esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Deep Learning Model  
### Lstm  
LSTM is the architecture of a recurrent neural network (RNN). An RNN is a neural network having a recurrent structure of output and input. Figure 1 shows the basic structure of an RNN. When a sequence with a large number of time steps is used in an RNN, the initial values decrease by the chain rule. This is because the values between \(-1\) and \(1\) are multiplied by the hyperbolic tangent function (tanh) in the back propagation through time (BPTT) used for training as the network becomes deeper. Therefore, an RNN involves the problem of information loss because the initial input data do not influence the output results owing to the vanishing gradient problem.  
An LSTM with a structure more complex than that of an RNN was proposed to solve the long-term dependency problem of an RNN. An LSTM consists of an input gate, forget gate, and output gate for preventing information loss. The sigmoid activation function outputs a value between zero and one to determine the amount of information based on the output value. Thus, it can add or remove the information of the cell state. The sigmoid and hyperbolic tangent functions are used as the activation functions of an LSTM. The input gate determines whether new information is saved in the cell state, whereas the forget gate determines whether past information is deleted from the cell state. Meanwhile, the output gate determines which information is to be output from the cell state. Figure 2 shows the structure of an LSTM.  
Figure 1: Structure of RNN.  
Equations (1)-(6) show the process of updating the cell state and the output values of each gate by using the LSTM calculations. \(h_{t-1}\) represents the previous state, \(x_{t}\) represents the cell input, and \(h_{t}\) represents the cell output. \(w\) and \(a\) represent the weight and bias, respectively.  
\[f_{t}=\sigma\Big{(}w_{f}\cdot[h_{t-1},\ x_{t}]+a_{f}\Big{)} \tag{1}\]  
\[i_{t}=\sigma(w_{i}\cdot[h_{t-1},\ x_{t}]+a_{i}) \tag{2}\]  
\[\widetilde{c}_{t}=\tanh(w_{C}\cdot[h_{t-1},\ x_{t}]+a_{c}) \tag{3}\]  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\times\widetilde{C}_{t} \tag{4}\]  
\[o_{t}=\sigma(w_{o}\cdot[h_{t-1},\ x_{t}]+a_{o}) \tag{5}\]  
\[h_{t}=o_{t}\times\tanh(C_{t}) \tag{6}\]
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 3 Material and Methods
### Performance Evaluation  
We evaluate the performance of the different classification algorithms quantitatively and qualitatively. Following traditional quantitative evaluations, we calculate confusion matrices by comparing the reference labels with the predicted ones. Then, we compute the standard Overall Accuracy (OA) measure, as well as user's accuracy (UA), producer's accuracy (PA) and F-Score per-class. In addition, we also evaluate the results qualitatively through visual inspection.
## 2 Materials and Methods
Moreover, both materials are considered hybrid ceramics; thus, further studies on mechanical aspects could benefit this selection. All restorations were cemented on the respective casts following a standardized pre-treatment procedure and using a self-adhesive cement. On both cast and restoration sides, the pretreatment comprised a 5 min ultrasonic bath in alcohol and drying. Sandblasting, even if clinically indicated, was avoided to reduce the possible bias of particle abrasion. A self-adhesive cement (Panavia SA, Kuraray Noritake, Tokyo, Japan) was applied, with the restorations that were seated applying a 10 N force with a weight. Gross cement excesses were removed before polymerization, and then, specimens were light-cured with an LEDlamp (Celalux 2, VOCO) at 1400 mW/cm\({}^{2}\) for 20 s on each side. After 24 h, specimens were polished using fine and extra-fine diamond burs, rubber points (Twist DIA, Kuraray Noritake), and nylon brush. A summary of all materials, alongside their manufacturer and composition, is presented in Table 1.  
A scan of all specimens was performed using a micro-CT (SkyScan 1172 Micro-CT, Bruker, Bremen, Germany). Acquisitions were performed with the following parameters: voltage = 80 kV; current = 124 \(\upmu\)A; aluminum 0.5 mm filter; pixel size = 14.8 \(\upmu\)m; averaging = 5; rotation step = 0.6\({}^{\circ}\); random movement = 10; scanning 360\({}^{\circ}\) = OFF; total scan duration = 44 min. Images were reconstructed though NRecon software (version 1.7.4.6, Bruker) in order to obtain Digital Imaging and Communications in Medicine (DICOM) files with standardized parameters: beam hardening correction = 20%; smoothing = 0; ring artifact reduction = 9. The so-obtained DICOM files were processed with a segmentation software (Mimics Medical version 24.0, Materialise, Leuven, Belgium) to measure both the volumetric and linear adaptation of the restorations. To measure the volumetric interfacial adaptation in the present paper, the authors avoided considering the cement volume alone. In that way, voids inside the cement itself and in the resin-cement interfaces would not be included in the calculation even if it was clear that they represented an essential part of the volumetric adaptation. Thus, resins, cement, and void volumes were separately segmented by selecting the corresponding spikes in the Hundsfield Unit Graph. Then, a standardized protocol consisting of Boolean (intersection) and morphology (close) operations was used to extract all voids in the interfacial area. After that, cement voxels were added to interfacial void voxels with the \"unite\" function, and the total volume was recorded from the \"mask propriety\" section, expressed in mm\({}^{3}\). An example of the procedure is reported in Figure 1A-C.
## 2 Materials and Methods
### Multivariate Analysis
#### 2.4.5 Software and Model  
The resizing of hyperspectral images was conducted on ENVI 4.6 (ITT, Visual Information Solutions, Boulder, CO, USA). The resizing was to cut the image with only samples and a single background from the original hyperspectral images. The spectral data extraction was conducted on Matlab R 2010b (The Math Works, Natick, MA, USA). PLSR was performed on Unscrambler(r) 10.1 (CAMO AS, Oslo, Norway). The optimal wavelength selection methods and the image visualization were conducted on Matlab R 2010b (The Math Works, Natick, MA, USA).  
The performances of the PLSR models using full spectra and the optimal wavelengths were evaluated by correlation coefficients of the calibration set and the prediction set (\(r_{c}\) and \(r_{p}\)), and the root mean square error of the calibration set and the prediction set (RMSEC and RMSEP). A better model should have higher \(r_{c}\) and \(r_{p}\), and lower RMSEC and RMSEP.
## 2 Materials and Methods  
### Study Regions  
Our study focused on four regions in central and eastern Indonesia where reef manta ray populations have been identified and regularly observed: Berau in East Kalimantan; Nusa Penida in Bali; Komodo in East Nusa Tenggara; and Raja Ampat in SouthwestPapua (Figure 1). Nusa Penida and Komodo, located in the Lesser Sunda Islands in south-central Indonesia, host populations of approximately 624 and 1085 individuals, respectively [7; 8]. The Raja Ampat Archipelago, situated within the Bird's Head Seascape of eastern Indonesia, supports a large population of over 1375 reef manta rays [6], which appear to be in a healthy state, exhibiting high survival rates and population growth over a decade of monitoring [24]. By contrast, the reef manta ray population in Berau, primarily observed in the waters surrounding Sangalaki island, consists of approximately 155 individuals and remains relatively understudied [25].  
Figure 1: Fastloc GPS and Argos positions (colored dots) recorded by the SPLASH10F-321A satellite tags deployed on reef manta rays across four regions: Berau, Nusa Penida, Komodo, and Raja Ampat from July 2014 to July 2022. Each color represents a different tagging region.  
Nusa Penida and Komodo are geographically the closest of the four regions, with a distance of approximately 450 km. In contrast, Berau is more isolated, separated by \(\sim\)1200 km, \(\sim\)1300 km, and \(\sim\)1500 km from Komodo, Nusa Penida, and Raja Ampat, respectively. Berau is, moreover, separated from these other three regions by the 2500 m deep Makassar Strait, the main pathway for the immense movement of water from the Pacific to the Indian Oceans known as the Indonesian Throughflow [26]. Finally, Komodo and Raja Ampat are about 1500 km apart, and while there are \"stepping stones\" of volcanic islands with shallow reefs between the two, the 5800 m deep Banda Sea which separates them [26] may serve as a dispersal barrier for manta rays.
# Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review
## 3 Intelligent Detection Methods for Driver Fatigue and Distraction
### Intelligent Detection Methods Based on Driver's Head Posture  
Detection methods based on a driver's head posture work by calculating the relative positions of various feature points in a three-dimensional space to obtain head posture angles, thereby detecting whether the driver is engaged in distracted behaviors, such as diverting their gaze from the driving task. Detecting the driver's head posture can also help to identify behaviors such as looking down at a phone or nodding off due to drowsiness.  
Since head rotation is a continuous process, models that consider the relationship between frames often achieve better detection results than methods that rely solely on real-time detection using single frames. [PERSON] et al. [45] optimized the fully connected layers of a residual error network with a composite loss function and expanded the training dataset through transfer learning. This approach accurately and continuously monitors a driver's distracted state in real driving environments but lacks an in-depth analysis of adaptability to environmental changes and real-time requirements. [PERSON] et al. [46] argued that a driver's psychological fatigue and sleepiness are reflected in changes in head posture. They introduced an improved linear unit layer into a bidirectional Long Short-Term Memory (LSTM) network structure to effectively process a 3D time series of head angular acceleration data, allowing for the effective recognition of complex head movement states. However, this method requires extensive training data to accurately identify different driver states, and data labeling is challenging.  
Detection methods based on driver head posture share similarities with those based on facial features, as they can be conducted using only camera sensors, making them low-cost and easy to integrate. However, the accuracy of their detection is easily affected by head occlusions and the diversity of postures.
# Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies
## 5 Reporting Overhead Analysis  
The closed-form expressions for the number reporting bits over a reporting channel for four different fusion schemes are demonstrated in Table 1, the analysis includes one-bit hard, two-bit softened hard, simple combined, and modified combined fusion algorithms. The adaptive combined algorithm is not included because the number of reporting bits varies with the variation of the eligibility threshold. Therefore, close form expression for this algorithm cannot be extracted. The communications between the CHs and their members or the FC incur reporting overhead, i.e., collaboration cost. The reporting overhead, \(H\) is computed as a product of the number of reporting bit, \(N\) and the bandwidth, \(B\), i.e., \(H=NB\).  
Note that \(D_{1}\) is the number of the clusters that employ two-bit fusion, \(D_{2}\) is the number of the eligible clusters when using simple combined algorithm. \(D_{3}\) is the number of the clusters that employ two-bit fusion and \(D_{4}\) is the number of the clusters that employ one-bit fusion when using modified combined algorithm. Recalling that \(C\) is the number of the clusters and \(S\) is the number of the users in the cluster.  
On the other hand, the power consumption also adds an extra overhead. The power consumption of a scheme reflects the energy efficiency of that scheme. The power consumption of \(N\) CRs in a CRN is computed as a sum of sensing power, \(P_{1}\), and reporting power, \(P_{2}\). The \(P_{1}\) is computed as \(P_{1}=NPs\), while the \(P_{2}\) is computed as, \(P_{2}=N_{r}P_{t}\), where \(N_{r}\) is the total number of reporting CRs in a CRN, \(P_{s}\) is the power consumed by a CR to detect a PU, and \(P_{t}\) is the transmission power of a CR.
# Multi-Scale Aggregation Stereo Matching Network Based on Dense Grouping Atrous Convolution
## 3 Experiments
### Evaluation Metrics  
Because the essence of binocular stereo matching is to find the homonymous points of the left and right images, the error directly affects the accuracy of the stereo matching. Thus, the end-point error (EPE) and 1-pixel error are used to represent the accuracy of the matching. The calculation formula of the EPE is as follows:  
\[EPE=\frac{1}{N}\sum_{m\in N}\sqrt{\Big{(}d_{m}-\hat{d}_{m}\Big{)}^{2}} \tag{7}\]  
where \(N\) represents the total number of pixels, \(d_{m}\) represents the ground truth at the pixel \(m\), and \(\hat{d}_{m}\) represents the predicted disparity value at the pixel \(m\).  
The EPE is the average disparity error in pixels, and the error is inversely proportional to the matching accuracy. The smaller the EPE, the higher the matching accuracy. The 1-pixel error is the average percentage of pixels that have an EPE bigger than 1 pixel.  
In addition, the real-time nature of the network requires special attention in the field of automatic driving, and its data processing must meet the constraints of driving speed and safe distance. We used the length of inference time to reflect the speed of the network modeling. The inference time is inversely proportional to the modeling speed. The longer the inference time is, the slower the modeling speed is. The shorter the inference time is, the faster the modeling speed is.
## 5 Relationship between Contact Forces and Wear as well as New Insights
### Importance of the Proposed Modeling Approach and Track Parameters  
Since the foundational work of [PERSON] [61; 62; 63], modeling corrugation has generally been based on wheel and rail sub-models that consist of beams and plates, and the wheel-rail contact is solved separately via statics and then coupled to the wheel-rail system. These calculations were limited by the state of the art. For contact mechanics, \"the mechanics of steady or slowly varying creep forces has been very thoroughly studied, but the behavior under transient or rapidly oscillating conditions is virtually unexplored\" [63]. The longitudinal dynamics of the wheel-rail interaction were investigated experimentally in [62], and longitudinal resonances higher than 900 Hz were observed. Such resonances were not considered important and thus were ignored in further investigations, and the focus for corrugation has been on vertical, lateral and wheelset torsional dynamics [1; 3; 39; 40; 56; 57]. In this paper, by analyzing the frictional rolling in the vehicle-track system under transient and rapidly oscillating conditions, the influence of both the vertical and the longitudinal vibration modes on corrugation initiation and growth was captured. However, further research should be performed that includes the influence of the track structure, especially the fastening, which plays a major role in constraining the rail and affecting its modes [59]. Studies have modelled fastenings and sleepers and identified their parameters [19; 50; 64; 65], and these works have revealed that the alignment of the fastening system influences the frequency of normal and longitudinal dynamics and that the natural frequencies are determined by the system parameters.  
Figure 1 shows different cases of corrugations under different track types, i.e., mono-block sleeper versus duo-block sleeper and W-shaped tension clamp versus Deenik clips. Among the three figures in Figure 1, Figure 1a is at a gentle curve, and the latter two figures are on straight tracks. These observations agree with the description of the short pitch corrugation [1]. Comparing the three photos reveals that the characteristics of the corrugation differ from track to track. Certain corrugations are more or less uniformly distributed along the track (Figure 1a), whereas others are more severe near the sleeper support (Figure 1b,c). The difference between Figures 1a and 1b,c is the types of sleepers and fastenings. According to Figure 1b, the wavelength of the corrugation may vary significantly within a sleeper span. Moreover, Figure 1b,c is from the same track, thus having the same traffic and same types of sleeper and fastening; however, large differences in the corrugations are apparent. These observations all indicate that corrugation is sensitive to track conditions, thus highlighting directions for future work.
## 2 Materials and Methods  
### Gas Mixing and Distribution System  
The proposed gas supply system is shown in Figure 1. Three Mass Flow Controllers (MFCs) are used to accurately regulate the flow rate and volume of N\({}_{2}\), O\({}_{2}\), and CO\({}_{2}\) from the high-pressure cylinders to the gas mixer [17; 18]. The gas mixer facilitates the mixing of gases within the system. Each individual chamber in the system is connected to a micro pump and a check valve. These pumps circulate gas from the mixer to the culture chambers and back, creating a closed-loop gas supply system. The check valve prevents air from entering the system when a chamber lid is opened. The state of each chamber lid (open or closed) can be identified by a switch sensor. The pump is automatically deactivated when the chamber lid is open and only operates when the chamber lid is closed. The gas sensor is positioned downstream of the gas mixer and upstream of the chambers, along the flow direction, to measure the gas concentrations (volume fraction). Additionally, a solenoid valve, which is a three-way valve, is positioned between the gas sensor and the culture chambers. It can be controlled to switch between two positions: either to the atmosphere or the system.  
Figure 1: Schematic of the gas mixing and distribution system for a multi-chamber cell incubator. The system requires compressed gases of pure N\({}_{2}\), O\({}_{2}\), and CO\({}_{2}\). The red arrows indicate the gas flow direction.  
The proposed system calculates the required volume of various gases using real-time gas concentrations obtained from gas sensors. It then accurately regulates the gas volume entering the system from the gas cylinder through MFC. Mixing and distribution processes are subsequently conducted within the system. By employing automatic control and adjustment mechanisms, the gas concentration within the system can be maintained within the acceptable error range of the desired value. In summary, to enhance mixing efficiency and accuracy, the system raises two main concerns: determining the optimal mixing parameters and calculating the inflow volume of N\({}_{2}\), O\({}_{2}\), and CO\({}_{2}\).
## 4 Discussion
### Drug Reaction  
Erythroderma caused by drug reaction was observed in 10.78% of patients. Of all 22 patients diagnosed with drug-induced erythroderma, 5 of them had a previous history of allergic reactions. The observed incidence of a drug reaction as a cause of erythroderma is in accordance with the literature [2,5,6,8]. In a study conducted by [PERSON] and [PERSON], it was found that 12.3% of patients experienced drug-induced erythroderma [2]. [PERSON] et al. reported a comparable incidence, with 18.4% of affected subjects, which is similar to the findings of [PERSON] et al. (17%) [5,8]. In addition, [PERSON] and [PERSON] observed drug-induced erythroderma in 6.5% of cases [6]. Drug-induced erythroderma may occur days or weeks after exposure, and it evolves quickly [1,15,16]. The number of drugs can be the causative factor [1,2]. The most common cause of drug-induced erythroderma in our sample was allopurinol (\(n=5\); 20.8%) and anticonvulsants (\(n=4\); 16.7%), such as carbamazepine (\(n=3\); 12.5%) and lamotrigine (\(n=1\); 4.2%). Table S2 presents the characteristics and timing of the development of drug-induced erythroderma. This agrees with the findings of other authors [3,5]. The above-mentioned medications are some of the most common agents responsible for severe drug-induced skin reactions, such as drug reaction with eosinophil and systemic symptoms (DRESS) and Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TNT) [17]. In a study by [PERSON] et al., allopurinol was the most common medication causing severe cutaneous adverse reactions (SCAR), including SJS, TNT, SJS/TNT overlap, and DRESS [18]. These drug-induced skin reactions can lead to erythroderma [19]. Isolated cases of drug-induced erythroderma, according to our observations, were caused by venlafaxine (\(n=1\); 4.2%), tolperison (\(n=1\); 4.2%), telaprevir (\(n=1\); 4.2%), telaprevir (\(n=1\); 4.2%), torphazine (\(n=1\); 4.2%), bromazine (\(n=1\); 4.2%), fenofibrate (\(n=1\); 4.2%). In previously published literature, single studies report telaprevir and torphazine as possible causes of erythroderma [20,21,22]. In the remaining patients (\(n=7\); 29.2%), we were unable to determine which drug was the direct trigger. It seems that it may be primarily attributed to the multimorbidity of the subjects and the diverse array of medications they were concurrently taking. This group included patients who were simultaneously treated for various conditions, such as cardiovascular, neurological, psychiatric, and metabolic diseases. Identifying a specific causative factor may have been challenging due to the complexity of these interactions. Predominantly, polypharmacy was associated with the number and severity of comorbidities and older age of patients. What is more, drugs commonly used in comorbidities could also induce erythroderma, according to some studies [23; 24; 25; 26; 27; 28]. Cases of \(\beta\)-blocker-induced erythroderma (e.g., bisoprolol, metoprolol) have also been reported in the literature [29; 30]. Moreover, erythroderma can (rarely) be caused by metformin, insulin, and other diabetes drugs [25; 26; 27; 28].