esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
These penalties can be manually assigned through class or sample weights [67], systematically determined [68], estimated from occurrence probabilities [69], or dynamically adjusted during training based on sample difficulty [70]. The issue of class imbalance was addressed in this study through the adoption of strategies at both the data and algorithm levels. This study employed a tailored approach of random undersampling to reduce the number of samples in majority classes by a specific ratio, alongside utilizing the \"Compute Class Weight\" function provided in the scikit-learn library [71]. The IID assumption of data, fundamental to both ML and DL models, simplifies modeling processes and enables reliable inferences about the underlying population from observed data. However, in practical situations, meeting the IID assumption may not always be feasible or realistic. Spatio-temporal datasets often exhibit spatial and/or temporal dependencies and variations that contradict this assumption [72]. For instance, time series data may show temporal dependencies among samples collected at different intervals, while spatial data may demonstrate spatial dependencies among samples from neighboring locations, both challenging the independence assumption. Additionally, class-imbalanced datasets, typical in classification tasks, violate the assumption of identical distribution as sample distribution varies across different classes. Although ML and DL algorithms may yield satisfactory outcomes on a given dataset, it is essential to thoroughly evaluate the performance and generalizability of these models when applied to an independent dataset, particularly in cases where the IID assumption may be violated. Our findings underscored the limited generalizability of DL models due to real-world datasets not adhering to the IID assumption. By applying the trained DL models to various independent datasets, this research demonstrated the limited generalizability of the DL models, highlighting the necessity for specifically tailored methods to effectively handle spatial autocorrelation and heterogeneity (intrinsic to spatial data) and temporal dependencies in the time series data, which contradict the IID assumption. The primary limitation of this study was associated with the ground reference data. While this research utilized ground reference data from two different sources, both sources had their own inaccuracies, resulting in errors being introduced during the model training phase. Supervised EO-based crop mapping relies heavily on accurate ground reference data, which are challenging to obtain due to restricted access to certain regions, the need for consistent and up-to-date surveys, and potential inaccuracies in observations. These challenges can affect the precision and reliability of EO-generated crop maps, particularly in areas with insufficient or inconsistent ground reference data.
## 3 Related Methods
### Long Short-Term Memory (LSTM)  
LSTM network is an ANN architecture that is developed to tackle the vanishing gradients problem [38]. LSTM preserves useful information about previous data in the sequence and propagates into the network if needed. In order to restrain the vanishing gradients problem, LSTM utilizes forget gates to retain useful data. The design of the LSTM cell is visualized in Figure 3.  
Figure 3: The LSTM architecture.  
Figure 2: The dynamic time warping clustering.  
LSTMs use a set of \"gates\" (forget, input and output) to control how a sequence of data enters to the network, is stored, and leaves the network. The forgetting gate determines which data are useful given both input (\(X_{t}\)) and the previous hidden layer (\(h_{t-1}\)) data. At the forget gate, a sigmoid activation function (given in Equation (6)) is used to generate a vector where each element is within [0, 1].  
\[f_{t}=\sigma(W_{f,x}\ X_{t}+W_{f,h}\ h_{t-1}+b_{f}) \tag{6}\]  
The input gate first determines which data will be stored in long-term memory given both input (\(X_{t}\)) and previous hidden layer (\(h_{t-1}\)) data. At this process, \(tanh\) activation function (Equation (7)) is used to combine input and the previous hidden layer data and obtain candidate data (\(\tilde{C}\)). Then, which components of the candidate data are worth keeping is determined by using sigmoid activation function (Equation (8)). Finally, new long-term memory is obtained as in Equation (9).  
\[\tilde{C}_{t}=tanh(W_{c,x}\ X_{t}+W_{c,h}\ h_{t-1}+b_{c}) \tag{7}\]  
\[i_{t}=\sigma(W_{i,x}\ X_{t}+W_{i,h}\ h_{t-1}+b_{i}) \tag{8}\]  
\[C_{t}=C_{t-1}f_{t}+i_{t}\tilde{C}_{t} \tag{9}\]  
The output gate decides output data (\(o_{t}\)) (Equation (10)) and new hidden layer (Equation (11)) given the previous hidden layer and new input data.  
\[o_{t}=\sigma(W_{o,x}X_{t}+W_{o,h}h_{t-1}+b_{o}) \tag{10}\]  
\[h_{t}=o_{t}\ tanh(C_{t}) \tag{11}\]
## The 8 April 1893 Earthquake Data  
### Overview of the Seismological Data from Various Sources  
On 8 April 1893, just before 3:00 PM local time (one among various catalogs reports a different value for the minutes of the occurrence of the event; see Table 1), an earthquake struck near the town of Svilajnac, Serbia. Gaining widespread notice from both the general populace and professionals in the regions that comprised Serbia, Austria-Hungary, Bulgaria, Romania, and segments of the Ottoman Empire at that time, this earthquake drew significant attention owing to its formidable intensity and the consequential impact it had on individuals, structures, and the surrounding environment. Notably, Serbia and Hungary experienced pronounced effects from this seismic event. As a result, the Geological Institute in Serbia and the Earthquake Committee of the Hungarian Geological Society and Central Meteorological Institute carried out systematic data collection from the regions where the earthquake manifested itself with destructive effects on buildings.  
In the 1893 issue of the Hungarian Nature Journal, we found a contribution by [PERSON] [8], which included information that the Svilajnac earthquake was recorded in the seismological observatory of Rocca di Papa, located in the vicinity of Rome, Italy (unfortunately, this record is no longer available). According to [PERSON], the suggested origin time is 14 h 43 m 20 s Roman time, that is, at 14 h 53 m 31 s Central European time. Bearing in mind that the Rocca di Papa seismological station is located about 750 km from the location of the epicenter and that at that distance, the \(P_{n}\) waves arrive first, we can estimate the travel time of the \(P_{n}\) wave from the epicenter to the location of Rocca di Papa to be 95.54 s or approximately 1 m 36 s, assuming the \(P_{n}\) wave velocity of about 7.85 km/s [9]. By subtracting the estimated travel time from 14 h 53 m 31 s, we obtain a time of 14 h 51 m 55 s. This time is very similar to the value of 14 h 52 m provided in the preliminary report on the earthquake written by [PERSON] and [PERSON] [10] and, later, in the Registry of the Serbian Royal Academy [11]. [PERSON] et al. [5] also reports this value (Table 1).
# Digital-Twin-Based Monitoring System for Slab Production Process
## 4 Experiment and Verification
### Verification Process  
#### 4.2.1 Target Detection Model Effect Verification  
This paper adopts the following parameters as evaluation metrics: Precision (Pre), Recall (Rec), average precision (AP), AP at IoU threshold 0.5 (AP\({}_{50}\)), AP at IoU threshold 0.75 (AP\({}_{75}\)), and accuracy. These metrics collectively assess the localization accuracy of the object detection model. The formulas are presented below:  
\[recall=\frac{TP}{TP+FN} \tag{6}\]  
\[AP=\int_{0}^{1}P(R)dR \tag{7}\]  
\[accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{8}\]  
Recall represents the proportion of correctly detected targets to the total number of targets. AP (average precision) is the area under the precision-recall curve within the [0, 1] range. AP\({}_{50}\) denotes the AP value when IoU is fixed at 50%, and AP\({}_{75}\) represents the AP value when IoU is fixed at 75%. Accuracy indicates the proportion of correctly predicted instances in the given test sample set by the detection model. The evaluation metrics employed in this study are designed to reflect superior model performance, with higher scores indicating better detection capabilities.  
The two control sample sets required for validation were utilized to train state-of-the-art (SOTA) object detection models. Following the completion of training, the effectiveness of the proposed model was assessed through a comparative evaluation against the control group. The selected object detection models in this study include CBAM [46], EW-YOLOv7 [47], DS-Cascade RCNN [48], Cascade R-CNN [49], and SSD. The control group was established to validate the superiority of the proposed model.  
Upon completion of training of the aforementioned models and the proposed model, a total of 5000 in situ slab images were collected for testing. As shown in Table 1, the evaluation metrics for different SOTA models and the proposed model were computed. The results demonstrate that the preprocessing algorithm proposed in this study significantly improves the recognition accuracy and capability of each object detection model. This suggests that the proposed model, compared to SOTA models, notably enhances the detection accuracy of slab defects. Figure 6 shows the classification, localization, and probability diagram of slab defects identified using recognition algorithms. It can be seen that our algorithm has excellent recognition performance.  
Figure 5: Experimental validation process.
# A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification
## 4 Experimental Results
### CNN Visual Result  
To compare the behavior of the CNN architectures in the drowsiness detection and classification process, the best test results for each of the CNNs were considered, taking into account the highest accuracy and recall of the Drowsy class. Based on Figure 12, for the InceptionV3-based CNN, the fourth experiment with 99.51% accuracy and 99.41% recall was considered; for the VGG16-based CNN, the sixth experiment with 99.71% accuracy and 99.61% recall was considered and for the ResNet50V2-based CNN, the fifth experiment with 99.9% accuracy and 99.8% recall was considered.  
For a better understanding of the behavior, the gradient-weighted class activation mapping (Grad-CAM) [33; 34] method was used. Using Grad-CAM, it is possible to visualize the regions that are important for detection; this method seeks to identify parts of the image that guide the CNN to make the final decision for class determination. The method involves generating a heat map representing the ROI regions with the highest relevance for classification of the received input image.  
Figure 13 shows five examples of different scenarios for visualization of the heat maps with Grad-CAM, in scenarios 1 and 2, the driver is in a normal state, i.e., no drowsiness; in scenario 3, the driver is in a wakeful state, which is the transition to drowsiness; in scenarios 4 and 5, the driver is drowsy. In each heat map, the red color represents the regions of highest importance for the prediction of each of the three trained CNNs, and the blue color represents the regions of lowest importance. The five examples were tested for each CNN, generating ROIs I-1 to I-5 corresponding to the outputs of the InceptionV3-based CNN, V-1 to V-5 corresponding to the VGG16-based CNN and R-1 to R-5 corresponding to the ResNet50V2-based CNN. In ROIs I-1 to I-5, we observed that the heat maps focus on the right eye (I-1), lower left eye and nose (I-2 and I-3), lower right eye (I-4) and the whole right eye (I-5). While the heat maps of ROIs V-1 to V-5 have a focus on V-1 and V-2 on the right eye, V-3 and V-4 on the left nose and cheek and V-5 on the right nose and cheek. In ROIs R-1 to R-5, it was observed that in R-1 and R-2, the heat map was on the right eye and part of the nose; in R-3, R-4 and R-5, it was focused between both eyes. Below each ROI, the Grad-CAM display is shown with its respective percentage prediction for drowsiness and non-drowsiness. Considering the example scenarios and the respective classification, it can be stated that the ResNet50V2-based CNN presents a higher focus on the eyes for better drowsiness detection.  
Figure 12: Radial behavior of two test metrics. (**a**) Accuracy of the three networks in radial; (**b**) Recall of Drowsy class of the three networks in radial.
## 4 Results  
### Characteristics of the Matchup Database  
A total number of 284,175 matchups between the COMS/MI and in situ measurements were obtained from April 2011 to March 2015. Figure 3a shows the spatial distribution of the number of matchup points in a bin of \(2^{\circ}\times 2^{\circ}\). The matchup data covers the entire COMS/MI domain, but the spatial density distribution of matchup points was not uniform. For example, in the Philippine Sea and the northwestern region of Australia, there are more than 1000 matchup points in a bin, whereas there were few or no matchups in the tropical ocean. The spatial distribution of the matchup data was affected by the topography, the presence of clouds, and the distribution of currents.  
The monthly characteristics of the number of matchup points were the lowest in July, with 17,717 and the highest in October, with 31,692 (Figure 3b). For local time, more than 7000 matchup points were generated per hour except at 23 h (Figure 3c). The latitudinal distribution of matchups is shown in Figure 3d. The latitudes of the matchups were mostly distributed between 15\({}^{\circ}\) N and 30\({}^{\circ}\) N in the Northern Hemisphere and between 10\({}^{\circ}\) S and 20\({}^{\circ}\) S in the Southern Hemisphere. However, matchups were rarely produced in the equatorial region and in the high latitudes above 40\({}^{\circ}\). The in situ temperatures of the matchup database were in the range of \(-\)0.2 \({}^{\circ}\)C to 33.8 \({}^{\circ}\)C, and the number of matchups showed the maximum value between 28.5 \({}^{\circ}\)C and 29.5 \({}^{\circ}\)C, as shown in Figure 3e.
## 2 Literature Review  
### Growing Self-Organizing Clustering Algorithms  
In general, the major drawback of classical clustering algorithms such as the Gaussian Mixture Model (GMM) [17] and \(k\)-means [18] is that the number of clusters/partitions has to be specified in advance. GNG [7] and ASOINN [8] are typical types of growing self-organizing clustering algorithms that can handle the drawback of GMM and \(k\)-means. GNG and ASOINN adaptively generate topological networks by generating nodes and edges for representing sequentially given data. However, since these algorithms permanently insert new nodes into topological networks for learning useful features/information, they have the potential to forget learned features/information (i.e., catastrophic forgetting). More generally, this phenomenon is called the plasticity-stability dilemma [19]. As a SOINN-based algorithm, SOINN+ [20] can detect clusters of arbitrary shapes in noisy data streams without any predefined parameters. Grow When Required (GWR) [21] is a GNG-based algorithm that can avoid the plasticity-stability dilemma by adding nodes whenever the state of the current network does not sufficiently match to the data. One problem of GWR is that as the number of nodes in the network increases, the cost of calculating a threshold for each node increases, and thus the learning efficiency decreases.  
In contrast to GNG-based algorithms, ART-based algorithms can theoretically avoid the plasticity-stability dilemma by utilizing a predefined similarity threshold (i.e., a vigilance parameter) for controlling a learning process. Thanks to this ability, a number of ART-based algorithms and their improvements have been proposed for both supervised learning [22; 23; 24] and unsupervised learning [25; 26; 27; 28]. Specifically, algorithms that utilize the CIM as a similarity measure have achieved faster and more stable self-organizingperformance than GNG-based algorithms [14; 15; 29; 30]. One drawback of the ART-based algorithms is the specification of data-dependent parameters such as a similarity threshold. Several studies have proposed solving this drawback by utilizing multiple vigilance levels [31], adjusting parameters during a learning process [24], and estimating parameters from given data [16]. In particular, CAEA [16], which utilizes the CIM as a similarity measure, has shown superior clustering performance while successfully reducing the effect of data-dependent parameters.
## 3 DSP Algorithm
### DSP Description
#### 3.2.1 Phase 1: Finding All Possible Paths  
In this phase, the DSP algorithm searches all possible paths between the source and destination nodes in the network based on the network link table. A new search algorithm 1, called the _Search Path_ algorithm, was developed to perform the searching task. The searched paths associated with their path distance are then added to a path list.  
The pseudocode of the Algorithm 1 is as follows:
# Simulation Model for Correction and Modeling of Probe Head Errors in Five-Axis Coordinate Systems
## 3 Results
### Example of Probe Head Error Correction  
The possibility of correcting probe head errors was presented on the example of measurements of the standard ring with a diameter of 34 mm. The values of diameter and roundness deviation were determined during the measurements. The ring was mounted in the plane perpendicular to the probe oriented using \(A=43^{\circ}\) and \(B=91^{\circ}\). Table 2 presents the results obtained during the real measurements without compensation of the probe head errors and after the compensation done using the methodology presented in Section 2.4. Results were compared with the data given on the calibration certificate of the ring.  
Figure 6: Comparison between results of experimental probe head errors identification and simulation using developed model for \(A=75\) and \(B=80\).  
Figure 7: Comparison between results of experimental probe head errors identification and simulation using developed model for \(A=-100\) and \(B=30\).
## 5 Grid-Connected Photovoltaic Power Generation System
### System Simulation Verification Under Dynamic Solar Irradiance  
In order to comprehensively validate the superiority of the photovoltaic grid-connected system in practical applications, this study simulates varying solar irradiance conditions and explores the impact of solar irradiance variations on system performance. Solar irradiance is one of the key factors affecting photovoltaic power generation efficiency. By conducting experiments under different irradiance conditions, the stability, response speed, and energy output capability of the photovoltaic grid-connected system under varying weather, time periods, and seasonal changes can be more accurately reflected. The variation of solar irradiance is shown in Table 3, and Figure 18 illustrates the current, voltage, and power output of the photovoltaic module under varying irradiance conditions.  
Figure 19 demonstrates that, as the solar irradiance changes, the AVA-LADRC control method continues to effectively stabilize the DC-side voltage. Compared to the other three control methods, this approach exhibits superior performance under dynamic irradiance conditions, ensuring the stability of the voltage waveform and control accuracy. After grid connection, the load current, compensation current, and phase A grid current waveforms of the system are shown in Figure 20.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Time** & **Irradiance** \\ \hline
0–0.3 s & 1000 W/m\({}^{2}\) \\
0.3–0.6 s & 700 W/m\({}^{2}\) \\
0.6–0.9 s & 800 W/m\({}^{2}\) \\
0.9–1.2 s & 1200 W/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dynamic variation of solar irradiance.  
Figure 17: ANN control structure diagram.  
It can be observed that the control strategy proposed in this study still maintains good control performance in the photovoltaic grid-connected system, with stable output current waveforms. The THD of the phase A current at different time points under varying irradiance conditions are shown in Figure 21.  
Figure 19: DC-side voltage waveform under dynamic solar irradiance.  
Figure 18: Photovoltaic module output: (**a**) Current; (**b**) Voltage; (**c**) Power.  
Figure 21: THD of phase A current at different times under dynamic solar irradiance using the AVA-LADRC method: (**a**) 0.1 s; (**b**) 0.4 s; (**c**) 0.7 s; (**d**) 1 s.  
Figure 20: Current waveform under dynamic solar irradiance using the AVA-LADRC method: (**a**) Load current; (**b**) Inject current; (**c**) Grid current.  
As shown in Figure 21, the proposed VAV-LADRC control method not only effectively reduces the impact of harmonics but also maintains excellent current quality despite fluctuations in the photovoltaic output power. The harmonic distortion rate remains at a consistently low level and meets the grid-connected system control requirement of 5%. This further validates the strong adaptability and stability of the method.
# Downregulation of Squalene Synthase Broadly Impacts Isoprenoid Biosynthesis in Guayule
## 2 Results
In contrast, MEePP (2-C-methyl-d-erythmitol 2,4-cyclodextrin diphosphate) was strongly and significantly reduced in root tissue of _SQSi_ lines compared to the WT control (but not compared to the EV). Finally, CDP-ME (4-diphosphocytidyl-2-C-methylerythritol) levels were slightly enhanced in the leaf tissues of _SQSi_ lines but not significantly (Figure 5, Table S1). Other metabolites, including the prenyl phosphates (except GPP), were unchanged, not detected, or varied inconsistently in response to _SQS_ downregulation (Table S1). Figure 5: Effect of SQS downregulation on isoprenoid intermediates. Concentrations (ug/g dry weight) of metabolites in 5-month-old guayule plant tissues including: (**A**) the cytosolic mevalonic acid pathway intermediates: acetyl coenzyme A (AC-CoA); acetoacetyl coenzyme A (Acac-CoA); hydroxymethylglutaryl coenzyme A (HMG-CoA); mevalonate (MVA); mevalonate-5-phosphate (MVA-P); and mevalonate diphosphate (MVA-PP). (**B**) The methylerithritol phosphate pathway (chloroplast) intermediates: pyruvate and glyceraldehyde-3-phosphate (G3P); 1-deoxylulose-5-phosphate (DXP); methylerythmitol-4-phosphate (MEP); 4-(cytidine-5’diphospho)-methylerythmitol (CDP-ME); methylerythmitol-2,4-cyclodiphosphate (CDP-MEP), 2-C-methyl-d-erythmitol 2,4-cyclodextrin diphosphate (MEePP); and 4-hydroxy-3-methylbut-2-enyl diphosphate (HMBPP). (**C**) Also shown, common products of both pathways: isopentenyl pyrophosphate (IPP)/dimethylallyl diphosphate (DMAPP), geranyl diphosphate (GPP), farnesyl diphosphate (FPP), squalene, and natural rubber. Values represent the average of 3 biological reps. Finally, the relative expression of isoprenoid metabolic and rubber biosynthetic pathway genes was determined by Q-RT-PCR. MVA pathway genes encoding _HMGR_, _MVK_, and _FPPS_ were all consistently downregulated in _SQSi_ lines compared to the WT and VC control plants (Figure 6). Putative rubber biosynthetic genes encoding _SRPP_ and _CBP_ showed no difference for _SQSi_ lines, but, remarkably, _CPT3_, the rubber-particle associated _cis_-phenyl transferase, was significantly upregulated in _SQSi_ lines.