esa-sceva/satcom-chunk-collection huggingface dataset:
# MFHE: Multi-View Fusion-Based Heterogeneous Information Network Embedding
## 5 Experiment
### Comparative Experiment
#### 5.2.2 Experimental Setup  
The experimental settings of the algorithms are shown in Table 2. The division of the training set, validation set, and test set of datasets is shown in Table 3. Models based on random walk, such as DeepWalk, HERec, and Metapath2 Vec, have the random walk window of 5, the walk length of 100, the number of walks of 40, and the number of negative samples of 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Optimizer & Adam \\ Learning rate & 0.005 \\ Regularization & 0.001 \\ Dimension of node & 64 \\ Dimension of \(q\) & 128 \\ Attention head \(K\) & 8 \\ Dropout & 0.6 \\ Early stop & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment parameter setting.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **ACM** & **DBLP** \\ \hline Training & 600 & 800 \\ Validation & 300 & 400 \\ Test & 2125 & 2857 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The division of datasets.  
The node classification results of MFHE on ACM and DBLP datasets are better than all baselines, with a 1.72% and 0.9% improvement in classification performance, respectively.
2. On the ACM dataset, MFHE performs best, followed by GAT algorithm. DeepWalk, Metapath2 Vec, HERec, and GraphSAGE perform worse in turn. GAT and MFHE consider the influence of neighbor nodes in the view of information extraction stage and use the multi-head attention mechanism to calculate the normalized attention value between nodes. However, MFHE introduces multi-view information fusion compared with GAT, and GAT directly learns the node representation on the graph, without considering the weight information of each subview.
3. On the DBLP dataset, MFHE performance is also improved. Compared to the effect on the ACM dataset, the node classification performance of HERec is significantly improved, even better than GAT. The reason is that the meta-path A-P-C-P-A in subviews is more important in DBLP. And HERec is the best result of experimental records on all meta-paths. It is equivalent to giving the maximum weight to A-P-C-P-A in the settings, while the same weight in GAT does not extract the most important information. At this time, MFHE calculates weights when considering correlations between subviews, extracting the significant and complete HIN embedding, and hence has an improved classification of the DBLP dataset.
## 3 Methodology  
This section discusses the approach followed in this work. Fault detection is performed through a pattern recognition neural network. The process of building an artificial neural network for pattern recognition, which includes data processing, training the network, and testing its performance, is examined here.  
Artificial neural networks have gained significant importance in detection and classification applications because of their ability to perceive data behavior. The performance of ANNs mainly depends on the training methods, the data used for training, and the network structure. To analyze the influence of preprocessing the data utilized for training the network, in this work, two methods of data preprocessing, namely, the D-value method proposed by [PERSON] [53] and PCA, are used. Figure 4a shows the steps involved in training the neural network, and Figure 4b shows the steps involved in testing the detection rate of the trained neural network.  
In the training process, the simulated stiction data from the Choudhury striction model are generated for various combinations of the deadband plus stick band (S) and slip jump (J) values (represented in Figure 2). These simulated data are then sent to the preprocessing block where the D value and PCA are calculated. These processed data are input to the neural network to train the network. The trained network is then subjected to offline industrial data with and without stiction to test the performance of the networks. A comparison of these methods based on the detection rate is discussed in the Results Section. data fusion is mainly performed in the preprocessing and training stages of the neural network, as the result mainly depends on the combination of PVs and MVs. In this paper, a comparison of fault detection methods based on two preprocessing methods is carried out.  
Figure 4: Block diagram showing the development of a neural network for fault detection. (**a**) Training phase. (**b**) Testing phase.
## 3 Results and Discussion
### Basic Chemical Composition  
There was a statistically significant increase in the dry matter content only in potato tubers enriched with 5-Cl-7-I-8-Q (Figure 2). The ash content of the potato tubers was statistically significantly different in tubers and ranged from 0.16 to 0.23 g\(\cdot\)100 g\({}^{-1}\) d.w. The lowest ash content was in tubers biofortified with the 8-OH-7-I-5 QSA treatment and the highest content was found after the KIO\({}_{3}\) application. Biofortification had no statistically significant effect on the fat content of potato tubers enriched with 5-Cl-7-I-8-Q. However, there was a statistically significantly higher fat content in tubers enriched with KIO\({}_{3}\) and 8-OH-7-I-5 QSA compared to the control. The results of the analyses showed that the protein content of potato tubers varied and ranged from 5.79 to 6.13 g\(\cdot\)100 g\({}^{-1}\) d.w. There was a statistically significantly lower protein content in tubers biofortified with the 8-OH-7-I-SQSA and 5-Cl-7-I-8-Q treatments relative to the control. Biofortification with iodine compounds had no significant effect on the dietary fibre content of tubers. The fibre content ranged from 10.5 to 11.2 g\(\cdot\)100 g\({}^{-1}\) d.w. The assimilable carbohydrate content ranged from 76.65 to 78.24 g\(\cdot\)100 g\({}^{-1}\) d.w. All results were relatively similar to each other.  
Biofortification with KIO\({}_{3}\), 8-OH-7-I-5 QSA, 5-Cl-7-I-8-Q did not engender changes in the basic composition of the potato tubers (dry matter, protein, fat, fibre and digestible carbohydrates). In conclusion, this process in no way reduces the nutritional attractiveness of potato tubers.  
Figure 2: The dry matter content in fresh weight (**A**), and the chemical composition of ash, crude fat, protein (**B**), and dietary fibre and digestible carbohydrates (**C**) in dry weight. The designations a, b, c are those representing the results of three different analyses.
###### Abstract  
Our goal is to show that temporomandibular disorder (TMD) patients with orthopaedic instability can be effectively treated by the combination of occlusal splint therapy and molar intrusion. Diagnostic records of 18 patients reporting previous TMD and treated with splint therapy were evaluated. Postsplint anterior open bite was treated by skeletally anchored molar intrusion. Changes in overjet (O), overjet (OB) were measured on articulator mounted models: initially in maximal intercupidation (MI), centric \"de jour\", postsplint centric relation (CR) and postintrusion CR. Changes in ANB (A point-Nasion-B point) angle, mandibular plane-palalal plane angle and facial axis angle were assessed on lateral cephalograms. Morphological changes of the condyle were detected on pre-and posttreatment CBCT images. When compared screening mountings to MI models, significant differences were found in OJ and OB. Following splint wear, there was a significant increase in lower facial height and significant decrease in facial axis angle, which in turn increased ANB angle. OB and OJ showed a significant change on the postsplint mountings when compared to MI. After intrusion, mandible exhibited counterclockwise rotation, which decreased lower facial height, increased OB and facial axis angle and decreased ANB and OJ. Posttreatment CBCTs confirmed improved condylar morphology.Occlusal splint therapy followed by orthodontic molar intrusion provides MI-CR harmony, therefore, it seems to be an effective method for treating TMD patients.  
Keywords:molar intrusion; orthodontic treatment; temporomandibular disorder; occlusal splint; centric relation +
Footnote †: journal: Applied Sciences
# Correlation Study of Auroral Currents with External Parameters During 10-12 October 2024 Superstorm
## 4 Discussion  
This study investigated the variations in FACs and PEJs during the intense geomagnetic storm of 10-12 October 2024 separately in four local time sectors (dawn/dusk, day/night) and in two hemispheres. The Swarm orbital local time during this geomagnetic storm was similar to those during the May 2024 geomagnetic storm. This provides an opportunity to further investigate the not-well-understood issue of the dawn-dusk asymmetry of FACs and PEJs. Moreover, in October (May), the northern auroral region is in winter (summer) while the southern auroral region is in summer (winter). This seasonal contrast allows for an investigation into whether the observed hemispheric asymmetries in FACs and PEJs are mainly linked to the different seasonal conditions in each hemisphere.
# Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain
## 4 Results
### Double Seasonal Models
The randomness of the method chosen to analyse variability makes many forecasts done in winter to use a model optimised with data from autumn season. The special events during autumn impact greatly on the winter forecasts. This analysis was completed with a study on the influence of the size of the training dataset on prediction accuracy. The date of 11 July 2016 was set for the 24-hours-ahead forecasts during two weeks, and the size of the training dataset increased from 8 weeks to several years. In this way, it is intended to observe the behaviour of the model with respect to the sample size, following the indications of [41]. Figure 4 presents the MAPE of the 24-hour forecasts according to the size of the sample for the \(NMC_{24,168}\) and \(AMC_{24,168}\) models in order to compare their behaviour. It can be observed that the \(AMC_{24,168}\) model presents higher variability and the model starts to stabilize around the average value 1.7% when a sufficiently large set is used, in particular from 20,000 h. However, the \(NMC_{24,168}\) model is much more stable, and with relatively small training sets, in particular, with a time series composed of 5000 h, it maintains accuracy values around 1.6%. The \(AMC_{24,168}\) model is more unstable due to the trend equation (Equation (6)) as the electricity demand time series has no clear trend as shown in Figure 1. Using a larger dataset helps the \(NMC_{24,168}\) model to stabilize while \(AMC_{24,168}\) model depends clearly on the season the dataset starts. Although \(NMC_{24,168}\) model shows better stability than \(AMC_{24,168}\), the smoothing parameter associated with the trend does not tend to 0 in order to converge to the same model. This is due to the parameters are local optimal, but not global optimal. Thus, the need for an analysis of these parameters and their stability is supported. Figure 3: Radar diagram of the average of the parameters for the model \(NMC_{24,168}\) depending on the time. Figure 5 shows the evolution of the value of each parameter according to the size of the data set used for the adjustment of the \(NMC_{24,168}\) model. It can be seen how there is a stabilization of the values in an asymptotic way. It is surprising how the values of the parameters associated with the seasonality exchange the weights as the data set grows. It can be noticed some peaks where the values go up. These peaks coincide with the beginning of the series on holidays. In fact, the most intense peaks coincide with the dates of the Immaculate Conception or Christmas. Once again, a stability in the predictions is observed demonstrating a great predictability. This characteristic does not have the same pattern for all models, and depends largely on the values of the seasonal component, always keeping low values for the \(\alpha\) smoothing parameter.
# Adaptive Quick Reduct for Feature Drift Detection
## 4 Conclusions  
Feature drift in data stream is a recent research area that is attracting great interest. In this paper, an effective feature shift detection algorithm based on QuickReduct has been proposed, together with an easily reproducible method to inject drift into a dataset for testing purposes.  
The Adaptive QuickReduct has proven to effectively capture the shift artificially injected in three real world datasets under different dependency conditions and to work well in the classification of real world data streams, with a very low memory footprint. While not showing always the best absolute performance in classification and not guaranteeing always the optimal reduct in feature selection, the low memory requirements and the fast response to the eventual shift make it ideal in those application fields where such shift are common and the data volume is high: IoT environments, sensor networks and social media analysis, just to name a few.  
Future work will follow two directions: the first is extending the proposed algorithm to deal with gradual and incremental drifts; the second is to hybridize it with leading feature drift frameworks, like VFDT and HAT, in order to further improve their classification performances. Work on a publicly available tool to inject feature drift into a dataset for testing purposes is ongoing.  
Authors contributed equally to the paper. Both authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **kNN** & **kNN-FW** & **NB** & **NB-FW** & **VFDT** & **HAT** & **kNN-AQR** \\ \hline Electricity & 54.31 & **84.08** & 57.62 & 73.39 & 79.23 & 83.46 & 80.21 \\ GMSC & 92.48 & 92.67 & 93.09 & 93.32 & 93.25 & 93.37 & **93.56** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prequential accuracy (best results are reported in bold).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **\#Instances** & **\#Features** \\ \hline Electricity & 45,312 & 9 \\ GMSC & 150,000 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the datasets used in the classification experiment.  
Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: [[http://archive.ics.uci.edu/ml](http://archive.ics.uci.edu/ml)]([http://archive.ics.uci.edu/ml](http://archive.ics.uci.edu/ml)) and [[https://www.kaggle.com/datasets](https://www.kaggle.com/datasets)]([https://www.kaggle.com/datasets](https://www.kaggle.com/datasets)) (accessed on 27 December 2020).
## 2 Materials and Methods
### Model Selection
#### 2.4.5 Categorical Boosting  
CatBoost, a modified GBDT algorithm proposed by [PERSON] [47], is renowned for its comparatively low parameter count, robust handling of categorical data, and superior predictive accuracy. Compared with GBDT-based algorithms such as XGBoost and LightGBM, CatBoost more efficiently solves statistical learning problems. A major strength of CatBoost lies in its processing of categorical features. XGBoost and LightGBM normally require these features to be one-hot encoded or processed via alternative methods, producing high-dimensional feature representations and high computational costs. CatBoost addresses raw categorical features without encoding. Avoiding the dilemmas associated with traditional methods (e.g., one-hot encoding and feature interactions) conserves memory and computational resources while boosting training speed. CatBoost also uses the symmetric tree growth algorithm to optimize trees' structure. Conventional GBDT algorithms apply depth-first growth strategies, which cause some branch nodes to have small sample sizes. Model generalization can potentially be influenced as well. To mitigate this issue, the symmetric tree growth strategy regards each node as a binary tree structure. It employs a local greedy algorithm to determine thresholds for node splits. This approach alleviates the problem of small sample sizes and enhances generalizability. In addition, CatBoost assuages concerns about gradient bias and prediction shift. The risk of overfitting therefore diminishes while the model's accuracy and generalizability improve.
## 3 Results and Discussion
### Accuracy Analysis  
A confusion matrix was created to assess the accuracy of the decision tree classification method to classify the results for litchi, cropland, and other forest and vegetation (Table 4). OA was 90.34%, with a kappa coefficient of 0.84. The PA value for litchi was 94.31%, PA for other forest and vegetation was 87.33%, and PA for cropland was 88.02%. The UA of litchi was 94.64%, the UA of other forest and vegetation was 85.40%, and the UA of cropland was 94.64%, and the UA of cropland was 94.  
89.89%. The extraction results of the lithi fruit trees indicate that the decision-tree-based vegetation recognition method has a high recognition rate for lithi fruit trees. However, the extraction results for both cropland and other forest and vegetation were less than 90.0%, indicating that the method has a low recognition rate for cropland and other forest and vegetation. This is mainly because it is difficult to distinguish the crops planted in the cropland from the wide variety of tree species in the other forest and vegetation types, resulting in a small number of mixed scores among the three crops.  
In terms of area, the extracted area of lithi fruit trees matches the officially reported area in Maoming. However, due to the differences in the growth cycles of different lithi species, the method will incorrectly identify some lithi fruit trees as non-litchi fruit trees. Overall, the method in this paper possesses high accuracy in the identification of lithi, other forest and vegetation, and cropland. Due to the insufficient sampling sites of longan and citrus, the extraction of spatial distribution information of these two fruit trees was not assessed. These were also the main shortcomings of this study.
## 2 Antenna Array Design  
The Ka-band transmit beamformer chip, shown in Figure 2, is based on a SiGe BiCMOS technology and consists of eight identical channels that feed the orthogonal polarizations of four radiating elements of the phased array.  
Each channel contains a power amplifier, a variable gain amplifier with 16 dB gain control and a phase-shifter programmable with a 5-bit control. At the frequency range of operation from 27.5 to 30 GHz, the maximum gain per channel is 22 dB with a 12 dBm compression point at 1 dB (P1 dB), while the total power consumption at P1 dB is 1.35 W from a 1.8 V voltage supply.  
Within current flat antenna technologies, a multilayer structure printed circuit board (PCB) has been chosen for the overall design, due to its low cost and mass production approach. Figure 3 shows a microphotography of a transversal cut of the manufactured PCB, in which the multi-layer stack-up structure consists of thirteen copper layers and different dielectric substrates, alternating cores and prepregs.  
This PCB can be divided into four functional parts: Antenna elements are implemented using layers from L1 to L6. Layers L6 to L10 are used for routing DC power and the serial-peripheral interface (SPI) bus to program the beamformer chips. Layers L10 to L12 are assigned to the RF signal distribution network in stripline technology with integrated Wilkinson power dividers, and finally, layers L12 to L13, interconnection lines between PCB and surface mount components in grounded coplanar waveguide (GCPW) technology, are integrated.  
To carry out the design, digital and DC layers are fabricated using FR4 substrates because of its lower cost, while the rest of the structure employs ceramic materials from  
Figure 3: Transversal cut of the multi-layer PCB showing the stack-up structure.  
Figure 2: Block diagram of the Ka-band transmit beamformer chip.  
Rogers and Isola with relative dielectric constants of 3.6 and low losses at high frequencies to achieve a better RF performance. This combination results in obtaining the advantages of high gain, low cost, and easy processing.  
Vertical transitions are fabricated by through-hole, blind or buried vias, and micro-via connections with a diameter of 0.2 and 0.15 mm, respectively, creating coaxial-like structures to ensure the RF signal integrity [25]. In this kind of structure, it is known that the electric field is more tightly constrained inside the ground via as the amount of these increases [26]. For this reason, the number of grounded vias implemented in the design corresponds to the maximum allowed if fabrication rules are considered.
## 3 Results and Discussion
### Removal Efficiencies of Solids and COD
#### 3.3.2 tCOD and sCOD  
Figures 6 and 7 show the average concentrations and removal efficiencies of tCOD and sCOD in the influent and effluent of the bioreactor, for each tested HRT.  
The HRT6 assay showed the highest removal efficiencies of tCOD and sCOD with values of 45.5% and 17.2%, respectively. The highest removal efficiencies registered for tCOD rather than for sCOD suggest that one significant part of the organic matter used by the DF consortium was present in the OFMSW in the form of particulate matter. The tCOD and sCOD removal efficiencies increased with the increase in HRT, which means that lower COD loads were more beneficial for the COD removal.  
According to [PERSON] et al. [28], a removal rate of 12.86% was obtained during the DF of OFMSW, with an HRT of 5 d, at a pH of 5.5, and under thermophilic conditions. This value is much lower than that recorded in the present study under similar operational conditions (37.2% for an HRT of 5 d and pH of 5.2).
# Kernel-Density-Based Particle Defect Management
## 1 Introduction
In the field of semiconductor manufacturing, a particle defect indicates a failure resulting from fine particles, such as dust, that are present on the wafers during FAB. The size of the particle defect owing to the manufacturing environment has remained unchanged, but the FAB process has been improved to develop capabilities for the fabrication of fine patterns. During one inspection, as shown in Figure 4, there was no defect when the pitch was 130 nm, but a pitch of 45 nm resulted in a defect because the size of the particle was greater than the pitch size. Therefore, the structure and stability of the deposited chemical must be carefully controlled, and reduction in contamination in particular becomes increasingly crucial as device sizes shrink [7]. The particles in the chamber of a facility need to be monitored to maintain the chamber's stability. Current manufacturing processes manage particle defects through a simple control chart (c-chart) that monitors the number of particles. As these monitoring charts do not consider the distribution or density of the particles on the wafers, engineers do not know whether the particles are assignable or what the common causes are. Therefore, this study proposes a particle defect management method to overcome the limitation of this monitoring chart. This paper is organized as follows. Section 2 reviews the conventional particle management method. Section 3 provides the details of the proposed method, and Section 4 presents the experimental results and evaluation. Finally, Section 5 concludes this study. Figure 3: Distribution of defects in a semiconductor manufacturing process. Figure 2: Pitch change in the lithography process (2002–2020). DRAM, Dynamic Random-Access Memory; NAND, Negative-AND gate flash memory.