esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### GIS Interface
#### 3.2.1 Autumn  
For consistency, the first day of October was chosen in this scenario to test the automation model. Figure 7 shows the spatial distribution of pressure and flow over the network area obtained from the IDW. The cream colour in Figure 7 indicates the lowest observed values and the burgundy colour indicates the highest values. The pressure IDW indicated low pressure values (0.6 to 1.2 bar) observed at a few areas including Butina, Al Ghuwaiir, Industrial Area 4, Industrial Area 6, Al Ghaphia, Maysaloon, Al Fayah, and Al Nasserya. However, the remaining vicinities covering the Al Rahmaniya 1, Al Rahmaniya 3, Al Qadsia, Al Sabkha, and Barashi areas indicated normal pressure behaviour across its network (ranging from 2 to 3.75 bar). The IDW returned for the net flow data for the analysed day indicated a major increase covering the eastern sector of the network which included Al Rahmaniya 1, Al Rahmaniya 3, Butina, Maysaloon, Al Nasserya, Barashi, and Al Fayha, while the remaining locations indicated normal flow patterns (110 to 7380 m\({}^{3}\)/day). The individual pressure and flow drops were selected by the model (shown in brick red) and both the selected vicinities in the pressure and flow layers were intersected. Since pressure and flow drops are indicative of multiple defects other than leaks, the individual layers were intersected to determine the regions with a high risk of a leak based on both parameters. Finally, one chlorine monitoring point showing a low chlorine value of 0.15 mg/L narrowed down the suspected area to a vicinity extending from Butina to Industrial Area 4, including a pipe length of about 320 km.  
Figure 7: Analysis maps for October.
## 2 Networking Architectures
### Multi-Group UAV Ad-Hoc Network  
A multi-group UAV ad-hoc network is, primarily, an integration of a UAV ad-hoc network and a centralized network architecture, as depicted in Figure 2c. In this architecture, UAVs within a group form an ad-hoc network, and the backbone UAV of each group is further connected to the GS in a centralized manner. Intra-group communication is performed within a UAV ad-hoc network without involving the GS, while inter-group communication is conducted via the backbone UAV. This architecture is best suited for missions where a large number of heterogeneous UAVs need to be deployed. However, this networking architecture lacks robustness due to its semi-centralized nature.
## 4 Experimental Validation  
### Experiment Setup  
The double pulse test is used to evaluate the physical parameter of IGBT modules and obtain the turn-on and turn-off energy by the offline measurement of the oscilloscope, as shown in Figure 9a.  
The PMSM test bench depicted in Figure 9b is adopted to validate the proposed losses model based on the inverter applications. The load motor provides the load to the tested motor and the inverter can output corresponding voltages according to the FOC algorithm. In this process, the load currents flow through the IGBT modules and the power losses is generated. The power analyzer is adopted to measure the loses of the inverter, that is, the difference of dc input losses and ac output  
Figure 8: Comparison between the proposed losses model and the traditional losses model.  
Figure 9: The experiment equipment. (**a**) The double-pulse test equipment. (**b**) The permanent magnet synchronous motor (PMSM) test bench.  
losses is the inverter losses. It should be noted that the inverter losses come from IGBT modules, the dc/ac bus-bar and the parasitic resistances of the bus capacitance. The parameters of the tested motor are listed in Table 3.
# Energy Reduction Multipath Routing Protocol for MANET Using Recoil Technique
## 6 Conclusions  
The energy reduction multipath routing protocol using recoil technique (AOMDV-ER), ad hoc on demand distance multipath routing with life maximization (AOMR-LM) and ad hoc on demand multipath distance vector routing protocol with fitness function (FF-AOMDV), are all an improvement over the AODV protocol. In this paper, the proposed AOMDV-ER outperforms the existing algorithms, namely AOMR-LM, AOMDV and SRMP, in terms of routing overhead, network lifetime, packet delivery ratio and energy consumption. This is given that in the AOMDV-ER protocol, the nodes perform transmission of packets to destinations smartly using varying recoil off time based on their geographical location. The experimental result shows that AOMDV-ER reduces 22% and 6% of the routing overhead, an improvement of 26% and 33% of network lifetime, 6% and 12% of packet delivery ratio and reduces 14% and 31% of power consumption in comparison to AOMDV and AOMR-LM protocols respectively.  
[t]Acknowledgements: R.K.S. and N.S.C. conceptualized all three algorithms of AOMDV-ER. RKS has carried out the formal analysis of algorithm and software implementation on NS2 simulation tool. R.K.S. created and investigation of all possible scenarios while taking results, whereas the writing review and editing is completed by R.K.S. and N.S.C. together. The authors declare no conflict of interest.  
Figure 15: (a) Energy cons. vs. Node speed; (b) Energy cons. vs. Pack. Size; (c) Energy cons. vs. time.
## 2 Literature Review
For the imbalanced SER [30] introduced Attention-Integrated Convolutional RNN. The experimental results on Emo-DB and IEMOCAP demonstrate better performance for imbalanced speech emotions. In another related work, a self-attention mechanism [31] is added to the Bidirectional Long Short-Term Memory (BLSTM) based classification. The proposed solution not only accomplished correlation of speech signals in the statements but also improved the diverse information using directional self-attention (DSA). The algorithm performed well on IEMOCAP (comprising complete, script, and spontaneous) and EMO databases. There are few works on the domain of Arabic spoken speech emotion recognition. Therefore, this study focused on the recognition of Arabic speech emotion. In 2018, according to [32], the first study on emotion recognition in spoken Arabic data is proposed. The speech corpus is collected from Arabic TV shows. Videos were labeled into three categories happy, angry or surprised. The study used 35 classification methods. The best classification performance is provided by the Sequential Minimal Optimization (SMO) classifier, which achieves 95.5%. Another study on Arabic speech emotion was introduced in [33]. This study provides a dataset called \"Semi-natural Egyptian Arabic speech emotion (EYASE)\". This study was focused on Egyptian sentences. This study adopts SVM and KNN classifiers. The highest accuracy achieved was 95% using SVM. The highest accuracy was obtained in Male emotion recognition. Happiness was the most difficult to detect, while anger was the easiest. A study by [34] that built dataset based on real-world Arabic speech dialogs for detecting anger in Arabic conversation. The result revealed that acoustic sound features such as fundamental frequency, energy, and formants are more suitable for detecting anger. The experimental findings demonstrated that support vector machine classifiers can identify anger in real time at detection rate of more than 77%.
## 2 Materials and Methods
### Machine Learning Algorithm  
#### 2.5.1 Random Forest Classifier  
The RF is a combination of tree predictors with each tree depending on an independently sampled random vector value with a similar distribution in all trees (Figure 3) [40]. Boosting and bagging are two ensemble methods capable of squeezing additional predictive accuracy out of classification algorithms. Bagging algorithms are used to reduce the complexity of the models that overfit the training data, while the boosting algorithm increases models' complexity. The training samples, which are not used in the training sample, were included in the evaluation and were referred to as 'out of bag' samples [4]. In addition, the RF classifier is easy to use since it only uses two parameters (e.g., number of variables at each node and number of trees), which is not sensitive to the parameter value [41]. The number of trees and predictors in RF classification are vital parameters to achieving the highest accuracy possible. For assessing the accuracy of the current RF output, these parameters were set at 50 for the number of trees, and the maximum number of tree depth and samples per class were set as 30 and 500, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Index** & **Formulas** & **References** \\ \hline \multirow{2}{*}{Vegetation Index} & DVI & \(NIR-Red\) & [36] \\ \cline{2-4}  & NDVI & \(\frac{NIR-Red}{NIR+Red}\) & [37] \\ \hline \multirow{2}{*}{Urban index} & NDBI & \(\frac{SWIR-NIR}{NIR+NIR}\) & [38] \\ \cline{2-4}  & UI & \(\frac{SWIR2-NIR}{NIR2-NIR}\) & [39] \\ \hline \multirow{2}{*}{Barren land index} & NBII & \(\frac{Red-TIR}{Red+TIR}\) & [39] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Remote sensing indices for highlighting LULC classes.  
Figure 3: A pictorial view of the random forest working principle.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**LULC Class** & **Description** \\ \hline Agriculture & Cultivated land, crop fields, vegetable fields \\ Urban & Residential, commercial, industrial, mixed urban, other urban \\ Barren Land & Exposed soil, construction site, fallow land \\ Forest & Deciduous forest and mix forest, shrubs, and other \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of land-use and land-cover classes used in this study.
# UVsq-Sat NG, a New CubeSat Pathfinder for Monitoring Earth Outgoing Energy and Greenhouse Gases
## 3 Description of the Uvsq-Sat NG Space-Based Mission  
Uvsq-Sat NG is a French cutting-edge CubeSat developed by LATMOS in collaboration with the international INSPIRE team. This project is integrated into the \"Academic spatiale d'Ie-de-France\" initiative, which was selected in the 2023 call for proposals of the \"Appel a manifestation d'interet Competences et Metiers d'Avenir\" (expression of interest for future skills and professions for France 2030). Uvsq-Sat NG is designed for scientific and technological exploration but also as an educational tool since students can gain practical skills in satellite design, construction, and operation, fostering a deeper understanding of space sciences and engineering principles.  
The Uvsq-Sat NG space program consists of both a ground segment and a space segment. The ground segment includes all the infrastructure and facilities that are used to control and manage the space missions. It includes the LATMOS UHF/VHF stations (Hermes and Elsa) located at Observatoire de Versailles Saint-Quentin-en-Yvelines (OVSQ, France), enabling radio communication with the Uvsq-Sat NG CubeSat. Another station located in the French Riviera at Grasse (France) will be used and operated by ACRI-ST to facilitate satellite data downlink in the S-band. The space segment, on the other hand, comprises the Uvsq-Sat NG CubeSat, which plays a pivotal role in fulfilling the mission's objectives and transmitting crucial data back to Earth for analysis and informed decision making. Figure 1 shows the satellite's architecture.  
Figure 1: Computer-aided design of the Uvsq-Sat NG satellite with its platform and its scientific payloads (NIR Spectrometer, NanoCam, Earth Radiative Sensors, photodiodes).
# Distributed Fault Detection and Isolation Approach for Oil Pipelines
###### Abstract
in combination with a leak detection method (to localize pipeline leaks) would discern between component fault and leakage occurrence, avoiding, for example, long response times or false alarms, which also would improve OPS performance. Thus, here it is considered that the FDI be performed by combining two methods. The first method aims to detect and isolate faults in OPS components, so this first method is used to determine in which component a fault occurs; and, in the case of pipeline segment fault (which implies a leak), once the faulty segment is detected and isolated, the second method would aim to localize the leak. In this paper, in order to detect and isolate faults in an OPS component, a model-based distributed FDI approach is proposed, which uses a structural model of the system in conjunction with algorithms to generate diagnostic tests. The latter would be implemented in local diagnosers along the OPS to ensure detection and isolation of faults in pipeline segments, as well as in process control equipment (e.g., sensor fault, valve fault, etc.). The proposed approach is combined with a second method to localize the leak that occurred in a pipeline segment. For this purpose, a method as described in [4; 34; 35; 36] may be used (this second method is not addressed in this work). The major contribution of this research is the extension of the structural analysis approach [17; 23; 37] to propose a distributed FDI approach for the OPS, considering an optimization process in the selection of tests and the inclusion of neighbors subsystems at different levels to achieve the diagnostic goals. In order to guarantee maximum diagnosability and least exchange of sensor information between subsystems, local diagnosers should be efficiently built for each subsystem, in this sense, two algorithms for the design of local diagnosers are proposed, first only with local information and if necessary with progressive incorporation of information from neighboring subsystems to achieve detection and isolation of a set of faults of interest in OPS. This paper is organized as follows. In Section 2, the theoretical framework of structural analysis for model base FDI is provided. A model-based distributed FDI approach, which uses a structural model of the process in conjunction with algorithms to generate diagnostic tests, is proposed in Section 3. In Section 4, a case study of FDI for an OPS is presented. Section 5 shows the discussions of the obtained results. Finally, Section 6 gives some conclusions.
## 1 Introduction
proposed a combined coded excitation method by modulating Golay code sequence with Barker sequence for ultrasound imaging [14]. The combined codes, which are 3-bit Barker code, 8-bit Golay code pair and 3-bit Barker code modulated 4-bit Golay code pair, were used in their ultrasound imaging experiments. Recently, [PERSON] and [PERSON] proposed a new pseudo-Chirp-Barker-Golay (PCBG) coded excitation method for ultrasound imaging [15]. They used the Field II toolbox to simulate phantom and cyst phantom of B-mode image. The research for the usage of modulated codes is the most popular in ultrasound imaging applications now. In their research, the combined sequences of codes have relatively low sidelobe levels and high main lobe level. Meanwhile, their lengths are easily tuned. That means the combined coded pulse compression algorithm is practically promising to reduce the coherent noise level at a higher resolution and SNR of the inspection systems. [PERSON] et al. [16] proposed a frequency-phase modulation method based on Barker code sequence. This method enhances the depth resolution and the experiment on glass fiber reinforced polymer demonstrated their robustness at various noise levels. [PERSON] et al. [17] applied the Gold sequence to code the guided wave actuation pulses for reducing the coherent noise caused by multiple transducers. In this paper, a hybrid coded excitation method that uses a designed sequence of a Barker code convolution Golay code pair (BCG) is developed to improve the echo SNR of ultrasonic guided wave testing systems. The technique combines the advantages of these two coding methods and increases the diversity of code lengths. The technique performance will be evaluated by SNR and peak sidelobe level (PSL) of the processed signals. The main contributions of this paper are presented as follows:* The developed hybrid coded excitation technique is applied to the guided wave mode excitation for pipe inspection for the first time. * The proposed method has been theoretically evaluated that has better performance than other common techniques. * The experimental results that the proposed method has the best echo SNR and PSL at various frequencies (30 kHz-50 kHz). * This work can be extended to other potential applications for a significant SNR improvement. The rest of this paper is organised as follows. First, we generate the dispersion curves of wave modes at frequencies of interest and code generation and compression are presented in Section 2. Then, Section 2 also illustrated the methodology and its superiority. Next, the experimental set-up and data analysis are described in Section 3. Finally, Section 4 gives the discussions and conclusions.
## 4 Results  
### Exemplary Result for Real Parathyroid Glands  
Real parathyroid glands are processed using the proposed approach as an illustrative example and selected slices are shown (Figure 3). The achieved results are shown for global best ring topology. The computation time was \(\sim\)4 min for single test using the AMD Ryzen 7 2700 processor 3.2 GHz (8-cores), 2133 MHz DDR4 16 GB RAM, Samsung SSD 960 Evo, and Debian 9 Linux distribution.  
The proposed approach uses optimization techniques, therefore the question of convergence arises. Global minimum is not achieved in all cases, which is typical for complex optimization problems [51; 52]. The analysis of convergence is possible with the use of the Monte Carlo test. This test assumes multiple repetition of optimization for random starting parameters. A histogram of MSE is shown in Figure 4. The number of iterations is limited, because infinite iterations correspond to testing all possible cases. Single value of MSE should be achieved if the global minimum is found and that is indicator of the optimality. Different values that could be depicted in the compact form using histogram means that there are local minima, so parameter values are not equal. This test allows the testing of coupling between parameters. Independent parameters lead to simple optimization problem for random starting point of optimization. Such independence gives quick convergence to identical final results that is global minimum, because independent parameters are optimized independently. Histogram for such a case is the single value of error. Dependent parameters introduce complex relation between them so error space is very complicated with a lot of local minima. Histogram of optimization after finite number of iterations is multivalued.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Minimal Value** & **Maximal Value** & **Type** \\ \hline \(m_{x}\), \(m_{y}\), \(m_{z}\) & \(\frac{1}{4}B\) & \(\frac{3}{4}B\) & selected by range \\ \hline \(\theta_{x}\), \(\theta_{y}\), \(\theta_{z}\) & 0 & 360 & folded by range \\ \hline \(s_{x}\), \(s_{y}\), \(s_{z}\) & 5 & 10 & selected by range \\ \hline \(A\) & 5 & 20 & selected by range \\ \hline \(\beta\) & 0.5 & 1.5 & selected by range \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ranges used for parameters.
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 4 Joint Digital Signal Processing Schemes for Carrier Recovery
### Algorithms for Joint Carrier Recovery
#### 4.1.1 Master-Slave Carrier Recovery  
Master-slave carrier recovery is relatively straight forward. The frequency offset and phase is estimated from the master channel and then applied to the slave channels. The main principle does not depend on the algorithm used for the master frequency and phase estimation as long as the estimated frequency and phase can be readily extracted and applied to the slave channels.  
There are different motivations for using master-slave schemes for phase estimation and frequency offset estimation. For phase estimation, the underlying rationale is that the phase estimation is computationally demanding and therefore attractive to share between many channels. Master-slave frequency offset estimation on the other hand can be used to avoid frequency offset differences between the channels. Traditional fourth-power spectral methods to estimate the frequency offset generally have a limited frequency resolution [107], which leads to a small frequency offset remaining on the signal. This offset is easily tracked with the fine phase estimator, but it might be different for different channels and therefore hinders joint phase recovery.  
Master-slave frequency offset compensation however requires some additional considerations depending on the specific LO comb implementation. If the spacings of the transmitter and LO combs are synchronized, the channels will have the same frequency offset, and the master-slave concept can be directly used. However, if the spacings are not synchronized, the channels will have different frequency offsets. In this case, the comb spacing difference needs to be estimated separately and also applied to the slave channels. The principle of such a scheme is illustrated in Figure 7a. The comb spacing generally varies only slowly and can be estimated relatively seldomly.
# A Geospatial Approach to Improving Fish Species Detection in Maumee Bay, Lake Erie
## 2 Methods
### Geospatial Analysis  
The spatial distribution of fish species richness by gear type was analyzed using spatial statistics in ArcGIS 10.5. All distance methods were set to Euclidean distance (i.e., straight line distance). Moran's I, a spatial autocorrelation analysis, was used to evaluate the presence of spatial clustering between the sampling locations [20; 21]. The zone of indifference was the spatial relationship used for Moran's I and the hot spot analysis (Getis-Ord Gi*). The zone of indifference treats features within a specified area as neighbors, features outside the area are also neighbors but they are assigned a smaller and smaller weight as distance increases. The Moran's I distance band threshold (i.e., search radius) setting was the ArcGIS default value ensuring every feature had at least one neighbor. The hot spot analysis distance band threshold was determined using the first significant peak from the incremental spatial autocorrelation analysis, which represents a distance where spatial clustering was pronounced [20].  
The number of fish species caught at a sampling location by gear type from 2013-2016 were input into the hot spot analysis to determine high or low spatial clustering. The output from the hot spot analysis provides z-scores and \(p\) values that identify significance of clustering at a given distance [16]. Z-scores greater than 1.96 or below \(-\)1.96 indicate statistically significant hot or cold spots with \(p\) values \(\leq\) 0.05. The z-scores from the hot spot analysis were interpolated using the inverse distance weighed (IDW) tool to generate gradient layers of potential fish species richness to better visualize patterns and identify areas for targeted sampling in Maumee Bay (Figure 1).