esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Challenging Open Issues
### Other Open Issues
#### 2.2.1 Network Topology  
There are two main types of network topology, depending on how many hops the data traverses from the source node to the sink node: single-hop and multi-hop topology. A single-hop topology means that every sensor node is directly connected to a sink node, while in the multi-hop topology, the data transmitted from the sensor nodes to the sink node will traverse through one or more intermediate nodes before reaching the sink node. In case of single-hop, no routing protocol is needed, since all sensor nodes are one hop away from the sink node. However, due to the lossy nature of the human body, it is not always possible to have a direct communication between the source node and the sink node. It is more likely that the data will go through intermediate nodes before reaching the sink node. Moreover, [PERSON] _et al_. [7] conducted an experiment to investigate the reliability between the two topologies. The packet delivery ratio parameter is used to measure reliability. It turned out that the multi-hop topology is more reliable than single- hop topology. Based on the reasons above, we can conclude that for WBSNs, multi-hop topology is the best choice.
## 2 Problem Description
### Production-Maintenance Interaction  
The interaction process between production and condition-based maintenance needs to be described. In a production cycle, the system produces various products in sequence. During this production process, degradation information can be recorded continuously, but maintenance decisions are made only at the intervals of set up based on the state of degradation.  
For the sake of clarity, now consider the production in one cycle. The system starts producing each type of product sequentially from the moment \(t_{0}\) (\(t_{0}=0\)). The time period for producing the first product is \((t_{0}^{\prime},t_{1})\) and then it is ready to produce the second product until the \(m-\)th product is produced. \((t_{i},t_{i}^{\prime})\) is the set-up time and \((t_{i}^{\prime},t_{mt}^{i})\)\((i=0,1,\ldots n)\) is the maintenance time. This process is shown in Figure 1. If the magnitude of system degradation does not exceed the PM threshold at \(t_{i}^{\prime}\), then neither PM nor CM is taken. If the degradation exceeds the PM threshold but is lower than the CM threshold, then PM is immediate at \(t_{i}^{\prime}\). PM is imperfect in that the degradation level after PM will be reduced to a level below the PM threshold. Otherwise, if the failure threshold is exceeded, a perfect CM is immediate at \(t_{i}^{\prime}\), which will bring the system back to a brand-new state.
## 2 Materials and Methods  
Multispectral images are a record of reflected or emitted electromagnetic energy. Each material absorbs and reflects the incoming radiation in a characteristic way [46]. Spectral reflectance of lava flows plays an important role in visible (VIS) and near-infrared (NIR) remote sensing [63; 64; 65; 66; 67; 68; 69]. However, there are three main reasons that make it difficult to map an individual lava flow from multispectral satellite data: Vegetation cover, spatial overlapping, and spectral similarity [70; 71; 72]. In particular, in high eruption frequency areas, lava flows often overlap each other; when the overlapping lava flows are erupted within a short period and have similar mineralogical composition and morphology, distinction will be made even more difficult by their similar spectral signatures [72]. Therefore, at the end of an eruption, it is often hard to identify the new emplaced lava flow over the older ones. To overcome this problem, we designed a semi-automatic supervised classifier based on machine learning techniques that simulates the human processes guiding the expert decision [73; 74; 75]. Thus, multispectral satellite data about the observed region before and after the eruption are used as input together with the pre-eruptive digital topography of the imaged area. Automated classification evaluates each pixel in a scene and then groups the pixels with similar values (e.g., digital number, reflectance, radiance) into a specified number of classes. Combining high spatial resolution digital topography with multispectral satellite data would be optimal, as it would allow a more complete extraction of the lava flow fields removing known noise without eliminating known lava flows.
# A Coupling System for Prediction of Physiological Parameters in an Immersed Condition
## 2 Methods
### Subjects' Test
#### 2.2.3 Physiological Measurements  
The study used a swallowable telemetry capsule with a capsule thermometer from e-Celsius Performance Systems (BodyCap, Caen, France; accuracy: \(\pm\)0.2 \({}^{\circ}\)C) with a temperature range of 30 to 40 \({}^{\circ}\)C for continuous monitoring of core body temperature. The system was capable of uploading a set of core temperature data every 60 s after the subject swallowed the capsule. An 18-channel temperature and heat flow density tester (JINT-1, China; temperature accuracy: \(\pm\)0.1 \({}^{\circ}\)C; heat flow accuracy: \(\pm\)0.1 W/m\({}^{2}\)) was used to monitor skin temperatures and heat flow densities on the chest, back, upper arm, arm, thigh, and leg. The four-point modeling method was used to determine the mean skin temperature [27].  
\[MST=0.3T_{chest}+0.3T_{upperarm}+0.2T_{thigh}+0.2T_{leg} \tag{1}\]  
All experiments started at 9:00 a.m. each day to eliminate the effects of different experimental times on individual subjective perception and circadian rhythms. At the beginning of the experiments, subjects first sat in the climate chamber wearing life preservers for 20 min to stabilize their physiological parameters, and then they entered the water tank and kept their bodies immersed in the water from the neck down for 40 min (see Figure 4). Subjects wore LP1 and LP2 for two separate experiments.
# Path Planning Strategy for Implementing a Machine Control System in Grader Operations
## 4 Case Study  
In this study, we selected a section from the construction site of a high-speed national highway in Pyeongtaek, Gyeonggi-do, South Korea, as a case study. This section included a mix of curved and straight segments (Figure 12). The road width is approximately 10 m, with slight variations along the section, and the total length is about 40 m. Of this, the actual working length is roughly 25 m, while the remaining 15 m are designated as a non-working area for the motor grader to change its working line. The motor grader used in the test was a Fiatallis FG85A, with its specifications provided in Figure 13.  
Figure 12a shows the test section, and Figure 12b illustrates how this test section was converted into a 3D target model. In these figures, the road direction is set from left to right. Accordingly, the (x, y) coordinate lists of the upper and lower outer boundary lines were extracted at 2-m intervals and stored as df1 and df2, respectively (Figure 9). The road width was approximately 10 m, with slight variations along the section. The safety line was set to 5 cm, and the repeated rate range was set from 0.5-0.6 (see Table 3). Consequently, the number of lines was five, the repeated rate was 0.581, and the effective width was 1.54 m (see Table 4).  
Figure 11: Direction vector.  
The line-changing method employed was the 'turning after reversing' technique, and paths were generated for a single layer. The results are as follows:  
Figure 13(a) displays the list of coordinates generated by the proposed algorithm in this study. The motor grader followed the X and Y coordinates, with the Target Z representing the blade height. A direction value of 1 indicates forward movement, while \(-1\) indicates reverse. We evaluated the grader's ability to follow accurately the generated path on the construction site. For this evaluation, we developed a navigation system that utilized a tablet PC integrated with GPS. The work path was visually presented to the operator on the tablet PC, which was connected to a GPS device attached to the grader for real-time path following. This setup allowed the operator to view the grader's exact location and path in real time.  
\begin{table}
\begin{tabular}{c c} \hline
**Data** & **Value** \\ \hline Number of lines & 5 \\ Repeated rate & 0.581 \\ Effective width (m) & 1.54 \\ \hline \end{tabular}
\end{table}
Table 4: Output data value.  
Figure 12: Test section and 3D target model representation.  
Figure 13: Grader specifications.
# A Deep Learning Approach for Automatic Hate Speech Detection in the Saudi Twittersphere
## 4 Experiment Setup
### Neural Network Models
#### 4.2.4 CNN + GRU Architecture  
The third experimented architecture is a combination of both CNN and GRU, as illustrated in Figure 3. In this architecture, CNN is used as a feature extractor that passes the \"extracted feature\" to a GRU layer, which treats the generated feature dimension as timesteps. The CNN + GRU architecture contains 6 layers: an input layer (embedding layer), a convolution layer with 100 filters and a kernel size of 4, a max pooling layer, a GRU layer, another max pooling layer, and finally the output layer. The embedding layer starts by mapping the tweets into a 300-dimensional vector space, producing an (\(m\times 300\)) tweets matrix, with \(m\) being the length of longest tweet. This output matrix is then passed to a dropout layer with a rate of 0.2 to avoid the overfitting problem. Then, the output of the dropout layer is fed into the convolution layer, which has 100 filters with kernel sizes of 4. The rectified linear unit function is used for activation. The filters perform convolution on the tweets matrix and generates (\(n\times 100\)) feature maps, where \(n=m-(\textit{kernel size}-1)\). The feature maps are then passed into a dropout layer with a rate of 0.2 and a max pooling layer with a pool size of 4. This produces vector output, which can be considered as \"extracted features\". These extracted features are then passed to the GRU layer with 200 units, followed by a dropout layer with a rate of 0.2. Finally, the output of the dropout layer is then fed into the output layer with sigmoid activation to produce the final predictions.
# Consolidating ICESat-2 Ocean Wave Characteristics with CryoSat-2 during the CRYO2 ICE Campaign
###### Abstract  
Using the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) global high-resolution elevation measurements, it is possible to distinguish individual surface ocean waves. With the vast majority of ocean surveying missions using radar satellites, ICESat-2 observations are an important addition to ocean surveys. ICESat-2 can also provide additional observations not possible with radar. In this paper, we consolidate the ICESat-2 ocean observations by comparing the significant wave height (SWH) with coincident CryoSat-2 radar observations during the CRYO2 ICE campaign from August 2020 to August 2021. We use 136 orbit segments, constrained to the Pacific and Atlantic oceans as well as the Bering Sea, to compare observations to show the level of agreement between these systems. Three models based on ICESat-2 are used in the comparison: the standard ocean data output (ATL12), a method of modeling the individual surface waves using the geolocated photons and, functioning as a baseline, an approach using the standard deviation of the ocean surface. We find the following correlations between the SWHs from the models and the SWHs from CryoSat-2-0.97 for ATL12, 0.95 for the observed waves model, and 0.97 for the standard deviation model. In the same comparison, we find mean differences relative to the observed SWHs for each model, as well as errors, which increase as the SWH increases. The SWH observed from ICESat-2 is found to agree with observations from CryoSat-2, with limitations due to changes in the sea state between the satellite observations. Observing the individual surface waves from ICESat-2 can therefore provide additional observed properties of the sea state that can be used alongside other global observations.  
ocean altimetry; significant wave height (SWH); CRYO2 ICE; ICESat-2; CryoSat-2 2022 14 1300 14601300 16 February 2022
# Prototype of Augmented Reality Technology for Orthodontic Bracket Positioning: An In Vivo Study
###### Abstract
Following advances in information and communications technology, digital applications in dentistry have increased, including three-dimensional (3D) intraoral scanning, cone beam computed tomography, computer-aided design and manufacturing, and the production of surgical guides. Digital dentistry has increased the quality and accuracy of dental treatment. The technique of 3D intraoral scanning can digitalize the intraoral structure, facilitating treatment planning and simulation with the aid of 3D software. To prevent inaccurate bracket positioning and help dentists make appropriate decisions, researchers have proposed the incorporation of customized orthodontics into digital orthodontics. Advances in digital orthodontics allow the use of 3D models in customizing brackets according to the patient's malocclusion type and individual problems. Studies [11; 12; 13] have focused on bracket positioning methods, including positioning methods using orthodontic surgical guides, positioning gauges, and 3D-printed guides [11; 12; 13]. Despite the progress of digital orthodontic techniques in recent years, the use of advanced techniques (e.g., using 3D-printed guides) for bracket positioning can incur heavy costs and thus reduce the generalizability of digital orthodontics. To solve this problem, the present study proposed an AR-assisted bracket navigation system to shift preoperational planning to clinical practice, with the aim of reducing guide production time and cost, enhancing the accuracy of bracket bonding, and achieving customized orthodontics. To facilitate the accurate localization of bracket positioning, we developed an AR-assisted bracket navigation system. This study analyzed the accuracy of bracket placement between the conventional direct bonding technique with Boone gauge and AR-assisted bracket navigation system by the novice and the expert (Figure 1).
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 4 Experimental Results
### Setup
#### 4.1.4 Data Preprocessing  
We apply consistent preprocessing to all datasets.  
First, we apply Least Absolute Shrinkage and Selection Operator (Lasso) feature selection to identify the most relevant attributes, removing redundant or low-impact features. This step reduces input dimensionality, thereby improving training efficiency and stability across all GAN variants.  
Categorical features (e.g., protocol type, service, flag) are one-hot encoded, while continuous numerical features are normalized to the \([0,1]\) range using Min-Max scaling. Rows with missing values are discarded to preserve data integrity.  
To train WGAN, CWGAN, and WGAN-GP, we adopt a two-stage augmentation pipeline. We first apply SMOTE to expand each minority class to the size of the majority class. This SMOTE-resampled dataset is then used to train the respective GAN model. Once training converges, the GAN is used to generate additional synthetic minority-class samples. These generated samples are then combined with the original training data to construct the final dataset used for classification.  
For CWGAN, class labels are additionally used to guide generation, enabling label-consistent augmentation. WGAN-GP follows the same pipeline as WGAN but replaces weight clipping with a gradient penalty for improved training stability.  
For comparison, traditional oversampling methods--SMOTE, ADASYN, and ROS--are applied directly to the original training set.
# Network Attack Classification in IoT Using Support Vector Machines +
## 2 Background  
Support vector machines (SVM) are a class of supervised machine learning algorithms that create a binary classification model out of complex highly non-linear problems [5]. The SVM requires data samples to create a hyperplane, the decision surface, and maximize the margin around it (see Figure 1). It undergoes a training phase in which each data sample, denoted as \(x_{i}\), is assigned to the class it belongs to, denoted as \(y_{i}\), the predicted value. Therefore, the training set is labeled in pairs \((x_{i},y_{i}),i=1,\ldots,l\) where \(x\in\mathbb{R}^{n}\) and \(y\in\{1,-1\}\). The data sample includes the so called features, which are the data parameters that are used to define the activity of the data sample vector. The end result of the SVM training phase is a set of support vectors that create the optimal hyperplane and the weights \(w_{i}\), which correspond to each input feature that is used to predict the value of \(y\). The difference of the SVM from other neural networks is that it uses the optimization of maximizing the margin to reduce the number of weights that are nonzero to just a few. These correspond only to the important features that provide useful information in deciding the hyperplane.  
One important step in the SVM is the kernel function that helps change the data dimensions and in turn determine the shape of the hyperplane. In simple words, the kernel function adds dimensions in the hyperplane so that a clear distinction between the classes can be made. There are various types of kernels available to use, which include the Linear, Polynomial, Gaussian Radial Basis Function (RBF) and Sigmoid. The results of each kernel depend on the nature of the data sample. The Linear kernel is the simplest kernel that has better results when used in linear problems. The polynomial, RBF and Sigmoid kernels  
Figure 1: The optimal hyperplane that separates the positive and negative values. The position of the hyperplane is determined by the training set pairs that are closest, called the support vectors [6].  
attempt to generate support vectors from a combination of the features given. They are best used with non-linear data, but their complexity relies on the number of new features they extract.
## 4 Experiment and Results
### Training Accuracy of the Models  
Figure S1 shows the accuracy of implemented models for eight epochs. Furthermore, a comparison of the optimized models are shown with the existing CNN, ANN, and LSTM models. The training's accuracy of the OCNN starts at 98.12% and ends at 96.88%. For OANN training, accuracy ranges from 97.19% to 98.12%, and the training accuracy of OLSTM starts at 92.50% and ends at 97.81%.  
Compared to the optimized models, the existing CNN, ANN, and LSTM models have much lower performance accuracy overall. For CNN, ANN, and LSTM, the training accuracy ranges from 88.37% to 90.4%, 90.88% to 88.72%, and 82.15% to 91.22%, respectively.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 4 Effect of Metal-Based NPs on Medicinal and Aromatic Plants
### Copper-Based NPs
#### 4.2.3 Genotoxicity  
CdS and CuO NPs induced growth inhibition and caused cytological aberrations in both mitotic and meiotic cells in _Coriandrum sativum_ L, the effect being like that of conventional mutagen, ethyl methanesulfonate, which was used as control [213]. Damaged root plasma membrane and altered genome of hydroponically cultivated coriander plants exposed to higher CuNP concentrations suggested that genotoxic impact of CuNPs was observed as well [207].  
Considering the beneficial impact CuO NPs on the production of gymnemic acid II in _Gymema sylvestre_ (Retz.) R. Br cell suspension, which is 9.0-fold higher than that of the control [136] (Table 1), this method can be recommended for recovery of this anti-sweet compound.
## 5 Simulation Results
### Test System 2  
This system is constituted of ten generators; valve-point effect and transmission loss are neglected here. The total load demand is set to be 2700 MW. Moreover, test system 2 is divided into three areas as shown in Fig. 2. Area 1 consists of four generating units; area 2 is comprised of three generating units; area 3 is  
convergence performance is significantly influenced by the selection the penalty parameter. In this paper, we propose a self-adaptive APP iterative scheme, which allow the penalty parameter increase or decrease according to iterative information. Simulation results illustrate that the proposed SAPP is superior to the traditional APP in terms of stability in convergence.
## 4 Constrained SAC Algorithm
### The Proposed Constrained SAC Algorithm
\[\mathcal{H}\Big{(}\pi\Big{(}a^{(t)}\Big{|}s^{(t)}\Big{)}\Big{)}=-\sum_{a^{(t )}}\pi\Big{(}a^{(t)}\Big{|}s^{(t)}\Big{)}\log\pi\Big{(}a^{(t)}\Big{|}s^{(t)} \Big{)}. \tag{23}\]0  
\[\mathcal{H}\Big{(}\pi\Big{(}a^{(t)}\Big{|}s^{(t)}\Big{)}\Big{)}=-\sum_{a^{(t )}}\pi\Big{(}a^{(t)}\Big{|}s^{(t)}\Big{)}\log\pi\Big{(}a^{(t)}\Big{|}s^{(t)} \Big{)}. \tag{23}\]1  
where \(a^{(t)}=f_{\phi}\Big{(}e_{t};s^{(t)}\Big{)}\) is the reparameterization, which could lower the variance estimation. \(\epsilon_{t}\) is an input noise sampled from Gaussian distribution. The update process of \(\lambda\) could also be realized by neural networks; however, this will slow the learning process and destablize the network. So, we use the gradient ascend method for the update. Moreover, since the learning process is time-consuming, if CSI changes little in the network, it is wasteful to learn a new action that almost equals to the last one. So we introduce an on-off mechanism to avoid unnecessary learning. When the difference between the current state and the last state is small enough, we will adopt the last action instead of learning a new one. With all the information above, we present our constrained SAC in Algorithm 1. ```
1:initialize network parameters: \(\psi,\theta,\phi\)
2: initialize Lagrangian multiplier and its learning rate, temperature parameter and discount factor: \(\lambda,\mu_{\lambda},\mu_{\pi},\rho\)
3: empty replay memory \(\mathcal{D}\), initialize last state \(\bar{s}=0\) and initialize last action \(\bar{u}=0\)
4: copy parameter to target network: \(\bar{\psi}\leftarrow\psi\)
5:for each episode (\(t=1,2,\cdots,T\)) do
6: observe environment \(s^{(t)}\)
7: compute the difference between \(s^{(t)}\) and \(\bar{s}\): \(\Delta=\Big{|}s^{(t)}-\bar{s}\Big{|}\). 8:if\(\Delta\) is greater than the threshold then
9: sample action \(a^{(t)}\) from policy network: \(a^{(t)}\sim\pi_{\phi}\Big{(}\cdot\Big{|}s^{(t)}\Big{)}\)
10: set \(\bar{s}=s^{(t)}\) and \(\bar{u}=a^{(t)}\)
11:else
12: take action \(a^{(t)}=\bar{u}\)
13:endif
14: calculate reward according to Equation (21)
15: update replay memory: \(\mathcal{D}\leftarrow\mathcal{D}\cup\Big{(}s^{(t)},a^{(t)},r,s^{(t+1)},\epsilon ^{(t)}_{k}\Big{)}\)
16:if it is time to update then
17: sample a mini batch from \(\mathcal{D}\). 18: update \(\psi\) via stochastic gradient ascent on (27)
19: update \(\theta\) via stochastic gradient ascent on (29)
20: update \(\phi\) via stochastic gradient ascent on (31)
21: update \(\lambda\) according to (20)
22: update \(\bar{\psi}\) by \(\bar{\psi}\leftarrow\tau\psi+(1-\tau)\bar{\psi}\)
23:endif
24:endfor
```  
**Algorithm 1**Constrained SAC algorithm.