esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill
## 6 Conclusions and Future Work  
In this study, a novel method was developed for RUL prognosis in the hot rolling milling process. The RUL estimation methodology was implemented in a software application, along with a method for estimating the confidence of the predicted RUL values. The proposed approach was validated in a case study in which two different types of trailing arms were produced by a rolling mill machine. The prediction capability of the method was evaluated based on historical datasets available by the manufacturing company, and the results indicate that the proposed method is accurate enough to be used in industrial practice.  
Based on the experimental results, it can be supported that, by using this method, the industry can accurately--and according to the production plan--predict which day the maintenance activities have to be performed. Moreover, the method is characterized by robustness, since its performance is affected neither by product type variations on the machine nor by the user-tuned maximum accepted wear level that defines the tool replacement point.  
Future work will focus on (1) enrichment of the proposed energy analysis approach to include additional parameters affecting segment wear, such as rolling speed or oil pressure measurements; (2) application in a real-world industrial environment together with a maintenance tasks scheduling tool; (3) application of the proposed approach in other industrial equipment, to assess its generality; and (4) investigation of cybersecurity aspects.  
Figure 9: Indicative confidence increase curve during a prediction simulation session.  
**Author Contributions:** I.A., conceptualization, data curation, formal analysis, investigation, methodology, software, validation, visualization, and writing--original draft; N.N., project administration, supervision, and writing-review and editing. K.A., supervision and writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research leading to these results was partially funded by European Commission, under the H2020-IND-CE-2016-17 program, FOF-09-2017, Grant Agreement No. 767561 \"SERENA\" project, VerSatiE plug-and-play platform enabling REmote predictive maineNAnce.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs
## Study Area and Data
### Satellite Data and Preprocessing  
MODIS and Landsat surface reflectance images from 2015 were used in the data fusion experiments. Contemporaneous land cover maps from the USDA Cropland Data Layer (CDL) [31] were used in analyzing the results.  
Figure 1: Experimental sites and the boundaries of the Landsat scenes (**a**) with corresponding land cover maps from the Cropland Data Layer for 2015 (**b**).
## 1 Introduction
This receiver is an essential subsystem used in all the subsequent OPS demonstrations. Section 3 demonstrates a 10.24 Tb/s/port optical switch for PDM-16 QAM optical packets [18]. One of the main aspects of optical switches for higher multi-level modulation format is transparency. A semiconductor optical amplifier (SOA) switch, which was often used as a time-division optical switch in the past, may cause serious distortion on multi-level QAM signals due  
Figure 1: Evolution over time of the switching capacity of recent Optical packet switching (OPS) experiments at the National Institute of Information and Communications Technology (NICT). to the patterning effect [19; 20]. To alleviate this, we employ electro-optical (EO) phase shift switches made of PLZT [12; 18]. Additionally, the PLZT switch can provide a wider switching wavelength band than the SOA switches. Section 4 describes a full 2 \(\times\) 2 OPS optical switching node with a control plane capable of dealing with packet contention by means of packet dropping or packet buffering in fiber delay lines (FDLs). This 2 \(\times\) 2 OPS node is first demonstrated in back-to-back with a switching capacity of 10.24 Tb/s/port [21]. Section 5 demonstrates the 2 \(\times\) 2 OPS switching node with an improved transmitter and achieves 12.8 Tb/s/port in a transmission experiment consisting of two spans of 50 km of SMF and dispersion compensating fiber (DCF) [22]. Finally, the paper is summarized and the main conclusions are drawn.
## 3 Spoofing Detection Method  
By analyzing the impact of spoofing on parameters under a single rotating antenna, hypothesis testing is established based on observations of the CNR and carrier phase. Then, the GLRT method is used to solve the hypothesis-testing problem, thus achieving spoofing detection.
## 2 Model Presentation
### Current Model  
\(\psi_{S2}\) can be calculated with \(\psi_{S1}\) using the linear expression (5). This method is computationally efficient but not accurate for body thicknesses of less than 2 nm. A more general and accurate charge density model (CDM), which is valid for any silicon body thickness, is developed to obtain an explicit relation between the surface electric fields \(E_{S1}\) and \(E_{S2}\).  
The potential \(\psi\) and the electric field \(E\) at any point in the same cross-section of the silicon body satisfy the following equation:  
\[E=-\frac{d\psi}{dx}=\text{sign}(E)\cdot\sqrt{G\cdot exp\left(\frac{\psi}{V_{lm }}\right)+C} \tag{12}\]  
where \(\text{sign}(E)\) is the sign of the electric field \(E\), \(G=K\cdot exp(-V_{ch}/V_{tm})\) and \(C\) is a constant.  
The relation between the surface electric field \(E_{S1}\) and \(E_{S2}\) can be obtained by integrating Equation (4) twice, with \(C>0\), \(C=0\) and \(C<0\), respectively:  
\[\left\{\begin{array}{rl}\frac{|E_{S2}|-\sqrt{C}}{|E_{S2}|+\sqrt{C}}=e^{- \text{sign}(E)\frac{\sqrt{C}}{V_{tm}}T_{Si}},\frac{|E_{S2}|-\sqrt{C}}{|E_{S1}| +\sqrt{C}},&C>0\\ 2V_{tm}\left(\frac{1}{E_{S2}}-\frac{1}{E_{S1}}\right)=T_{Si},&C=0\\ \tan^{-1}\frac{E_{S2}}{\sqrt{-C}}-\tan^{-1}\frac{E_{S1}}{\sqrt{-C}}=-\frac{ \sqrt{-C}}{2V_{tm}}T_{Si},&C<0\end{array}\right. \tag{13}\]  
By applying the boundary condition (2), \(E_{S1}\) can be calculated with \(\psi_{S1}\). The value of \(C\) can be obtained by Equation (12) with \(\psi_{S1}\) and \(E_{S1}\). The back-gate potential \(E_{S2}\) can be computed through (13).  
From Gauss' law, the unit area inverse charge density is given by  
\[Q_{i}(y)=\varepsilon_{Si}(E_{S1}-E_{S2}) \tag{14}\]  
The drain current is derived based on the charge sheet approximation [18]:  
\[I_{DS}=\mu\frac{W}{L}\left\{\frac{Q_{i}(L)+Q_{i}(0)}{2}[\psi_{S1}(L)-\psi_{S1 }(0)]+V_{tm}[Q_{i}(0)-Q_{i}(L)]\right\} \tag{15}\]  
where the surface potential and inversion charge density at the source and drain ends are solved by substituting \(V_{ch}(0)=0\) and \(V_{ch}(L)=V_{ds}\), respectively.
# Automatic Speech Recognition Advancements for Indigenous Languages of the Americas
## 3 Experimental Setup and Dataset Description
### Data  
For this work, we have used transcribed speech data collected from the following two sources: (i) the primary dataset provided by the organization of the AmericasNLP  
Figure 1: Sketch of the dataset used for fine-tuning the ASR system, the CNN and transformer-based architecture wav2 vec2.0, the fine-tuning process, the Bayesian hyperparameter search and the Sobol sensitivity analysis.  
challenge and (ii) a corpus collected from other publicly available sources. The NeurIPS competition advocated using ancillary external resources to complement the primary dataset during the training, but imposed usage restrictions on some databases. A summarized description of the train-dev-test database is depicted in Table 1.  
The database published by the competition included 0.72 h from The Pandialectal Corpus of the Bribri Language [69], 0.46 h from the Guarani Common Voice Mozilla database [64], 3.49 h from the Endangered Languages Archive (ELAR) (dk0137 and dk0491) for Kotiria [70,71], 1.8 h from ELAR for Waikhana [71], and 3.95 h from the Siminchik dataset for Quechua [72]. All of these mentioned databases were subject to restrictions during the competition; it was not permitted to use data from those sources differently than set out by the rules provided by the organization. In addition to the primary dataset, we enhanced the training process by collecting supplementary speech data. Specifically, we amassed 1.14 hours of transcribed speech for the Bribri language [73], 21.8 h for Kotiria [74], and 7.04 h for Quechua [75]. However, it should be noted that no external data collection was obtained for Guarani or Waikhana.  
To augment the diversity and variability of the assembled speech audios, we applied offline speed augmentation techniques [21] to the primary database. Specifically, we augmented the audios at \(\times\)0.9 and \(\times\)1.1 speed rates, effectively widening the spectrum of speech patterns encountered during training. This augmentation technique fortifies ASR models and enhances their adaptability to varied speech styles and tempo changes [21]. Summing up, the cumulative duration of recordings in the training dataset varied across different languages as follows (see Table 1): 2.61 h for Bribri, less than one hour for Guarani, 29.87 h for Kotiria, 4.35 h for Waikhana, and 12.05 h for Quechua.
###### Abstract  
With the rapid advancement of mobile robotics technology, Simultaneous Localization and Mapping (SLAM) has become indispensable for enabling robots to autonomously navigate and construct maps of unknown environments in real time. Traditional SLAM algorithms, such as the Extended Kalman Filter (EKF) and FastSLAM, have shown commendable performance in certain applications. However, they encounter significant limitations when dealing with nonlinear systems and non-Gaussian noise distributions, especially in dynamic and complex environments coupled with high computational complexity. To address these challenges, this study proposes an enhanced particle filtering method leveraging particle swarm optimization (PSO) to improve the accuracy of pose estimation and the efficacy of map construction in SLAM algorithms. We begin by elucidating the foundational principles of FastSLAM and its critical role in empowering robots with the ability to autonomously explore and map unknown territories. Subsequently, we delve into the innovative integration of PSO with FastSLAM, highlighting our novel approach of designing a bespoke fitness function tailored to enhance the distribution of particles. This innovation is pivotal in mitigating the degradation issues associated with particle filtering, thereby significantly improving the estimation accuracy and robustness of the SLAM solution in various operational scenarios. A series of simulation experiments and tests were conducted to substantiate the efficacy of the proposed method across diverse environments. The experimental outcomes demonstrate that, compared to the standard particle filtering algorithm, the PSO-enhanced particle filtering effectively mitigates the issue of particle degeneration, ensuring reliable and accurate SLAM performance even in challenging, unknown environments.  
Simultaneous Localization and Mapping; Extended Kalman Filter; particle swarm optimization +
Footnote †: journal: Applied sciences
## 7 Positioning Performance of LEO NR Signal
### LEO Navigation Scenario  
The propagation channel is a key part of the NR navigation system and is generated by LEO scenario simulation. We use Starlink's orbit data to establish the LEO satellite motion scenario [77]. The static receiver is located in Xi'an Jiaotong University, Shaanxi, China (108.9788\({}^{\circ}\)E, 34.2481\({}^{\circ}\)N), at an altitude of 450 m. The dynamic receiver moves around the campus at a speed of 60 km/h. The simulation starts at 12:03 UTC on 5 November 2024, and lasts 15 s. As shown in Figure 13, the elevation angles of six visible LEO satellites are higher than 10 degrees, and their orbit parameters are shown in Table 4. Therefore, the satellite-receiver kinematic simulation generates their geometric distance and relative velocity. According to the distance and velocity, we obtain three key propagation channel characteristics: (1) path loss, (2) time delay, and (3) Doppler shift. These parameters are sampled at \(T_{\rm coh}\) interval to capture rapid channel variations. The received NR signal can be built based on the transmitting signal and propagation channel characteristics. The NR receiver includes multiple received signals from different LEO satellites, which experience various channels. We use DLL and FLL to measure the time delay and relative velocity of the received NR signals. Finally, at each \(T_{\rm coh}\) interval, the EKF updates these measured values to estimate the receiver state vector, such as position, velocity, and clock error.  
Figure 12: DLL tracking accuracy with different local replica signals. (**a**) Transmitting power range: 55–65 dBm. (**b**) Zoomed-in range: 58–62 dBm.
## 2 Experimental Details
### Desulfurization Tests of Absorbents  
The performance tests of the desulfurization absorbent were carried out in a fixed bed reactor, as shown in Figure 4. The experimental apparatus consisted of a gas supply device, a 1/2-inch quartz tube reactor installed vertically in an electric furnace and a gas analyzer. The content of COS flowing into the reactor was diluted with nitrogen gas, and  
Figure 3: Images of the pellet-type Cu-based desulfurization sorbents shaped by the extrusion method, (**a**) CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\), (**b**) CuO/macro-porous Al\({}_{2}\)O\({}_{3}\).  
Figure 2: Procedure for the preparation of macroporous alumina by template method.  
the flow rate of all gases was adjusted using a mass flow controller (MFC). The Cu-based absorbents supported on \(\gamma\)-alumina and the macro-porous alumina were filled with the powder or pellet sample in the center of the reactor, and the change in COS content was measured by gas chromatography connected to the reactor outlet after supplying the COS gas. The reaction temperature was fixed at 200 \({}^{\circ}\)C, and a thermocouple was installed directly in the absorbent packed bed inside the reactor to measure the temperature of the absorbent layer. For gas chromatography (GC, Shimadzu GC-8A), the multi-packed column, which was connected to Parapak T (2 ft) and Hayasep Q (8 ft) as GC column material capable of separating nitrogen, COS, and CO, was used. The column temperature was set to 140 \({}^{\circ}\)C, and the injector and thermo-conductivity detector (TCD) temperatures were set to 150 \({}^{\circ}\)C. The concentration of COS supplied into the reactor was approximately 5000 ppmv and approximately 3000 ppmv in the case of powder absorbent and pellet absorbent, respectively. In the experimental process of the pellet-shaped absorbent, the error due to the channeling phenomenon caused by the difference in the packing density of the absorbent was minimized by flowing a low COS concentration. Moreover, with the powder absorbent sample, approximately 1.0 g was packed in the reactor, and in the case of the pellet-type absorbent, and 2.0 g was packed to conduct the reaction experiment. The sulfur capacity of the sorbents was calculated based on the time in which COS was detected at the reactor outlet. The desulfurization experiment was carried out for approximately 2 h after COS detection. The sulfur capacity was calculated as in Equation (1).  
\[\text{Sulfur capacity, mgS/g - sorbent}=(\text{F}_{\text{COS}}\times\text{M}.\text{W}_{\text{ Sulfur}})/\text{W}_{\text{sorbent}}\times\text{BT}\times 1000 \tag{1}\]  
\(\text{F}_{\text{COS}}\): mole flow rate of COS, mol/min  
\(\text{M}.\text{W}_{\text{sulfur}}\): gram mole mass of sulfur, 32 g/mol  
\(\text{W}_{\text{sorbent}}\): weight of sorbents packed in the reactor, g  
BT: breakthrough time, min  
Figure 4: Schematic diagram of the experimental setup for the desulfurization tests.
## 2 Related Work
### Drone-Based 5G NR Positioning  
As the field of UAV technology advances rapidly, its application in SAR solutions for localization purposes has experienced a notable surge. The integration of UAVs into SAR missions offers a compelling solution for precise localization, enhancing the efficiency and effectiveness of search and rescue efforts [8; 9; 18; 20; 21]. [PERSON] et al. [8] proposed the SARDO system, an automated UAV-based SAR solution that takes advantage of the high penetration of cell phones to locate missing persons. SARDO is designed to locate missing persons by combining pseudo trilateral measurements and machine learning techniques to efficiently localize cell phones in a given area. In addition, [PERSON] et al. [19] introduced the latest progress in the field of 5G converged positioning in terms of both system architecture and key technologies, focusing on analyzing the technical routes of 5G in-band wireless positioning and 5G converged positioning, and looking forward to the architectural evolution and technical challenges of the 6G-oriented space-time service system, discussing the converged positioning technologies based on the 5G mobile communication system. The latter work through the integration of the 5G in-band positioning, satellite positioning, and visual positioning, which significantly improve the positioning accuracy and coverage in complex environments. [PERSON] et al. [9] proposed a 3 DSAR solution that utilizes 5G-NR technology to achieve a single UAV 3D localization SAR solution, which significantly improves the positioning accuracy and coverage in complex environments by means of four main components, i.e., a distance estimator, an angle estimator, 3D localization, and a 3D UAV prediction trajectory algorithm. Ref. [20] proposed a solution for joint 3D positioning and trajectory planning of flying base stations (FBSs) with the objectives of determining the total distance between users and FBSs and minimizing the sum of FBSs flight distance by developing a fuzzy candidate points selection method. Ref. [21] proposed an intelligent solution based on a novel multilayered cellular automata for positioning flying cells and provided a solution for rapid and efficient positioning of multiple UAV base stations to respond in real time to urgent network changes. The scheme considers both backhaul and radio access network constraints, and user requirements in terms of downlink throughput.
## 3 Results
### Initiative Metadata: Country, Organization Type, and Founding Year (MQs 1, 2, and 3)  
The number of initiatives found per country or region is given in Table 1 ([19], Tab 3). Regions are listed when the initiative describes itself as working at that transnational level, \"Worldwide,\" for example. Initiatives with a presence in multiple countries are tagged as operating in all of them.  
Organization type data are given in Figure 2 ([19], Tab 4). Projects are defined as short-term, time-constrained initiatives; organizations are groups that may run many projects, events, or other activities; networks are groups that connect interested people; associations are groups that offer membership and associated benefits; foundations are groups that provide funding or consulting, and that may be less directly connected with on-the-ground work; and laboratories are physical spaces that allow participants to do hands-on work. It should be noted that initiatives are tagged only as one organizational type, meaning that, for example, an initiative that runs several projects and operates a laboratory space would still likely be classified as an organization.  
Figure 1: PRISMA flow diagram followed in the analysis. As no databases were consulted in the search, those flowchart boxes do not appear.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Country** & **Initiatives** & **Country** & **Initiatives** \\ \hline Spain & 56 & Norway & 3 \\ \hline Italy & 29 & Switzerland & 3 \\ \hline Germany & 19 & The Czech Republic & 3 \\ \hline Turkey & 17 & Bulgaria & 2 \\ \hline UK & 17 & Europe & 2 \\ \hline US & 14 & Lithuania & 2 \\ \hline Belgium & 12 & Slovakia & 2 \\ \hline The Netherlands & 9 & Bosnia and Herzegovina & 1 \\ \hline Portugal & 9 & Chile & 1 \\ \hline Worldwide & 8 & Columbia & 1 \\ \hline Austria & 7 & Costa Rica & 1 \\ \hline Finland & 7 & Croatia & 1 \\ \hline Greece & 7 & Ecuador & 1 \\ \hline Poland & 7 & Estonia & 1 \\ \hline Sweden & 6 & Iceland & 1 \\ \hline Romania & 5 & Latin America & 1 \\ \hline Slovenia & 5 & Latvia & 1 \\ \hline Denmark & 4 & Liechtenstein & 1 \\ \hline France & 4 & Luxembourg & 1 \\ \hline Hungary & 4 & Mexico & 1 \\ \hline Ireland & 4 & Ukraine & 1 \\ \hline Cyprus & 3 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of initiatives per country. Regions (“Worldwide,” “Europe,” and “Latin America”) correspond to initiatives that define themselves as working at the given level. Initiatives may be active (and thus, listed) in multiple countries.  
Figure 2: Initiative organization type.  
Founding-year data since 1983 are given in Figure 3 ([19], Tab 7). To show the trend more clearly, one initiative founded in 1965 is omitted from the graph.
## 3 Catalytic Advanced Oxidation Processes (AOPs)
### Photocatalysis and Ozonation  
In addition to peroxide and persulfate systems, SDB catalysts are used in photocatalytic and ozone treatment processes. Photocatalysts like TiO\({}_{2}\) and ZnO are limited in solar energy utilization due to their wide bandgap energies (3.2-3.6 eV), restricting their activity primarily to the ultraviolet range, which accounts for only 3-5% of the solar spectrum. Coupling these semiconductors with SDB has been shown to reduce the bandgap and improve responsiveness under visible light, thereby enhancing photocatalytic performance [50]. Under light irradiation, the carbon substrate can absorb light or act as a support for semiconductor particles. Simultaneously, the biochar can adsorb contaminants, increasing their local concentration and enhancing the photocatalytic efficiency. For instance, [PERSON] et al. [50] investigated a novel SDB/TiO\({}_{2}\) composite, highlighting the role of metallic elements present in sewage sludge in enhancing photocatalytic hydrogen generation from water. The composite achieved a hydrogen generation rate of 2523 umol/(g-h), which was a 5.5-fold improvement compared to pristine TiO\({}_{2}\). During the co-synthesis process, structural defects formed in the SDB were conductive to the development of multilayered architectures, which in turn improved charge carrier mobility and facilitated the photo-reforming of glycerol. In micropollutant removal, SDB/TiO\({}_{2}\) composites have shown improved photocatalytic degradation of antibiotics, leveraging SDB's adsorptive enrichment and electron shuttling to reduce charge recombination [51]. In ozonation, recent studies have demonstrated that SDB can function as an effective catalyst, promoting the decomposition of ozone into \(\cdot\)OH and thereby enhancing oxidative degradation efficiency [52; 53].  
Overall, SDB demonstrates multifunctional catalytic capacities, enabled by its diverse surface functionalities, intrinsic metallic species, and hierarchical porosity. These structural attributes facilitate efficient activation across various AOPs, including iron-mediated redox systems, persulfate activation, PAA catalysis, and light-driven or ozone-coupled degradation mechanisms. These advancements establish SDB as a low-cost and sustainable catalyst for advanced oxidation in wastewater treatment.