esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper presents an adaptive super-twisting sliding mode control (STSMC) strategy for a single-phase active power filter (APF), which integrates an interval type-2 fuzzy neural network (IT2 FNN) with a self-feedback recursive structure. The proposed controller effectively addresses the challenges of harmonics suppression in APF systems by approximating the unknown system model using the IT2 FNN. By leveraging interval type-2 fuzzy membership functions and historical information, the network enhances its nonlinear processing capability and accuracy, thereby improving the controller's approximation ability. Moreover, the adaptive STSMC reduces the chattering phenomenon and simplifies parameter setting by incorporating an adaptive law for network parameters. The stability of the system is rigorously established using the Lyapunov method, ensuring reliable performance in practical applications. The experimental results validate the significant improvement in the total harmonic distortion (THD) rate compared to traditional  
Figure 9: Curves of power supply voltage, load current, compensation current, and power supply current measured when the additional load is disconnected under different methods: (**a**) IT2 FNNSFR STSMC algorithm; (**b**) ASMC algorithm; (**c**) traditional STSMC algorithm.  
adaptive sliding mode control and super-twisting sliding mode control, demonstrating commendable steady-state performance and dynamic response. It can be inferred that the controller effectively suppresses harmonics in the APF system, highlighting its potential for real-world implementation and contributing to the advancement of power quality improvement techniques.  
Conceptualization, J.F.; methodology, J.W.; writing--original draft preparation, J.W.; writing--review and editing, J.F. and X.L. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the National Science Foundation of China under Grant No. 62273131.  
All relevant data are within the paper.  
The authors declare no conflicts of interest.
## 4 ARCOMEM Data Management
### ARCOMEM Data Model  
One of the first tasks to be addressed in ARCOMEM was the identification of the concepts that are relevant for knowledge capturing, crawling, and preservation. After a requirements analysis, a structured domain representation was created in OWL (Web Ontology Language) [9] that reflects the informational needs of ARCOMEM. Then, from the perspective of data interoperability, access and reuse we have concentrated on embedding the ARCOMEM conceptual vocabulary in the wider semantic Web context. We have done this in several ways.  
First, we have ensured interoperability with the Open Archival Information System [10] (OAIS, ISO 14721). OAIS constitutes a conceptual framework for long term digital information preservation and access. As such, it provides a central point of entry into OAIS related standardization, and a basis for comparing the data models of digital information preserved by archives. Therefore, interoperability with the OAIS information model is a prerequisite for the ARCOMEM data to be taken into account for the purpose of long term preservation.  
In addition, the ARCOMEM information concepts have been linked to concepts from the wider semantic Web context including the LOD (Linked Open Data) cloud, in order to harmonize the semantic coverage and ensure a proper embedding of the ARCOMEM conceptual vocabulary in the wider semantic Web context. The basic idea behind Linked Data is the use of globally valid identifiers for referencing resources. This reduces the integration effort required for semantic interoperability. Linked Data are typically published in W3C-Standards RDF (Resource Description Framework) [11] or OWL [9]. ARCOMEM ontology elements have been linked with elements from foundational ontologies such as Dolce UltraLight [12] and officially established or de facto standard vocabularies for conceptual constructs such as event, e.g., Linked Open Descriptions of Events (LODE) [13], Event-Model-F [12].  
The central concepts in ARCOMEM's configuration are _InformationObject_, _InformationRealization_ and _CrawlingConcept_. InformationObject is the class that subsumes all ARCOMEM's information types: entities, events, opinions, and topics. Multilingual instances of this class are classified according to the language they belong to. InformationRealization captures the concrete instantiations of these information objects in the form of multimedia Web object such as texts and images. CrawlingConcept describes required aspects of the crawling workflow.  
Figure 4 illustrates the resulting data model in the form of a simplified UML notation in the form of classes and their relations. Open arrows represent subClassOf relations, whereas closed arrows indicate any other type of relation. The ARCOMEM data model is represented in OWL. This enables structured access to its information content through SPARQL queries. The query structure relies on statements expressed in the form of subject-predicate-object _triples_, denoted as \(\{S,P,O\}\)[14].
## 3 Results and Their Analysis  
The study presents results from the stage of characterization, grouping, and weighing obtained with the use of modeling by Eco-indicator 99. An analysis of negative environmental effects generated during one life cycle of the land and offshore wind farm was performed including four kinds of impacts, eleven impact categories, and three impact areas. The first stage of analysis within LCIA (impact assessment) was characterization. All three categories (operators' health--unit: DAIY; the environment quality--units: PAF\(\cdot\)m\({}^{2}\)/a and PDF\(\cdot\)m\({}^{2}\)/a; materials--unit: MJ surplus energy) and other eight (out of eleven) impact categories make up the nonergonomic impact. The highest level of negative impacts, both for the land-based and offshore power plant, was reported for, respectively: human health--emission of inorganic compounds--cause of respiratory system diseases (0.999 DALY--land-based wind farm, 0.954 DALY--offshore wind farm), for deterioration of the environment quality--emissions of ecotoxic compounds(879,423.9 PAF-m\({}^{2}\)/a--land wind farm, 1,320,083.6 PAF-m\({}^{2}\)/a--offshore wind farm) and for exhaustion of natural resources--processes connected with extraction of fossil fuels--(2,437,145.9 MJ)--land wind farm, 1,752,643.1--offshore wind farm). In terms of nonergonomic impacts, it is the land-based wind power plant that produces a higher level of adverse effects on the human health and exhaustion of natural resources, whereas, in terms of the environment deterioration, this is the offshore wind power plant (Table 1). Nonfunctionality impact is described with the use of four impact categories: emission of radioactive elements (human health--DALY unit), use of land (quality of the environment--PDF-m\({}^{2}\)/a unit), and processes connected with extraction of minerals and fossil fuels (sources--MJ unit, surplus energy). The last of the listed categories is characterized by the highest negative impact in the considered type of impact (2,437,145.9 MJ--land wind power plant 1,752,643.1--offshore wind power plant). In respect of nonfunctionality impact, higher levels of adverse impact on the environment and depletion of resources was found for a land-based wind farm, whereas an offshore wind power plant was found to pose more threat to human health (Table 1). Nonecological impact is the sum of negative impacts of eight impact categories from three areas: operators' health (emission of radioactive elements causing climate changes and ozone layer depletion), the environment quality (emission of ecotoxic substances causing acidification or eutrophication and processes connected with land use), and resources (processes connected with extraction of minerals and fossil fuels). The highest impact on the health of humans and animals of both considered wind farms was found for emission of substances causing climate changes (0.351 DALY--land-based wind farm, 0.379 DALY--offshore wind farm). Emissions of ecotoxic
## 2 Materials and Methods
### Area of Study  
Africa is the second-most populous and second-largest continent in the world. It covers 6% of the earth's total surface (20% of total land area) with a total area of about 30.3 \(\times\) 10\({}^{6}\) km\({}^{2}\)[37]. Solar energy distribution in Africa is fairly uniform and the global solar horizontal irradiance for a larger proportion (85%) of the landscape is over 2000 kWh/m\({}^{2}\)/year. The continent has a solar power generation potential of 1000 GW which is largely untapped to date [38] and the theoretical estimated solar power production is 60 \(\times\) 10\({}^{6}\) TWh/yr [39]. Ten out of the 54 countries in Africa have been selected to test the hybrid neural network developed in this study. These countries are from the five geopolitical zones in Africa (Nigeria and Ghana from West Africa; Algeria and Egypt from North Africa; South Africa and Namibia from Southern Africa; Ethiopia and Somalia from East Africa; the Central Africa Republic and Chad from Central Africa) and are highlighted with location tags in Figure 2. The case study details in terms of latitude, longitude, elevation, optimum azimuth, and optimum slope are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Geopolitical** & **Country** & **City** & **Latitude** & **Longitude** & **Elevation** & **Optimum** & **Optimum** \\
**Zone** & **Country** & **City** & **(\({}^{\circ}\))** & **(\({}^{\circ}\))** & **(m)** & **Azimuth (deg.)** & **Slope (deg.)** \\ \hline West Africa & Ghana (GH) & Bongo & 10.903 & \(-\)0.812 & 216 & 6 & 16 \\ West Africa & Nigeria (NG) & Kano & 12.022 & 8.524 & 465 & 5 & 16 \\ East Africa & Ethiopia (ETI) & Dijbouti & 10.647 & 41.286 & 538 & 0 & 13 \\ East Africa & Somalia (SOM) & Bari & 10.271 & 50.141 & 308 & \(-\)4 & 12 \\ Central Africa & Chad (CH) & Ennedi & 18.064 & 22.576 & 545 & 3 & 20 \\ Central Africa & Central African & Vakaga & 9.826 & 22.508 & 494 & 8 & 16 \\ North Africa & Algeria (ALG) & Tamanrasset & 24.072 & 4.679 & 874 & 26 & 27 \\ North Africa & Egypt (EGY) & Mut & 24.475 & 28.466 & 332 & 5 & 25 \\ Southern Africa & South Africa (SA) & Northern Cape & \(-\)29.186 & 20.464 & 874 & \(-\)180 & 0 \\ Southern Africa & Namibia (NAM) & Erongo Region & \(-\)21.963 & 15.331 & 1179 & \(-\)180 & 0 \\ \hline \end{tabular}
\end{table}
Table 1: Case study location description.
## 4 Experimental Results
### Network Utilization  
Streaming large images for realistic SDO distance education requires a high network bandwidth. This study used the multichannel-based data transfer method mentioned in Section 3.3 to utilize the high-bandwidth network resources provided by KREONET. Figure 12 shows the data transfer rate and network utilization results according to the number of channels when the SDO images are split-transmitted in six channels from the SDO server to the relay server. The data transfer rate and network utilization for stable transmission were verified in an actual transmission experiment while increasing the number of channels by one to fully utilize the network bandwidth when large data such as SDO images are transmitted. The experimental results in Figure 12b show that the data transfer rate and network utilization increased linearly in proportion to the number of channels and 10 SDO image data can be transmitted without delay.  
Figure 10: Network configuration for IVR-SDO streaming distance education for user experiment. Users A and B are students, and User C is an expert.  
Figure 13 shows the changes in the network utilization of the relay server when the three participants, Users A, B, and C, enter the distance education system at regular intervals. When the education participants access the relay server, the SDO images are relayed. Hence, network utilization gradually increases at each point in time when the server is accessed. Quantitatively, the relay server receives approximately 287 Mbps of data from the SDO image server and relays three times (855 Mbps) the received data to the three participants.  
Figure 11: Experiencing the IVR-SDO streaming distance education system: (**a–d**) Users A and B (students) receive explanations about the SDO images from User C (expert) and ask questions; (**e–h**) display as seen from the viewpoint of User C (expert).  
Figure 12: Experiment results using the multichannel-based SDO image streaming module. The data transfer rate and network utilization increased in proportion to the number of channels: (a) multichannel-based SDO image streaming from the SDO image server to the relay server; (b) measurement results of data transfer rate and network utilization according to the number of channels.  
Figure 13: Increase in the network utilization of the relay server when three users connect to it one by one while SDO images are transmitted to the relay server.
# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 6 Experimental Results
### Simulation Results
#### 6.4.3 Convergence Speed Under Latency Constraints  
We adopt the normalization from [41] by fixing the ratio of edge communication to the local computation time at 10 and then setting the normalized edge round cost to 1.  
Figure 8: Performance comparison of three FL frameworks on GTSRB with a Dirichlet non-IID split (\(\alpha=0.1\)). Bars denote the mean accuracy, precision, recall, and F1-score after 200 communication rounds (averaged over 30 independent runs). Error bars indicate 95% confidence intervals.  
Figure 7: Performance comparison of three FL frameworks—single-server FL, proposed MS-FL with DWAA, and cloud-based FL—on MNIST with a Dirichlet non-IID split (\(\alpha=0.1\)). Bars denote the mean accuracy, precision, recall, and F1-score after (**a**) 5 communication rounds and (**b**) 10 rounds (averaged over five runs).  
Similarly to [18], we use the cloud to edge latency ratio as \(\mathrm{t_{c}}=\frac{\mathrm{\tau_{edge}(t)+\mathrm{\tau_{cloud}(t)}}}{\mathrm{ \tau_{edge}(t)}}\), which is greater than 1 because client-edge-cloud communication in hierarchical FL is substantially slower than client-edge communication. Consistent with [10; 41], we use \(\mathrm{t_{c}}\)= 5 and \(\mathrm{t_{c}}=10\) for cloud-based FL.  
Concretely, we model each edge server round as 1 ms of wireless transmission plus 0.1 ms of local computation (so 1.1 ms per round) for both MS-FL and single-server FL. For the cloud-based hierarchy scheme, we use a 5 ms and 10 ms end-to-end client-cloud latency plus 0.1 ms of computation, yielding 5.5 ms and 10.5 ms per round. These settings--drawn from [10; 18; 19; 41]--allow us to compare convergence speed under realistic network and processing delays.  
Figure 9 compares test accuracy over cumulative running time for three FL schemes on both MNIST and GTSRB. In both plots, the DWAA-based MS-FL (orange curve) achieves near-final accuracy in far fewer time units than the other methods. On MNIST (Figure 7a), DWAA reaches over 90% accuracy within roughly 10 ms, whereas single-server FL (blue) needs about 30 ms and the two cloud-based variants (green and red) lag behind. On the more challenging GTSRB dataset (Figure 7b), the same ordering holds: DWAA converges most rapidly, single-server FL follows, and cloud-synchronized FL suffers the greatest delay.
# Development and Field Validation of Wireless Sensors for Railway Bridge Modal Identification
## 1 Introduction
In Section 2, the smart wireless monitoring system developed for bridges' SHM is described. The focus is on the design of the wireless sensors equipped with MEMS accelerometers. which are the sensing nodes to be positioned on the structure. A field test arranged to evaluate the performances of the developed system is presented in Section 3; contextually, a comparison is made with the results coming from a traditional monitoring system including wired velocimeters, already installed for other activities. This comparison allowed for the validation of the proposed solution. Some preliminary results of the experimental activity are then shown in Section 4, and a basic diagnostic indicator is obtained for the case study. In the conclusions, the potential of the proposed system to be used for SHM of bridges is highlighted as well as actual limitations and future improvements are discussed.
# Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots
## 4 Homography Estimation for a Non-Center-Aligned Image Pair
### Results and Discussion  
We compared our hierarchical HomographyFpnNet with the baseline of classical ORB/SIFT+RANSAC methods for homography estimation of non-center-aligned image pairs. We report the metric of mean corner error for each approach on the 30,000 test samples generated from the MS-COCO 2014 test set. To measure this metric, we computed the L2 distance between the ground truth corner position and the estimated corner position in the original image_a. In other words, for hierarchical HomographyFpnNet, we first resized image_a to a size of \(128\times 128\) pixels for the prediction, and then we resized the predicted corners back to the original image_a size to compute the error in the corner positions. The error was averaged over the four corners of the image, and the mean corner error was computed over the entire test set (30,000 samples). We used the default OpenCV parameters in the traditional ORB/SIFT+RANSAC homography estimator, which computes ORB/SIFT features at multiple scales and uses the top 25 scoring matches as input to the RANSAC estimator. To avoid extremely large corner error, the estimated four-point homography values of ORB/SIFT+RANSAC method were clipped within the shape size of image_a. In scenarios where the ORB/SIFT+RANSAC estimate failed, we ignored the estimate and excluded that test sample.  
As shown in Figure 8, the ORB+RANSAC method performed poorly in the homography estimation of non-center-aligned image pairs, with a mean corner error of 173.59 pixels. The method of SIFT+RANSAC performed better and obtained a mean corner error of 98.16 pixels, while the detection speed of SIFT features was much slower than that of ORB features. The reason that ORB/SIFT+RANSAC methods performed poorly in the homography estimation of non-center-aligned image pairs is that the size of image_patch_b is smaller than image_a, causing many mismatches of ORB/SIFT feature points between image_a and image_patch_b. Our hierarchical HomographyFpnNet performed much better in this task, and the mean corner error of the three-stage model was only 6.05 pixels. Figure 9 shows some prediction results obtained with our three-stage hierarchical HomographyFpnNet method compared with those obtained with ORB/SIFT+RANSAC methods, in which the corners of the green boxes are ground truth four-point homography values and the corners of the red boxes are predicted results.  
Figure 8: Test error comparison of our hierarchical HomographyFpnNet with other methods for non-center-aligned image pairs. Pixel errors are all computed in the original image_a size.  
Figure 9: Homography estimation results of different methods for non-center-aligned image pair. The corners of green box are ground truth four-point homography values and the corners of red box are predicted results.
# From Modeling to Failure Prognosis of Permanent Magnet Synchronous Machine
## 6 Conclusions  
Hybrid electric vehicles are gaining popularity nowadays. The challenge of reliability pushes research to apply prognosis on different elements constituting the electrical propulsion system of the vehicle. This research focuses on the prognosis of the electrical machine in the HEV.  
Several axes are treated and presented in this paper. We started with machine modeling where a surface permanent magnet machine used in hybrid electric vehicle application is modeled using finite element modeling. Then, the hidden Markov model for prognostic purposes is developed to detect the presence of short circuit, demagnetization and eccentricity faults. Prognosis aims to detect faults at their early stage or before they occur; hence, the probability of false alarms is high. To avoid this problem and eliminate any possibility of ambiguity in the prognostic decision, we used three vital parameters that are useful for machine monitoring, torque, temperature and vibration, as the inputs of the HMM. Torque, temperature and vibration signals were synthesized from electromagnetic, thermal and vibration finite element models respectively. After that, the RUL of the PMM is calculated when a turn-to-turn short circuit in one slot occurs. A suggested RUL calculation strategy that takes advantage of the aging equation of insulation and aging behavior of magnet is presented.  
Figure 27: Illustrative example for the RUL calculation in the case of turn to turn short circuit.  
Results show that torque, temperature and vibration are good vital parameters to predict the presence of demagnetization, turn-to-turn short circuit and eccentricity faults. The hidden Markov model is suitable for prognosis as it is easy to implement and its input is directly collected from data sensors.  
Conceptualization, R.G., G.H. and N.M.; Supervision, G.H. and N.M. All authors have read and agreed to the published version of the manuscript.  
This work is co-funded by the European Union and Normandy Region. Europe is involved in Normandy through the European Funds for Regional Development.  
The authors declare no conflicts of interest.