esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 AI Applications and Research in NAFLD
### Liver Cirrhosis
DL approaches have developed over time and have led to the automatic classification of liver fibrosis, in comparison with non-deep learning-based algorithms (artificial neural networks, multinomial logistic regression, support vector machines and random forests) [58]. CNNs used in this particular study lead to higher areas under receiver operating characteristic curve, with values of up to 0.85-0.95 when automatically scoring liver fibrosis stages. Another study focused on the prediction of the METAVIR score based on a -four-class model (F0 vs. F1 vs. F2-3 vs. F4) based on 3446 patients and 13,608 US images, validated on a separate internal set (266 patients and 300 images) and an external set (572 patients and 1232 US images) [59]. The areas under the receiver operating curve (AUROC) for classification of liver cirrhosis were 0.901 (internal test set) and 0.857 (external test set). Moreover, the AUROC for the DCNN classification of liver cirrhosis based on US imaging was significantly higher than all five radiologists, at least for the prediction of the METAVIR score. ML is useful to predict bleeding oesophageal varices in compensated advanced chronic liver disease, based on laboratory measurements and liver stiffness measurements used to generate an AI algorithm based on extreme-gradient boosting [60]. The study included 828 patients with advanced chronic liver diseases and oesophageal varices, mostly with NAFLD, AFLD and hepatitis B and C, with the DL-assisted algorithm exhibiting an accuracy of 98.7% in predicting variceal bleeding, better than endoscopic classification alone, which reached only 58.9%. A similar multicentric approach (17 institutions from China, Singapore and India) used an ML-based strategy to predict the presence of high-risk varices in compensated cirrhosis to avoid unnecessary endoscopies based on liver stiffness, platelet count and total bilirubin [61]. The model worked well for the prediction of high-risk varices, and is also accompanied by a web-based calculator ([[http://www.pan-ches.cn/calculator/MLEGD_score](http://www.pan-ches.cn/calculator/MLEGD_score)]([http://www.pan-ches.cn/calculator/MLEGD_score](http://www.pan-ches.cn/calculator/MLEGD_score)), accessed on 4 March 2023). Thus, the ML-based model spared 52.6% of unnecessary endoscopies (in the training cohort) with a missed high-risk varices rate of 3.6%, and spared 58.1% (in the validation cohort) with a missed high-risk varices rate of 1.4%. Similar numbers were also obtained based on two separate external test cohorts. Another ML model was used to predict portal vein thrombosis (PVT) after splenectomy in portal hypertension patients, with a good accuracy and a satisfactory agreement between prediction models and real life observations [62].
# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 4 Proposed Spoken Language Identification System
### Layers Description  
1. The Sequence Input Layer for 2-D image sequence input contains the vector of three elements. Depending on the used parameters, this article includes 26 dimensions: 13 for GTCC and 13 for MFCC; there are 50 feature vectors per sequence (Because the features that were extracted from segments have different feature vectors depending on speech file length, we need the framing to buffer the feature vectors into fixed sequences of size 50 frames with 47 overlaps); hence the input size is [26 50 1], where 26, 50, and 1 correspond to the height, width, and the number of channels of the image, respectively.
2. To use Convolutional Layers to extract features (that is, to apply convolutional operations to each speech frame independently), we use a Sequence Folding Layer followed by convolutional layers.
3. The Batch Normalization Layer follows the Convolution Layer, where Batch Normalization is responsible for the convergence of learned representations. Then the ELU Layer.
4. Average Pooling 2 d = 1 \(\times\) 1 with stride [10 10] and padding [0 0 0 0]. A 2-D average pooling layer performs downsampling by dividing the input into rectangular pooling regions and computing the average values of each region.
5. We use a Sequence Unfolding Layer and a Flatten Layer to restore the sequence structure and reshape the output to vector sequences.
6. We include the LSTM Layers to classify the resulting vector sequences.
7. The Dropout Layer, with a dropout possibility of 40%, always follows every LSTM layer.
8. The Fully Connected Layer contains seven neurons.
9. The Softmax Layer applies a softmax function to the input.
10. The Classification Output Layer acts as an output layer for the proposed system.
## 3 Results
### Influence of Empirical Parameters on SIF Retrievals
#### 3.1.1 Influences of Fitting Window and SNR on SIF Retrievals  
SIF retrieval is based on the filling effect in the absorption lines and the best fitting window may be dependent on the SNR. Thus, we calculated the RMSE of SIF retrieval using different fitting windows and simulation datasets with different SNRs (Figure 4).  
The retrieval accuracy was low in narrow fitting windows. For the far-red band, the RMSE was greater than 0.70 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\) when \(\mathrm{\lambda_{1}}\) was greater than 740 nm, and the accuracy did not appear to be significantly improved with the increase in the SNR in narrow fitting windows. In wider fitting windows (\(\mathrm{\lambda_{1}}\) < 740 nm, \(\mathrm{\lambda_{2}}\) > 691 nm) that contain enough absorption lines, a relatively low RMSE of less than 0.70 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\) can be achieved, even when the SNR is 322, by appropriately adjusting other parameters in Table 4.  
Figure 4 shows the best retrievals of SIF with different fitting windows, but it has not been tested whether the best retrievals in these fitting windows are robust with the changing of \(\mathrm{n_{SV}}\) and \(\mathrm{n_{p}}\). To select an optimal fitting window that can achieve an accurate and robust retrieval using SIFIS data, the simulation dataset with an SNR of 322 was used to investigate the distribution of RMSE in different fitting windows (Figure 5).  
Figure 4: The root mean square error (RMSE) of (**a**) far-red and (**b**) red SIF retrieval using different fitting windows (\(\mathrm{\lambda_{1}}\)—758 nm at far-red band and 682—\(\mathrm{\lambda_{2}}\) nm at red band) and simulation datasets with different SNRs. Other parameters listed in Table 4 are arbitrarily changed, and the minimal value of the RMSE is taken.  
For far-red SIF retrieval, the RMSE varied within wide limits when \(\lambda_{1}\) was greater than 735 nm, which increased the difficulty of the optimization of empirical parameters in these fitting windows. When \(\lambda_{1}\) was 735 nm, the RMSE was the smallest and most robust with a value of 0.93 \(\pm\) 0.44 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\). For red SIF retrieval, the varying range of RMSE continued to decrease as the fitting window widened. A fitting window of 682-697 nm achieved an optimal RMSE (0.95 \(\pm\) 0.38 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\)).
# Nonlinear Effect and MAI Impact on SAC-OCDMA System Based on 2D Multi-Diagonal Code and Laser Array
## 5 Results and Discussion
### First Scenario: Without Considering MAI Effects
#### 5.1.1 Q-Factor versus Bit Rate  
Figure 3 presents the SAC-OCDMA system performance in terms of Q-factor versus bit rate (10-100) Gb/s, while taking into account the case of GVD in wavelength domain (i.e., \(\beta_{1}\) only) and GVD in frequency domain (i.e., \(\beta\)2 and \(\beta\)3). The total distance simulated is 250 km of SMF. As shown in Figure 3, the degradation in system performance is observed at bit rates above 55 Gb/s per network user corresponding to Q-factor below the Q limit (i.e., Q limit equal to 15.5 dB required to obtain a BER less than \(10^{-9}\)), when the GVD in wavelength domain is considered (i.e., D = 16 ps/nm/km and S = 0.08 ps/nm\({}^{2}\)/km). We also note that when the second and the third order dispersion are considered in the frequency domain (i.e., \(\beta\)2 = \(-\)20 ps\({}^{2}\)/km and \(\beta\)3 = 0 ps\({}^{3}\)/km, respectively), a degradation in system performance of 1 dB, 5.9 dB and 10.6 dB are observed at 30 Gb/s, 40 Gb/s and 50 Gb/s, respectively. Finally, the Q-factor limit of 15.5 dB is maintained at a total bit rate of 55 Gb/s, when the effects of GVD is manifested in the wavelength domain. Furthermore, when the GVD is simulated with frequency domain parameters (\(\beta\)2, \(\beta\)3), the system performance at a bit rate of 40 Gb/s results, to a certain extent, in a Q-factor within the vicinity of the Q-factor limit of 15.5 dB. The results in Figure 3 illustrate the potential of utilizing Forward Error Correction (FEC) to mitigate the performance penalty due to the frequency domain parameters (\(\beta\)2, \(\beta\)3). For example, a hard decision FEC limit at a BER threshold of 3.8 \(\times\)\(10^{-3}\) corresponding to a Q limit of 8.5 dB [34] can be applied with a 7% overhead. As such, the results in Figure 3, can be used in principle to estimate the potential gain from applying FEC encoding/decoding. By way of an example, a bit rate of 42.8 Gb/s which accounts for the 7% overhead and the target data bit rate of 40 Gb/s maintains a Q-factor above the FEC limit of 8.5 dB, resulting in error free transmission for the target data bit rate of 40 Gb/s.
# Solution of Orifice Hollow Cathode Plasma Model Equations by Means of Particle Swarm Optimization
## 1 Introduction  
Orifice Hollow Cathodes (OHCs) [1] are used both as electronic sources and/or neutralizers for electric propulsion (EP) devices, e.g., Hall Effect Thrusters (HETs), Gridded Ion Thrusters (GITs), and High-Efficiency Multistage Plasma Thrusters (HEMPTs) [2].  
stability because abrupt changes in geometry, flow rate or discharge current can cause the solution to diverge. This has given motivation to explore different solution strategies.  
In this work, the possibility of solving the whole system of models' equations by means of a Particle Swarm Optimization (PSO) algorithm is investigated. PSO is a population-based stochastic optimization technique developed by [PERSON] and [PERSON] in 1995 [PERSON] and [PERSON] (1995), inspired by the social behaviour of bird flocking or fish schooling. PSO has been successfully applied in many research and application areas ([PERSON], 2017), and recently, it has also been successfully used in EP applications to solve the complex systems of non-linear equations used to model Helicon Plasma Thrusters' (HPTs) plasma ([PERSON], 2018).  
Hence, because of the relevant number of involved variables and the associated strong non-linearity, in order to benefit from this approach, a code that searches for all the possible solutions of the plasma equations for OHCs has been built and tested.  
The article is structured as follows: the main definitions and assumptions of the plasma model (whose equations are recalled in Section 2.3 for completeness) are discussed in Section 2; the optimization technique and its detailed model are presented in Section 3; an application of the methodology is presented in Section 4, where the results of the simulation are discussed; finally, the conclusions are drawn in Section 5.
# Orbit Decomposition Method for Rotordynamic Coefficients Identification of Annular Seals
## 5 Results and Discussion
### Rotordynamic Results Comparison Between Two numerical Methods
#### 5.1.3 Computational Time  
The CFD software is run on a computer with Intel Xeon E5\(-\)2650 v2 2.6 GHz CPU. Table 5 compares the computational times of two methods. For each frequency, the computational time for one transient calculation is about two days, while less than two hours is required for the orbit decomposition method. The computational time of the present method decreases by 96.44% relative to the transient CFD method.
## 1 Introduction
This study compared the performance of CNN and Vision Transformer models for classifying thermal images of Mount Etna's volcanic activity. The dataset included 3000 images divided into six classes, where each represented distinct volcanic phenomena. The aim was to determine whether Transformer-based architectures, such as the Vision Transformer and Swin Transformer, can outperform CNNs, including AlexNet, in terms of classification accuracy and efficiency. This paper is structured as follows. In Section 2, we describe the dataset of thermal images used in this study, including the volcanic activity classes represented within it. In Section 3, we outline the CNN and Transformer architectures evaluated in our experiments, detailing their structure and relevance to our classification task. Following this, in Section 4,we introduce the evaluation metrics applied to objectively measure each classifier's performance, providing a basis for comparison across different model types. The subsequent Section 5 presents the findings of our comparative analysis, highlighting the strengths and limitations observed for each model. Finally, in Section 6, we discuss the implications of our results and suggest directions for future work in applying these models to volcanic image classification and potentially other geophysical applications.
# Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications
## 6 Performance Evaluation
### Energy Consumption in the Network  
Energy consumption is a vital efficiency performance factor in network operations. Here, the total power consumption in the network is gauged during the caching and provisioning process for the NFs across the network nodes and links. It also includes the beamforming power consumption at the RRHs to communicate with the MS requests. The total power consumption during the caching and provisioning times across the fog BBU nodes, links and the beamforming architectures account for the energy consumption in the network. First, for the network infrastructure, the work here adopts the power consumption model in [36], which measures the power usage in the cloud for a certain processing duration. Along with this, the power consumption at a single BBU node is \(z\),  
\[z(n_{i})\ =\ \beta(n_{i}).z(n_{i})|_{max}+(1-\beta(n_{i}))\sigma(n_{i}).z(n_{ i})|_{max}, \tag{14}\]  
Figure 4: Overall Network Cost for Various MS Requests.  
where \(\textit{fi}(n_{i})\) is the power consumption for the node in idle status, and \(\left.z(n_{i})\right|_{max}\) accounts for the maximum power consumption at the node. Furthermore, the variable \(\alpha(n_{i})\) denotes the node saturation rate, which depends on the utilization rate. Along these lines, the total power consumption for all the nodes in the path for request \(r\) is determined by,  
\[z_{cons}\ =\ \sum_{n_{i}}z(n_{i}),\ n_{i}\in N. \tag{15}\]  
This is added to the power consumption due to the number of switches in the network and their power consumption as,  
\[z_{total}\ =\ z_{cons}+X.z(x), \tag{16}\]  
where \(z(x)\) is the power consumption for switch \(x\) for \(X\) total switches.  
Figure 5 shows the total energy consumption levels. Similar to the analysis for the delay and cost, the clustered method groups cached NFs on the secondary node, which requires higher power consumption as compared to the primary nodes. The power consumption here also includes the utilization rate of the beamforming architectures at the F-RAN. Overall, the clustered method suffers from higher energy consumption, i.e., 11% higher at 200 requests versus the distributed caching counterpart. The behavior of the uncached methods follows the trend of the cost analysis attributed to the increased number of nodes and links in the request path. This increased operational cost suffers from an increase in power and energy consumption as well.
# Lamb Wave-Based FDM-PPM Method Data Transmission Scheme in Plate Structures
## 2 Lamb Wave Communication Method Based on FDM-PPM
### Construction of FDM-PPM Signals
#### 2.1.1 Phase Modulation of Frequency-Division Multiplexing Signals  
Traditional PPM is a signal modulation technique with constant pulse width and amplitude, conveying information by controlling the position of the pulse within the symbol window [26]. In contrast, FDM modulates the transmitted information in the frequency domain using an on-off keying strategy, with each subcarrier mapping to a data bit. In this paper, based on PPM, FDM is applied to the signal pulse waveform to increase the number of information bits the signal can represent.  
The FDM method modulates the transmission information in the frequency domain using an on-off keying (OOK) strategy, where each subcarrier maps to a single data bit. When the subcarrier frequency is excited, the transmitted data bit is a '1', and otherwise, the transmitted data bit is a '0'. In this study, a Hanning window was used for excitation. The expression for a signal with N subcarriers is as follows:  
\[x_{FDM}(t)=0.5\times\left[1-\cos\left(\frac{2\pi}{T}\right)\right]\cdot\left[ \sum_{i=1}^{N_{i}-1}\sin(2\pi f_{si}t+\phi_{si})\right] \tag{1}\]  
where \(T\) denotes the width of the windowing function, and \(f_{si}\) denotes the frequency of the \(i\)-th subcarrier in the \(s\)-th subcarrier sequence; \(\phi_{si}\in\left\{0,\,\pi\right\}\) denotes the phase of the \(i\)-th signal in the \(s\)-th subcarrier sequence; and \(x_{FDM}\) denotes the modulated signal.  
In traditional FDM methods, to ensure that adjacent subcarriers do not interfere with each other, the frequency spacing between the center frequencies of subcarriers was set to be at least equal to the main lobe bandwidth of each subcarrier. To improve the waveguide frequency band utilization, the FDM-PPM method defines the two non-interfering subcarriers as the first sequence. It designates the subcarrier positioned between these two subcarriers as the second sequence. Only one of the two subcarriers is used when representing a single piece of information. Suppose the waveform of the first set of subcarriers is insufficient to represent the bit information adequately. In this case, the system switches to the second set of subcarriers for transmission, while the first set remains unexcited.  
A specific example for illustration: The first set of subcarriers consists of two subcarriers with frequencies of 190 kHz and 270 kHz, while the second set includes a subcarrier with a center frequency of 230 kHz. For a modulated signal representing the information\"00101,\" the signal waveform is shown in Figure 1a. However, when the modulated signal needs to represent \"00110,\" the first set of subcarriers has reached its capacity limit and cannot continue to carry subsequent bit information. At this point, the system switches from the first set of subcarriers to an unused state and activates the second set for modulation. Figure 1b shows the corresponding signal waveform, while Figure 1c displays the frequency-domain distribution of both sets of subcarrier signals.
## 4 Soil and Water Conservation and Its Impacts on Hydrology and Soil Erosion
### Impact on Soil Erosion  
Indonesia's topography is dominated by sloped land, and most areas have high rainfall. With a high population with a majority of farmers, making agricultural land with ideal conditions is increasingly difficult to obtain. The expansion of agricultural land and plantation is mostly carried out in sloping areas, which are often not accompanied by SWC measures, which contributes greatly to soil erosion [9; 10]. If this condition is not handled, it will lead to a decrease in land productivity and can result in more severe disasters. Appropriate SWC application not only suppresses runoff but effectively reduces erosion. One of the successful indicators for soil and water conservation can be indicated by tolerable erosion value. Tolerable erosion values, as determined by the Indonesian National Standard (SNI) 7943:2014 are presented in Table 3[66].  
In oil palm plantations, several SWC methods have been tried, such as bench terrace, individual terrace, cover crop, sediment trap, vertical muld, and some combinations of measures. The measurement results using erosion plots show that SWC measures are more effective if they consist of two or three measure combinations. The combination of bench terrace, cover crop, and manure produces a reduction in the erosion of up to 70% compared to the control, while the other best combination is a combination of silt pit, cover crop, and manure with a reduction of up to 60% [68]. The application of silt pits has also proven to be effective in suppressing erosion in date palm plantations at Aceh Province. Byimplementing silt pits, erosion that occurs during extreme rainfall can be reduced by up to seven times [78].  
The application of SWC is not only effective in agricultural and plantation lands, but has also proven effective when applied to reforestation areas. The use of vertical mulching can reduce erosion by 37% at 6-m intervals, while at 12 m intervals, erosion can be reduced by 30%. A significant reduction in erosion is also accompanied by five times the prevention of nutrient loss compared to the control [31].
## 2 Materials and Methods
### Extraction of Features Using MediaPipe  
The method used for model development is the MediaPipe Holistic model for facial, hand gesture and body pose feature extraction. This model provides a robust landmark set. For each image, MediaPipe processes, detects and draws the following landmarks:  
1. **Arms Gestures**: A set of 21 landmarks for each hand corresponding to key points for it with proper x, y, z coordinates. These landmarks capture the positioning of fingers and the hand motions;
2. **Body Pose**: A set of 33 landmarks for the body stance corresponding to key points for it with proper x, y, z coordinates. These landmarks capture the skeleton of the body providing a full-body pose estimation;
3. **Facial Features**: A set of 468 landmarks for the face corresponding to key points for it with proper x, y, z coordinates. These landmarks capture facial expressions and can be relevant in expressions of feelings such as aggression.  
This coverage gives us the ability to analyze hand gestures, body posture and facial expression simultaneously, which is crucial for classifying and detecting different emotions and behaviors. For proper model training, landmark extraction for each image was performed by combining them into a single feature vector containing the following:  
1. **63 + 63 values for each arm landmarks** (21 landmarks for each hand multiplied by the count of coordinates [x, y, z]);
2. **99 values for the body pose landmarks** (33 landmarks for body stance multiplied by the count of coordinates [x, y, z]);
3. **1404 values for the facial expressions landmarks** (468 landmarks for body stance multiplied by the count of coordinates [x, y, z]).  
The process for landmark extraction was as follows:  
* The image file was first converted to RGB from BGR, which is the default format for OpenCV, since MediaPipe expects an RGB image as the input;
* The model then processes the converted images in order to detect and return the landmarks for the body pose, hands and face;
* If failure for landmark detection occurs, then NaN values are assigned to the list for the specific parts.  
In order to successfully detect human behaviors, we prioritized specific essential components for our model. Our aim was to classify argumentative and non-argumentative behaviors, and therefore, we chose specific expressions and motions on the target behaviors. By prioritizing facial landmarks, emotions such as fear, sadness or anger can be indicated. Body language also can indicate such emotions based on the torso posture or the curve of the spine. Hands gestures are often used to express our feelings, such as dominance in a situation or disapproval. The choice of the selected features aligns with the existing literature [34; 35] on behavioral analysis. This emphasizes the significance of interpreting human emotions based on the selected features.