esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study aimed to evaluate the accuracy and reliability of two RTK GNSS-based methods--Method 1 (vertical position minima) and Method 2 (vertical velocity minima)--for estimating the \(SF\), \(SL\), and _running velocity_ in a 400 m sprint. The results demonstrated that both GNSS-based methods successfully estimated the \(SF\), \(SL\), and _running velocity_, but Method 2 exhibited superior accuracy and reliability. In the 3D video analysis sections, Method 2 showed lower RMSEs and higher ICC values compared with Method 1. Similarly, in IMU-based comparisons across the full 400 m sprint, Method 2 achieved better agreement with the IMU-derived \(SF\), particularly in curved sections and in the analysis of left-right asymmetry, where Method 1 exhibited larger biases. The RTK GNSS provided stable measurements throughout the entire 400 m sprint, with the total sprint time matching the 2D camera reference by over 99.95%, confirming its robustness for continuous monitoring. These findings indicate that a single RTK GNSS receiver, particularly when employing the vertical velocity minima approach, enables accurate step-by-step analysis of sprinting performance in outdoor conditions.  
Conceptualization, K.O., N.M., K.H. and M.T.; methodology, K.O., N.M., K.H. and M.T.; software, K.O., N.M., K.H. and W.K.; validation, K.O., N.M. and K.H.; formal analysis, K.O., N.M. and K.H.; investigation, K.O., N.M., K.H., A.K., W.K., H.N., S.U. and M.T.; resources, K.O., N.M., K.H., A.K., W.K., H.N., S.U. and M.T.; data curation, K.O., N.M., K.H. and M.T.; writing--original draft preparation, K.O.; writing--review and editing, K.O., N.M., K.H. and M.T.; visualization, K.O. and M.T.; supervision, M.T.; project administration, M.T.; funding acquisition, M.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Experimental Practice Fund of the Faculty of Health and Sports Science at Doshisha University.  
Studies involving human participants were reviewed and approved by the Research Ethics Committee of Doshisha University in Kyoto, Japan (approval code 23034, approved on 19 December 2023) and conducted in accordance with the Declaration of Helsinki. All participants were informed about the experimental methods and associated risks, and they provided written informed consent to participate in this study.  
Written informed consent was obtained from the patient(s) to publish this paper.  
The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.  
We are deeply grateful to the athletes in the experiment.  
The authors declare that this research was conducted in the absence of any commercial or financial relationships which could be construed as potential conflicts of interest.
# Laser-Based Mobile Visible Light Communication System
###### Abstract  
Mobile visible light communication (VLC) is key for integrating lighting and communication applications in the 6G era, yet there exists a notable gap in experimental research on mobile VLC. In this study, we introduce a mobile VLC system and investigate the impact of mobility speed on communication performance. Leveraging a laser-based light transmitter with a wide coverage, we enable a light fidelity (LiFi) system with a mobile receiving end. The system is capable of supporting distances from 1 m to 4 m without a lens and could maintain a transmission rate of 500 Mbps. The transmission is stable at distances of 1 m and 2 m, but an increase in distance and speed introduces interference to the system, leading to a rise in the Bit Error Rate (BER). The mobile VLC experimental system provides a viable solution to the issue of mobile access in the integration of lighting and communication applications, establishing a solid practical foundation for future research.  
visible light communication; LiFi; laser lighting; semiconductor laser +
Footnote †: journal:  
0000-0001-9980-3331][PERSON]  
1,2  
[PERSON]  
1 Key Laboratory for Information Science of Electromagnetic Waves (MoE), School of Information Science and Technology, Fudan University, Shanghai 200433, China; [EMAIL_ADDRESS] (Y.H.); [EMAIL_ADDRESS] (Z.W.); [EMAIL_ADDRESS] (Z.L.); [EMAIL_ADDRESS] (J.H.); [EMAIL_ADDRESS] (C.M.); [PERSON]_ADDRESS] (S.J.); [EMAIL_ADDRESS] (Z.L.); [EMAIL_ADDRESS] (U.Z.); [EMAIL_ADDRESS] (N.C.); 22 CGC Institute of Ubiquitous-X-Innovation and Applications, Beijing 100876, China; [EMAIL_ADDRESS] (X.W.); [EMAIL_ADDRESS] (L.X.); [EMAIL_ADDRESS] (G.L.)
## 2 Class-D PA for Polar-Mode Architectures
### Class-D PA Design and Simulations  
The search for the most appropriate active device is crucial to implement an RF PA. In this particular case, considering peak envelope power (PEP) \(P_{OUT}\) = 100 W and \(v_{E}\) = 28 V  
Figure 4: Polar-mode Class-D HCV PA diagram and waveforms.  
(drain supply voltage), the GaN HEMT CGH40090 PP from Wolfspeed is a good candidate for this application.  
As GaN HEMTs can be considered almost ideal switching devices at VHF, the PEP proposed for this class-D HCV PA demonstrator can be obtained from the following expression:  
\[P_{O}(t)=\frac{32N^{2}}{\pi^{2}R_{L}}v_{E}{}^{2}(t) \tag{9}\]  
which derived from (8), where \(R_{L}\) corresponds to the 50 \(\Omega\) RF load impedance.  
Considering (9), the required 1:N ratio for the LCMN is \(N=\sqrt{2}\), which means an impedance conversion from \(R_{L}\) (50 \(\Omega\)) to 25 \(\Omega\). This results in values of \(C_{MN}\) = 25 pF and \(L_{MN}\) = 30 nH, as well as inductance, which is provided by two inductors in the proposed LCMN balance structure. To complete the design of the class-D RF PA, the inductance and capacitance values for the series resonant tanks LC1 (\(L_{R1}\) = 22 nH, \(C_{R1}\) = 68 pF) and LC2 (\(L_{R2}\) = 22 nH, \(C_{R2}\) = 68 pF) were derived from AWR simulations (starting from the corresponding values for a resonance frequency of \(f_{res}\) = 128 MHz and a quality factor of \(Q=\sqrt{2}/2\)) to provide a flat output power response over the entire bandwidth, while the rest of the components were also tuned to achieve the desired amplifier performance.  
To achieve broadband operation, a reactance compensation effect was used in the load network. A similar effect has already been extensively described in the wideband class-E PA literature [20; 21; 22], but in the case of the class-D PA, an impedance profile was chosen that keeps the admittance phase of the load network around zero degrees and its magnitude constant over the entire operating bandwidth.  
The full simulation results with the current component values, including the modeled input and output coaxial cable (50 \(\Omega\)) baluns, as well as the coaxial hybrid coupler (T2) and the HEMT (CGH40090 PP) simulation model (provided by Wolfspeed), are shown in Figure 5. The result of these simulations shows the performance of the RF PA operating in class D, where the transistor acts as a switching device requiring at least 30 dBm (\(P_{IN}\)) of RF continuous wave (CW) drive signal (\(v_{DR}\)) at its input port. Another parameter to control is the bias operating point (\(V_{GS}\) and \(I_{DQ}\)) of the RF PA, which, in switchmode PAs, defines the duty cycle of the drain waveforms and affects the power efficiency.
## 2 Materials and Methods
### Volatile Organic Composition  
The analysis of volatile compounds was performed using gas chromatography/mass spectrometry (GC/MS) following solid-phase micro-extraction (SPME). The SPME fiber (divinylbenzene/carboxoen/polydimethylsiloxane, DVB/CAR/PDMS, 50 \(\upmu\)m, Supelco) was utilized to adsorb the volatile compounds emitted from 5 g of each bread sample kept at 40.0 \(\pm\) 0.1 \({}^{\circ}\)C. The samples were exposed to the fiber for 60 min at 25 \({}^{\circ}\)C, followed by 40 min. Subsequently, the fiber was exposed to the GC inlet for 10 min for thermal desorption at 250 \({}^{\circ}\)C. The temperature program for GC was set from 40 to 230 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C/min, with an isothermal hold for 40 min. A DB-624 capillary column (Agilent Technologies, 60 m, 0.25 mm, 1.40 \(\upmu\)m) was employed for chromatographic analysis. The GC-MS instrument operated at 70 eV in the EI mode, scanning the m/z range from 30 to 550. Helium was used as the carrier gas at a flow rate of 1 mL/min. Compound identification was achieved by comparing the fragmentation patterns of the experimental mass spectra with a commercial library (NIST05). The relative proportions of individual components were expressed as percentage peak areas. Three replicates of each sample were analyzed.
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 3 Proposed Method  
### Overall Framework  
As shown in Figure 2, we propose ProSAF-Net, which adopts YOLOv5 [33] as the baseline detector. While YOLOv12 [34] introduces more advanced architectural designs and achieves high performance on general object detection tasks, it also comes with significantly increased model complexity and computational costs. In contrast, YOLOv5 offers a favorable balance between accuracy and efficiency, which is critical for small object detection in remote sensing scenarios where high-resolution images and limited computational resources are common. Moreover, YOLOv5 provides better extensibility for integrating our proposed modules, making it a more practical choice for this study. The pipeline initiates with the backbone network extracting hierarchical features [C2, C3], which capture multi-scale visual patterns critical for subsequent processing. To counteract the feature degradation inherent in deep convolutional operations, a shallow detail aggregation module (SDAM) is integrated during the feature fusion phase, where C3 features are synergistically combined with upsampled outputs to preserve high-frequency texture details. Building upon this, three cascaded deep semantic fusion modules (DSFM) progressively refine feature representations by establishing bidirectional interactions between low-level spatial details and high-level semantic abstractions, thereby enhancing cross-scale information propagation. Prior to detection heads, spatial context awareness modules (SCAM) implement multi-granularity context modeling through parallel global scene analysis and local pattern correlation, effectively suppressing background interference while amplifying discriminative object characteristics. To address the substantial scale variations of remote sensing targets, an auxiliary dynamic loss is introduced with a self-adjusting weight allocation mechanism that dynamically prioritizes optimization objectives based on target size distribution, ultimately enhancing cross-scale detection consistency. Technical implementations of SDAM, DSFM, SCAM, and the novel loss function will be systematically detailed in subsequent sections.
# First Appraisal of Effective Microplastics Removal from the Textile Manufacturing Processes
## 3 Results  
### Mass Balance  
Volumes of 50 L were supplied to the ASEC equipment, and about 50 L of each sample were recovered after the treatment (Figure 1) with complete transparency (\(\sim\)10 mg/L of suspended solids). The pH ranged between 6.87 and 7.01, with an electrical conductivity of 16 \(\upmu\)S/cm [21]. So, the complete recuperation of the liquid volume was achieved after the treatment of all the samples. On the other hand, the dried solids and crystallized salts were collected from the output. Effluents from both industries before the WWTP have a dried conglomerate production (humidity \(<\) 1%) of 7 g per liter for Sf and 4 g per liter for Nf (Table 1).  
The samples with the highest MPs particle load (in mg) retained in the filter were all from the raw samples: S2\({}_{\text{in}}\) (182) \(>\) N\({}_{\text{in}}\) (69) \(>\) S3\({}_{\text{in}}\) (55) \(>\) S1\({}_{\text{in}}\) (44). The MP mass in the depurated water samples (\({}_{\text{out}}\)) was below 0.3 mg for the samples studied.  
The number of MPs found in dried solids were 146 mg in N, 101 mg in S3, 95 mg in S1 and 0.27 mg in S2. The highest amount of MPs retained was in the N sample with a ratio of 0.014; this meant that most of the mass of the salt formed was composed of MPs.  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Samples** & **Salt Formation (g/L)** & \multicolumn{2}{c}{**MP Mass (mg)**} & \multicolumn{2}{c}{**Retained Ratio**} & **MPs Removal** \\  & **Aqin** & **Aqout** & **Solidout** & **(mg MP/Solid)** & **(\%)** \\ \hline S1 & 19 & 44 \(\pm\) 1 & 0.3 \(\pm\) 0.1 & 95 \(\pm\) 1.0 & 0.005 & 99.97 \\ S2 & 5.8 & 182 \(\pm\) 3 & 0.1 \(\pm\) 0.1 & 0.27 \(\pm\) 2.0 & 4.65 \(\times\) 10\({}^{-5}\) & 99.99 \\ S3 & 7 & 55 \(\pm\) 2 & 0.1 & 101 \(\pm\) 2.0 & 0.014 & 99.99 \\ N & 4 & 69
# Structural Damage Detection through Dual-Channel Pseudo-Supervised Learning
## 2 Methodology
### The Process of the Algorithm  
Figure 2 illustrates the pipeline of the proposed model that combines the DCPS learning strategy and Conformers for detecting structural damage. Initially, the raw data from various acceleration response signals undergo different noise-addition processes and are then input into dual-channel networks. Two Conformer networks, having the same architecture but different randomly initialized weights, are used to predict the segmentation confidence maps of the two signals and obtain one-hot labels. In the next step, one network utilizes the output of the other network as a label and applies a cross-entropy loss for cross-channel learning and network training.  
The procedures are summarized as follows. Step 1: Data Collection and Preprocessing--collect structural acceleration response data related to structural damage preprocess and process it. Step 2: Data Partitioning and Annotation--divide the dataset into training, validation, and testing sets, and each dataset is annotated with its impairment category. Step 3: Model Building and Training--train a DCPS learning and Conformer model using the training and validation data. Step 4: Model Evaluation--evaluate the trained model using the testing set. Step 5: Application--Deploy the trained model into practical applications for the real-time identification and monitoring of structural damage. This can involve integrating the model into existing systems or creating a new application for this purpose.
# A Robust Conformal Framework for IoT-Based Predictive Maintenance
## 6 Experimental Results and Performance Evaluation: A Case Study on CMAPSS Datasets
### Visual Analysis of Conformal Prediction Intervals  
In order to provide an insightful and practical visualization of a single engine unit, Figure 12 offers a detailed view of how weighted calibration performs at \(\alpha=0.05\). The orange curve signifies the clamp-regulated RUL prediction, while the green-shaded region indicates the conformal lower bound. Thanks to weighted tendency, which refers to an estimator that tracks recent residuals more closely. The interval is sufficiently tight to convey meaningful uncertainty estimates but remains broad enough to encapsulate abrupt changes in degradation levels.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Margin** & **Coverage** \\ \hline Naive & 32.92 & 0.476 \\ Weighted & 40.57 & 0.510 \\ Bootstrap & 41.66 & 0.513 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Naive, weighted, and bootstrap conformal methods evaluation using FD2.  
Figure 11: Weighted CP performance evaluation in FD2 validation set.  
This flexibility surpasses that of purely probabilistic bounds or naive row-based conformal intervals, which may fail to adapt when sensor signals shift dramatically. Figure 13, on the other hand, expands the analysis to multiple engine units, each concatenated on the \(x\)-axis.  
Every colored block corresponds to a separate unit's non-increasing RUL forecast and weighted-based CI. Units with higher starting RULs display narrower CIs that grow gradually, whereas those nearing their EoL reveal broader intervals early on, capturing the heightened sensor volatility. The uniform behavior across units reinforces weighted's capability to deliver consistent, scenario-adaptive intervals that balance coverage and interval tightness to reduce variability in PdM.  
Figure 12: Single-unit conformal interval for unit 43 with weighted calibration at \(\alpha=0.05\), illustrating a stable yet adaptive lower bound.  
Figure 13: Multi-unit conformal intervals aligned on the \(x\)-axis, with each segment depicting clamp-regulated predictions and weighted-based bounds for a distinct engine unit.
## 3 Results and Discussion
### The Strength Reduction Results of Water Immersion Experiments  
Table 2 has shown the uniaxial strengths of two kinds of red-bed soft rocks under different water immersion time. The clay minerals are prone to be broken causing an asymptotic deterioration in rock. Meanwhile, the strengths of red-bed soft rock will gradually decrease with the time. Figure 15 shows the strength reduction law of light-grey silty mudstone and light-yellow silty mudstone.  
Figure 13: The schemes of experiments.  
Figure 14: SEM images of red bed soft rocks under 3000 and 10,000 times magnification. (**a**) 3000 times magnification of light-yellow silty mudstone; (**b**) 10,000 times magnification of light-yellow silty mudstone; (**c**) 3000 times magnification of light-grey silty mudstone; (**d**) 10,000 times magnification of light-grey silty mudstone.  
Figure 15: The strength reduction coefficients of soft rock.  
According to renormalization group theory, there is a critical point that distinguish stable and unstable state. In Figure 15, we can find that the strength reduction degree of light-grey silty mudstone become stable around 0.8 and the strength reduction degree of light-yellow silty mudstone become stable around 0.6. Therefore, we believe that the strength reduction ratio of 0.8 and 0.6 is the red bed soft rocks' critical strength reduction coefficient under long-term water corrosion in this article. Comparing the results with calculation results in Section 2.3, the granular unit model has a critical failure probability of 0.8 and the stripy unit model has a critical failure probability of 0.6, which prove that the application of renormalization group theory in multi-scale deterioration issue of red-bed soft rock is effective.  
For the water induced failure, we have given the calculation results and verified them by the experimental results. The water induced deterioration is a long-term process which is time-consuming when carrying out experiments. Therefore, the renormalization group theory herein is more effective. However, regarding the stress-induced failure, we find it difficult to give a precise solution when adopting renormalization group theory. The reason is that the unit model's failure probability is not the same during loading process, and the meso-structure's failure probability obey the Weibull Distribution. We have given the analytical solution of critical points for red-bed soft rock deterioration induced by stress in Section 2.4. Actually, the stress induced failure problem is more convenient when carrying out experiments.
# Low-Temperature Direct Bonding of SiC to Si via Plasma Activation
## 3 Results and Discussion
### Surface and Interface Characterizations
Due to the hydrophilic bonding and O\({}_{2}\) plasma activation, numerous O atoms were detected in the interlayer. From the element analysis, we can confirm that the amorphous layer basically consisted of oxide. In addition, the O\({}_{2}\) plasma treatment can increase the porosity of amorphous layer, which will introduce additional absorbed water molecules [17]. When the annealing temperature was still quite low, many water molecules absorbed by the hydrophilic surface remained in the interface. The water-stored oxide layer can be responsible for the thick amorphous layer. In addition, the remaining water molecules diffused toward the Si bulk below the native oxide, which can be proved by the concentrated distribution of O atoms near the Si layer. Therefore, a curved boundary between the Si layer and oxide was detected as shown in Figure 5a. Figure 4: XPS Si 2p spectra of (**a**) bare Si, (**b**) Si after O\({}_{2}\) plasma activation, (**c**) bare SiC, and (**d**) SiC after O\({}_{2}\) plasma activation. Compared to the interface in Figure 5a,b, the interface was greatly improved when the annealing temperature increased to 300 \({}^{\circ}\)C, as is shown in Figure 5d,e. The thickness of the amorphous layer shrank from 48 nm to 11 nm due to the increase in annealing temperature. In addition, the interface became straighter and flatter, which presented a more controllable interface. All these improvements can be attributed to the increased annealing temperature. As long as the annealing temperature was high enough, the absorbed water molecules were expelled along the interface under the chamber pressure and vacuum pressure. As the water molecules were eliminated in time, there was no additional oxide formed, and a straight boundary was obtained. However, the TEM results also demonstrated that the amorphous oxide layer cannot be eliminated for low-temperature O\({}_{2}\) plasma-activated wafer bonding.
# Summertime Aerosol Radiative Effects and Their Dependence on Temperature over the Southeastern USA
## 1 Introduction
[28] estimated the direct aerosol radiative effect in the metropolitan Atlanta to be \(-11\pm 6\) W/m\({}^{2}\) in the summer of 1999, [PERSON] et al. [18] estimated the aerosol radiative effect due to seasonal changes in aerosol load to be \(-3.9\) W/m\({}^{2}\), and [PERSON] et al. [24] estimated that betweenthe summers of 2001 and 2013 the diurnally averaged surface radiative effect changed by 8 W/m\({}^{2}\) due to decreasing aerosol mass. The main objective of this study was to provide a quantitative estimate of the regional aerosol direct radiative effect caused by the temperature-dependent biogenic emissions over the southeastern USA. The research was carried out using remote sensing observations and the findings were evaluated with aerosol-climate model sensitivity simulations. With this combination of observations and model simulations the significance of biogenic emissions on the temperature dependence of AOD and the corresponding radiative effect was quantified. Despite the large amount of research done in this region, to our knowledge this is the first time the temperature-dependent summertime aerosol radiative effects over the southeastern USA have been quantified.