esa-sceva/satcom-chunk-collection huggingface dataset:
## Electrical Response of the Spinel ZnAl2O4 and Its Application in the Detection of Propane Gas
When the sensor signal is processed with analog circuits, the electronic circuit design is specific because it depends on the signal's features. For other characteristics, it would not be functional. However, its advantages are easy construction, fast response, low cost, and high sensitivity. For its application as a detector of toxic gases, our group synthesized powders of the semiconductor oxide ZnAl\({}_{2}\)O\({}_{4}\) at a relatively low temperature applying a wet chemistry process assisted with microwave radiation. Pellets were manufactured from the powders to study the oxide's ability to detect propane (C\({}_{3}\)H\({}_{8}\)) atmospheres in direct current (DC). The oxide showed good thermal stability, high sensitivity, and excellent dynamic response at an operating temperature of 250 \({}^{\circ}\)C. We found that the ZnAl\({}_{2}\)O\({}_{4}\) is suitable for being applied as a propane gas sensor. Furthermore, we modeled its electrical response based on the time-invariant linear systems theory. Through the model, the transient response of the sensor was verified by exposing it to air and propane gas, checking its stability (and stabilization time). With that, an operating point was found for the detector's design, whose features were, in summary, an operating temperature of 250 \({}^{\circ}\)C, a concentration of 1000 ppm, size of 10 cm \(\times\) 10 cm, low cost, applicable to high-temperature processes, and short response times.
## 2 Materials and Methods
### Methods  
#### 2.3.1 Separability Calculation  
Separability (\(M\)) is a commonly used statistic for quantitatively evaluating the separation of spectral bands and indices in classification tasks for specific categories [48]. It has been widely applied in forest fire monitoring [30; 49] and other fields [50; 51]. The calculation formula is shown in Equation (4):  
\[M=\frac{|\mu_{\rm b}-\mu_{\rm a}|}{\sigma_{\rm b}+\sigma_{\rm a}} \tag{4}\]  
Figure 4: The data for validation and sample in this study. (**a,b**) Sentinel-2 MSI image of pre-fire and post-fire, respectively, with the synthesized bands of Red-NIR-Green; (**c**) The different fire intensity regions divided by dNBR; (**d**) The distribution map of the sample points, and the bottom map is the ZY1E AHSI image with the synthesized bands of Red-NIR-Green.  
In Equation (4), \(\mu_{\text{b}}\) and \(\mu_{\text{a}}\) represent the mean values of samples for different fire severity categories, while \(\sigma_{\text{b}}\) and \(\sigma_{\text{a}}\) are the corresponding standard deviations. Generally, a larger value of \(M\) indicates better separability between categories. If \(M<1\), it suggests poor separability, while \(M\geq 1\) indicates good separability.
# A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment
## 3 MC-BDP Reference Architecture
### MC-BDP Architectural Layers
#### 3.4.1 The Persistence Layer  
The persistence layer consists of file stores, disk space, and relational and non-relational databases. In the context of MC-BDP, it is used differently by components in other layers, namely, the node, container, service, and messaging layers. Depending on how monitoring is implemented, it can also use some form of persistence although, if a cloud service is used, its representation falls outside the scope of the diagram represented in Figure 2.  
Typically, bare metal nodes hosted on-premises contain a hard disk drive (HDD) which uses magnetic recording technology to store information [66]. Likewise, virtual machines commissioned from commercial cloud providers include a configurable amount of disk space which can be increased or decreased as needed. Although this research focuses on the stream aspect of big data and therefore on processes which are not disk-intensive or storage-focused, it is aware that some degree of disk usage does happen at the node level. Likewise, containers running on physical or virtual machines are configured with an amount of disk space which can be fixed or flexible up to the maximum amount available on the host.  
Services deployed to containers could be configured to use external storage such as databases or file stores hosted on bare metal or in the cloud. In MC-BDP's context of stream data processing, for instance, external storage is used primarily in checkpointing, a common approach used by large scale distributed architectures to provide fault tolerance [31]. The Flink framework, used in the prototype implementation of MC-BDP, recommends that states be stored in an external file system or database for extra resilience [67]. The cloud simplifies the provision of these storage resources, which can be commissioned as services and easily configured to provide an adequate level of resilience. MC-BDP's messaging layer also involves an element of persistence, albeit for a short (configurable) amount of  
Figure 2: MC-BDP architectural layers (source: [57]).  
time. One of the reasons for this requirement is to allow consumers of the data to handle backpressure. Thus, should data processing slow down, the messaging layer can continue to receive new data while old data remains stored in the brokers waiting for its consumers to catch up.  
Finally, for general persistence requirements such as the introduction of a sink to store data after processing, MC-BDP recommends a physical or cloud-based database or distributed file system such as Hadoop [68] or Aurora [69]. The advantages of using a cloud-based service are high scalability and fault tolerance, security out-of-the-box, and a potentially lower initial investment and maintenance cost, since the service is fully managed by the cloud provider [69; 70; 71].
# Multi-Scale Recurrence Quantification Measurements for Voice Disorder Detection
## 5 Conclusions  
In order to accurately quantify the nonlinear features in the speech signal to effectively detect and classify pathological voice, MRQMs were proposed. A recognition rate of 99.56% was achieved, and each evaluation index was the best, demonstrating the effectiveness of the proposed method. However, due to the high similarity of different vocal fold diseases, the method in this paper could not classify different pathological voice types well. However, it also achieved a three-class recognition rate of 89.05%. This article points out that the nonlinear analysis method can effectively detect pathological voices.  
Cross-database training and multi-class recognition are still challenges for pathological voice detection. Considering the diversity of different pathological voice databases, pathological voice detection will be carried out in different databases in the future. In addition, we will continue to study MRQMs or other complex measures combined with the occurrence model to explore the phenomenon of turbulent noise in pathological voices to improve the multi-class recognition rate. With the development of the Internet of Things and artificial intelligence, Wise Information Technology of 120 is gradually entering the link of medical treatment. This work can provide ideas for the self-diagnosis of patients.  
Conceptualization, Z.T.; Validation, X.-J.Z.; Visualization, Y.-H.Z.; Writing--original draft, X.-C.Z.; Writing--review & editing, X.-C.Z. and D.-H.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 6: The confusion matrix for pathological voice multiclass classification with 10-fold cross validation.  
**Funding:** This research was funded by the National Natural Science Foundation of China under Grant No. 61271359.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The MEEI database is commercialized and not publicly available.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms
## 5 Simulation Results
### Performance of the Genetic Algorithm Used by GARSUD
In particular, the initial random deployment is widely modified during the first generations, achieving a completely different distribution of RSUs after 20 generations. This corresponds to the major gain in the fitness of the solutions as previously mentioned. After this stage, the variability of the solutions is reduced, and some RSUs are assigned to their final position, whereas some others still vary their location to slightly reduce the warning notification time in the solution. As expected, genetic algorithms progressively reduce the variation of the solutions until they converge towards their final solution. Figure 9 presents the average warning notification time when deploying four and nine RSUs, in both map layouts (i.e., Madrid and Valencia), where different vehicle densities are considered. As shown, the warning notification time is reduced with each new generation of solutions for almost all the selected densities, in particular in low-density scenarios. It is reduced in Madrid from 30.25 s to 5 s when only 100 vehicles are present in the scenario, which represents over an 80% improvement. The final reduction is also about 80% for 200 vehicles and about 60% for 300 vehicles. The improvement when simulating 400 vehicles is very reduced, since increasing the density of vehicles over this threshold increases the probability of finding a connected path of vehicles between a sender vehicle and an RSU. As shown, the biggest improvement in terms of warning notification time is obtained  
Figure 8: Evolution of the deployment of nine RSUs in the Valencia scenario using GARSU. (**a**) After 20 generations; (**b**) after 40 generations; (**c**) after 60 generations; (**d**) after 80 generations. during the first 40 generations, whereas the last 20 generations only provide a slight reduction when 100 vehicles are simulated. Studying the results in Valencia, we observe that similar to Madrid, the biggest improvement in terms of warning notification time takes place during the first 40 generations of the search process, reducing by about 85% the warning notification time when 100 vehicles are simulated and by about 60% when 200 vehicles are present in the scenario. The improvement during the final part of the process is lower because the genetic algorithm is mostly refining the solutions instead of exploring new areas of the search space. Again, the improvement for high vehicle densities is less noticeable as it is more likely to find a forward path from the warning nodes to the RSUs. Nevertheless, the number of hops needed to overcome the complex layout and reach an RSU is higher, thus increasing the warning notification in all vehicle densities compared to Madrid. These results prove that random RSU deployments are highly inefficient, particularly for low vehicle densities, and the effect of traffic distribution should be taken into account to obtain efficient RSU deployment policies.
# RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating
## 3 Proposed Method
### Radar Plots Feature Extraction  
For a classification problem, the first thing to focus on is reasonable feature selection. We selected range, azimuth, elevation, distance width, azimuth width, total number of resolution units, maximum amplitude, minimum amplitude, average amplitude, and number of resolution units exceeding the threshold as the 10 dimensional recognition features of the radar plots. The design of the extracted features here refers to those features mentioned in [8; 9; 16]. The specific meanings about these features are shown in Table 1.  
There is a significant difference in the range of different sub-features of radar plots. Normalization can eliminate dimensional differences between different sub-features, making them  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Symbol** & **Feature Information** & **The Specific Meaning of Feature Information** \\ \hline R & Range & The distance between target and radar \\ \hline A & Azimuth & The azimuth of the target in the radar coordinate \\ \hline E & Elevation & The elevation angle of the target in the radar coordinate \\ \hline D\({}_{\text{w}}\) & Distance width & The width of target at distance can be characterized by the number of distance resolution units \\ \hline A\({}_{\text{w}}\) & Azimuth width & The width of target in azimuth can be characterized by the number of azimuth resolution units \\ \hline T\({}_{\text{n}}\) & Total number of resolution units & The number of units contained in the range and azimuth resolution area of the target \\ \hline A\({}_{\text{max}}\) & Maximum amplitude & The maximum amplitude of the echo participating in condensation \\ \hline A\({}_{\text{min}}\) & Minimum amplitude & The minimum amplitude of the echo participating in condensation \\ \hline A\({}_{\text{a}}\) & Average amplitude & The average amplitude of the echo participating in condensation \\ \hline E\({}_{\text{t}}\) & Number of resolution units exceeding threshold & The number of units participating in plot condensation that exceed the threshold \\ \hline \hline \end{tabular}
\end{table}
Table 1: The extracted features and their meanings.  
comparable and improving the performance of the model. Therefore, the extracted original feature \(F=[\text{R},\text{A},\text{E},\text{D}_{\text{w}},\text{A}_{\text{w}},\text{T}_{n },\text{A}_{\text{max}},\text{A}_{\text{min}},\text{A}_{\text{tr}},\text{E}_{t}]\) is normalized by the following formula.  
\[F_{q}^{\prime}=\frac{F_{q}-F_{q}^{\text{min}}}{F_{q}^{\text{max}}-F_{q}^{\text{ min}}},\ q=1,2,\cdots 10 \tag{11}\]  
Here, \(F_{q}\) represents the sub-feature of \(F\), whose dimension is \(q\). \(F_{q}^{\text{max}}\) and \(F_{q}^{\text{min}}\) are the maximum and minimum values that can be taken for this sub-feature, respectively. \(F_{q}^{\prime}\) is the \(q\)th dimensional sub-feature obtained by normalization, with a value interval of \([0,1]\).
## 3 Proposed Method
### Model Design  
We constructed a feature enhancement module based on CNN, GRU, and multi-head attention. Simultaneously, our model opted for Swin Transformer [29] as the visual encoder and BART [32] as the language decoder. The overall structure of our proposed SwFB model is depicted in Figure 3.  
The models based on CNN [27; 33] or transformer [29] can be used as visual encoder. [PERSON] et al. [10] pointed out that transformer-based models exhibit superior performance. In our research, we employ Swin Transformer as our visual encoder, which is a vision model based on a transformer architecture that extracts features by partitioning an input image into non-overlapping patches and employing a window-based self-attention mechanism. The most important tasks in image analysis are classification, detection, and segmentation [34]. Considering the success of the U-Net network in image segmentation tasks, we drew inspiration from the convolutional network concept of U-Net [13]. In our feature enhancement module, we adopt the convolutional neural network to capture spatial structural features. Through convolutional layers, the model can capture subtle features and patterns in the image, thereby enhancing the richness of feature representation.  
Figure 2: An example of conversion from JSON to SEQs.  
BiGRU [14] is a neural network model stacked with GRU units, providing a more comprehensive contextual understanding by simultaneously considering forward and backward information. It performs well in sequence-related tasks. Multi-head attention [15] is an improvement to attention mechanisms, allowing the model to focus on different parts of the input sequence in parallel, with each head concentrating on different aspects. This makes the model more effectively capture global and local information, enhancing its expressive and generalization capabilities. Thus, based on CNN, GRU, and multi-head attention, we proposed the feature enhancement module Co-G-Ma; this module effectively integrates the connection between local features and global context, improving the performance and accuracy of cross-border waybill image text extraction tasks.  
Our language decoder is responsible for generating sequences with special tokens. Noting the high compatibility of BART with this task, we selected the publicly available multilingual pre-trained BART model. In order to obtain effective feature vectors during the early stages of training, we initialized our model with weights [10] pre-trained on the IIT-CDIP dataset [35]. During training, we employed a teacher-forcing training strategy. For BART, apart from receiving the input feature vector set, the model also takes the ground truth as input. This approach allows the model to directly observe the correct output sequences during training, facilitating faster gradient propagation and contributing to a more stable and rapid convergence.
# Prediction and Control of Broadband Noise Associated with Advanced Air Mobility--A Review
## 2 Sources/Types of BBN
### Inflow Turbulence Noise  
Ingestion noise is broadband noise caused by a propeller ingesting turbulence. This turbulence may originate from a variety of sources, such as atmospheric turbulence or wake turbulence shed by other propellers or airframe components, as shown in Figure 3. Anisotropic turbulence will be chopped several times as it passes through the rotor, which leads to narrow-band noise around the harmonics of the blade-passing frequency, also known as haystacking, which was first reported by [PERSON] [6]. Additionally, [PERSON] [7] assumes the inflow turbulence noise as leading-edge noise.
# Resilience in the Context of Cyber Security: A Review of the Fundamental Concepts and Relevance
## 3 Cyber Resilience: Fundamental Concepts
### The Relevance of Cyber Resilience in Various Segments
#### 3.2.3 Electricity  
Power generation and distribution systems are critical infrastructures operated under the control of intelligent devices and interrelated to cyber-physical processes characterized by vulnerability to cyber-attacks [42]. Strengthening cyber security and resilience in the electricity sector has become a global necessity [43]. The use of intelligent energy networks (known as Smart Grids) makes it possible to automate, monitor, and control energy consumption, offering extremely reliable energy systems. Conversely, the use of smart grids for communication increases the probability of attacks on these grids. These attacks can lead to disruptions, such as inaccurate meter readings, false electricity demands, and impaired protective mechanisms. Consequently, a resilient communications network that can withstand cyber threats is necessary [44]. Still, on the subject of the electricity sector, the concept of the microgrid--widely used for integrating distributed generation units (e.g., photovoltaic units, diesel engines and wind turbines)--is capable of providing improved services, but the cyber and physical structures of microgrids are more prone to cyber and physical attacks [45].
# Building a Realistic Virtual Simulator for Unmanned Aerial Vehicle Teleoperation
## Appendix A Complete Dynamic Model of the Quadrotor  
### Conservative Model  
The closed-form translational and rotational motion equations of the quadrotor are given by (15). For the translational equation, the inertia matrix \(M_{T}(\xi)\) is given by:  
\[M_{T}(\xi)=\left[\begin{array}{ccc}m&0&0\\ 0&m&0\\ 0&0&m\end{array}\right] \tag{11}\]In the same equation, the gravity vector \(g_{T}(\xi)\) is:  
\[g_{T}(\xi)=[0,0,mg]^{T} \tag{10}\]  
Concerning the rotational equation, the inertia matrix \(M_{R}(\mathbf{\eta})\) is:  
\[M_{R}(\mathbf{\eta})=\left[\begin{array}{ccc}m_{11}&m_{12}&m_{13}\\ m_{21}&m_{22}&m_{23}\\ m_{31}&m_{32}&m_{33}\end{array}\right] \tag{11}\]  
with:  
\[\begin{array}{l}m_{11}=I_{xx}\cos^{2}\theta+I_{zz}\sin^{2}\theta\\ m_{12}=0\\ m_{13}=(I_{zz}-I_{xx})\sin\theta\cos\theta\cos\phi\\ m_{21}=0\\ m_{22}=I_{yy}\\ m_{23}=I_{yy}\sin\phi\\ m_{31}=(I_{zz}-I_{xx})\sin\theta\cos\theta\cos\phi\\ m_{32}=I_{yy}\sin\phi\\ m_{33}=I_{yy}\sin^{2}\phi+I_{xx}\cos^{2}\phi\sin^{2}\theta+I_{zz}\cos^{2}\theta \cos^{2}\phi\end{array}\]  
On the other hand, the rotational matrix of centrifugal and Coriolis forces \(C_{R}(\mathbf{\eta},\dot{\mathbf{\eta}})\) is:  
\[C_{R}(\mathbf{\eta},\dot{\mathbf{\eta}})=\left[\begin{array}{ccc}c_{11}&c_{12}&c_{13 }\\ c_{21}&c_{22}&c_{23}\\ c_{31}&c_{32}&c_{33}\end{array}\right] \tag{12}\]  
with:  
\[\begin{array}{l}c_{11}=\dot{\theta}(I_{zz}-I_{xx})\sin\theta\cos\theta\\ c_{12}=\dot{\phi}(I_{zz}-I_{xx})\sin\theta\cos\theta\\ \qquad\quad+\psi\frac{1}{2}\Big{[}(I_{zz}-I_{xx})\cos^{2}\theta\cos\phi-(I_{ zz}-I_{xx})\sin^{2}\theta\cos\phi-I_{yy}\cos\phi\Big{]}\\ c_{13}=\dot{\theta}\frac{1}{2}\Big{[}(I_{zz}-I_{xx})\cos^{2}\theta\cos\phi-(I _{zz}-I_{xx})\sin^{2}\theta\cos\phi-I_{yy}\cos\phi\Big{]}\\ \qquad\quad+\dot{\psi}\Big{(}-I_{yy}\sin\phi\cos\phi+I_{xx}\cos\phi\sin\phi \sin^{2}\theta+I_{zz}\cos^{2}\theta\cos\phi\sin\phi\Big{)}\\ c_{21}=\dot{\phi}[I_{xx}-I_{zz}]\cos\theta\sin\theta\\ \qquad\quad+\dot{\psi}\frac{1}{2}\Big{[}I_{yy}\cos\phi-(I_{zz}-I_{xx})\cos^{2} \theta\cos\phi+(I_{zz}-I_{xx})\sin^{2}\theta\cos\phi\Big{]}\\ c_{22}=0\\ c_{23}=\dot{\phi}\frac{1}{2}\Big{[}I_{yy}\cos\phi-(I_{zz}-I_{xx})\cos^{2} \theta\cos\phi+(I_{zz}-I_{xx})\sin^{2}\theta\cos\phi\Big{]}\\ \qquad\quad+\dot{\psi}(I_{zz}-I_{xx})\cos^{2}\phi\sin\theta\cos\theta\\ c_{31}=\dot{\phi}(I_{zz}-I_{xx})\sin\theta\cos\theta\sin\phi+\theta\frac{1}{2} \Big{[}(I_{zz}-I_{xx})\cos^{2}\theta\cos\phi\\ \qquad\quad-(I_{zz}-I_{xx})\sin^{2}\theta\cos\phi+I_{yy}\cos\phi\Big{]}\\ \qquad\quad+\dot{\psi}\Big{(}I_{yy}\sin\phi\cos\phi-I_{xx}\cos\phi\sin\phi \sin^{2}\theta-I_{zz}\cos^{2}\theta\cos\phi\sin\phi\Big{)}\end{array}\]\[c_{32}=\dot{\phi}\frac{1}{2}\Big{[}I_{yy}\cos\phi+(I_{zz}-I_{xx}) \cos^{2}\theta\cos\phi-(I_{zz}-I_{xx})\sin^{2}\theta\cos\phi\Big{]}\] \[\qquad\qquad+\dot{\psi}(I_{xx}-I_{zz})\cos\theta\sin\theta\cos^{2}\phi\] \[c_{33}=\dot{\phi}\Big{(}I_{yy}\sin\phi\cos\phi-I_{xx}\cos\phi\sin \phi\sin^{2}\theta-I_{zz}\cos^{2}\theta\cos\phi\sin\phi\Big{)}\] \[\qquad\qquad+\dot{\theta}(I_{xx}-I_{zz})\cos^{2}\phi\sin\theta\cos\theta\]