; methodology, B.A., F.A.K. and S.M. ; validation, B.A., F.A.K. and S.M. ; formal analysis, B.A., F.A.K. and S.M. ; investigation, B.A., F.A.K. and S.M. ; data curation, B.A., F.A.K. and S.M. ; writing--original draft preparation, B.A. ; writing--review and editing, F.A.K. and S.M. ; visualization, B.A., F.A.K. and S.M. ; supervision, F.A.K. and S.M. ; funding acquisition, F.A.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Saudi Data and AI Authority (SDAIA) and King Fahd University of Petroleum and Minerals (KFUPM) under SDAIA-KFUPM Joint Research Center for Artificial Intelligence Grant No. JRC-AI-RFP-12. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflicts of interest.
# Optimal Scheduling of Hydro-PV-Wind Hybrid System Considering CHP and BESS Coordination
## 2 Coordinated Optimal Scheduling Model of Hybrid System
### Objective of Optimal Scheduling Model in Hybrid System
#### 2.2.5 The Model of EV  
Different from that of a traditional load, the charging and discharging power of electric vehicles is based on driving behavior of users, battery characteristics, and charging and discharging device. It is uncertain in the two dimensions of time and space, so it is necessary to simplify it in order to solve it in an actual situation. Equation (29) shows the limit of the charging power, not exceeding the maximum charging power of charging pile. Equation (30) represents the capacity relationship between \(t-1\) and \(t\) moment. Equations (31) and (32) show the capacity limit, and Equation (33) expresses the charging time is assumed as 8 h.  
\[0\leq P^{t,s}_{ev}\leq p^{\max}_{ev} \tag{29}\]  
\[SoC^{t,s}_{ev}=SoC^{t-1,s}_{ev}+\eta^{c}_{ev}P^{t,s}_{ev} \tag{30}\]  
\[SoC^{\min}_{ev}\leq SoC^{t,s}_{ev}\leq SoC^{\max}_{ev} \tag{31}\]  
\[SoC^{\mathit{event,s}}_{ev}\geq SoC^{end}_{ev} \tag{32}\]  
\[\mathit{event}=8 \tag{33}\]  
Above all, the objective and constraints of hybrid energy system are shown in Equations (1)-(4). The hydro power constraints are shown in Equations (5)-(10), and the wind and photovoltaic power generation constraints are shown in Equations (11) and (12). Equations (13)-(20) represent the constraints of CHP, battery energy storage constraints are shown in Equations (21)-(28), and electric vehicles constraints are shown in Equations (29)-(33).
# A Cumulant-Based Method for Acquiring GNSS Signals
## 4 Simulation and Experimental Results
### Real Galileo Signal Acquisition Results
As observed in the figures, the traditional method exhibits two side-peaks near the correct peak position. In contrast, the proposed method effectively eliminates these side-peaks, resulting in a cleaner acquisition result.
## 3 Mathematical Modeling Formulation
### Mathematical Formulation of Technical Objectives
#### 3.4.2 Voltage Deviation Index  
The aim of this research is to minimize voltage variations in the distribution grid during the integration of BSSs. The extent of voltage fluctuation can be quantified by employing the formula provided in Equation (24) [26]. This study focuses on developing strategies to mitigate voltage deviations effectively, thereby enhancing the stability and reliability of the distribution network when incorporating BSS technology.  
\[\text{Obj}_{4}=\text{VD}=\sqrt{\sum_{i=1}^{N}\left(\frac{V_{\text{Standard}}}{V _{\text{Base}}}-\frac{V_{i}}{V_{\text{Base}}}\right)^{2}} \tag{24}\]
# Fault Imaging of Seismic Data Based on a Modified U-Net with Dilated Convolution
###### Abstract  
Fault imaging follows the processing and migration imaging of seismic data, which is very important in oil and gas exploration and development. Conventional fault imaging methods are easily influenced by seismic data and interpreters' experience and have limited ability to identify complex fault areas and micro-faults. Conventional convolutional neural network uniformly processes feature maps of the same layer, resulting in the same receptive field of the neural network in the same layer and relatively single local information obtained, which is not conducive to the imaging of multi-scale faults. To solve this problem, our research proposes a modified U-Net architecture. Two functional modules containing dilated convolution are added between the encoder and decoder to enhance the network's ability to select multi-scale information, enhance the consistency between the receptive field and the target region of fault recognition, and finally improve the identification ability of micro-faults. Training on synthetic seismic data and testing on real data were carried out using the modified U-Net. The actual fault imaging shows that the proposed scheme has certain advantages.  
dilation rate; deep learning; data processing; receptive field +
Footnote †: journal: Applied  
1
Footnote 1: School of Bohai Rim Energy, Northeast Petroleum University, Daqing 163318, China  
2
Footnote 2: School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China; [EMAIL_ADDRESS] (YS.); [EMAIL_ADDRESS] (W.W.)
# Estimating Destination of Bus Trips Considering Trip Type Characteristics
## 2 Literature Review
### Implication  
Table 1 shows the various methodologies and accuracies from the destination inference studies. To date, various methodologies have been developed and applied to estimate alighting stops, including the trip-chain-based estimation and AI/probability model-based estimation methodologies. Research has been advanced to validate the effectiveness and accuracy of the methods, and improve the methodologies to facilitate more accurate alighting stop estimation for more trips. However, when a single methodology is applied, there is the limitation that the scope of the applicable trips is limited [19; 25; 26]. This study proposes a methodology for estimating the alighting stop in stages, by considering the characteristics and limitations of the trip-chain-based alighting stop estimation and past history data-based alighting stop estimation methodologies. This study aims to build more accurate and complete data for the travel patterns of the public transport users for monitoring and decision-support of public transport operations. Accordingly, we classify trip types by considering the continuity and recurrence of trips based on the trip history data, and applying an appropriate alighting stop estimation method for each type.  
In order to improve the accessibility of cities and the efficiency of traffic flow, the connection with infrastructure and services surrounding public transportation is an important factor [27; 28; 29]. In particular, in order to implement MaaS (Mobility as a Service) through connection with parking and bike sharing systems, it is essential to estimate accurate alighting stops for public transportation.
# VFLD: Voxelized Fractal Local Descriptor
## 4 Experimentation
### Comparison with Other State-of-the-Art Feature Descriptors  
As mentioned, FPFH is the current state of the art for local 3D point descriptor despite emerging in 2009. Nonetheless, we tested other descriptors in order to place our approach in context. In this section, we repeat the descriptiveness experiments on the ModelNet10 and Simple Figure datasets but involving Point Feature Histogram (PFH) [11] and 3D Shape Context (3 DSC) [8] as well as VFLD and FPFH.  
All the parameters of the descriptors involved were empirically tuned to provide the best performance and enable a fair comparison. The results are shown in Figure 17.  
As can be seen, VFLD outperformed the rest of the competitors in terms of descriptiveness in both datasets. 3 DSC clearly performed worst, providing very poor accuracy and precision-recall rates. PFH and FPFH performed similarly, albeit with the latter being slightly better. This is to be expected, as both descriptors share similar theoretical basis.  
Finally, it is worth noting that VFLD has 18 components in its descriptor, FPFH has 33, PFH has 125 and 3 DSC has 1980. Not only does our proposal outperform the methods tested in terms of descriptiveness, but it also has significantly fewer components. This will help optimize their processing, for instance, in the matching step performed in the registration pipeline.
# Mo(VI) Complexes Immobilized on SBA-15 as an Efficient Catalyst for 1-Octene Epoxidation
## 2 Results and Discussion
### Catalytic Performance in 1-Octene Epoxidation
#### 2.2.3 Influence of the Oxidant  
This study was carried out by testing tert-butyl hydroperoxide (TBHP), ethylbenzyl hydroperoxide (EBHP), and isopropylbenzene hydroperoxide (usually called cumene hydroperoxide, CHP) as oxidant, using a molybdenum/1-octene molar equal to 1:1000 and n-decane as solvent. These are conventional oxidation hydroperoxides used in industry in propylene oxide production processes. Figure 8 shows the evolution of epoxide formation with the reaction time as well as the oxidant efficiency obtained after 360 min. TBHP and EBHP led to very similar results, reaching epoxide yields up to 45% in the evaluated period. Nevertheless, the use of CHP as oxidant provokes a clear reduction of both the epoxide yield and the oxidant efficiency. Some authors have previously described that CHP can be easily decomposed into acetone and phenol in presence of some vanadium, tungsten and molydenum catalysts [28]. According to the epoxidation results, it seems to indicate this decomposition is taking place on our Mo(VI) heterogeneous catalyst leading to a poor epoxide yield. Therefore, TBHP will be the best choice to be used with this catalytic system since epoxidation results are quite similar to those obtained by using EBHP and is a much cheaper oxidant.  
Figure 8: 1,2-epoxioctane yields and oxidant efficiency obtained with different organic hydroperoxides (Reaction conditions: Temperature = 75 \({}^{\circ}\)C; decane as solvent; 1-octene:oxidant = 1; Mo:1-octene = 1:1000).
# Review of GaN Thin Film and Nanorod Growth Using Magnetron Sputter Epitaxy
## 5 Conclusions and Future Outlook
Another important issue is the possibility of material damage due to high-energy ions. Currently, the flux and energy distribution of arriving ions and sputtered species from a Ga target is still not explored yet. Furthermore, these numbers may change over the course of the deposition run depending on the condition of the Ga target (solid, liquid, fresh, or poisoned). Detailed characterization of plasma using liquid Ga is important in order to set the proper growth condition and minimize point defects, as this will ultimately affect the electrical and properties of GaN. The capability of MSE to produce high quality GaN thin films and nanorods within a large substrate area makes it an interesting alternative for GaN epitaxy. With better understanding in process physics and improvements in the material quality in the future, MSE can become one of the main technologies for the growth GaN-based materials and devices, moving it from an academic research area to practical industrial applications. Conceptualization, A.P., J.B., M.J., and C.-L.H. ; methodology, A.P., M.J., E.A.S., and C.-L.H. ; validation, A.P., J.B., M.J., E.A.S., L.H., and C.-L.H. ; resources, J.B., L.H., and C.-L.H. ; data curation M.J. and E.A.S. ; writing--original draft preparation, A.P. and C.-L.H. ; writing--review and editing, A.P., J.B., L.H., and C.-L.H. ; project administration, C.-L.H. ; funding acquisition, J.B., L.H., and C.-L.H. All authors have read and agreed to the published version of the manuscript. This research was funded by Energimyndigheten (grant number 46658-1), Vetenskapsradet (grant number 2018-04198), and Stiftelsen Olle Engkvist Byggmastare (grant number 197-0210) and the APC was funded by the Energimyndigheten. The Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University (Faculty Grant SFO-Mat-LiU 2009-00971) is acknowledged for financial support. The authors declare no conflicts of interest.
## 3 Results and Discussion
### Comparisons of Different Streams and Their Combinations  
Table 3 shows 10-fold cross-validation results using separate networks and fused networks respectively, where 'U' represents upper face network, 'L' represents lower face network, 'U+L' represents fused local network.  
Figure 4: Performance comparison of five kinds of fusion strategies and five kinds of depth of local network.  
Upper face network extracting features of eyes and eyebrows region achieves an accuracy of 31.27% and lower face network extracting features of mouth region achieves an accuracy of 43.87%. It can be seen that the performance of the upper face network is lower than that of the lower face network. This could be due to the effect of glasses, i.e., if the subjects have glasses, the features of eyes could be lost to some extent.  
Meanwhile, we can see that the performance of the global network (72.05%) outperforms the local network (50.13%). This may be because the local features extracted in this research do not cover all features of facial expressions. However, the local network fused with the global network can improve the recognition accuracy. It is seen that NIRExpNet (global network + local network) can achieve a recognition accuracy of 78.42%, which is the highest in all kinds of networks. This may indicate that either global or local networks cannot represent facial expression information completely, that the global features and local features are complementary to each other, and that the combination of them can improve the recognition rate.
## 5 Discussion
### Limitations of This Research  
While the results of this study are promising, there are several limitations that should be considered. Firstly, the private dataset used in this research is relatively small, containing only one thousand data points. This limited sample size may restrict the generalizability of the model to a broader range of content or contexts. Smaller datasets can also lead to overfitting, where the model may perform well on the training data but struggle to generalize to unseen data. Although our model achieved high accuracy on the available data, future research could benefit from collecting larger and more diverse datasets to ensure that the findings hold true across different populations or content types.  
Secondly, the nature of the data itself presents a challenge. The dataset consists of short video comments, which are inherently noisy. These comments may include irrelevant information, slang, misspellings, or ambiguous language, all of which can reduce the quality of the data. While preprocessing techniques like noise removal were applied, there is a risk that some useful information or unusual data points, which could potentially contribute to better classification, may have been discarded. In particular, unique or outlier data points sometimes provide valuable insights that enhance model performance. Therefore, a more refined approach to noise filtering could improve the quality of the dataset without sacrificing important features.
# FPGA Implementation of a Higher SFDR Upper DDFS Based on Non-Uniform Piecewise Linear Approximation
###### Abstract  
We propose a direct digital frequency synthesizer (DDFS) by using an error-controlled piecewise linear (PWL) approximation method. For a given function and a preset max absolute error (MAE), this method iterates continuously from right to left within the input interval, dividing the entire interval into multiple segments. Within each segment, the least squares method is used to approximate the objective function, ensuring that each segment meets the error requirements. Based on this method, We first implemented a set of DDFS under different MAE to study the relationship between SFDR and MAE, and then evaluated its hardware overhead. In order to increase the frequency of the output signal, we implement a multi-core DDFS using time interleaving scheme. The experimental results show that our DDFS has significant advantages in SFDR, using fewer hardware resources to achieve high SFDR. Specifically, the SFDR of proposed DDFS can reach 114 dB using 399 LUTs, 66 flip flops and 3 DSPs. More importantly, we demonstrate through experiments that proposed DDFS breaks the SFDR theoretical upper bound of DDFS based on piecewise linear approximation methods.  
direct digital frequency synthesis;non-uniform piecewise linear approximation; spurious free dynamic range