esa-sceva/satcom-chunk-collection huggingface dataset:
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 4 Experiments and Results
### Effect of Audio Duration and Number of Words on Performances  
In this section, we investigate the effect of the longer audio-text pair on the baseline alignment accuracy. This is where we explicitly tackle the task of very long audio-to-text alignment at the word level.  
For speech, we added the three chapters before and/or after the reference Chapter 10 to produce longer data. We thus performed the alignment between the audio and the corresponding text for Chapters 7-10, Chapters 10-13, and Chapters 7-13. For the singing voice, we added 50 songs before and/or after the Playlist 50. We thus performed the alignment between the audio and the corresponding lyrics for up to 150 songs.  
We computed the alignment on a single Intel(R) Xeon(R) Gold 5118 CPU @ 2.30 GHz on a server with 131.6 GB RAM. We rely on the linear memory-forced alignment that we presented in Section 3.4 to deal with long audio-text context. We systematically report the number of pivots that we use.  
We computed the AAE for Chapter 10 and Playlist 50 for each case, taking the offsets incurred by the other chapters/songs. The results are shown in Tables 1 and 2.  
These results demonstrate that the alignment error remains remarkably stable, both for speech and the singing voice, even when preceded and/or followed by several extra hours of audio and the corresponding transcripts. This indicates that the system is able to precisely align several hours of audio with the corresponding text and with the same level of accuracy.  
The encoding module is insensitive to increasing audio duration thanks to its fully convolutional architecture. The forced-alignment module is also insensitive to increasing audio duration and corresponding transcript length thanks to the use of pivots.
## 3 Experimental Considerations
### Experimental Results and Considerations
#### 3.2.1 Temperature Characteristics of Single Reinforced Concrete by High-Frequency Induction Heating
Furthermore, an increase in the voids in induction heating is a result of the generation of fine cracks in concrete, dehydration of water from gel and capillary voids, and decomposition of S-C-H hydrates and calcium hydroxide chemically bonded to evaporate the bonded water. Chemical Vulnerability Characteristics  
Introduction: Concrete is a material composed of aggregates, cement, and water. After hydration reactions, 60-70% of the mass is composed of C-S-H(CaO-SiO\({}_{2}\)-H\({}_{2}\)O) gel, and the remaining 20-30% is calcium hydroxide (Ca(OH)\({}_{2}\)). When concrete is exposed to high temperatures, the chemical and physical structures change, resulting in the dehydration of calcium hydrate and thermal expansion of the aggregates, which cause cracks in the concrete at temperatures above 300 \({}^{\circ}\)C.  
Calcium hydroxide (Ca(OH)\({}_{2}\)) in the hardened cement body is disassembled at 530 \({}^{\circ}\)C, while CSH gel, which has a large effect on the physical properties of concrete, is disassembled at temperatures of 600 \({}^{\circ}\)C or higher. Further, concrete melts at a temperature of 1150 \({}^{\circ}\)C or higher [53]. Concrete is a porous structure composed of hardened cement bodies and voids, which contain aggregate, cement, chemically bonded water, and gel water, and the voids are filled with free water and air. The moisture content change in concrete is defined as free water moving through voids, and the free and gel waters vaporized between room temperature to 105 \({}^{\circ}\)C. In addition, the release of chemically bonded water starts at 105 \({}^{\circ}\)C or higher and is completely released at 850 \({}^{\circ}\)C [54]. However, the thermal conductivity of ordinary concrete depends on the type and preparation of the aggregate. At room temperature, the thermal conductivity exceeds 2.0 kcal/mh \({}^{\circ}\)C, but decreases with temperature rise, and at 600 \({}^{\circ}\)C or higher, it becomes approximately 1.0 kcal/mh \({}^{\circ}\)C. Although thermal deformation for generating thermal stress in concrete is affected by the aggregate, it expands to 600-800 \({}^{\circ}\)C at room temperature, and the coefficient of thermal expansion is approximately 1.0 \(\times\) 10\({}^{-5}\)-1.5 \(\times\) 10\({}^{-5}\)\({}^{\circ}\)C\({}^{-1}\). Furthermore, the strength of concrete decreases at high temperatures, which also varies depending on the type of aggregate and the stress conditions, but typically falls to 50-60% at room temperature at 500 \({}^{\circ}\)C or greater, and even if moisture is replenished after cooling, it is difficult to recover strength because the cracks caused by the difference in the coefficient of thermal expansion between the cement paste and aggregate affects the internal structure.
# A Novel Charging Station on Overhead Power Lines for Autonomous Unmanned Drones
## 4 Results
### Experimental Robotic Manipulation  
Robotic manipulation experiments were conducted to demonstrate the application of the charging station under various scenarios. The testing process was divided into three phases: laboratory experiments, controlled outdoor mockup and real power line.  
The first set of experiments was conducted in an indoor mockup equipped with a five-meter section of power line suspended three meters above the ground by a metal structure. The experiments shown in Figure 12 consisted of installing the charging station using the robotic arm, the elevation platform and the end effector. First, the charging station was attached to the MLMP; then, the MLMP was manually placed on the line (Figure 12a). The installation of the charging station was carried out in a teleoperated mode, manually controlling the movements of the elevation platform (Figure 12b) and the arm (Figure 12c). Finally, the undocking maneuver was performed (Figure 12d).  
In addition, a second experiment was performed to test the mechanical coupling between the drone and the charging station when the station is installed and the drone is charging its batteries.  
The second set of experiments was conducted in a similar but larger mockup at an outdoor facility at the ATLAS Tactical Centre in Spain. The same experiments were  
Figure 12: Indoor experiments installing the charging station using the elevation platform and the robotic arm with end effector. (**a**) MLMP perched on the line; (**b**) elevation platform positions the charging station on the cable; (**c**) robotic arm actuates the worm gear with an ad hoc end effector to close the clamp; (**d**) undocking of the MLMP and the charging station.  
carried out as in the laboratory mockup but, this time, fully autonomously. The results demonstrated the installation of the station and the coupling between the MLMP and the charging station. More details about the results of the outdoor mockup can be found in Supplementary Materials Video.  
Finally, the third set of experiments was carried out on a real power line (Figure 13). The aim was to perform a charging maneuver by coupling the MLMP to the station already installed on the cable (Figure 13). The MLMP performed the maneuver of approaching the power line. The vehicle then perched on the line (Figure 13) and moved along the cable to approach the charging station (Figure 13). Finally, the UAV performed the coupling of the MLMP to the charging station (Figure 13).  
These tests validated the mechanical design of the charging station, MLMP and end-effector integration in a real operational environment.
## 2 Study Area and Data Sources
### Landslide Conditioning Factors
#### 2.3.2 Environmental Factors  
The Normalized Difference Vegetation Index (NDVI), shown in Figure 2g, represents vegetation cover density closely related to landslide susceptibility [36]. Higher NDVI values usually indicate better vegetation cover, where plant roots help stabilize the soil and reduce surface erosion, lowering the likelihood of shallow landslides [37]. Soil types (Figure 2h) also significantly influence landslide occurrence, as different soils have distinct properties, such as particle size, porosity, permeability, and cohesion. Soil types further affect vegetation growth, indirectly impacting slope stability.  
Distance from rivers (Figure 2i) and distance from roads (Figure 2j) are also critical factors. Slopes closer to rivers are more vulnerable to failure due to river erosion, which removes slope material and increases the frequency of landslides [38]. Similarly, areas closer to roads often exhibit higher susceptibility to landslides, although this relationship can vary depending on local terrain and geological conditions [39].
## 6 Conclusions
\begin{tabular}{l l} \(q\), \(\dot{q}\) & angular position and velocity of the joint \\ \(\theta\), \(\dot{\theta}\) & angular position and velocity of the motor \\ \(M(q)\) & the inertia matrix \\ \(C(q,\dot{q})\) & the centripetal/Coriolis matrix \\ \(D(\dot{q})\) & the friction torque vector \\ \(G(q)\) & the gravity torque vector \\ \(\tau_{d}\) & the aggregate disturbance \\ \(u\), \(u_{s}\), \(u_{f}\) & the system input vector, control command of the reduced slow subsystem \\  & and control command of the reduced fast subsystem \\ \(K\) & the stiffness matrix of the spring between the motor and the link \\ \(J\) & the inertia matrix of the motors \\ \(x_{1},x_{2}\) & \(q\) and \(\dot{q}\) \\ \(\tau_{1},\tau_{2}\) & the elastic torque and its derivative with respect to \(t_{\varepsilon}\) \\ \(h(x,t)\) & the equilibrium of \(\tau_{1}\) \\ \(\Lambda\) & the difference between \(\tau_{1}\) and \(h(x,t)\) \\ \(W^{*}\), \(\Phi\) & the NN weight matrix and regression functions vector \\ \(N\) & the number of neurons used in the hidden layer \\ \(\zeta\) & the NN approximation error \\ \(t_{\varepsilon}\) & the fast time scale \\ \(e_{1},e_{2}\) & the position tracking error and the velocity tracking error \\ \(\rho(t)\) & the prescribed performance function \\ \(e_{i}(t)\) & the transformed tracking error of \(e_{1i}\) \\ \(s_{s},s_{f}\) & the slow sliding manifold and the fast sliding manifold \\ \(K_{1}^{s},K_{2}^{s},K_{3}^{s}\) & the feedback gain matrices of \(u_{s}\) \\ \(K_{1}^{f},K_{2}^{f},K_{3}^{f}\) & the feedback gain matrices of \(u_{f}\) \\ \(\Omega_{ew}\) & preset compact sets for \(\hat{W}_{i}\) \\ \(\Omega_{Ei}\) & the ellipsoid set of \(\hat{W}_{i}\) \\ \(x_{2f}\), \(\hat{H}_{f}\), \(\Phi_{f}\), \(\tau_{1f}\) and \(\zeta_{f}\) & filtered results of \(x_{2}\), \(-M^{-1}H_{r}\), \(\Phi\), \(M^{-1}\tau_{1}\) and \(\zeta\) \\ \(F_{i}\) & the learning gain matrix of the NN weight's updating law \\ \(\hat{\psi}_{fi}\) & the filtered output of the NN \\ \end{tabular}
## 4 Data Products and Processing  
### Currently Available Data Products  
CYGNSS is the first mission that released its full raw IF data set, which is publicly available through NASA's Earth Observing System Data and Information System (EOSDIS) Physical Oceanography Distributed Active Archive Center (PO.DAAC). Under the support of HydroGNSS mission's [23] system consolidation study, the ICE-CSIC/IEEC team also got access to the full raw IF datasets collected by the UK TDS-1 mission. In addition, a limited sets of raw IF data from BuFeng-1 mission and Spire GNSS RO satellites have also been provided under the supports of different projects. Currently, these raw IF data sets have been processed following the procedure explained in Section 3.2 for all visible GNSS satellites from GPS, Galileo and BDS-3 constellations. The processing and results are organized by track, which is defined as the GNSS-R measurements obtained from one set of raw IF data and corresponding to one GNSS satellite. To guarantee the quality of the complex waveform products, the tracks with the mean SNR lower than \(-6\) dB are removed.  
The data products from CYGNSS and TDS-1 missions are fully available under the Creative Commons (CC) license, while these from Spire GNSS RO satellites and Bufeng-1 mission are available upon request. The geographical distributions of the product tracks for TDS-1 and CYGNSS are presented in Figure 3, with different colors indicating the specular points from different GNSS systems. There are 828 tracks of complex waveform products from the TDS-1 satellite (between 1 September 2014 and 25 March 2019) and 2646 tracks from the CYGNSS constellation (between 25 August 2017 and 20 January 2022). It is noted that the CYGNSS mission is still in operation and continues collecting new Raw IF data sets, from which the complex waveform products will be also updated periodically.
## 3 Result and Discussion
### Extraction of the Final Regenerative Areas
#### 3.3.1 Intersection with the Results of the Vegetation Indices
Furthermore, if a pixel had a value of less than 0.1, it was determined to represent a non-regenerative area. EVI indicated a regenerative region when the pixel value was 0.48 or higher. With SAVI, values above 0.9 indicate regeneration. NBR can be used to extract a vegetative regeneration site based on the precise criteria for fire severity classification. However, because such clear criteria have not been defined for the vegetation indices, it is difficult to extract a regenerative area solely via a comparison of the vegetative indices. 3.2 Comparison between the Final Vegetation Regeneration Area and the Area Extracted Using NDVI and dNBR  
For benchmarking, we compared the performance of the proposed method with the state-of-the-art alternative method using NDVI and NBR [19]. The method using NBR and NDVI detected more vegetation regeneration areas than the proposed method in Figure 10. The reason behind this is that the method using NBR and NDVI is sensitive to trees and light scattering. Our method can remove these distortions by using EVI and SAVI indices, thereby leading to smaller regenerative areas than the alternative method using the NBR and NDVI. Thus, the proposed method can detect vegetation regeneration areas with high probability. Figure 9: Final vegetation regenerative regions based on the proposed method.
# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
## 2 Principles and Methods
### Remote Sensing of Snow
#### 2.2.1 Snow Cover Area  
Snow cover area has been estimated using satellite optical sensors such as Landsat TM, Aqua/Terra MODIS, and NOAA AVHRR (advanced very-high-resolution radiometer). Snow cover can be identified under clear-sky conditions using the Normalized Difference Snow Index (NDSI), which exploits the contrasting reflectance of snow in the visible and short-wave infrared bands [100; 101]. Utilizing ancillary spectral indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Forest Snow Index (NDFSI) helps incorporate vegetation information in snow detection and improves performance in mapping forest snow cover [102; 103]. Radiation transfer models can also be used for improved snow mapping over forest areas [104]. Recent snow mapping efforts have focused on generating long-term snow cover products using observations from multiple satellite sensors [105], high-spatial resolution snow mapping using Sentinel-2 and Landsat optical imagery [106; 107] and machine-learning techniques [108; 109]. In addition, satellite sub-pixel snow cover is valuable for improved estimation of snowmelt runoff and understanding energy exchanges between the land surface and atmosphere [110]. Two main approaches to derive sub-pixel snow fraction include empirical linear regression of NDSI [111] and spectral mixture analysis [112; 113; 114; 115; 116].  
Cloud contamination can significantly limit the signal quality of snow property detections made by satellite optical-IR remote sensing. Daily composites of half-hourly to hourly observations from geostationary satellites enable optimized snow detection while suppressing cloud contamination effects. Automated snow mapping has been achieved for a variety of geostationary satellites including GOES (Geostationary Operational Environmental Satellite) over North America [117]; Meteosat Second Generation (MSG) satellites over Europe [118; 119]; and Multifunctional Transport Satellites (MTSAT)-2, Himawari-8, and Feng Yun (FY)-2 over Asia [120; 121]. A linear interpolation method [117] has been used to derive fractional snow cover from FY-2 satellite observations [122]. Cloud-freesnow cover products have also been derived using combined observations from polar-orbiting and geostationary satellites [121], additional observations from satellite microwave sensors [123; 124; 125; 126]; and through processing of satellite data using advanced spatiotemporal filters and interpolation techniques [127; 128]. Optical-IR sensors have limitations in distinguishing between dry and wet snow, which can be effectively addressed by incorporating SAR observations.
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 3 System Model
### Communication Links  
The transmission involves two types of links, direct line-of-sight (LOS) and indirect sight (NLOS) links.  
Direct link: LOS connection between BS and UN.  
Indirect link: NLOS condition, where signals reflect off the IRS before reaching the user.  
The mobile millimeter-wave (mmWave) bands have unique propagation and scattering behavior, which are characterized by high path loss, sparse multipath propagation, and a variety of blockages; thus, the mmWave channel can be modeled using the Saleh-Valenzuela model, which can model both the large-scale fading (path loss and shadowing) and the small-scale fading, such as the multipath components with different angles of arrival/angles of departure. The signal received by the user \(k\) can be modeled as follows:  
\[y_{k}=H_{BU}W+H_{IRS}\Theta H_{B1}W+n_{k} \tag{1}\]where \(H_{BU}\) represents the BS-to-user channel matrix, \(H_{IRS}\) and \(H_{BI}\) are the IRS-to-user and BS-to-IRS channel matrices, \(W\) is the hybrid beamforming matrix at the BS, \(\Theta\) represents the IRS reflection coefficient, \(x\) is the transmitted signal coefficient, and \(n_{k}\) is the additive noise.  
Environmental Description:  
Channel model: The mmWave channel is described as a mixture of large-scale fading (path loss, shadowing) and small-scale fading (multipath components). The Saleh-Valenzuela model is utilized to represent the channel as follows:  
\[H=\sum_{l-1}^{L}\alpha_{l}a_{r}(\theta_{r,l})a_{t}^{H}(\theta_{l,l}) \tag{2}\]  
where \(L\) is the number of multipath components, \(\alpha_{l}\) denotes the complex path gain of the \(l^{th}\) path, \(\theta_{r,l}\) and \(\theta_{l,l}\) are the angles of arrival and departure, and \(a_{r}\) and \(a_{t}\) are the array response vectors at the receiver and transmitter.
## 2 Related Work
### Weight Aggregation in FL  
Weight aggregation in FL revolves around the idea of transmitting client weights to a server for global model updates. Given the diversity of IoT scenarios, numerous weight-aggregation techniques have surfaced to improve model accuracy. [PERSON] initiated FL, using average weight aggregation from clients. This resulted in reduced communication rounds compared to synchronized Stochastic Gradient Descent (SGD) [13]. [PERSON] et al.'s FedPSO uses particle swarm optimization for weight aggregation, selecting optimal client weights to improve accuracy [22]. [PERSON] et al.'s FedHQ focuses on heterogeneous quantization precision, allocating weights based on quantization heterogeneity [23]. [PERSON] et al. addressed the challenge of divergent data distributions by employing DRL to evaluate client contributions for global model aggregation [24]. [PERSON] et al. showcased AdaFed, a scalable FL aggregation architecture that leveraged cloud functions for resource efficiency and resilience [25]. [PERSON] et al. presented FedLA, reducing aggregation frequency for efficient gradients in non-IID settings. It employed weight divergence change rate for aggregation timings [26]. [PERSON] et al.'s \(\gamma\)-mean method, anchored in a minimum divergence estimation, curtails the influence of byzantine clients [27]. [PERSON] et al. focused on the probability distribution of features, estimating local distributions for weight calculations in heterogeneous industrial IoT data [28]. [PERSON] et al. introduced DeFL, a decentralized aggregation method for cross-silo FL to tackle the central server's vulnerabilities [29]. [PERSON] et al.'s FedBuff blends synchronous and asynchronous FL benefits, proving superior or equal to FedAvgM under differential private training [11].  
The aggregation of locally computed updates from clients with heterogeneous data distributions, communication constraints, and unstable network environments still poses challenges in terms of model accuracy and convergence. The FL algorithms proposed by various researchers use different methods for global model updates and weight aggregation to address these challenges. However, some of these methods have limitations, such as inefficient communication, vulnerability to central servers, sensitivity to byzantine clients, and reduced accuracy due to non-IID data distributions. To overcome these limitations, a novel aggregation method should be proposed to assign weights to each client based on the quality of their local model updates. This method can ensure that clients with more accurate and reliable updates receive more weight during global model aggregation.
## 2 Mathematical Analysis of Boost Converter  
Figure 1 depicts a common DC-DC boost converter diagram. It consists of an inductor (\(L\)), a filter capacitor (\(C\)), a semiconductor switch (\(S_{w}\)), and a diode (\(D\)). The input voltage, load resistance, and output current are denoted with \(E\), \(R\), and \(I_{0}\), respectively. During the converter modeling, the converter parameters and power switch are assumed to be ideal. It is also assumed that the converter is operating in continuous conduction mode. According to the ON and OFF conditions of the switch, the boost converter has two separate functioning modes. Based on two different modes, the average state space model of the converter can be constructed as [8]:  
\[\begin{array}{c}\frac{dI_{L}}{dt}=\frac{E}{L}-\frac{(1-u)V_{0}}{L}\\ \frac{dV_{0}}{dt}=\frac{(1-u)I_{L}}{C}-\frac{V_{0}}{RC}\end{array} \tag{1}\]  
where \(I_{L}\) is the inductor current, \(V_{0}\) is the output voltage, \(u\) is the control signal, which takes '0' for the OFF state and '1' for the ON state of the switch.