esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principle of Three-Dimensional GPR  
The typical GPR consists of antennas with a transmitter and a receiver, a computer as the control system, and the power supply. In Figure 1, the antennas transmit EM waves penetrating through pavement structure layers. When the EM waves touch the interface of two-layer materials with different dielectric constants, part of the EM waves will reflect back from the interface of the two layers to the receiver antennas. The other EM waves are transmitted across the two-layer interface to the next layer. The GPR software stores the EM waves back and arrival time. The GPR also stores the amplitude of the reflected EM waves. The dielectric constant represents the ability of the material to absorb and release EM waves. The difference of dielectric constants in two adjacent layers affects the amplitude of the EM waves. As debonding occurs in the asphalt concrete layer, the amplitude of the reflected EM waves increases because of different dielectric constants with air, water, and asphalt concrete. The dielectric constant of the asphalt pavement contains information of asphalt concrete integrity, including water contents, chemical composition, and volume fraction of asphalt mixtures. For dry asphalt mixtures, the dielectric constants are between 2 and 4; for the wet asphalt mixtures, the dielectric constant are between 5 and 12.  
The 2D GPR operates a scan of only one single line with a transmitter and a receiver. It is unable to obtain information from different depths of the pavements with a single frequency. The 3D three-dimensional GPR (3D GPR) provides step-frequency EM waves and can solve the contradiction between frequency and the depth. Moreover, the 3D GPR has the ability to operate a scan of multiple lines simultaneously with the antenna.
# Path Loss Measurements of Indoor LTE System for the Internet of Things
## 2 Principles  
In this section we describe the main principles that we adopt in this paper, including the path loss model and the coverage range model.
## 6 Evaluation and Discussion
### Security Analysis
#### 6.1.2 Blockchain Tempering Attack  
In this attack scenario, an adversary \(adv\) attempts to insert fake blocks into the blockchain. Suppose that a legitimate consumer vehicle requests the blockchain so it can start verifying the received \(Data\). It sends an \(Interest\) packet to \(MS\) asking for the blockchain: \(CV\hookrightarrow MS:i(/NDN-BDA/MS/BC/request)\). Since NDN supports in-network caching, a network cache may satisfy the consumer's request, providing the \(adv\) with the ability to fulfill the request with a falsified blockchain. The Consumer Vehicle (\(CV\)) can detect a fake blockchain using the \(Data\)'s certificate by relying on the \(MS\)'s key \(K_{MS}\). The \(CV\) can also detect a manipulated block when it receives \(Data\). For instance, when the \(CV\) receives a \(Data\) packet from the \(PV\) with a particular VID, it searches for the associated certificate in the blockchain. Upon finding it, it verifies the validity of the block by authenticating its hash, checking if it was signed by the \(MS\)'s key \(K_{MS}^{-1}\) or not. If the verification fails, the \(CV\) will consider the blockchain as invalid and then request another version from the \(MS\).
# An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid
## 4 Results and Discussion
### Aggregated and Total Energy Consumption Results
This is because of the fact that the total capacity of the burst load is limited to 4.28 kWh, which is merely 2% of the total regular load. Therefore, this case shows a reduction of 43.64 kWh energy consumption as compared to the first case and a further decrease of 0.33 kWh as compared to the second case. Figure 19: Aggregated energy consumption (case 3). Figure 18: Aggregated energy consumption (case 2). Case four of CHEMA has been designed in order to integrate the effects of users' comfort in the energy cost minimization model. The basic idea is to relax the temperature threshold limits of the regular loads as described in Equation (12). The changing of threshold limits is subject to activation of burst loads. Results of case four are mentioned in Figure 20. This case further reduces the aggregated energy consumption by 4.55 kWh by taking advantage of the regular loads' temperature limit relaxation. Total energy consumption of cases 1 to 4 is 188.486 kWh, 145.173 kWh, 144.84 kWh and 140.29 kWh, respectively. It is clearly seen that there is a visible and reasonable reduction in case 2 to case 4 as compared to case 1 while a little bit more reduction is also caused among cases 2 to 4 by application of different strategies. In summary, case 2 corresponds to 23.11%, case 3 to 24% and case 4 to 25.7% energy cost reduction as compared to case 1. Table 4 shows the total energy consumption results of four cases. Our proposed model consists of six layers and has many unique features including the partial base line load concept. Regarding the energy cost minimization, the effect of the partial base line load is compared with the reference paper [19]. Figure 21 shows the ToU pricing scheme, cost of the reference paper (Cost 1) and the CHEMA cost (Cost 2). The appliances' ratings are taken as the same as the reference paper, and the base line load is assumed to be 1000 W, which is dealt with the Equation (1). \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Case** & **Total Load (KWh/day)** \\ \hline
1 & Unscheduled without PPC & 188.4860 \\
2 & Unscheduled with PPC & 145.1734 \\
3 & Scheduled without PPC & 144.8401 \\
4 & Load Flexibility (UCI) & 140.2925 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Load comparison of different cases. Figure 20: Aggregated energy consumption (case 4). It is clearly seen that, in peak hours, the cost of our proposed model is less than the reference paper.
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 2 Background and Related Work
### Incident Response with NIDS, SIEM, and SOAR
#### 2.1.1 IDSs  
It is critical to detect intrusions to handle incidents efficiently. Detection capabilities can be provided by HIDSs installed on critical systems or NIDSs distributed at various network access points, which issue alerts to security incident management when unauthorized activities are detected on monitored systems. Numerous commercial IDS products are available, and research into new IDS implementations is ongoing. IDS products can be signature-based or anomaly-based. Signature-based detection relies on a database of known malicious patterns, and anomaly-based detection uses a model of normal traffic to detect deviations. An NIDS with response capabilities, e.g., blocking traffic when an intrusion is detected, is typically referred to as an intrusion prevention system [17]. As surveyed in the literature [18], recent research on IDSs has focused on applying the most recent AI and ML methods to detect intrusions [19; 20]. Noteworthy work includes extending IDS schemes to protect network environments, e.g., the Internet of Things [21; 22], cloud environments [23; 24], software-defined networks [25], and automation and control systems [26].
###### Abstract  
Text annotation is an essential element of the natural language processing approaches. The manual annotation process performed by humans has various drawbacks, such as subjectivity, slowness, fatigue, and possibly carelessness. In addition, annotators may annotate ambiguous data. Therefore, we have developed the concept of automated annotation to get the best annotations using several machine-learning approaches. The proposed approach is based on an ensemble algorithm of meta-learners and meta-vectorizer techniques. The approach employs a semi-supervised learning technique for automated annotation to detect hate speech. This involves leveraging various machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (KNN), and Naive Bayes (NB), in conjunction with Word2 Vec and TF-IDF text extraction methods. The annotation process is performed using 13,169 Indonesian YouTube comments data. The proposed model used a Stemming approach using data from Sastrawi and new data of 2245 words. Semi-supervised learning uses 5%, 10%, and 20% of labeled data compared to performing labeling based on 80% of the datasets. In semi-supervised learning, the model learns from the labeled data, which provides explicit information, and the unlabeled data, which offers implicit insights. This hybrid approach enables the model to generalize and make informed predictions even when limited labeled data is available (based on self-learning). Ultimately, this enhances its ability to handle real-world scenarios with scarce annotated information. In addition, the proposed method uses a variety of thresholds for matching words labeled with hate speech ranging from 0.6, 0.7, 0.8, to 0.9. The experiments indicated that the DT-TF-IDF model has the best accuracy value of 97.1% with a scenario of 5%:80%:0.9. However, several other methods have accuracy above 90%, such as SVM (TF-IDF and Word2 Vec) and KNN (Word2 Vec), based on both text extraction methods in several test scenarios.  
hate speech detection; machine learning; sentiment analysis; semi-supervised learning; self-learning; text mining +
Footnote †: journal: Applied Sciences
# Multi-Resolution SPH Simulation of a Laser Powder Bed Fusion Additive Manufacturing Process
## 4 Application: Laser Powder Bed Fusion
### Computational Performance  
To better appreciate the improvement gained by the proposed particle refinement approach, a runtime analysis was performed on 4 models:  
1. Single resolution w/o dynamics particle refinement w/uniform discretization size \(\Delta/2\)
2. Multi resolution w/dynamics particle refinement w/o neighbor-list optimization
3. Multi resolution w/dynamics particle refinement w/neighbor-list optimization
4. Single resolution w/o dynamics particle refinement w/uniform discretization size \(\Delta\)  
The simulation setup in all cases is the same as the reference model taken from [28], corresponding to a laser power of 100 W at the scan speed of 2 m/s using a discretization size of 4 \(\upmu\)m. Elapsed simulation times are recorded for a serial C++14 implementation, run on a single CPU core of Intel(r) Core(tm) i5-4690 at 3.50 GHz. Shown in Figure 15 is a comparison of the runtime associated with each model. The only difference between models 2 and 3 is using the optimized algorithm proposed for constructing the neighbor list of multi-resolution SPH particles (see in Section 2.5). This explains why the number of particles in both cases is the same (yellow graph of the left plot in Figure 15), but their runtime is different (orange vs. yellow graphs on the right plot in Figure 15). As can be seen, using the proposed optimization saves an additional 25% of the computational cost in multi-resolution SPH schemes. Consequently, a 2D LPBF simulation of a 1-mm long track with a reasonable discretization size of 4 \(\upmu\)m can be finished in almost 90 min.  
Figure 14: Laser tracks with different powers at scan speed 2.5 m/s. Snapshots of the temperature distribution shown at \(t=0.13\) ms, when 1/3 of the powder-bed length is melted, and at \(t=1.0\) ms, when the LPBF track is fully solidified. The color-bar limit is set to a maximum of 2173 K for better visualization. The SPH particles are dynamically refined in all simulations, where the coarsest and finest discretization size used is 6 and 3 \(\upmu\)m, respectively.  
On the other hand, both multi-resolution cases (models 2 and 3) could cut the final number of particles down to almost half of the reference case (model 1). The bar-chart comparison shows that the optimized multi-resolution simulation saves 50% of the computational time, thus 2\(\times\) faster in providing the same result. Although not quite similar in terms of the computing platform and hardware, it is still beneficial to point out that the (single-resolution) SPH model of [28] published in 2018 was claimed to take 36 h on a single CPU core.
# Optimal Power Dispatch of an Offshore Wind Farm under Generator Fault
## 4 Optimal Power Dispatch with Fault Accommodation  
According to the previous sections, the objectives of optimal active power dispatch under generator faults can be divided into the following:  
* When a fault occur on the WT, the safety of the WT is the most important.
* The power output of the WF should follow the power demand from the TSO as much as possible.
* Through the reasonable power dispatch strategy, the power losses caused by the wake can be reduced.  
Figure 8: Power loss curve.  
Figure 9: Temperature rise curves under different thermal resistances.  
* The power fluctuation of the WTs due to the power dispatch strategy should be reduced as much as possible.  
In this paper, the optimization process of active power dispatch is actually a process of solving nonlinear constrained multi-objective optimization problems. If the number of WTs is small and the controller's computing capacity is high enough, the enumeration method can find the optimal solution that meets the requirements after calculating each power reference vector. However, this algorithm will take a long time when the number of WTs increases. PSO is an optimization algorithm that has been applied in many engineering applications. Compared with other optimization methods, PSO has the advantages of less adjustable parameters, fast convergence, and easy programming. Therefore, we adopted the PSO algorithm to optimize the power dispatch of the WF.
# IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial
## 2 Several Promising Techniques: IRS, LIS, and Radio Stripes
### Categorizing Recent Studies on Large Intelligent Surfaces
#### 2.2.3 Complexity Analysis of LIS-Aided Communications  
Despite its potential use, the implementation and computational complexity are big challenges in the LIS structure [7]. Moreover, LIS deployment is complex and raises a variety of new research issues [30]. One potential issue is hardware impairments (HWI), which include transmitting errors in analog systems, quantization issues, RF deficiencies, power amplifier nonlinearity, and time and frequency synchronization faults, among other issues. These HWI are common in modern communications systems, but they are more severe in LIS because of the larger contact area (for example, when using LIS in front of long buildings or walls). As a result, in real-world implementations, the HWI may limit the capabilities of LIS [27].
# Multi-Classifier Pipeline for Olive Groves Detection
## 2 Materials and Methods
### Satellite Imagery Classification
#### 2.3.2 Multi-Classifier Pipeline  
The multi-classifier pipeline presented in Figure 3 was built by combining the baseline pipeline with either a binary classification or a unary (one-class) classification. The first classification overclassifies all vegetation types to ensure that all possible vegetation types are captured in one vegetation class, and those pixels are re-classified by the second classifier to separate the vegetation into different classes. The final classification map is obtained by combining the result from the first and second classification processes.  
Figure 2: IOTA2 classification module flow chart [18].  
In this study, two possibilities for the second classifier have been tested and are listed as follows: the Multi-layer Perceptron (MLP) [19] and the One-class Support Vector Machine (OCSVM) [20]. The MLP performs a binary classification using the architecture shown in Figure 4, and it consists of one input layer, two hidden layers and one output layer. The input layer has a number of nodes equal to the number of features corresponding to the five dates, ten bands and six spectral indices that were used. Hence, it made an input layer have 80 nodes. For the two hidden layers, the activation function used is Rectified Linear Unit (ReLU). After the activation function, a Batch Normalization layer (BatchNorm) is added to allow faster model training and allow the use of a larger learning rate. Before the output layer, a Softmax function is set to turn the output values into probabilities such that all nodes in the output class will amount to 1. The output layer consists of nodes equal to the number of classes, which is 2 because the MLP will perform binary classification between Olive and Non-olive classes. For the training phase, the Cross Entropy loss function was used, and the weights and biases of the model were adjusted by the Adam optimizer. OCSVM on the other hand, conducts a unary classification that focuses only on the identification of one class. The kernel used for the OCSVM model was the Radial Basis Function (RBF) kernel. The \(\
u\) parameter, which is the noise tolerance when the model learns to set the boundary for the training data, was set to 0.5, and the \(\gamma\) parameter, which is the width of the Gaussian curve in the RBF kernel, was set to 0.1.  
Figure 4: The architecture of Multi-Layer Perceptron (MLP) in this study.  
Figure 3: The multi-classifier pipeline.  
The multi-classifier pipeline firstly classifies the pixels into Forest, Impervious, and Vegetation classes and then further classifies Vegetation pixels into Olive, and Non-olive classes. Impervious class includes the urban areas, bare land and other non-vegetation objects. Forest class comprises forest areas and dense dark vegetation areas. Vegetation class contains olive groves, trees and other vegetation that is not categorized as Forest. The reference data for the multi-classifier pipeline was also created by interpreting the satellite imagery, analyzing spectral indices, and referencing RPG dataset.
# Development Trend of Electronic Nose Technology in Closed Cabins Gas Detection: A Review
## 3 Concept and Principle of the Electronic Nose
### System Components  
Various electronic nose sensor sections are used for gas detection in enclosed cabins, although each sensor has its advantages and disadvantages, which involve changes in the configuration mechanisms for re-response variables and output data. Over the past few decades, the evolution of electronic noses over the years has centered on three main areas: volatile collection, sensor arrays, and pattern recognition algorithms. (1) Research on volatile collection focuses on increasing the sample concentration before the gas reacts with the sensor to improve sensor response sensitivity. (2) Research on sensor arrays focuses on changing sensor materials and sensor array composition to respond to different gas detection requirements and to improve the response sensitivity. (3) The research on pattern recognition mainly revolves around different learning algorithms to reduce error and improve recognition accuracy.  
Current high-quality electronic noses can be designed with dozens of different organic polymer sensors [52]. Different types of sensors react to different chemicals and produce different changes in current, and dozens of them combine to form a special fingerprint, and the odor is recognized [53]. The electronic nose is a new type of bionic gas inspection equipment that imitates human olfactory senses, and its working process is divided into three steps, odor signal extraction, pre-processing, and recognition function [54]. The main components of the electronic nose system are power supply, air intake/exhaust device, sensor array, signal processing circuit, and computer. The intake/exhaust device is mainly composed of a micro vacuum pump and a three-way valve; the signal processing circuit is mainly a sensor signal conditioning circuit; the computer is mainly used for signal processing, including signal pre-processing and pattern recognition algorithms. The electronic nose system is shown in Figure 3.
# Ocean Surface Topography Altimetry by Large Baseline Cross-Interferometry from Satellite Formation
## 3 Satellite Formation and Interferometry Baseline
### Baseline Variation Cycle  
Under the helix orbit configuration, in consideration of the along-track baseline limitation, the angle between orbit plane, satellite altitude, and orbit inclination is set to be 7.88 m \({}^{\circ}\), 891 km, and 78\({}^{\circ}\), respectively. The orbit center deviates \(\sim\)53 m from the earth center, to maintain a minimum safety distance of 150 m during the orbital cycle, as shown in Figure 6a. The variation cycle of cross-track and along-track baseline length are shown in Figure 6b,c, respectively. The cross-track baseline length reaches its maximum at the equator and decreases as latitude increases. The along-track baseline length reaches its minimum at the equator and increases as latitude increases, and the positive/negative sign stands for the relative position change of two satellites. In order to avoid severe time decorrelation, the suggested interferometry region should be within 44.6\({}^{\circ}\) northern/southern latitude, where the along-track baseline length is shorter than 40 m, and the across-track baseline length ranges between 629-1000 m, as indicated by the blue shaded area in Figure 6b,c. The baseline inclination angle is small and poses a minor influence to OST altimetry within the 44.6\({}^{\circ}\) latitude range.  
Figure 6: _Cont._  
According to Figure 6c, under the helix orbit configuration, OST altimetry near the high-altitude region will suffer from severe time decorrelation. In order to improve the altimetric accuracy, the orbit perigee can be adjusted for proper baseline length near the latitude region of interest.