esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results and Discussion
### User Applications  
The number of connected users that Appdaptivity can handle can limit the possibilities of applying Appdaptivity in the IoT. In this test, we will evaluate the number of user applications that can interact concurrently with Appdaptivity. The tests have measured the response time since user applications send an RD discovered until the information of the behaviours defined (list of resources) is received with multiple user applications concurrently. Therefore, the location discovery time measured above is also included. The CoAP network comprises 8 CoAP end points along with their 4 corresponding resources as presented above. In the same line of the connection with the CoAP network, Appdaptivity also guarantees a secure and private communication with user applications through the fundamental protocol in the Internet transport security: TLS/SSL. In the comparison, the scenarios with a secure and non-secure communication channel have been included. Figure 11 displays the average response time, and the 95% confidence intervals in the aforementioned scenario. The variation between a secure and non-secure channel varies slightly until it remains stable with 80 user applications concurrently. Confidence intervals show a higher difference due to the use of secure communications. We have taken 80 users as the upper bound as an example of workers of a medium-size building. Therefore, in this case it is recommendable to activate the secure channel at all times. Appdaptivity can comfortably handle a large number of user applications. The worst case time may seem a bit high, however it only has to be done once and includes the communication response from all the clients, the discovery of the CoAP network and a CoAP request for each one of the 8 CoAP servers to get their locations.
###### Abstract  
In the rapidly evolving field of cybersecurity, the integration of multi-source, heterogeneous, and fragmented data into a coherent knowledge graph has garnered considerable attention. Such a graph elucidates semantic interconnections, thereby facilitating sophisticated analytical decision support. Central to the construction of a cybersecurity knowledge graph is Named Entity Recognition (NER), a critical technology that converts unstructured text into structured data. The efficacy of NER is pivotal, as it directly influences the integrity of the knowledge graph. The task of NER in cybersecurity, particularly within the Chinese linguistic context, presents distinct challenges. Chinese text lacks explicit space delimiters and features complex contextual dependencies, exacerbating the difficulty in discerning and categorizing named entities. These linguistic characteristics contribute to errors in word segmentation and semantic ambiguities, impeding NER accuracy. This paper introduces a novel NER methodology tailored for the Chinese cybersecurity corpus, termed CSBERT-IDCNN-BiLSTM-CRF. This approach harnesses Iterative Dilated Convolutional Neural Networks (IDCNN) for extracting local features, and Bi-directional Long Short-Term Memory networks (BiLSTM) for contextual understanding. It incorporates CSBERT, a pre-trained model adept at processing few-shot data, to derive input feature representations. The process culminates with Conditional Random Fields (CRF) for precise sequence labeling. To compensate for the scarcity of publicly accessible Chinese cybersecurity datasets, this paper synthesizes a bespoke dataset, authenticated by data from the China National Vulnerability Database, processed via the YEDDA annotation tool. Empirical analysis affirms that the proposed CSBERT-IDCNN-BiLSTM-CRF model surpasses existing Chinese NER frameworks, with an F1-score of 87.30% and a precision rate of 85.89%. This marks a significant advancement in the accurate identification of cybersecurity entities in Chinese text, reflecting the model's robust capability to address the unique challenges presented by the language's structural intricacies.  
Keywords:cybersecurity knowledge graph; Chinese named entity recognition; CSBERT; few-shot data; cybersecurity dataset +
Footnote †: journal: Applied
## 1 Introduction
### Our Contributions  
We model the flying base station in relation to the stationary terrestrial base station and jointly study the mobility and RF communications of a flying base station. We introduce and analyze _the flying base station channel capacity_, which is the capacity or the upper-bound limit of the reliable communication rate that the communication channel can achieve, assuming support from physical-layer signal processing and mobility. The flying base station channel capacity builds on the Shannon channel capacity from the Shannon-Hartley Theorem but, because of the flying base station's mobility capability and purpose, assumes the ideal location and mobility control of the base station. Because the flying base station channel capacity provides the upper bound of the reliable communication rate, it holds true for any digital/signal processing technique and any flying base station position/location (given the locations of the user and the stationary base station). Therefore, we analyze the ideal positioning of the flying base station, which depends on the stationary base station's relative position to the user's. To highlight the dependency on the stationary base station, we express the flying base station's channel capacity with respect to the stationary base station's parameters; the flying base station's capacity depends on the stationary base station's channel and its SNR performance to the user. Furthermore, we analyze the flying base station's capacity gain over the stationary base station in order to analyze the beneficialness of the flying base station; we identify the flying base station's transmission power requirement for it to be beneficial over the stationary base station. We analyze the cases when the flying base station supports a full duplex vs. half duplex, the latter of which reduces the bandwidth by half and, thus, requires greater transmission power for the flying base station to be beneficial.
## 6 Numerical Example: Trajectory Tracking of a Wheeled Mobile Robot
### Kinematic Model of the Mobile Robot  
Let us consider the wheeled mobile robot shown in Figure 1.  
The kinematic model of the robot is given by  
\[\dot{x}=\begin{bmatrix}\dot{x}_{1}\\ \dot{x}_{2}\\ \dot{x}_{3}\end{bmatrix}=\begin{bmatrix}\cos x_{3}&0\\ \sin x_{3}&0\\ 0&1\end{bmatrix}\begin{bmatrix}u_{1}\\ u_{2}\end{bmatrix}:=g(x)u, \tag{81}\]  
where \((x_{1},x_{2})\in\mathbb{R}^{2}\) is the position and \(x_{3}\in\mathbb{R}\) is the orientation. Control inputs \(u_{1},u_{2}\in\mathbb{R}\) are translational and angular velocities, respectively. In the following, we consider the robot model (81) subject to the following input constraint:  
\[\|u\|^{2}=u_{1}^{2}+u_{2}^{2}\leq\beta^{2}, \tag{82}\]  
where \(\beta>0\) is a constant.  
We suppose that a \(C^{2}\) desired trajectory \(x_{d}(t)=[x_{1d}(t),x_{2d}(t),x_{3d}(t)]^{\top}\) and the corresponding \(C^{1}\) reference input \(u_{r}(t)=[u_{1r}(t),u_{2r}(t)]^{\top}\) satisfying  
\[\dot{x}_{1d} =u_{1r}\cos x_{3d}(t)\] \[\dot{x}_{2d} =u_{1r}\sin x_{3d}(t) \tag{83}\] \[\dot{x}_{3d} =u_{2r},\]  
are given. Moreover, we also assume that \(u_{1r}(t)>\gamma,\forall t\in[0,\infty)\) holds for some constant \(\gamma>0\). Then, system (81) can transform the following error system:  
\[\dot{x} =\tilde{f}(t,e)+\tilde{g}(t,e)\tilde{u} \tag{84}\] \[\tilde{f}(t,e) =\begin{bmatrix}u_{1r}[\cos\left(e_{3}+x_{3d}\right)-\cos x_{3d}] \\ u_{1r}[\sin\left(e_{3}+x_{3d}\right)-\sin x_{3d}]\\ 0\end{bmatrix},\ \tilde{g}(t,e)=\begin{bmatrix}\cos\left(e_{3}+x_{3d}\right)&0\\ \sin\left(e_{3}+x_{3d}\right)&0\\ 0&1\end{bmatrix}.\]  
The input constraint for \(\tilde{u}\) is given by  
\[\tilde{u}\in\tilde{U}(t) =\left\{u\in\mathbb{R}^{2}\mid\tilde{G}(t,u)\leq 0\right\}, \tag{85}\] \[\tilde{G}(t,u) =\|\tilde{u}+u_{r}(t)\|^{2}-\beta^{2}=[\tilde{u}_{1}-u_{1r}(t)]^ {2}+[\tilde{u}_{2}-u_{2r}(t)]^{2}-\beta^{2}. \tag{86}\]  
As mentioned in Remark 3, (85) is not a simple norm constraint, but a convex constraint on \(\tilde{u}\). Hence, we can apply the proposed controller design method.
## 3 Proposed Methods
### Probabilistic Fusion  
OD-SPH uses the classification results of OD-PNV when fitting the road plane, and its purpose is to better fit the road plane. The probability that the superpixel output by the OD-PNV and OD-SPH methods belongs to an obstacle is only determined by the height and the plane normal vector of the superpixel, respectively. That is, the probability that a superpixel belongs to an obstacle based on the height feature will not be affected by the plane normal vector feature. There is no direct influence between the probability results output by the two methods. The bayesian framework is suitable for decision inference based on the multi-factor probabilistic methods, which is more in line with the human driver's judgment and decision-making process than the methods of obstacle detection using single information. In order to accurately detect obstacles in complex traffic scenes, this paper proposes the OD-LSF method, which uses the Bayesian framework to fuse the output results of the above two methods to achieve a comprehensive judgment. The probability \(PR_{LSF}\) that the superpixel belongs to an obstacle is calculated by the following formula:  
\[PR_{LSF}=\frac{{{p_{pr}}\cdot{PR_{PNV}}\cdot{PR_{SPH}}}}{{{({p_{pr}}\cdot{PR_{ PNV}}\cdot{PR_{SPH}})+(1-{P_{pr}})(1-{PR_{PNV}})(1-{PR_{SPH}})}}}. \tag{10}\]  
Among them, \(P_{pr}\) represents the prior probability; \(PR_{PNV}\) and \(PR_{SPH}\) represent the probability that the superpixel is judged as belonging to an obstacle by OD-PNV and OD-SPH, respectively. Figure 9 shows the probabilistic distribution of the above image represented by \(PR_{LSF}\) and the classification result of the OD-LSF method (the decision threshold of \(PR_{LSF}\) is set to 0.45). It can be seen that the OD-LSF method comprehensively considers the output results of the OD-PNV and OD-SPH methods. There is sufficient  
Figure 8: The probabilistic distribution of \(PR_{SPH}\) and classification result of OD-SPH method: (**a**) \(PR_{SPH}\) probability distribution; (**b**) OD-SPH method classification result.  
discrimination in areas such as the hood and tires of the vehicle, and the correct detection results are output.
# An Improved Orthogonal Matching Pursuit Algorithm for CS-Based Channel Estimation
###### Abstract  
Wireless broadband transmission channels usually have time-domain-sparse properties, and the reconstruction of these channels using a greedy search-based orthogonal matching pursuit (OMP) algorithm can effectively improve channel estimation performance while decreasing the length of the reference signal. In this research, the improved OMP and SOMP algorithms for compressed-sensing (CS)-based channel estimation are proposed for single-carrier frequency domain equalization (SC-FDE) systems, which, in comparison with conventional algorithms, calculate the path gain after obtaining the path delay and updating the observation matrices. The reliability of the communication system is further enhanced because the channel path gain is calculated using longer observation vectors, which lowers the Cramer-Rao lower bound (CRLB) and results in better channel estimation performance. The developed method can also be applied to time-domain-synchronous OFDM (TDS-OFDM) systems, and it is applicable to the improvement of other matching pursuit algorithms.  
compressed sensing; single-carrier frequency domain equalization; orthogonal matching pursuit; simultaneous orthogonal matching pursuit; Cramer-Rao lower bound 2023 23 9509. [[https://doi.org/10.3390/s23239509](https://doi.org/10.3390/s23239509)]([https://doi.org/10.3390/s23239509](https://doi.org/10.3390/s23239509))
# Sentiment Analysis on Algerian Dialect with Transformers
## 2 Problem Statement and Research Objectives  
Sentiment analysis has long been a formidable challenge, particularly when striving for a profound comprehension of nuanced behaviors [15; 16]. Historically, sentiment analysis models struggled to surpass the 80% accuracy threshold. However, in contemporary times, these models have made remarkable strides, achieving accuracy rates upwards of 95% in tasks such as IMDB review classification [17].  
However, this progress has not extended to sentiment analysis in Arabic, despite its usage by over 400 million people. Most Arabic sentiment analysis models continue to fall short of the 90% accuracy mark, even on relatively simple tasks, as evidenced by survey studies conducted in [18] and detailed in [19]. These studies revealed that only 20% and 30% of Arabic sentiment analysis systems, respectively, have managed to surpass this accuracy threshold. Furthermore, these systems struggle to achieve 88% accuracy when confronted with more complex tasks like mental health analysis, as proven in [20].  
A further challenge is that discerning sentiment and extracting the fundamental message from Arabic content demands substantially greater effort. This complexity is exacerbated by the multitude of dialects within the Arabic language, with variants like the Algerian dialect adding further layers of intricacy to the challenges at hand.  
Based on the presented challenges, this research aims to accomplish the following broad objectives:  
* Creating an extensive and specialized corpus of Algerian Arabic text samples to enhance Arabic language resources and strengthen natural language processing capabilities for this specific dialect.
* Investigate and implement pre-processing techniques that are specifically optimized for the Algerian Arabic dialect, considering the unique linguistic features and nuances of this dialect.
* Emphasize the potential of state-of-the-art deep learning models, like BERT, for natural language processing in this specific linguistic and cultural context.  
* Ensure that the developed model can be applied to infer sentiment from any Algerian text, making it a practical tool for understanding the opinions and emotions of the Algerian population.
## 3 FLARoute: Fair Locality Aware Routing
### FLARoute Design
#### 3.2.3 Phase 3: Estimating the Maximum Burden on Neighbouring Nodes  
Knowledge about the maximum burden in the network allows nodes to make fair routing decisions accordingly. The maximum burden is the burden on the most burden among a node's neighbours. Global knowledge is required to obtain a precise value at any given instant. However, due to the characteristics of OppNets, obtaining precise global knowledge at any given instant is impractical. Updating local knowledge on encounter basis is a possible solution, since a precise estimate in not required. However, this may result in undesirable overhead. To minimize the overhead incurred in acquiring and maintaining global information, the proposed mechanism, namely Maximum Burden Estimation (MaxBE), only requires each node to embed its maximum burdenestimate in summary vectors. [PERSON] et al. [73] show that embedding such information in summary vectors does not incur significant additional control overhead in most cases.  
The neighbour burden estimate, denoted by \(g\), is first initialized to the local instantaneous relative burden on the node. After each time interval, \(\Delta Tg_{i}\), \(g\) is updated if the instantaneous burden on the node is higher. \(g\) is also updated if an encountered node presents a higher estimate. At the end of every time slot \(\Delta Tb_{i}\), \(G_{i}\), the maximum burden estimate, is computed by Equation (3). \(g\) is then reset to 0 for the next time slot, \(\Delta Tb_{i+1}\).  
\[G_{i}=\left\{\begin{array}{c}g,i=1\\ (g+G_{i-1})/2,i>1\end{array}\right. \tag{3}\]  
For updating \(g\), the instantaneous maximum burden estimate is exchanged in summary vectors when two nodes encounter each other. In that case, the instantaneous estimate is also computed with Equation (3) (note that \(g\) is not reset until the current time slot is elapsed). Since nodes estimate the maximum burden from encounters, the estimated value depends on the burden on neighbouring nodes. This is suitable for OppNets because users are satisfied by comparing themselves to their neighbours and acquaintances [19].
## 3 Results and Discussions
### RF Characteristics of the Microstrips under Strain  
To explore the RF characteristics of the microstrips under strain, S-parameters were monitored upon strain. S\({}_{11}\) spectra, which represented the reflection of the RF signals, are shown in Figure 4a. When the strain was lower than 5%, the S\({}_{11}\) values were typically bellow \(-\)16 dB at an RF frequency lower than 8 GHz and below \(-\)10 dB at frequencies from 8 GHz to 18 GHz. This indicated that the RF signals pass through the microstrip with low reflections. With the increase of strain from 5% to 10%, S\({}_{11}\) values gradually increased to approximately \(-\)5 dB at a frequency below 8 GHz, showing a significant increase of the reflections. However, the S\({}_{11}\) values kept stable at higher frequencies, which indicate the strain mainly affects the reflections of RF signals at a lower frequency range; below 8 GHz in our case.  
The S\({}_{21}\) parameter reflecting the transmission characteristics of the microstrips is plotted in Figure 4b. Upon strain, the S\({}_{21}\) values dramatically decrease with an increasing strain. When the strain increased to 5%, the S\({}_{21}\) values showed a slight, approximately 3 dB, decrease. However, when the strain increased from 5% to 10%, a significant drop in S\({}_{21}\) values was observed. The minimum S\({}_{21}\) value even went below \(-\)35 dB for a transmission line upon 10% strain. We noted that upon a certain strain, the S\({}_{21}\) values decreased gradually with the increase of frequency. Also, a larger strain leads to a sharper drop of the S\({}_{21}\) value over the frequency. Overall, the transmission of a microstrip shows inverse correlations with the strain.  
To give an insight into the RF characteristics upon strain, fittings were conducted from 1 GHz to 18 GHz according to the equivalent circuit stretched in Figure 3c and the measured S-parameters plotted in Figure 4. The fitting results are plotted in Figure 5 with  
Figure 4: The measured S-parameters under various strain: (**a**) S\({}_{11}\) spectra; (**b**) S\({}_{21}\) spectra.  
a good match of the data in Figure 4. Silver paste was used for the connection between SMA connectors and the microstrip, which caused impedance discontinuity and led to the differences in fitting and measurement results. The element parameters used for the fittings are listed in Table 2. For convenience, \(\Delta R_{1}\), \(\Delta R_{2}\), \(\Delta R_{3}\), \(\Delta R_{4}\), \(\Delta L_{1}\), \(\Delta L_{2}\) and \(\Delta C_{1}\) were induced to mark the changes of \(R_{1}\), \(R_{2}\), \(R_{3}\), \(R_{4}\), \(L_{1}\), \(L_{2}\) and \(C_{1}\) respectively, when the microstrip was stretched.
# Gaussian Distribution Model for Detecting Dangerous Operating Conditions in Industrial Fish Farming
## 2 Materials and Methods
### Gaussian Distribution Model Conceptualization
#### 2.2.2 Principal Component Analysis Application  
The principal component analysis (PCA) calculates new variables denoted as principal components which are obtained as linear combinations of the original variables. The first principal component is required to have the largest possible variance of the data. The second component is computed under the constraint of being orthogonal to the first component and to have the largest possible variance. The other components are computed likewise. The values of these new variables for the observations are called factor scores that are interpreted geometrically as the projections of the observations onto the principal components [32,33].In this research, to find the principal components of a \(m\times n\) data matrix \(X\), we employ the singular value decomposition (SVD) algorithm [34].
# An Evaluation Framework and Algorithms for Train Rescheduling
## 3 Related Work
They perform experiments on a Dutch railway network with mixed traffic and multiple delayed trains, using rescheduling time windows of 30 min and 1 h.  
In recent times, researchers have surveyed and discussed the different objectives and quality indicators for railway rescheduling in various contexts, e.g., [PERSON] et al. [16], [PERSON] [20], [PERSON] et al. [21], [PERSON] et al. [22]. While solving the train rescheduling problem, there is no general agreement in the literature on the objective function(s) to be adopted [16]. Similarly, often, there is no shared meaning for many quality indicators [21]. One example is the passenger inconvenience caused due to a rescheduled timetable, for which the literature adopts a wide range of definitions [23]. Based on the review of related work, some of the observed weaknesses and challenges are addressed in this paper by presenting: (i) a framework to evaluate and compare train rescheduling algorithms while using multiple quality indicators and a (ii) a proof-of-concept of the framework by comparing two multi-objective rescheduling algorithms. The two algorithms are extended versions of existing train rescheduling algorithms. The main contributions of the research presented in this paper are: (i) an evaluation framework for train rescheduling algorithms and a demonstration of its applicability and (ii) a systematic evaluation of the rescheduling solutions resulting from the two algorithms for realistic input data.
## 2 Data Description and Data Preprocessing
### Inversed Probability Density Function (IPDF)  
[PERSON] et al. [19] introduced the relative frequency distribution histogram (RFDH) metric to characterize anomalies in acceleration data. This feature utilizes the symmetric distribution characteristic of acceleration data around the zero-amplitude point and incorporates various statistical features, such as kurtosis and skewness. However, it is important to note that its identification performance is suboptimal when dealing with outlier patterns.  
In this paper, the same feature is employed with only slight modifications. Firstly, assuming the distribution range of acceleration amplitudes is within \(\pm\)50 mg, this range is divided into 1025 small intervals. The number of samples in each interval is counted, and the values are normalized by the maximum count. Subsequently, 1 is subtracted from the values of non-zero intervals, excluding the maximum value from this calculation. This process culminates in the formation of the IPDF feature.  
Figure 3 depicts the IPDF features of various data patterns. It is evident that the normal data type exhibits a single-peaked normal distribution centered around 0. The minor pattern's amplitudes concentrate in the vicinity of 0. The outlier type, due to the substantial disturbances from anomalies with large amplitudes, results in a broader amplitude distribution. However, the distribution near 0 still demonstrates the characteristics of a single-peaked normal distribution. The biased category exhibits asymmetric distribution in the IPDF metric due to the presence of abnormally low acceleration amplitudes in the negative range. The noise class manifests as a bimodal distribution in the amplitude domain.