esa-sceva/satcom-chunk-collection huggingface dataset:
# The Security Perspectives of Vehicular Networks: A Taxonomical Analysis of Attacks and Solutions
## 2 Preliminary Understanding of VANETs
### VANET Applications  
An application of VANET specifies where that VANET can be used. These networks are used for various purposes, and these applications can be categorized based on communication taking place between various entities. These entities may be vehicle to vehicle communication, vehicle to RSU communication, or any other [26]. Various applications are classified in four categories.  
* _Safety applications_[27]: Safety applications include all those services which ensure the safety of vehicles and passengers traveling in these vehicles. Collision detection systems, real-time traffic information, and finding congestion-free paths are some of the services in this category.
* _Comfort applications_[28]: These applications are used for entertainment of the drivers and passengers in the vehicles such as audio and video facilities, and even gaming applications. On tolls, payment may be collected electronically, which saves time and fuel of customers and also saves the time of toll collectors. In big cities, parking is a big challenge, but with the help of VANET-based applications, it is easy to identify parking locations using VANET communications.
* _Commercial applications_[29]: Vehicles can download personalized settings of vehicles using the internet. Many companies use VANETs for providing security for rented cabs/vehicles. Commercial advertisements are used by companies to attract customers who are general drivers of vehicles. These advertisements may relate to restaurants, petrol pumps, hotels, etc.
* _Environmental applications_[30]: These applications use many sensors to get information from the environment, which proves beneficial to travelers. Vehicles may get information related to weather, and based on this information, traveling-related decisions are taken. For example, applications may suggest not using a path which may have snowfall, rainfall, or storm-like condition.
# A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis
###### Abstract  
Although some traditional autoencoders and their extensions have been widely used in the research of intelligent fault diagnosis of rotating parts, their feature extraction capabilities are limited without label information. In response to this problem, this research proposes a hierarchical sparse discriminant autoencoder (HSDAE) method for fault diagnosis of rotating components, which is a new semi-supervised autoencoder structure. By considering the sparsity of autoencoders, a hierarchical sparsity strategy was proposed to improve the stacked sparsity autoencoders, and the particle swarm optimization algorithm was used to obtain the optimal sparsity parameters to improve network performance. In order to enhance the classification of the autoencoder, a class aggregation and class separability strategy was used, which is an additional discriminative distance that was added as a penalty term in the loss function to enhance the feature extraction ability of the network. Finally, the reliability of the proposed method was verified on the bearing data set of Case Western Reserve University and the bearing data set of the laboratory test platform. The results of comparison with other methods show that the HSDAE method can enhance the feature extraction ability of the network and has reliability and stability for different data sets.  
hierarchical sparse strategy; class aggregation and class separability strategy; intelligent fault diagnosis; auto encoder; particle swarm optimization
# Expansion Lemma--Variations and Applications to Polynomial-Time Preprocessing
## 3 Crown Decomposition and Applications  
In this section, we define a related technique called \"crown decomposition\" and its application to some problems. In Sections 3.1-3.5, we discuss how crown decomposition techniques are used to obtain kernels for Vertex Cover, \((n-k)\)-coloring, maximum SATISFIABILITY, \((n-k)\)-List coloring, and longest cycle problems, respectively. After that, in Section 3.6, we discuss a generalization of the crown decomposition technique recently introduced by Chen et al. [19].  
In the context of the vertex cover problem, a well-known reduction rule to discard the pendant (degree one) vertices is to pick their neighbors (and delete them and their neighbors) in the solution [1].  
A crown introduced by Fellows [20] generalizes the structure's underlying pendant vertices.  
**Definition 4** (Crown decomposition).: _Given a graph \(G=(V,E)\), a crown decomposition of \(G\) is a partition of \(V(G)=C\uplus H\uplus R\) such that_  
* \(C\
eq\emptyset\)_;_
* \(C\) _is an independent set;_
* _There is no edge between a vertex of_ \(C\) _and a vertex of_ \(R\)_, i.e.,_ \(H\) _separates_ \(C\) _from_ \(R\)_; and_
* _There exists a matching of size_ \(|H|\) _among the edges_ \(E\cap(H\times C)\)_._  
Set \(C\) can be seen as a crown placed on the head \(H\) of the remaining part (body) \(R\). If \(G\) has a pendant vertex \(x\) with its (unique) neighbor \(y\), then \(C=\{x\}\), \(H=\{y\}\) and \(R=V\setminus\{x,y\}\) constitute the crown decomposition.  
**Lemma 1** (Crown Lemma-1, [5; 20]).: _Let \(G\) be a graph without isolated vertices with at least \(3k+1\) vertices. Then there is a polynomial-time algorithm that either_  
* _Finds a matching of size_ \(k+1\) _in_ \(G\)_; or_
* _Finds a crown decomposition of_ \(G\)Fellows [20] applied Lemma 1 for kernelization in the following way. First, they observe that if there is a matching of size \(k+1\), then the instance is a trivial yes or no instance for some problems. Moreover, in the other case, the head of the crown can be argued to be in some optimum solution (as it hits a large number of obstructions) and, hence, can be deleted, resulting in a reduction rule.  
The following variation of Lemma 1 can be useful sometimes. This lemma also serves as a precursor to the more general expansion lemma described in the next section.  
**Lemma 2** (Crown Lemma-2, [5; 11; 21]).: _Let \(G=(V=A\uplus B,E)\) be a bipartite graph with no isolated vertices in \(B\), and \(|B|\geq|A|\). Then there is a polynomial time algorithm that either_  
* _Finds a matching saturating_ \(A\)_; or_
* _Finds a crown decomposition_ \((C,H,R)\) _of_ \(G\)_, with_ \(C\subseteq A\) _and_ \(H\subseteq B\)_._  
If there is no matching saturating \(A\), then by Hall's theorem (Proposition 1), there exists a minimal hall set \(C\subseteq A\), such that \(|N(C)|<|C|\). A minimal hall set gives rise to a crown decomposition; see [11] for proof.  
We exemplify these lemmas using some examples.
# Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network
## 2 Related Work
### GRU Based Models  
[PERSON] et al. [40] uses GRU based models to do the stock prediction. The model consists of 1 LSTM layer, 1 dropout layer, 1 GRU layer, 1 another dropout layer, and finally followed by a dense layer. In the experiment, S& P 500 historical time series data is used to train and test the model. It is shown that this model out performances former state-of-art methods including RNN, CNN and Ensemble methods. [PERSON] et al. [23] uses LSTM and GRU to do the stock market forecasting. The model consists of 3 data transformation process, which pre-process the stock and news data, and then concatenate into a time series sequence generator. After the transformation, the output will be put into an LSTM or GRU layer to get the final prediction. In the NEPSE dataset in the experiment, it is showed that news data helps with the prediction and GRU outperforms LSTM. [PERSON] et al. [21] intends to combine GRU with LASSO or PCA to predict the price trend. The model will first use LASSO or PCA to do the dimension reduction and then apply LSTM or GRU layer to get the final prediction. In the experiment, the data of the Shanghai Composite Index with a total of 3481 days are used to do the training and test. It is shown that GRU using LASSO dimension reduction is better. [PERSON] at al [41] uses GRU with a Tree Regularization technique(GRU-Tree) to forecast the price trend. In this method, a GRU based neural network is converted to a decision tree. In the experiment, it is shown that GRU-Tree is with a higher AUC both than the decision tree and GRU network under a dataset of SSE Composite Index in China. [PERSON] et al. [42] proposes a regularized GRU-LSTM neural network to do short-term closing price prediction. The model consists of 1 \(L_{2}\) regularized GRU layer, 1 another GRU layer, 1 LSTM layer and finally 1 dense layer. In the experiment, 2 stock prices with a time range of 10 years in China are used. It is shown that the hybrid model performs better that the existing GRU and LSTM network.
## 3 Results
### GC-Ms Analysis of VOCs Produced by S. arghel in EtOAc and MeOH Extractions
The GC-MS analysis of MeoH extract predominantly revealed the presence of the main constituents as Hentriacontane; Sulfutrous acid-hexyl octyl ester (11.34% each); \begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **VOCs Compounds** & **Formula** & **RT** & **MW** & **Area\%** \\ \hline
19 & Triacontane & C\({}_{30}\)H\({}_{62}\) & 9.587 & 422 & 2.645 \\ \hline
20 & Heptadecane, 9-octyl- & C\({}_{25}\)H\({}_{52}\) & 9.685 & 352 & 2.395 \\ \hline
21 & Hentriacontane & C\({}_{31}\)H\({}_{64}\) & 11.378 & 436 & 11.344 \\ \hline
22 & Sulfutrous acid, hexyl octyl ester & C\({}_{14}\)H\({}_{30}\)O\({}_{3}\)S & 11.378 & 278 & 11.344 \\ \hline
23 & Heptacosane & C\({}_{27}\)H\({}_{56}\) & 11.487 & 380 & 2.169 \\ \hline
24 & Octadecane & C\({}_{18}\)H\({}_{38}\) & 11.939 & 254 & 8.357 \\ \hline
25 & Sulfutrous acid, butyl heptadecyl ester & C\({}_{21}\)H\({}_{44}\)O\({}_{3}\)S & 12.271 & 376 & 2.020 \\ \hline
26 & 7,9-Di-tert-butyl-1-oxaspiro (4,5)deca-6,9 diene-2,8-dione & C\({}_{17}\)H\({}_{24}\)O\({}_{3}\) & 14.571 & 276 & 5.491 \\ \hline
27 & 1,3-Pentadiene, 1,1-diphenyl-, (Z) & C\({}_{17}\)H\({}_{16}\) & 14.571 & 220 & 5.491 \\ \hline
28 & Dibutyl phthalate & C\({}_{16}\)H\({}_{2}\)O\({}_{4}\) & 15.241 & 278 & 10.14 \\ \hline
29 & Heneicosane, 3-methyl- & C\({}_{22}\)H\({}_{46}\) & 19.092 & 310 & 2.024 \\ \hline
30 & 2-methyloctacosane & C\({}_{29}\)H\({}_{60}\) & 19.092 & 408 & 2.024 \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 1: Gas chromatography-mass spectrometry (GC-MS) analysis charts of released volatile organic compounds emitted from _S. arghel_ ethyl acetate (**a**) and methanol (**b**) extracts. Heneicosane; Heptadecane; Octadecane-1-iodo- (10.31% each) and Dibutyl phthalate (10.14%). While low VOCs percentage was recorded with Decane, 3,6-dimethyl-; 3-Ethyl-3-methylheptane (1.81% each); Octadecane, 2-methyl- (1.85%) and Dodecane; Tetradecane; 1-Iodo-2-methylnonate (1.88% each) (Table 2).
# Evacuation Management System for Major Disasters
## 2 Mathematical Model
### Evacuation Time Estimation
\[u=u_{c}-\frac{\big{(}u_{c}-u_{j}\big{)}\cdot\ln\Big{(}\frac{\min(k,k_{max})}{k _{c}}\Big{)}}{\ln\Big{(}\frac{k_{j}}{k_{c}}\Big{)}}\]  
Bearing in mind this relationship, two important constraints have to be addressed. First, the lane-changing problem in our particular case is solved using a random factor  
Figure 4: Vehicular model flow chart. to increase or decrease the vehicle's current speed, allowing passing between vehicles when the road conditions are favourable. Second, the gap acceptance is another factor to restrict the number of vehicles in a stretch by solving the intersection problems when the road capacity is exceeded. This problem was solved by considering a queue of vehicles with time priority until it exceeds the road capacity moment in which the current status of other vehicles in the queue is preserved, thereby emulating a traffic jam. Although this approach is quite simple, it allows the fundamental interaction dynamics to be captured when managing an evacuation in the vehicular phase, and can be used in a stochastic way by performing multiple iterations that contemplate many of the possible scenarios within the evacuation process. At the same time, the advantage is that its simplicity means that it does not require a large computational load, which allows simulations to be carried out in real time.
###### Abstract  
Essential oils are highly volatile, aromatic concentrated extracts from plants with wide applications. In this study, fast, easy-to-use attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) was combined with chemometric techniques to verify essential oils' taxonomy and purity. Principal component analysis (PCA) clustered 30 essential oil samples into three different groups based on plant botanical family and concentration. The first group contained highly concentrated oils from the Asteraceae family, the second group contained highly concentrated oils from the Lamiaceae family, while the last group contained three highly concentrated essential oils from different botanical families and commercial-grade essential oils. Thus, commercial-grade oil samples did not cluster with the corresponding concentrated oil samples despite their similar spectral patterns or botanical family. A loading plot identified infrared (IR) bands that correspond to carbonyl, vinyl, methyl and methylene group vibrations as the most important spectral bands that can be used as marker bands for discrimination between different botanical plant family groups. Hierarchical cluster analysis (HCA) confirmed the results obtained by PCA. ATR-FTIR spectroscopy combined with chemometric algorithms provides a direct and non-destructive method for chemotaxonomic classification of medicinal and aromatic essential oils and an assessment of their purity.  
Keywords:essential oils; ATR-FTIR; principal component analysis; chemometrics; hierarchical cluster analysis; quality evaluation +
Footnote †: journal: Applied Sciences
## 4 Codeword-Independent Localization
### Codebook Calculation  
In Section 3.3, we discussed that for the DANN model's training, except for fingerprints and corresponding locations, domain labels, i.e., the codewords \(\psi^{*}\), are also required in the training dataset. Hence, we need to generate all possible RIS codewords (codebook) for experiments given \(C=C_{l}^{2}\) (resolution counted in degrees) and the phase shift range \(R\) for RSSI calculation and domain adversarial learning. We applied the method in [73]. Suppose the RIS contains \(M=M_{H}\times M_{V}\) elements. Then, for the \(m_{H}\)-\(th\) (\(m_{H}\in[1,M_{H}]\)) column of the discrete Fourier transform (DFT)-based codebook [77], \(C_{H}\in\mathbb{C}^{M_{H}\times C_{l}}\) for the horizontal dimension is  
\[\frac{1}{\sqrt{M_{H}}}[1,e^{-i\frac{m_{H}}{M_{H}}R\cdot 1},e^{-i\frac{m_{H}}{M_{H} }R\cdot 2},\ldots,e^{-i\frac{m_{H}}{M_{H}}R\cdot(M_{H}-1)}]^{T}.\]  
Then, we can use it a similar way to calculate the DFT-based codebook \(\mathcal{C}_{V}\in\mathbb{C}^{M_{V}\times C_{l}}\) for the vertical dimension. Finally, the codebook \(\mathcal{C}=\sqrt{M_{H}M_{V}}\mathcal{C}_{H}\bigotimes\mathcal{C}_{V}\), where \(\mathcal{C}\in\mathbb{C}^{M\times C}\) and \(\bigotimes\) denotes the Kronecker product [78]. Each column in \(\mathcal{C}\) is a legitimate codeword.  
Codewords have a great impact on fingerprints. As shown in Figure 8, the RSSI values are different at the same locations when using different RIS codewords, which means as long as the fingerprint dataset for offline training does not contain RSSI measurements corresponding to all possible codewords, the fingerprints for online inference will have a different distribution from the training dataset. We provide our solution to this induced DG problem in this section.
## 4 Image Classification-Based Rail Defect Detection
### Classification Results  
The training process is completed via MATLAB R2022a. The minimum batch size for the training is 16 images, with the validation frequency set to be every 30 rounds of updating. The learning rate is initially 0.01, with a fixed dropping rate of 10% for each epoch. Considering the number of images for this classification is not large, the stochastic gradient descent with momentum (SGDM) optimizer is utilized to update the parameters for each layer. The stopping criterion for the training is the validation accuracy staying the same for 4 epochs. The training set is composed of a total of 900 images, with 300 images for each class, respectively. As for the validation, 300 images are randomly selected with 100 images for each class ('Intact', 'Cracked' and 'Bump'). For this experiment, the training stopped at iteration 336 (Figure 13a).  
From the training record shown in Figure 13b it can be concluded that the network performs well on the trainset, with a training error of only 0.1%. In contrast, the validation error is slightly higher, but still admissible, at 2.0%. To further demonstrate the classification results, classic measuring indices in the statistical analysis of multi-class machine learning tasks are introduced:  
\[\begin{split} precision=\frac{tp}{tp+fp}\\ recall=\frac{tp}{tp+tn}\\ accuracy=\frac{tp+tn}{tp+tn+fp+fn}\\ F_{1}=2\frac{precision\text{-}recall}{precision+recall}\end{split} \tag{5}\]  
where \(tp\), \(tn\), \(fp\) and \(fn\) are shorts for 'true positive', 'true negative', 'false positive' and 'false negative' predictions of a classifier.  
The drawn confusion matrix indicates that only a few samples in the validation set are misclassified. The precision and recall values are more than 97.0% and the overall accuracy is 98.0%. Additionally, the convergence speed of the network is also exceptionally fast, proving that the proposed approach is highly efficient and applicable. The \(F_{1}\) score in this experiment for each class is 100.0%, 97.0%, 97.0%. It is also worth mentioning that the CNN has 100.0% accuracy in identifying the damaged conditions from the healthy ones, which is of great significance in real engineering scenarios.
# Smart Inverters for Microgrid Applications: A Review
## 2 Smart Inverters
### Adaptability
#### 2.3.2 Islanding Detection  
Unintended islanding occurrence is inevitable but must be detected as fast as possible so the distributed system can be decoupled from the grid immediately. Otherwise, it can cause harm to line workers in the main grid or deviate the voltage and frequency from the standard level. Therefore, one of the characteristics of a smart inverter is being equipped with islanding detection methods. These methods are primarily divided into remote and local detection techniques.  
Remote methods are based on the detection on the consumer side and require communication between the utility and the inverter. These methods are out of the scope of this study [82; 83; 84]. Local islanding detection methods, as their name indicates, perform the detection based on local measurements. They are divided into two categories, passive and active methods. Passive methods monitor the voltage variation, frequency deviation, rate of change of power, rate of change of frequency or other trends of the system. When islanding happens these parameters will vary and if this variation exceeds a defined threshold, then islanding is detected [84]. However, if the loading of the DG does not change significantly after an islanding situation takes place, then it cannot be detected by monitoring fundamental parameters such as voltage change or frequency deviation so utilizing other parameters such as voltage unbalance or total harmonic distortion (THD) has been proposed [85]. In [86] another method which utilizes the rate of change of frequency over power has been introduced.  
Active methods are based on the perturbation and later monitoring of the response of system. If unintended islanding occurs, the changes of parameters due to these perturbations will be considerable [82]. One variant of these methods is the impedance measurement method. Here, a harmonic current is injected and by monitoring the voltage response, the grid impedance can be measured. If this impedance changes for more than 0.5 \(\Omega\) it can be an indication of grid failure and the DG must be decoupled. This is a fast and cheap anti-islanding method, and the problem of intentionally adding to the system disturbances can be reduced by using the harmonic signal injection at higher frequencies, 400-600 Hz [87; 88; 89].  
Naturally, both passive and active methods have advantages as well as drawbacks. Passive methods are fast and do not introduce harmonics and distortion. However, they are ineffective when the load and generation are closely matched at islanded mode and generally have larger none detection zone (NDZ). By contrast, the active methods are slower, introduce harmonics to the system and, therefore, the power quality is degraded, but at the same time, they present a decent detection possibility even if there is a perfect match between load and generation in islanded mode. In other words, the active methods contain a much smaller NDZ [82; 83; 84]. Figure 4 is an illustration of islanding detecting categories with a brief summary of their advantages and disadvantages.
# Chaotic Communication System with Symmetry-Based Modulation
## 3 Experimental Results
### SCM Transfer Analysis
By choosing different values of the synchronization accuracy threshold \(\Delta x\) and different delta values between the significant and idle synchronization coefficient \(\Delta S\) and between the significant and idle parameter \(\Delta a\), one can obtain the plot shown in Figure 7. The noise in the data appears due to the influence of the initial conditions at the moment of the synchronization beginning. Thus, the scatter of values on each line shows the best and worst cases of synchronization time. Figure 6: The behavior of synchronization errors at the receiver during transmission of message “10101100”.
## 2 Background  
The multi-sensor system (MSS) considered for geo-referencing in this contribution consists of a laser scanner and two GNSS antennas/receivers. As shown in Figure 1a, the laser scanner is the core sensor of the MSS, which rotates about its vertical axis with a constant angular velocity. GNSS receivers are connected to two eccentric GNSS antennas, which are mounted such that the centroid of the antenna reference points (ARPs) coincides with the scanner rotating axis. In addition to these GNSS receivers, it is assumed to have a nearby reference GNSS station with a known position (Figure 1b). During the data acquisition, the MSS makes a complete 360 degree rotation about its vertical axis, collecting both laser and GNSS measurements, which are synchronized through a GNSS receiver event marker.  
The objective of the navigation system is to provide the position (the centroid of ARPs) and the pointing direction (heading) of the laser scanner. In [8], standard real-time kinematic (RTK) positioning [21] is used to estimate individual rotating antenna positions, and then, a constrained nonlinear filtering method, in particular an extended Kalman filter, is used to obtain the above parameters. In this contribution, we use constrained integer least-squares (Section 3) and array-aided positioning (Section 4), enabling improved ambiguity resolution and improved positioning accuracy. In the following sections, we formulate a more general problem, estimating attitude angles and relative position between two platforms with multiple GNSS antennas/receivers, which enables us to demonstrate the potential of array-aided positioning. As shown in the following sections, array-aided positioning utilizes the reliable estimation of rotating frame ambiguities, which are obtained from array processing (MC-LAMBDA), improving the estimation of the relative position of the rotating frame with respect to the given reference station.  
Figure 1: Geo-referencing scenario. (**a**) The MSS formed by a laser scanner (blue) and two eccentrically-mounted GNSS antennas (green) [8]; and (**b**) the reference GNSS station with a known position.