esa-sceva/satcom-chunk-collection huggingface dataset:
# DeCGAN: Speech Enhancement Algorithm for Air Traffic Control
## 3 Results and Analysis
### Dataset  
The dataset utilized in this study comprises two components. The first component is an extended version of the VoiceBank [18] + Demand [19] dataset. Given the limited availability of ATC-related speech data, and to more accurately simulate environmental noise interference in ATC communications, the noise types in the VoiceBank + Demand dataset were categorized and expanded. Specifically, typical interference noises pertinent to real ATC scenarios, such as coughing, clapping, yawning, machine noise, mouse clicks, keyboard typing, and background conversations, were selected. This resulted in a total of 10,375 noisy-clean audio pairs, with audio durations ranging from 4 to 10 s.  
To address the complex electromagnetic interference and rapid speech rates commonly observed in ATC communications, various real ATC environmental noises, including electromagnetic interference and engine noise, were extracted and mixed with the VoiceBank + Demand dataset at SNRs of 0 dB, 5 dB, 10 dB, and 15 dB to simulate authentic ATC noise conditions. Furthermore, the speech rate in the dataset was adjusted to reflect the fast-paced nature of ATC communications. The enhanced VoiceBank + Demand dataset was subsequently utilized for both model training and testing. Importantly, the noise samples in our dataset were randomly balanced across the different categories. Each type of interference noise was uniformly sampled to ensure a roughly equal number of noisy-clean pairs for each noise class. Moreover, rigorous stratified sampling techniques were employed during dataset construction to maintain a consistent representation of all noise types. This balanced design not only reflects the diverse acoustic environments encountered in real-world ATC communications but also effectively mitigates the risk of model overfitting to any specific noise category. Therefore, the extended version of the VoiceBank + Demand dataset supports robust model training with improved generalization capability, ensuring that the model performs reliably across various noise conditions encountered in actual ATC scenarios.  
The second part of the dataset comprises authentic ATC speech collected from the Southwest Air Traffic Management Bureau of the Civil Aviation Administration of China. This dataset includes 4000 noisy-clean audio pairs, with a mean duration of 5.4 s per sample. The median duration is 4.8 s per sample, and the standard deviation of sample durations is 1.2 s, indicating moderate variability in the sample lengths.  
The test set was set to contain five types of noises relevant to real ATC scenarios but that were not present in the training set to assess the model's generalization capacity. These noises were introduced to the speech data to create test samples, with the SNR set at 2.5 dB, 7.5 dB, 12.5 dB, and 17.5 dB. This approach aims to evaluate the model performance regarding speech enhancement under previously unseen noise conditions and to validate its robustness in complex, noisy environments.
## Abbreviations \begin{tabular}{l l} AMI & Advanced Metering Infrastructure \\ DG & Distribution Grid Branch \\ DL & Distribution lines \\ DSMR & The Dutch standard for smart metering (NTA 8130) \\ E1 & Experimental training and testing case 1 \\ E2 & Experimental training and testing case 2 \\ E3 & Experimental training and testing case 3 \\ E4 & Experimental training and testing case 4 \\ K1-K5 & Relays \\ LV & Low voltage (230/400 V) \\ M1 & Meter class and random power error case 1 \\ M2 & Meter class and random power error case 2 \\ M3 & Meter class and random power error case 3 \\ MDMS & Meter Data Management System \\ MUT & Meter under test \\ MV & Medium voltage (typically 10-20 kV) \\ OM & Ohmmeter \\ P1 & Port of smart electricity meters \\ PCC & Power consumption change \\ PLC & Power Line Carrier \\ \(P_{L1}\), \(P_{L2}\) & Loads \\ RB & Relay control block \\ RMSE & Root mean squared error \\ RS-232 & A hardware standard for asynchronous serial communication \\ T1 & Training data/Testing data case 1 \\ T2 & Training data/Testing data case 2 \\ T3 & Training data/Testing data case 3 \\ USB & Universal Serial Bus \\  & A load model used in power system analysis to represent how the power consumption of a load changes with voltage. It combines three load characteristics: constant impedance (ZZ), constant current (ZI), and constant power (ZP) \\
**Variables** & \\ d\(I_{s}\) & The change of sum meter current (A) \\ d\(P_{A}\) & The PCC associated with a presence of the anomaly (W) \\ \(dP_{e}\) & An active PCC of the load located under the consumer MUT (W) \\ \end{tabular}
# New Insights into the Neuromyogenic Spectrum of a Gain of Function Mutation in SPTLC1
## 3 Results
### Microscopic Findings on a Quadriceps Biopsy Derived from the p.(A20S)-Sptlc1-Patient
Staining of juvenile non-disease controls are added (Supplementary Figure S2). The immunofluorescence-based analysis of phospho-FAK1 on the quadriceps muscle was carried out to confirm our proteomic findings, disclosing this protein as being the most increased one in patient-derived lymphoblastoid cells. In skeletal muscle, we detected expression in NDC on muscle fibers, physiological for focal adhesion. In the biopsy derived from the patient, phosphor-FAK1 was increased in perimysial areas along with increased abundance in immune cells (Figure 5). Figure 4: Immunohistological studies focusing on inflammatory proteins in a quadriceps muscle biopsy derived from the p.(A20S)-SPTLC1-patient: CD3 staining highlights single T cells in the endomysium and the perimysium (arrows). CD68+ macrophages are diffusely scattered in the endomysium and accumulate around and within a small perimysial nerve fascicle (arrows). Physiological complement accumulation in the perineurium of this nerve (arrow), but not on any sarcolemmal or sarcoplasmic structure of muscle fibers. Single myofib in perifascicular regions indicate upregulation of MHC cl. I sarcolemmaly (asterix), and some macrophages in the perimysium are also MHC cl. I-positive (arrows). MHC cl. II similarly highlights some macrophages and the partially enlarged capillary vessels (arrows). Small regenerating fibers also express CD56 (arrows). Coherent Anti-Stokes Raman Scattering (CARS) Is a Nonlinear Variant of the Raman Effect  
CARS allows investigations based on molecule-specific vibrations without using any dyes or labels. We used CARS to non-destructively analyze the biochemical composition of a quadricep biopsy derived from our SPTLC1-patient. Muscle fiber calibers were measured manually from CARS images utilizing the Leica LasX software. A muscle fiber caliber of 49.78 um \(\pm\) 13.17 um on average was determined for the SPTLC1 patient. The five controls had an average fiber caliber of 43.92 um \(\pm\) 23.29 um. Given that proteomic findings in immortalized lymphoblastoid cells indicated perturbed lipid homeostasis and increased lipid droplet accumulation was already associated with a HSANI [16], for the spectral analysis of biopsy sections, we focused on both wavenumbers characteristic for lipids (~2847 cm\({}^{-1}\) and ~2989 cm\({}^{-1}\)[17]) and proteins (2932 cm\({}^{-1}\)[18]). Based on the CARS images at wave numbers 2842 cm\({}^{-1}\), 2886 cm\({}^{-1}\), and 2932 cm\({}^{-1}\), no significant disease features, such as protein and lipid changes, could be detected (Supplementary Figure S3).
## 4 Optical Methods
### Streamer-to-Flame Transition Detection
By simply observing this series, it can be stated that the very first frames had no combustion, while, in the last two rows, flames were certainly present. The red curve in Figure 10 represents the corresponding area evolution of Figure 9, and it allows a visual understanding of the described method. For such a red curve, this method indicated the 14 th frame to be the transition one. As a further example, the blue curve (sixth combustion, first quadrant, \(\lambda=1.4\)) shows the same trend, and the transition occurred in the 16 th frame. Moreover, this picture allowed noting a different behavior between red and blue cases; after flame onset, the binarized area for the red case continued to grow, while that in the blue one reached a sort of plateau after the 20 th frame. This behavior was found to be recurrent for the lean cases and related more to flame propagation than to the flame onset. The subsequent flame kernel evolution was investigated in other works [15; 32]. Figure 8: **(a)** Example of binarized area evolution during combustion (\(\lambda=1.0\), combustion #2, first quadrant); **(b)** binarized area in motored conditions. Colored curves refer to each single discharge event; the black curve is the corresponding average. Figure 9: Streamer evolution for \(\lambda=1.0\), combustion #2, first quadrant. The igniter is in the bottom-left corner. The numbers above each picture refer to the corresponding frames from the start of discharge. Streamer-to-flame transition occurs at the 14 th frame after the start of discharge, according to the method presented in this section. It is worth mentioning that this method, even if maintaining a degree of uncertainty, strongly restricted, to a few frames, the range in which the streamer-to-flame transition occurred; an uncertainty of three frames denotes less than 0.25 CAD.
## 5 Experiments and Results
### Recursive Feature Elimination  
This section describes a methodical way to integrate XGBoost with XAI approaches for feature selection and evaluation. This approach efficiently improves prediction performance and addresses the advanced demands of 6G networks by enhancing accuracy and identifying critical features. We start by examining XGBoost insights and removing features that have a zero score, which suggests that they have little bearing on predictions. After that, we identify both important and unimportant features using XAI algorithms. As shown in Figure 15, by iteratively improving the XGBoost model with these chosen features, we were able to significantly increase accuracy from 95.59% to 97.02%. Feature 6 (rst_count) and Feature 12 (IAT), which were previously recognized as high-ranking features, are confirmed to have the most influence on forecasting the output by the feature importance score list. This highlights that prioritizing the most relevant features can substantially enhance model performance, delivering reliable and accurate predictions essential for addressing the sophisticated, data-driven, and autonomous operations anticipated in 6G networks.
## 2 Methodology
### Experimental and Numerical Setup  
The configuration of the experimental flow loop was an annulus, and the annular length was 5500 mm. The overall length of the V-shaped hole cleaning device was 150 mm, and the device was placed 1000 mm away from the annulus inlet, as displayed in Figure 1. The Carboxyl-Methyl-Cellulose (CMC) solution was selected as the experimental drilling fluid, and the rheological parameters of the fluid were measured by the Anton Paar rheometer (MCR 92). A high-speed camera (Revealer 2F04C) was used to capture the cuttings transport process [24]. The simulated drill cuttings were selected as spherical glass beads with diameters of 2 mm.  
When the cuttings bed reached a stable state, the transparent ruler on the outer wall of the wellbore was recorded in detail to calculate the arc length and to convert it into the cuttings bed height. At the same time, it could also measure the effective action distance under the action of the hole cleaning device. To minimize any uncertainty, each experiment was repeated in three replications, and their average values were taken.  
The geometry of blades mainly includes the blade height and blade helix angle. In this paper, only the influence of helix angle is considered. Due to the complex downhole working conditions, the blade height could not be designed too large. The maximum allowable height is only designed regarding the height of the drill pipe joint.  
Figure 1: Experimental flow loop used to evaluate hole cleaning effect of V-shaped hole cleaning device. (**a**) Front view of the drill pipe, (**b**) back view of the annulus.  
Other physical parameters and wellbore structure parameters are summarized in Table 1.  
The numerical setup was modeled based on the experimental setup so that all dimensions and operation conditions were equal. A velocity-inlet boundary condition was used as the inflow condition; a pressure-outlet boundary condition was used as the outflow. The no-slip boundary conditions were assumed for all the drill pipe and wellbore boundaries. The rotation of the blade in the annulus was simulated using the sliding mesh (SM) method, and the interface was set to realize the information transmission between the stationary and rotating domains. The flow field solution was obtained using a CFD code (Fluent) based on the finite volume method used to discretize the partial differential equations of the model using the Phase Coupled SIMPLE (PC-SIMPLE) algorithm for pressure velocity coupling. All calculations were performed using a constant time step of 5 \(\times\) 10\({}^{-4}\) s for satisfactory convergence. The ambient conditions required by the CFD were chosen to be in good agreement with the experimental conditions and are listed in Table 1.
# Building Geometry Simplification for Improving Mesh Quality of Numerical Analysis Model
## 4 Automatic Geometry Simplification Method for FVM
### Face Classification  
To preserve the geometrical characteristics in the simplification process, an insignificant face in the model is integrated into the nearby major face that determines the overall shape of the model. The surface on building geometries are generally parallel or perpendicular to each other like cuboid. Given this characteristics, the faces with large area and has a normal direction similar to that of other faces are considered as major faces. The area \(a\) and index \(p\) are calculated as the preservation priorities for each face of the B-rep solids. \(p\) is an index that is defined in this study, and calculated as follows:  
\[p_{i}=\frac{\sum_{j}a_{j}|\overrightarrow{n_{i}}\cdot\overrightarrow{n_{j}}|} {\sum_{j}a_{j}} \tag{7}\]  
Figure 2: Automatic geometry simplification process. where \(p_{i}\) is the proposed index, \(a_{i}\) is the area (m\({}^{2}\)), and \(\overrightarrow{n}_{i}\) is the unit normal of the target face \(i\). \(p\) is an average of the scalar product of the normal of the target face and another face's weighted with the area. By evaluating the priority sequence of the face through \(p\) along with the face area, it is possible to obtain a model consisting of Cartesian angles, and by preventing the formation of acute angles, the generation of high-quality meshes can be expected [15; 29]. The list of entire faces of the target solid is sorted with the area \(a\) and index \(p\) to extract the face priority sequence (\(F\)) and classified into major and minor faces (Figure 3). In the order of the priority sequence, it is determined whether to remove a face, for faces (\(f_{2}\)) having a smaller area than each surface (\(f_{1}\)) (Figure 3a). When the distance (\(d\)) and the angle (\(\angle(\overrightarrow{n}_{1},\ \overrightarrow{n}_{2})\)) between faces are smaller than the input thresholds, the face \(f_{2}\) is determined to be a minor face, that is, a surface to be removed. The distance (\(d\)) is evaluated as the distance from \(c_{2}\) (the center of \(f_{2}\)) to the \(p\) (the foot of the perpendicular from \(c_{2}\) to \(f_{1}\)), that calculated as \(|\overrightarrow{d}|=|\overrightarrow{n}_{1}\cdot(\overrightarrow{c}_{1}- \overrightarrow{c}_{2})|\). At this time, if there is any geometry that should not be removed according to the purpose of the numerical analysis (\(S_{preserve}\)), it is excluded from the evaluation of minor faces. For example, the opening geometry like door, which is important for CFD analysis, was assigned as \(S_{preserve}\) in Figure 3b). After classifying all minor faces (\(F_{minor}\)), the remaining faces are determined to be the major faces. Algorithm 1 shows the pseudocode for the face classification algorithm.
## 4 Results and Discussion
### Structure Responses  
Figure 11a compares the peak acceleration for the roof motion (\(\mathrm{PGA_{RM}}\)) of the S1 structure on layered profiles (i.e., L1 and L2) with that on the Uni profile. For tests with low input earthquakes, the \(\mathrm{PGA_{RM}}\) values for the S1 structure on the layered profiles were similar to those on uniform ground even though the FFM in the layered grounds were slightly lower. As input intensity increased, the \(\mathrm{PGA_{RM}}\) in the layered grounds were observed to drastically increase, up to 20% and 15% for the L1 and L2 profiles at the highest \(\mathrm{PGA_{BM}}\), respectively, making it higher than for uniform ground. The reduction in stiffness of the soft upper layer as well as in foundation rocking stiffness could contribute to these changes [17]. The structure response for the L1 profile, with a thinner, soft upper layer, increased more than for the L2 profile. At high input intensity, since there was a drastic reduction in soil stiffness for the soft, upper layer, as indicated in Figures 5b and 8b, it was observed a significant increase in structure response, especially in the profile with a thin layer near the foundation. The absolute displacement of the roof of the S1 structure was estimated by double integration of the recorded RM. By subtracting this absolute displacement from the horizontal displacement of the free-field motion, the relative lateral displacement of the structure was calculated.  
Figure 11b compares the maximum of relative lateral movement of the S1 structure observed on the layered grounds with that for the uniform ground at various levels of input excitation. Compared to the results observed on the homogeneous ground (Uni profile), the structure was displaced more on the layered grounds, especially at high input intensity, which shows a similar trend to what is seen with \(\mathrm{PGA_{RM}}\) in Figure 11a and \(\mathrm{PGA_{S}}\) in Figure 5b. In addition, the structure slid more after a decrease in the thickness of the soft, upper layer. This is because the increase in the response of the soil beneath the structure caused a greater reduction in its stiffness and, as a result, the relative displacement and peak acceleration of the structure further increased. The bending moments, in time series, for three positions on the wall of the S1 structure were measured using three pairs of strain gauges (Figure 1). The maximum bending moment at the bottom (Max.M\({}_{\mathrm{B}}\)) for the layered profiles was found and compared with those for the Uni profile in Figure 11c. The demand bending moment of the structure on the layered profiles increased. This can be attributed to the higher peak acceleration and relative displacement observed in the structure in Figure 11a,b, respectively. As the thickness of the soft upper layer decreased, the bending moments of the S1 structure increased.  
Figure 11: \(Cont.\)
# Sorption of Eu (III) onto Nano-Sized H-Titanates of Different Structures
## 1 Introduction  
The development of nuclear energy results in increasing discharge of radionuclides in natural environments due to accidents in nuclear power or reprocessing plants [1]. Among different products, actinides and lanthanides are formed in spent nuclear fuel (SNF) and radioactive waste (RW). Thus, storage and reprocessing of SNF and RW can release these elements, as well as other components, into the natural water and soil environment. Transplutonium actinides (mainly, Am and Cm) and lanthanides formed in nuclear fuel during its operation are important hazardous elements due to their high toxicity and radioactivity. Therefore, it is very important to develop materials and methods for their effective removal from wastewater and groundwater.  
Americium and curium present only in trivalent form under environmental conditions and their chemical behavior is very close to lanthanides, particularly Eu (III). Different techniques of An (III)/Ln (III) extraction from water solutions have been developed using inorganic sorbents [2; 3; 4; 5; 6; 7; 8; 9; 10; 11], organic/polymer sorbents [12; 13; 14; 15], and different types of chromatography [16; 17; 18]. Recently nanosized Na, H-titanates attracted attention due to their properties. They have layered crystal structure, high ion exchange capacity, high surface charge, and fast ion diffusivity. Layered nanosized titanates have different structures and morphology [19; 20]: Nanotubes, nanowires, nanospherulites, etc. It was demonstrated that these materials can effectively remove heavy metals [21; 22], cesium [23], strontium [22], thorium [24], and uranium [25; 26] from water solutions. Usually, only one type of nanotianate structure is studied in a research work. In this work we have investigated the sorption of europium (III), as an analogue of An (III), onto nano-sized H-titanates with different structures.
## 3 Energy Management Strategy
### Cooperative Eco-Driving (CED) Guidance
#### 3.1.2 Slope Speed Assistance (SSA)  
SSA allows the driver to decelerate by coasting before the vehicle enters a downhill area, as shown in Figure 8. The vehicle is then accelerated by gravity on the slope to reach the slope exit speed. This reduces brake maneuvers on the slope. The slope entry speed is calculated by taking into account the average slope and length of the segment of road and the expected slope exit speed. It is assumed that there is no pedal intervention from the driver and that the engine clutch is disengaged. Since the entry speed is not known, the speed for calculating the drag force is assumed to be the exit speed after consideration for a margin of error. To calculate the slope entry speed, the driving resistance force \(F_{R}\) of the vehicle is calculated as shown in Equation (2) below.  
\[\begin{array}{c}F_{R}=F_{roll}+F_{drag}+F_{grade}\\ =-f_{roll}\;mg\text{cos}\,\theta-0.5C_{d}\;A\rho v_{exit}{}^{2}-mg\text{sin}\, \theta\end{array} \tag{2}\]  
where \(F_{roll}\) is the rolling resistance force, \(F_{drag}\) is the aerodynamics drag force, \(F_{grade}\) is the grade resistance force, \(f_{roll}\) is the rolling resistance coefficient, \(m\) is the vehicle mass, \(\theta\) is the slope angle, \(C_{d}\) is the aerodynamics drag coefficient, \(A\) is the frontal area of the vehicle, and \(\rho\) is the air density.  
The slope entry speed \(v_{ent,\text{SSA}}\) is calculated using the trapezoidal rule for approximation of integrals. It uses the average slope of the segment of road and assumes an ideal situation where only \(F_{R}\) exists. Using the driving resistance force \(F_{R}\), the exit speed \(v_{exit}\), and the slope length \(d_{seg}\), the slope entry speed is calculated as follows.  
\[v_{ent,\text{SSA}}=\sqrt{v_{exit}{}^{2}-2d_{seg}\frac{F_{R}}{m}} \tag{3}\]  
Figure 7: Scheme of curve speed assistance for eco-driving guidance.