esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Economic Analysis for the Proposed Method
### Economic Benefit Analysis
Figure 12: Exceedance probabilities of economic loss under different earthquake intensities: (**a**) 3D original frame, frequent earthquake; (**b**) 3D retrofitted frame, frequent earthquake; (**c**) 3D original frame, occasional earthquake; (**d**) 3D retrofitted frame, occasional earthquake; (**e**) 3D original frame, rare earthquake (frame is collapse in this case); (**f**) 3D retrofitted frame, rare earthquake. Figure 14a-f compares the repair times between the original frame and retrofitted frame. These figures show that the probabilities are smaller than the specific repair time levels (using the number of workdays as an index). Figure 15 shows the median repair times, which are the values corresponding to a probability of 50% in Figure 14. Table 11 shows the total median repair times of the original frame and the retrofitted frame under a frequent earthquake, occasional earthquake and rare earthquake. The observations are that (1) There are no obvious differences when the two frames are hit by a frequent earthquake, in both cases the repair times are within one week. (2) The repair time for the retrofitted frame is shorter than that of the original frame under an occasional earthquake, requiring 7 workdays for the retrofitted frame and 34 workdays for the original frame. (3) The repair time for the retrofitted frame is obviously shorter than that of the original frame under a rare earthquake, where 53 workdays are required for the retrofitted frame and 402 workdays for the original frame. In this case, the original frame collapses and needs reconstruction. Similar to the calculation of repair cost, demolition and site clearance will increase the structural repair time by up to 20-30%; therefore, the total repair time under rare earthquakes increases by 20%, i.e., 402 \(\times\) 120% = 482 workdays. Therefore, the ratio between the repair time of retrofitting and the repair time of complete replacement is about 10%. Note that the shorter the repair time to a building, the less the impact of environmental problems. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Frame**} & **Frequent Earthquake** & **Occasional Earthquake** & **Rare Earthquake** \\  & **PGA = 0.07 g** & **PGA = 0.2 g** & **PGA = 0.4 g** \\ \hline
3D original frame & 5 & 34 & 482 \\
3D retrofitted frame & 2 & 7 & 53 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Median repair times of the original frame and retrofitted frame (unit: workday). Figure 13: Median values of repair costs under different earthquake intensities: (**a**) Frequent earthquake; (**b**) Occasional earthquake; (**c**) Rare earthquake. Figure 14: Exceedance probabilities of repair time under different earthquake intensities (Parallel construction): (**a**) 3D original frame, frequent earthquake; (**b**) 3D retrofitted frame, frequent earthquake; (**c**) 3D original frame, occasional earthquake; (**d**) 3D retrofitted frame, occasional earthquake; (**e**) 3D original frame, rare earthquake; (**f**) 3D retrofitted frame, rare earthquake. Figure 15: _Cont._
# Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
###### Abstract
In other words, the system health management has to handle unlabeled and unbalanced datasets and operate in an evolving environment. These two aspects are strictly correlated and can be addressed by integrating offline and batch processing with online and streaming models to recognize both known conditions (fault diagnosis) and detect unknown behaviors (novelty detection) [18]. Offline batch processing aims to uncover valuable information from accumulated data for fault prediction and is suitable for stored data, as it mainly focuses on the accuracy and comprehensiveness of analysis instead of focusing on real-time fault detection and diagnosis. On the other hand, real-time streaming processing is needed in variable and complex operating conditions to realize a real-time diagnosis and ensure timely maintenance. In a previous work [19], the authors proposed a novel framework for implementing PdM. In particular, the framework consists of an edge layer, in which raw signals are collected and processed to extract relevant information at a component and system level (i.e., feature extraction); in addition, novelty detection algorithms are also applied to streaming data to discover new system behaviors and component faults. This information is finally collected in the cloud, where supervised ML models are built on historical data and updated based on newly collected data. This paper is an extension of [19], in which the proposed framework is validated on a dataset collected from a test rig built in the Department of Industrial Engineering of the University of Bologna, Italy, and on four real industrial datasets, which differ for the components under analysis, the kind of collected signals, the quantity of the data, and the number of health and fault conditions. The remainder of the paper is organized as follows. Section 2 describes the research questions and the main contributions of this paper. Section 3 provides an overview of the existing literature on fault detection and diagnosis using incremental learning for streaming data. Section 4 describes the available datasets and the experimental scenario. Section 5 shows the results obtained by applying a clustering-based approach for novelty detection. Finally, Section 6 concludes the work by discussing the implications from the industrial point of view and providing future research directions.
# Integrated Components and Solutions for High-Speed Short-Reach Data Transmission
## 4 Some Integrated Solutions for Short-Reach Transmission Systems
### Transceiver Integration Scheme Based on Direct Detection
Next, receiver performance of the transceiver was verified though a discrete transmitter including a LiNbO\({}_{3}\) MZM modulator and a laser. The optical eye diagrams were detected by all receiver channels of the transceiver as depicted in Figure 13a. Just like the transmitter side, when all receiver channels were operated, the sensitivity of the test channel can be found to be slightly degraded, and the 1.4-dB penalty can be observed in Figure 13b. Figure 12: (**a**) Optical eye diagrams of all transmitter channels and (**b**) the sensitivity changes of ch_4 at multi-channel transmitters operations; Ch_1: the first channel; TX: transmitter; Rx: receiver; (From [28] © 2021 IEEE). Figure 13: (**a**) Optical eye diagrams of all receiver channels and (**b**) BER performance under the first channel; TX: transmitter; Rx: receiver; (From [28] © 2021 IEEE).
## 2 Experimental Section
### Exposure Methods  
The experiments included a general monitoring of the effect of toxic substances on selected physiological parameters of both male and female Wistar rats during the entire experiment,and a reproduction monitoring of reprotoxicity. For the experiments, two heavy metals, cadmium (Cd) and mercury (Hg), were selected. The experiments included 48 naive young rats (24 females and 24 males), 4 weeks old, of mean body weight 120 \(\pm\) 19 g. The rats were housed in cages in same sex pairs, with ad libitum access to water and food (standard Larsen diet), at 22 \(\pm\) 2 \({}^{\circ}\)C, 50% humidity with 12 h light and 12 h dark. Heavy metals were administered to Wistar rats in drinking water at concentrations of 0.5 mg\(\cdot\)L\({}^{-1}\) for Hg and 2.5 mg\(\cdot\)L\({}^{-1}\) for Cd, which are 500 times higher than MPC in drinking water, 0.001 and 0.005 mg\(\cdot\)L\({}^{-1}\) for Hg and Cd, respectively, given by the Decree of the Ministry of Health of the Slovak Republic No. 247/2017 Coll. The water used for Hg and Cd salts was tap water and the concentration of Hg and Cd was verified by Atomic adsorption spectroscopy.  
The control (K) group--16 rats (8 males and 8 females)--received untreated drinking water, the Cd treated group--16 rats (8 males and 8 females)--received cadmium chloride (CdCl\({}_{2}\) 2 H\({}_{2}\)O) at a concentration of 4.88 mg\(\cdot\)L\({}^{-1}\) (i.e., 2.5 mg Cd\(\cdot\)L\({}^{-1}\)) in water and the Hg treated group--16 rats (8 males and 8 females) received water containing mercuric chloride (HgCl\({}_{2}\)) at a concentration of 0.68 mg\(\cdot\)L\({}^{-1}\) (i.e., 0.5 mg Hg\(\cdot\)L\({}^{-1}\)). In all groups, the following parameters were monitored and evaluated:  
* Animal survival
* Weight changes
* Food intake
* Water intake  
The reproductive experiment also included 48 animals which were kept in pairs (1 male and 1 female). By random selection, they were divided into the K group and treated groups, Cd and Hg, as described above. The born pups were left with their mothers until weaning and the males were isolated from females with pups for that period. During the experiment, the following reproductive parameters were monitored and evaluated:  
* Number of births (litters), number of born pups, and number of raised pups (live pups assessed on the 21 st day after delivery);
* Number of born pups per litter and number of raised pups per litter In the toxicological experiment, the following parameters were monitored and evaluated:
* Average daily dose (ADD);
* Total dose received in 52 weeks in mg\(\cdot\)kg\({}^{-1}\) of live weight of a rat;
* The amount of toxic metal received in relation to the LD\({}_{50}\) dose.  
ADD and LD\({}_{50}\) were counted based on the standards and in accordance with the Methodological instruction of Ministry of the Environment of the Slovak Republic on preparation of risk analysis of polluted areas.
# A Quantum-Secure Cryptographic Algorithm Integrating Fractals and Prime Numbers
## 2 Construction of the Fractal Sub-Key
### Cantor Set  
As usual, the construction of the Cantor set has the following procedure: We start from the \([0,1]\) interval, we divide it into three equal parts, and we delete the open middle third \(\left(\frac{1}{3},\;\frac{2}{3}\right)\). In this way, we obtain the set  
\[C_{1}=\left[0,\;\frac{1}{3}\right]\;U\;\left[\frac{2}{3},\;1\right]\]  
If we repeat the same procedure for these new two intervals, we obtain  
\[C_{2}=\left[0,\;\frac{1}{9}\right]\;U\;\left[\frac{2}{9^{\prime}},\;\frac{1}{ 3}\right]\;U\;\left[\frac{2}{3},\;\frac{7}{9}\right]\;U\;\left[\frac{8}{9^{ \prime}},\;1\right],\]  
and so on.  
Cantor set is defined as the intersections of all the obtained values \(C_{n}\) in iterating through the procedure infinitely. The affine transformations that are used to generate the sub-key are the following:  
\[A_{1}=\left\{\begin{array}{l}X=\frac{1}{3}x\\ Y=y=0\end{array}\right.\]  
\[A_{2}=\left\{\begin{array}{l}X=\frac{1}{3}x+\frac{2}{3}\\ Y=y=0\end{array}\right.\]  
A graphical representation of the process is shown in Figure 1:
## 2 Experimental Setup and Methodology  
### Experimental Apparatus  
The experimental apparatus is schematically shown in Figure 1, and consisted of four main components: speaker system, burner system, camera system, and sound field measurement system. Sine wave signals of different frequencies (110-150 Hz) were generated by the signal generator, and then the sound waves were emitted by the speaker. The sound intensity was adjusted by the amplifier between the signal generator and the speaker. The jet diffusion flame was produced by a butane-fueled Bunsen burner with nozzle inner diameter of 0.39 mm, and the flame length was adjusted by the fuel flow controlled by a flow meter (produced by Sevenstar @0-100 mL/min) to avoid interference by differences in flame length on the experimental results. A high-speed camera (Phantom Miro eX4 @1800 frames per second) was used to capture the visual field, where the fuel visualization was realized by the schlieren system consisting of an LED and a concave mirror. In addition, the sound field measurement system consisted of an acoustic sensor (INV9202) and a dynamic signal acquisition instrument (DFT5204), and sound pressure was finally shown by the analysis software DFT6000. After the flame stabilized, the speaker was activated, and the flame was allowed to burn under acoustic excitation at a constant frequency and acoustic pressure until it self-extinguished or was quenched artificially after 1 s. Measurements of the sound pressure were then taken at the same position as the flame.
# Cooperative UAV Scheme for Enhancing Video Transmission and Global Network Energy Efficiency
## 2 Related Work
The main goal is to maintain the video with QoE support while supporting the nodes with a good connectivity quality level and flying for a long period of time. Based on an SDN architecture, the VOEI assumes the existence of a controller node to compute reliable and energy-efficiency routes, as well as to coordinate the movement and UAV replacement/recharge. In the following, we describe the considered network and system model, and also the VOEI operation.
## 3 Methodology
### FDS GMF Development  
In this subsection, the ECMWF reanalysis data and ASCAT observations from 1 July 2019 to 1 September 2019 are used as training datasets to develop FDS GMFs. The reanalysis data can supply a spatially continuous wider wind speed range corresponding to a larger amount of matchup pairs. On the other hand, the ASCAT observations have a higher accuracy on wind speed and limited swaths which result in less collocated winds.
The first phase alone can be viewed as a non-cooperative localization algorithm whereas the second phase is based on cooperative approach. In the first phase, a sensor node uses RSS to estimate the distances between itself and all visible beacons. These distance inputs are processed using the spring-relaxation technique to determine its location. Due to the small number of beacons and RSS ranging errors [12; 17], this estimated location may give low accuracy. The second phase is designed to refine the estimated location obtained from the first phase. In the second phase, each sensor except beacons exchanges its estimated location information and distance with each of its one-hop neighbors to refine its location again using the spring-relaxation technique. By introducing only a limited number of beacons and applying repeated the spring-relaxation technique in different domains, we aim to provide accurate location estimation with minimal system requirement and deployment effort. Besides, we provide in-depth study on system design and parameter design via both theoretical and experimental approaches. Specifically, we first analyze the convergence property of the spring-relaxation technique in order to ensure that the algorithm terminates within a finite time. We then show that in the presence of RSS ranging noise, cooperative localization with the spring-relaxation technique can ensure stationary outcomes. To further understand the impact of parameters on system performance when concrete wireless ranging characteristics are implemented, we conduct series of simulation experiments to investigate the system behavior by varying the values of design parameters in using the spring-relaxation technique. Finally, we explicitly compare the performance of our proposed solution to other candidates in the same simulation setup. The rest of the paper is organized as follows. Section 2 presents our localization solution in details, and briefly discusses some design issues. Section 3 provides analysis focusing on the convergence and stationary properties of our solution. Section 4 presents the simulation results covering studies in system design and parameter design, and comparison to other related techniques. Finally, Section 5 summarizes our conclusions.
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 4 Taxonomy of IoD
### Applications
#### 4.5.3 Construction  
IoD makes it easier to access the visual data of the construction site to the client's view simultaneously. Furthermore, connected sensors, GPS, and the high-quality camera provide various details to different stakeholders in the project. With sophisticated technologies, the project manager has real-time data access to the site to monitor and avoid waste in the construction process.  
In addition, worker smart helmets in the construction site provide data about the safety of an individual person to the drone. However, usage of IoD for the construction industry is quite new compared to other application areas which need clear regulatory approval and availability of experts [52].
# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
###### Abstract  
With the rapid development of unmanned aerial vehicle (UAV) technology and the rise of the low-altitude economy, the accurate tracking of UAVs has become a critical challenge. This paper considers a deep learning-based localization scheme that combines microphone arrays for audio source reception. The microphone array is utilized to capture sound source reception from various angles. The proposed TSNetIQ combines elaborately designed Transformer and convolutional neural networks (CNN) modules, and the raw in-phase (I) and quadrature (Q) components of the audio signals are used as input data. Hence, the direction of arrival (DOA) estimation is treated as a regression problem. Experiments are conducted to evaluate the proposed method under different signal-to-noise ratios (SNRs), sampling frequencies, and array configurations. The results demonstrate that TSNetIQ can effectively estimate the direction of the sound source, outperforming conventional architectures trained with the same dataset. This study offers superior accuracy and robustness for real-time sound source localization in UAV applications under dynamic scenarios.  
DOA estimation; deep regression; Transformer; microphone array +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000-00001-5000]Tian Jin
## 2 Related Works
### Arabic Language
[22] explored sentiment analysis of Arabic tweets about the Hajj pilgrimage, utilizing ML and DL models over six years. Their study involved preprocessing, feature extraction, and sentiment classification of a large dataset of tweets. They discovered that BERT, a transformer-based model, outperformed other models in accurately classifying sentiment, effectively capturing the complexities of Arabic text. [PERSON] et al. [23] proposed a DL ensemble model for sentiment analysis, using GRU, LSTM, and BiLSTM as base classifiers with AraBERT for embeddings and SVM as the meta-classifier. They also employed AraGPT for data augmentation. The model achieved accuracy rates of 88.89% on HARD, 90.88% on BRAD, and 88.23% on Twitter datasets, outperforming state-of-the-art models. [PERSON] et al. [24] addressed the detection of hateful and offensive speech on Arabic websites and social media platforms using a transfer learning approach. They compared the performance of various BERT-based models trained to classify comments as abusive or neutral. They utilized a dataset containing comments in standard Arabic and four dialects alongside their English translations. The models were evaluated using metrics such as accuracy, precision, recall, F1-score, and confusion matrix, highlighting the need for advanced solutions in Arabic-speaking online communities where controlling offensive speech remains less developed.
## 1 Introduction
This index reflects biophysical factors, such as vegetation concentrations and the degree of terrestrial vegetation vigor. Unlike the traditional broadband vegetation indices that are usually computed using the near-infrared and red bands, VIUPD is based on all observed bands. Narrowband hyperspectral data-based VIUPD has been proven to be sensitive to spectral operations and was more sensor-independent than the other 11 vegetation indices, including the NDVI and EVI [23]. VIUPD also showed great potential to discern variations in urban LST when it was used to analyze the urban heat island effect in Shijiazhuang, China [24]. VIUPD was also applied to the inversion of crop chlorophyll content, and was found to have greater accuracy and stability when estimating the chlorophyll content of winter wheat than the NDVI, the triangle vegetation index (TVI), and the ratio of modified transformed chlorophyll absorption ratio index [25]. However, the utility of VIUPD for drought monitoring in large and different climate regions remains unknown. In this study, we established another type of VCI with VIUPD as a new drought monitoring index. The strength and weakness of VIUPD-derived VCI for drought monitoring in various climate divisions across the continental United States (CONUS) were evaluated by comparing the VIUPD-derived VCI and other remote-sensing-based drought indices to traditional station-based drought indices.
## 2 Methodology
### Image Registration  
Given a fixed image \(I_{F}\) and a moving image \(I_{M}\) in a D-dimensional space, image registration is a problem that aims to find the spatial transformation \(T:I_{F}\longrightarrow I_{M}\) that aligns the images \(I_{F}\) and \(I_{M}\), where \(p\longmapsto T(p)\) assigns points from the image \(p\in I_{F}\) to \(T(p)\in I_{M}\). The objective is to optimize similarity criteria \(Sim(I_{F},I_{M}\circ T)\) that measures the quality of the transformation. This registration process is shown in Figure 1, where \(T^{*}=\arg\max_{T}Sim(I_{F},I_{M}\circ T)\).  
The authors in [11] consider \(Sim(I_{F},I_{M}\circ T)\) based on the mean squared error (MSE), which can be calculated using Equation (1):  
\[Sim(I_{F},I_{M}\circ T)=\frac{1}{2}\cdot||I_{F}-I_{M}\circ T||^{2}=\frac{1}{2 \cdot|\Omega_{p}|}\sum_{p\in\Omega_{p}}|I_{F}(p)-I_{M}(T(p))|^{2} \tag{1}\]  
where \(\Omega_{p}\) is the overlap region between \(I_{F}\) and \(I_{M}\circ T\). To do the registration of fixed and moving images, Equation (1) needs to be optimized in a space given the spatial transformations. Given that the registration is unimodal, that is, from CT scan to CT scan, the deformation is the voxel displacement in the different scenarios. The model is given by \(T:p\longmapsto p+\mathbf{u}(p)\), where \(\mathbf{u}\) is the DVF that is added to the identity transformation to  
Figure 1: Image registration process.  
obtain a nonparametric transformation [14]. Note that the goal is to find the \(\mathbf{u}\) that describes the contour propagation in each of the scenarios. The DVF \(\mathbf{u}\) is a 4-D vector structure for 3-D images since it includes the position of the gray-level intensity in the transformed image [18].