esa-sceva/satcom-chunk-collection huggingface dataset:
# Classifying X-Ray Tube Malfunctions: AI-Powered CT Predictive Maintenance System
## 5 Classifiers
### Classifier Selection
#### 5.1.1 Decision Trees, Logistic Regression and SVM  
DTs are among the oldest models used for classification and prediction [33]. It is characterised by recursively splitting the dataset based on the values of the selected features, creating decision rules. This way, it splits the datasets into smaller, more homogeneous datasets until only examples of one class remain in the dataset. This type of node is called a leaf node. How the tree is built and how to define homogeneity depends on the algorithm used.  
Logistic regression [34] is a probabilistic model using the logistic (sigmoid) function to model probability. It models the relationship between n independent variables and produces a binary output for a dependent variable. While logistic regression is a linear model in the log-odds space, the relationship between independent variables and probability is nonlinear due to the logistic function.  
With the modern form introduced in 1992 [35], SVM is also used for classification and regression. The simplest version tries to create a linear separation hyperplane between classes that is maximally and equally distanced from the nearest samples (support vectors) of both classes (maximum-margin hyperplane). Not all classes are separable by a linear hyperplane, so it uses a kernel trick to separate them in a higher-dimensional feature space.
## 3 Methods
### Calculating Azimuth and Elevation Angles for Automatic CRs and Conducting In Situ Measurements
 \tag{1}\]  
In Equation (1),  
\[ \begin{array}{ll}N_{L}(P,L,H)=&a_{1}+a_{2}L+a_{3}P+a_{4}H+a_{5}LP+a_{6}LH+a_ {7}PH+a_{8}L^{2}\\ &+a_{9}P^{2}+a_{10}H^{2}+a_{11}PLH+a_{12}L^{3}+a_{13}LP^{2}+a_{14}LH^{2}+a_{15 }L^{2}P\\ &+a_{16}P^{3}+a_{17}PH^{2}+a_{18}L^{2}H+a_{19}P^{2}H+a_{20}H^{3}\\ D_{L}(P,L,H)=&b_{1}+b_{2}L+b_{3}P+b_{4}H+b_{5}LP+b_{6}LH+b_{7}PH+b_{8}L^{2}\\ &+b_{9}P^{2}+b_{10}H^{2}+b_{11}PLH+b_{12}L^{3}+b_{13}LP^{2}+b_{14}LH^{2}+b_{15 }L^{2}P\\ &+b_{16}P^{3}+b_{17}PH^{2}+b_{18}L^{2}H+b_{19}P^{2}H+b_{20}H^{3}\\ N_{S}(P,L,H)=&c_{1}+c_{2}L+c_{3}P+c_{4}H+c_{5}LP+c_{6}LH+c_{7}PH+c_{8}L^{2}\\ &+c_{9}P^{2}+c_{10}H^{2}+c_{11}PLH+c_{12}L^{3}+c_{13}LP^{2}+c_{14}LH^{2}+c_{15 }L^{2}P\\ &+c_{16}P^{3}+c_{17}PH^{2}+c_{18}L^{2}H+c_{19}P^{2}H+c_{20}H^{3}\\ D_{S}(P,L,H)=&d_{1}+d_{2}L+d_{3}P+d_{4}H+d_{5}LP+d_{6}LH+d_{7}PH+d_{8}L^{2}\\ &+d_{9}P^{2}+d_{10}H^{2}+d_{11}PLH+d_{12}L^{3}+d_{13}LP^{2}+d_{14}LH^{2}+d_{15 }L^{2}P\\ &+d_{16}P^{3}+d_{17}PH^{2}+d_{18}L^{2}H+d_{19}P^{2}H+d_{20}H^{3}\end{array}  \tag{2}\]  
where \(a_{i}\), \(b_{i}\), \(c_{i}\), and \(d_{i}\) are the RPC model coefficients. \(b_{1}\) and \(d_{1}\) are typically 1. To ensure the stability of parameter solving, standardization of ground and image coordinates between \(-1\) and 1 is essential. Consequently, (\(P\), \(L\), \(H\)) represents the ground coordinates achieved by standardizing (_Latitude, Longitude, Height_), while (\(Y\), \(X\)) denotes the image coordinates achieved by standardizing (_Line, Sample_). The standardization formula is as follows:  
\[ \begin{array}{l}P=(Latitude-LAT\_OFF)/LAT\_SCALE\\ L=(Longitude-LONG\_OFF)/LONG\_SCALE\\ H=(Height-HEIGHT\_OFF)/HEIGHT\_SCALE\\ Y=(Line-LINE\_OFF)/LINE\_SCALE\\ X=(Samp-SAMP\_OFF)/SAMP\_SCALE\end{array}  \tag{3}\]  
where _LINE_OFF_ and _SAMP_OFF_ refer to the offset values of the image pixel coordinates and _LINE_SCALE_ and _SAMP_SCALE_ are the scale values of the image pixel coordinates. Similarly, _LAT_OFF_, _LONG_OFF_, and _HEIGHT_OFF_ denote the offset values of the ground coordinates. _LAT_SCALE_, _LONG_SCALE_, and _HEIGHT_SCALE_ are the scale values of the ground coordinates. Figure 7a,b depict the RPC geometric correction outcomes for the C-SAR/02 FSI mode image on October 18 and the C-SAR/01 FSII mode image on November 22, respectively. During the transit of the C-SAR/02 image on October 18, the first deployment scheme was employed, while the second scheme was used during the transit of the C-SAR/01 image on November 22. Figure 7c,d showcase the geometric correction results utilizing the RPC model for the two images and provide the location of the ground CRs and detailed image features. With the high-precision latitude and longitude data obtained from the ground  
measurements of each CR and the RPC parameters of the image, it becomes possible to compute the corresponding row \(Y\) and column \(X\) values for each automatic CR in the Level-1 image.
## 2 Related Work
### Ship Detection in Shallow Clouds Covered Images  
In ship detection tasks, cloud interference poses a major challenge, prompting the use of cloud removal algorithms during the preprocessing phase to enhance image quality. Various deep learning methods have been developed to reconstruct clear images from hazy inputs [34]. [PERSON] et al. [35] introduced DehazeNet, a trainable end-to-end system that estimates medium transmission to facilitate dehazing. By taking a hazy image as input, DehazeNet produces a transmission map used alongside the atmospheric scattering model to recover a clear image. Similarly, [PERSON] et al. [36] proposed the Dark Channel Network (DCNet), which estimates the transmission map for hazy scenes using a feature extraction layer to capture haze-related features and a CNN layer for convolutional processing. Ref. [37] introduces a depth-aware method built on a Generative Adversarial Network (GAN) architecture that estimates depth maps to enhance dehazing performance. By integrating depth features [38], the model can better distinguish varying haze levels from image content. Additionally, reference [39] employs the Zero-DCE method, which adjusts the dynamic range of images using higher-order curves for dehazing. Furthermore,reference [40] proposes a dual-decoder structure with a densely unpooling encoder to enhance structural features closely associated with haze.  
Real-world scenes often present complex degradation and uneven spatial haze distribution, which can hinder the generalization capabilities of dehazing models. To tackle this, [PERSON] et al. [41] developed the Spatial Dual-Branch Attention Dehazing Network (SDBAD-Net), an end-to-end framework utilizing the Meta-Former paradigm. This approach incorporates a robust Spatial Dual-Branch Attention (SDBA) module to effectively filter haze features with varying densities.  
[PERSON] et al. [42] introduced the Proxy and Cross-Stripes Integration Transformer (PCS-former), which features two novel transformer blocks: the sliding cross-stripes transformer block and the local proxy-based global transformer block. These blocks collectively provide a large receptive field while ensuring computational efficiency within a single transformer module. Additionally, [PERSON] et al. [43] proposed the Detail-Enhanced Attention Network (DEA-Net), which enhances CNN performance in restoring high-quality haze-free images by integrating Detail-Enhanced Convolution (DEConv) and Content-Guided Attention (CGA) into a Detail-Enhanced Attention Block (DEAB), thus improving feature learning and dehazing effectiveness.  
These advanced models significantly contribute to overcoming the challenges of cloud interference and image degradation in ship detection and other related tasks. Each approach offers unique solutions to handling complex haze conditions, improving image clarity and quality in real-world applications.
# Collaborative Analysis of Learners' Emotional States Based on Cross-Modal Higher-Order Reasoning
## 2 Related Works
### Cross-Modal Feature Fusion
#### 2.1.1 Early Fusion  
Early fusion is a simple and direct method of multimodal feature fusion. Its core idea is to directly integrate information from different modalities at the input level to form a comprehensive feature representation. In practice, this is often achieved in straightforward ways, such as concatenating feature vectors from different modalities, weighted summation, or using other forms of integration methods.  
The principle of early fusion lies in unifying multimodal data in the feature space to construct a comprehensive model input. Through merging information from different modalities at the input level, early fusion allows the model to directly access information from multiple data sources without additional processing steps. This direct fusion simplifies the design and implementation of the model, reduces system complexity, and enables rapid integration of multimodal information. The schematic diagram of early fusion is illustrated in Figure 1.  
However, early fusion also has some limitations, with the most significant being the potential for information confusion or conflict between different modalities. Because features from different modalities have different representations and semantic information, direct concatenation may lead to the loss or distortion of some information. Therefore, in practice, it is important to carefully select appropriate fusion methods to fully utilize the information from different modalities and improve the performance and effectiveness of the model.
## 3 Materials and Methods
### Experimental Design, Sodium Chloride Application and Nutrient Solution Management  
Eight treatments derived by a factorial combination of four NaCl concentrations in the nutrient solution (non salt control 1, 10, 20 or 30 mM) and two perilla cultivars (green and red) were delivered. The treatments were arranged in a randomized complete-block design with three replicates per treatment, amounting to a total of 24 experimental units (i.e., plots). Each experimental units (replicates) consisted of 8 plants for a total number of green and red perilla of 192 plants. The first and last plant of each experimental were considered as guards, and the biometric and chemical composition measurements were carried out on each of the six plants per experimental unit separately.  
The basic nutrient solution was a modified Hoagland formulation with a mmol\(\cdot\)L\({}^{-1}\) composition of: 14.2 N-NO\({}_{3}\)\({}^{-}\), 1.8 S, 1.0 P, 0.6 N-NH\({}_{4}\)\({}^{+}\), 6.5 K, 4.0 Ca, 2.2 Mg, 1 Na, and 1 Cl and (in mmol\(\cdot\)L\({}^{-1}\)) 20 Fe, 10 Mn, 0.4 Cu, 1.5 Zn, 20 B, and 0.3 Mo, with an electrical conductivity (EC) of 2.0 dS\(\cdot\)m\({}^{-1}\). The three saline nutrient solutions had the same basic nutrient solution plus an additional 9, 19 and 29 mM NaCl, resulting in an EC of 3.0, 3.9 and 5.1 dS\(\cdot\)m\({}^{-1}\), respectively. The pH of the four nutrient solutions was 6.0 \(\pm\) 0.3. All nutrient solutions were prepared using deionized water. Saline treatment started on 12 May, eight days after transplanting (DAT).  
The four nutrient solutions (control and three saline treatments) were pumped from independent 150 L tanks and delivered through a drip irrigation system through one emitter per plant at a rate of 2 L\(\cdot\)h\({}^{-1}\). Green and red perilla plants received the same solution volume, allowing for a leaching fraction of 20% to avoid any build-up of salinity in the substrate, thus maintaining the EC in the growing media at a level proximate to that of the treatment nutrient solution.
# A White-Box Sociolinguistic Model for Gender Detection
## 2 Automatic Gender Detection: From Discriminant Analysis to Deep Learning
### Third Stage  
In a third stage, automatic gender detection studies were interested in microblogging. The previous stages demonstrated that it was possible to predict the gender of authors from formal and informal texts of a certain length; however, microblogging platforms, such as Twitter, set a new challenge: to extract identifying information with little textual input.  
The first study on Twitter was conducted by [PERSON] et al. [27]. They manually annotated 1000 Twitter users to infer four user attributes: gender, age, political orientation, and regional origin. They achieved a classification accuracy of 72.3% using a Support Vector Machine classifier with unigrams and bigrams tokens (1,256,558 features) and sociolinguistic features. The latter set of features mainly consisted of punctuation mark frequencies, a list of emoticons compiled from the Wikipedia, and some words lists, such as exsexperation expressions or affection words.  
In [28], [PERSON], [PERSON], [PERSON] and [PERSON] created a large dataset of 184,000 Twitter users with 4.1 million tweets for training. Unlike the work presented by [27], they automatically annotated their dataset following the URLs included by the users in their profile description that linked to their blog sites. They experimented with a wide variety of classifiers, including Naive Bayes, Support Vector Machines, and Balanced Winnow 2. They reported 75.5% classification accuracy with a Balanced Winno2 classifier using character n-grams with \(n\) in the range (1, 5) and word unigrams and bigrams (15,572,522 features).  
In 2012, [PERSON], [PERSON] and [PERSON] [29] collected 78,853 Twitter users in order to infer authors' gender from the content of their tweets. They reached 80.6% classification accuracy using a Support Vector Machine classifier with word unigrams, Twitter hastags and LIWC categories as features (1,231,910 features).  
[PERSON], [PERSON] and [PERSON] [30] also experimented with a Support Vector Machine classifier on Twitter data. However, unlike previous work, they extracted tweets written in four languages: Japanese, Indonesian, French, and Turkish. They reached the highest accuracy of 87% on Turkish and the lowest accuracy of 63% on Japanese with the 20 top words unigrams, bigrams, trigrams, and hashtags, along with other metadata features, such as the 20 top mentions or links.  
[PERSON], [PERSON] and [PERSON] [31], in 2017, implemented the Sequential Minimal Optimization algorithm on the PAN-AP-17 dataset. They reported 72.25% accuracy on gender detection using character n-grams with \(n\) in range (2, 7).
# Assessment of Pohang Earthquake-Induced Liquefaction at Youngil-Man Port Using the UBCSAND2 Model
## 2 Methodology
### UBCSAND2 Constitutive Model
#### 2.1.4 Hardening Rule and Flow Rule on the Horizontal Plane  
For loading phase, the hardening rule and flow rule are similar for both maximum shear stress and horizontal planes. The only difference is the stress ratio \(\eta\).  
Upon loading, plastic deformation is controlled by conditions on the horizontal plane, in which consistence condition of its yield surface is derived from Equation (3) as follows:  
\[df_{2}\ =\ t_{2}-\sigma^{\prime}{}_{m}\dot{s}in\phi_{m2}-G_{2}^{p}\dot{d} \gamma_{2}^{p}\ =\ 0 \tag{10}\]  
The plastic shear modulus \(G^{p}{}_{2}\) of the horizontal plane for unloading phase is expressed as follows:  
\[G_{2}^{p}\ =\ G_{max}^{p}(1-\frac{\eta_{2}^{*}}{\eta_{f}^{*}}R_{f})^{2}\ =\ G_{max}^{p}(1-\frac{\tau_{xy}^{*}}{\tau_{f}^{*}}R_{f})^{2} \tag{11}\]  
where \(\eta\) * and \(\eta_{f}\) * are modified stress ratios based on modified shear stresses \(\tau\) *\({}_{xy}\) and \(\tau\) *\({}_{f}\) as shown in Figure 2.
# MEMS and IoT in HAR: Effective Monitoring for the Health of Older People
## 2 IoT and MEMS Technologies  
Ambient Assisted Living (AAL) uses IoT technology and artificial intelligence to support older people, making the home smart [12; 13]. The combination of IoT and AI creates advanced platforms to analyze the data collected, improving medical diagnosis and treatment [14]. With networks such as 5G/6G, data are transferred to cloud systems for telemedicine and remote robotic surgery.  
Wearables with MEMS sensors monitor diseases such as high blood pressure, heart failure, obesity, and diabetes, improving home care and reducing healthcare costs [15]. MEMS sensors, integrated with artificial intelligence models, recognize human activities (HARs) with high accuracy [16]. Sensors for activity recognition include wearable physiological sensors, IR motion sensors, acoustic sensors, household objects, magnetic sensors, environmental sensors, and video cameras [17]. However, video cameras have privacy and image quality concerns, so MEMS sensors are preferred for activity monitoring [18]. The technological development of MEMS sensors has made it possible to obtain reliable data on movement and orientation, stimulating research in various application fields, such as the study of synthetic touch sensors [19].  
The main MEMS sensors are accelerometers, gyroscopes, and magnetometers (IMUs Inertial Measurement Units). Accelerometers measure linear acceleration, gyroscopes measure angular velocity, and magnetometers measure the strength of the magnetic field. These sensors can be combined to form nine-axis inertial sensors, which are used to determine motion, orientation, and direction (Figure 1). The IMU can accumulate errors over time, but filtering and merging sensor data can reduce these errors.  
The Kalman filter is a widely used algorithm for estimating unknown variables, minimizing mean square error. For non-linear movements, techniques such as the extended Kalman filter (EKF) or the unscented Kalman filter (UKF) can be used.  
MEMS are used in healthcare to monitor the health of the elderly, collect vital data, and detect hazardous situations. Thanks to edge computing, they can process data locally, reducing cloud traffic. IMUs are used in various medical fields, from physiological monitoring to fall risk assessment and rehabilitation.
# Modified Generalized Feistel Network Block Cipher for the Internet of Things
## 5 Result and Discussion  
This section presents the statistical and cryptographic analysis findings to assess the security of the proposed MGFN lightweight block cipher's complete 24 rounds. Furthermore, we compare its results with those of other existing lightweight block ciphers.
# Knowledge Distillation-Based Multilingual Code Retrieval
## 3 Experiments
### Experiment Setup
#### 3.4.6 Experiment Equipment
 & \multirow{2}{*}{**Encoder**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Go**} & \multirow{2}{*}{**Java**} & \multirow{2}{*}{**Javascript**} & \multirow{2}{*}{**Php**} & \multirow{2}{*}{**Python**} & \multirow{2}{*}{**Ruby**} \\ \hline \hline \multirow{11}{*}{SuccessRate@1} & & & Go & **0.7233** & 0.4535 & 0.3750 & 0.3615 & 0.4645 & 0.3740 \\  & & Java & 0.5629 & **0.5859** & 0.3980 & 0.4496 & 0.5064 & 0.4150 \\  & & Javascript & 0.4828 & 0.4329 & 0.4400 & 0.3614 & 0.4715 & 0.3755 \\  & & Php & 0.5630 & 0.5208 & 0.4260 & **0.5645** & 0.6017 & 0.4555 \\  & & Python & 0.5594 & 0.4851 & 0.4158 & 0.4758 & **0.6781** & 0.4785 \\  & & Ruby & 0.3886 & 0.3428 & 0.2648 & 0.2787 & 0.4156 & 0.3710 \\  & & ALL & 0.6709 & 0.5542 & 0.4362 & 0.5362 & 0.6255 & 0.4700 \\  & & MPLCS & 0.6800 & 0.5643 & **0.4670** & 0.5295 & 0.6434 & **0.5035** \\ \hline \multirow{11}{*}{SuccessRate@1} & & Go & **0.7238** & 0.4730 & 0.3891 & 0.3889 & 0.4965 & 0.3830 \\  & & Java & 0.5862 & **0.5998** & 0.4229 & 0.4757 & 0.5453 & 0.4365 \\  & & Javascript & 0.5154 & 0.4616 & 0.4627 & 0.3925 & 0.5120 & 0.4045 \\  & & Php & 0.5933 & 0.5389 & 0.4434 & **0.5743** & 0.6240 & 0.4650 \\  & & Python & 0.6006 & 0.5144 & 0.4301 & 0.4782 & **0.6915** & 0.4890 \\  & & Ruby & 0.4671 & 0.3840 & 0.3032 & 0.3293 & 0.4794 & 0.4210 \\  & & ALL & 0.6758 & 0.5651 & 0.4461 &
## 2 Materials and Methods
### OFC Generation
(**b**) Path in space \((\Delta,\left|S\right|^{2})\) for the DKS. (**c**) OFC spectrum obtained by the path. (**d**) OFC intensity. generation. We select the path with the highest probability of generating a single pulse based on the obtained histogram. Obtaining DKS in optical microresonators is a complex process requiring a control strategy to achieve a single soliton state. Therefore, we propose the scheme illustrated in Figure 5 to overcome this challenge. In this proposal, we generate an OFC and subsequently employ a splitter to obtain the minimum fraction of the optical wave (1%) to verify the attainment of the DKS state. To validate the DKS state, we filter the signal with a reject band at the central frequency (1.55 um) and measure the power using an optical detector. If the power falls within a specific range, we conclude that we have generated the soliton; otherwise, we must repeat the generation process until we achieve the desired state. As we base our methodology on simulations rather than physical experiments, we use the intracavity power as an analog measure of the power measured by the optical detector. The power spectral density measures the energy accumulated within the MRR, indicating the presence of the DKS. With this strategy, we can validate the methodology without losing generality in the proposal. In our simulations, the ranges of intracavity power for the verification of the generation of the DKS are \(\left[2.5\times 10^{4},4\times 10^{4}\right]\) and \(\left[1\times 10^{5},2.1\times 10^{5}\right]\) a.u., for 50 GHz and 200 GHz MRR, respectively. This ranges should be adjusted experimentally by taking into account all the optical losses from the MRR to the detector and analyzing the soliton steps.
## 4 Parametric Study
### Effects of Changing the EM Field Propagation Direction  
The electric field (Ey) was applied towards the y-axis, while the magnetic field (Hx) was applied towards the x-axis (Figure 6a). The resonances recorded at both axes were 6 GHz and 15 GHz, respectively. If the fields are interchanged with each other, an electric field (Ex) was applied in x-direction and the magnetic field (Hy) in y-direction (Figure 6b); three major resonances were observed at 2.89 GHz, 9.42 GHz, and 15.16 GHz (Figure 7).  
Figure 5: S21 for different substrate materials.  
Figure 6: Simulation setup for changing field propagation, (a) magnetic field applied \(x\)-axis. (b) magnetic field applied \(y\)-axis.
# Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning
## 2 Concept and Principle of the Optical Encoding Model
Additionally, the property of orthogonality between \(LG\) modes allows the resulting intensity pattern to be unique for each data symbol [38].