esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Dynamic Assessment of Movement Performance
### Force Sensors in Swimming  
The action of the swimmer is the coordinated interaction between the trunk movements and upper and lower limbs [100]. The swimmers' capacity depends on the level of propulsive force applied during each stroke to allow the athletes to overcome the drag during the forward movement. Some studies affirm that 85-90% of the propulsive force is developed from the upper limbs, while the remainder is associated with the lower limbs [101; 102]. In the cyclic form of locomotion, for example, in swimming, rowing, and kayaking, the intracycle velocity fluctuation is due to the combined effect of propulsive force and drag [103; 104]. For these reasons, the study and the measure of propulsive force in swimmers play a crucial role in the athlete's functional assessment. The literature reports different studies about the direct or indirect methods to measure the propulsive force [105]; however, the standard practices proper to investigate the magnitude of the propulsive force in swimmers are tethered swimming, 3D video analysis, and the measured activity drag system [106].  
In recent years, the applications of sports engineering have been geared toward developing differential pressure systems (sensor paddles) [107; 105]. These systems base their functioning on measuring differential pressure through the two membranes situated respectively on the palm and the back of the paddles, measuring, in this way, the perpendicular component of the differential force acting between the palm and back of the hand. Subsequently, such a system can compute the dynamics parameters (force-time curves) of both hand swimmers' surface area via proper software. Furthermore, in some of these DAQ systems, such as the Kz system, the force data are integrated and synchronized (all the signals to be measured are acquired simultaneously) with an IMU that measures the athlete's kinematic parameters during the swim movement [107; 108]. Today, the usage of these sensors in swimming is still limited because few studies confirm the reliability and validity, with a gold standard of the dynamic parameters provided by instrumented paddles. Furthermore, these sensors can estimate only the applied force on the swimmer's paddles (so the force impressed by hands only) without considering the swimmer's arm or forearm's contribution to the propulsion [109; 110].
## 3 Methodology
### Data Perturbation Techniques  
Data perturbation approaches, noise injection, temporal scaling, and spatial perturbations have been used to further refine the robustness of the proposed technique and the generalization capabilities of the ML models. The main purpose of incorporating such approaches is to imitate the real-world noise and variability in personal actions and responses by increasing the ability of the proposal to effectively deal with different conditions and circumstances.  
* Spatial perturbations: In order to mimic natural hand movements, small random Perturbation has been applied to the spatial coordinates (X, Y) based on the following Equation (11): \[\left(X^{\prime},Y^{\prime}\right)=\left(X+\delta_{x},Y+\delta_{y}\right)\] (11) where \(\delta_{y}\) and \(\delta_{x}\) represent the random perturbations.
* Noise injection: To emulate variability in real-world interactions, random noise is augmented to the extracted features, e.g., velocity and touch pressure, as shown in Equation (12):\[X^{\prime}=X+\epsilon \tag{12}\]  
where \(\epsilon\) is random noise obtained from a Gaussian distribution, and \(X\) is the value of the original extracted feature.
* Temporal scaling: To emulate various interaction styles, interaction durations have been further scaled to reflect variations in user speed, as shown in Equation (13): \[T^{\prime}=T\cdot\alpha\] (13) where \(\alpha\) is the scaling factor, and \(T\) is the actual interaction duration [51; 52; 53].
## 5 Conclusions  
3D-RMS were characterized by scanning and transmission electron microscopy before and after RTA and compared with standard RMS. The difference in the morphology of these two systems was attributed to the shadow effect generated by the topography of nanostructured black silicon. XRD and SAED patterns revealed that the novel morphology of 3D-RMS during RTA promotes the formation of the two stable phases Al\({}_{0.42}\)Ni\({}_{0.58}\) and AlNi\({}_{3}\) for a system with 60% Ni atomic composition. Qualitative differential scanning calorimetry analysis of the 3D-RMS indicates that the curvature of the nanoscale multilayer favors the atomic diffusion in the early stage of the phase transformation. This study provides useful information for better understanding the microstructure and morphology of the 3D-RMS and its influence on the phase transformation during RTA and DSC. Further studies are necessary to quantify the energy release of the 3D-RMS and to identify the solid-state reaction sequence for both systems.  
Conceptualization, Y.H.S.C., P.S. and H.B.; methodology, Y.H.S.C. and H.B.; investigation, Y.H.S.C., S.S.R., C.S., K.J. and J.S.; supervision, P.S., F.M. and I.G.; writing, Y.H.S.C. and S.S.R.; review and editing, H.B., C.P. and P.S. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Deutsche Forschungsgemeinschaft (DFG grants Scha 32/30 and Scha 632/29), I.G. and S.S.R. acknowledge financial support from the German Research Foundation (DFG) through grant GA 1721/3-1. C.S. and F.M. acknowledge funding from DFG grant MU 959/48-1. Support by the Center of Micro- and Nanotechnologies (ZMN), a DFG-funded core facility of the TU Ilmenau, is gratefully acknowledged.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, [PERSON], upon reasonable request.  
The authors express deep thankfulness to all the staff of the Center of Micro- and Nanotechnologies (ZMN), especially to [PERSON], [PERSON], and [PERSON], for their support with some experiments (sputtering and characterization analysis).  
The authors declare no conflict of interest.
## 4 Overview: Signal Processing Methods
### Filters Applied  
The research will implement common filters utilized for EMG signals, which include the Band-pass filter, Notch filter, and High-pass filter. These filters are instrumental in eliminating unwanted common noise sources and narrowing down the frequencies to the range of the EMG signals.
# An Investigation of Optically Very Thin Ice Clouds from Ground-Based ARM Raman Lidars
## 4 Results
### Optically Very Thin Ice Clouds Adjacent Profiles
“Isolated” refers to both of the adjacent profiles being clear sky. The percentage breakdown is considered for columns (blue) and groups (red), where columns count all transparent ice cloud profiles with ice cloud column optical depths below 0.01 equally and groups count consecutive transparent ice cloud profiles with ice cloud column optical depths below 0.01 as one group. The total number of samples for each panel is given in the parentheses. Data for the RL at the SGP site are from August 2008 to August 2016 and at the TWP site from December 2010 to January 2015. Data for CALIPSO for both the SGP and TWP site are from June 2006 to August 2016. For CALIPSO, the cloud edge category is largest for columns but the in-between category is largest for groups for both SGP and TWP. Similar to the RL, combining the cloud edge and in-between percentages together yields 82% over both SGP and TWP, indicating that the vast majority of optically very thin ice clouds are directly next to optically thicker ice clouds. The in-between percentage is larger compared to the RL at the expense of a smaller percentage for the cloud edge. Figure 5 shows the CDFs for transparent ice cloud column optical depths below 0.01 as well as the CDFs for those associated with cloud edge, in between clouds and isolated for the RL and CALIPSO. The RL distributions indicate that isolated optically very thin ice clouds contain the thinnest optical depths, which is also supported by the smallest mean optical depth. In-between clouds have the largest optical depths and highest mean optical depth, unsurprisingly, given the larger optical depths of both the preceding and succeeding profile. The distribution for cloud edges is in the middle in terms of the mean and CDF. The cloud edge's CDF closely follows the total distribution of transparent ice cloud column optical depths below 0.01. In contrast to the RL, CALIPSO's distributions and means are nearly identical for cloud edge, in-between clouds and isolated. This suggests that the smaller optical depths only observed by the higher sensitivity RL contribute to the different distributions and means between cloud edge, in-between clouds and isolated clouds. Figure 5: Cumulative distribution functions (CDFs) of ice cloud column optical depths below 0.01 (solid black) as well as those separated into 3 categories: cloud edges (dot dashed dark blue), in-between (dashed light blue) and isolated (circle red) for (**a**) RL SGP, (**b**) RL TWP, (**c**) CALIPSO SGP and (**d**) CALIPSO TWP. Mean values are given. Data for the RL at the SGP site are from August 2008 to August 2016 and at the TWP site from December 2010 to January 2015. Data for CALIPSO for both the SGP and TWP site are from June 2006 to August 2016.
## 3 Results and Discussion
### Positioning Deviations of and Their Error Indicators for the Whole Measurement Period
The density of all three views of each image was calculated for Receiver A and Receiver B (**a,b**) from the displayed range (\(-\)1.5 m to 1.5 m) in the number of 1500 bins (resolution: 2 mm). Due to the large variance of the RTK deviations of Receiver C and Receiver D (**c,d**), the approximation of the displayed area of the graphs was adjusted. For these receivers, the density was calculated from the displayed range (\(-\)10 m to 10 m) in the number of 6500 bins (resolution: approximately 3.1 mm). low resolution and the boundary of the graphs did not allow displaying the situation when the positioning deviation of this receiver gradually increased from a deviation of 10 m to higher values. This situation occurred twice during the measurements, and in the worst case, this exponential drift of the deviation stopped at 426.2 m. For these three mentioned receivers (Receiver A, Receiver B and Receiver C) most of the measured positioning deviations occurred in the area of centimetre units (positioning deviations up to 0.04 m had a more than 20% occurrence rate in all the measurements, while the occurrence of larger deviations had a gradually declining trend in the density of the values). However, the same cannot be said about the results of Receiver D (Figure 4d). The highest occurrence density of approximately 6% was found to be around 0.2 m of the positioning deviation. From a value of 0.3 m, the density distribution at that receiver was much flatter, and in one case, the positioning deviation drifted down to a value of 46.5 m. The calculated accuracy indicators (\(\mu_{err}\), \(s_{err}\) and \(RMS_{err}\)) were the highest, which means that this receiver had, by far, the worst results in all the measurements. The \(RMS_{err}\) value of this RTK receiver was almost 94 times higher than the best result of Receiver A.
## 3 Test Results and Analysis
### Cutting and Rock-Breaking Effects  
The cutting effects of two cutting marks on the rock samples subjected to microwave irradiation for different durations are listed in Table 2. Only two normal cutting marks were left on the untreated basalt sample, and the basalt sample was subjected to 3 min of microwave irradiation in the cutting process using the reduced-scale TBM cutter. As the exposure to microwave irradiation increased to 6 min, cracks between the two cutting marks began to coalesce in the cutting process using the reduced-scale TBM cutter. With the further increase in the exposure to microwave irradiation to 9 and 12 min, the area of coalesced cracks in the basalt samples gradually expanded in the cutting process using the reduced-scale TBM cutter. For the basalt sample subject to 15 min of microwave irradiation, not only did the area of coalesced cracks enlarge, but also the cross-section of the sample was cracked in the cutting process, as shown in Figure 9.
## 4 Methodology
### User Spacecraft
#### 4.3.1 Ixpe  
The Imaging X-ray Polarimetry Explorer (IXPE) is a NASA space mission to advance our understanding of the astrophysical phenomena occurring in the universe. It has been specifically designed to measure the polarization of X-rays that emanate from various astrophysical sources, enabling scientists to gain a deeper insight into the structure and properties of these sources [11].  
On 9 December 2021, the IXPE spacecraft was successfully launched into space from the Kennedy Space Center in Florida, USA. The spacecraft was placed into a low-Earth orbit, with an altitude of about 540 km, an eccentricity of 0.00167, and an inclination of 0.2 degrees (equatorial orbit), enabling it to observe a wide range of astrophysical sources with great precision.  
The FDF played a critical role in the early orbit support of the IXPE mission. The FDF received six passes of DOWD measurements acquired from constellations of the  
Figure 5: The user satellites and their respective orbital geometries have been incorporated in the investigation of DOWD orbit accuracy. The length of the arc utilized in orbit determination is contingent on the duration of the DOWD measurements.  
TDRS satellites, namely TDRS-6, -7, -10, and -12, on 8 and 9 December 2021. In this study, these passes were reprocessed to reconstruct IXPE orbit using an arc length equivalent to 7 IXPE revolutions.
# Improved PPP Ambiguity Resolution with the Assistance of Multiple LEO Constellations and Signals
## 3 Results
### PPP AR with the Augmentation of Multi-Frequency LEO Observation
#### 3.2.1 PPP AR with the Different LEO Constellation
In order to investigate the position accuracy of the LEO-augmented PPP ambiguity resolution, the positioning errors of static PPP with different session lengths (1 min, 5 min, 10 min, and 30 min) were calculated. Figure 8 shows the statistical results of the GREC PPP fixed solutions with the inclusion of 0, 60, 192, and 288 LEO satellites. With the augmentation of the LEO satellites, the positioning accuracy of GNSS PPP can be significantly improved and the improvement is more remarkable when more LEO satellites are added. At the 1 min-observation session, the positioning accuracies of GREC-only and 60-LEO augmented PPP fixed solutions are greater than 8 cm in three components, while the positioning accuracy of 192-LEO and 288-LEO augmented GNSS fixed solutions can be 3-4 cm in the horizontal components and 4-6 cm in the vertical components. At the 5 min-observation session, the positioning accuracy of multi-GNSS fixed solutions is improved by about 60%, 80%, and 90% from (3.5, 1.8, 4.9) cm to (1.8, 0.6, 2.6) cm, (0.70, 0.23, 1.4) cm, and (0.34, 0.21, 1.34) cm in east, north, and up components, with the inclusion of 60, 192, and 288 LEO satellites, respectively. We are glad to see that, with the augmentation of 192 and 288 satellites, a centimeter-level accuracy can be achieved within 1 min, while a millimeter-level accuracy in the horizontal direction can be achieved within 5 min for GNSS PPP AR. Figure 7: Comparisons of LEO-augmented multi-GNSS PPP fixed solutions with different LEO constellations at stations ASCG, CEBR, and YEL2. The corresponding NSAT and PDOP are also shown in the figure.
## 2 Materials and Methods
### CFD Model Validation  
The air velocities along seven vertical lines for the Case 4 SWC (Summer case, Figure 1a) obtained from wind tunnel experiments and CFD simulations were compared for validating the CFD model (Figure 8). The CFD simulations reasonably revealed the characteristics of the measured velocity in terms of airflow pattern and magnitudes (Figure 8). However, there are slight discrepancies inline v2 (close to inlet wall) and v4 (inside feeding alley) between simulated and measured velocity. Small deviations were observed above the roof of the leeward side for line v5 and v6. One explanation could be that uncertainty might exist for the LDA precisely located at the specific measurement positions just after the inlet side wall and inside the feeding alley, although much attention was paid to it. However, weakness of the RANS models in predicting the air separation might also be the cause [17; 32]. Even though there were small discrepancies between the experiment and the simulation, the results indicated that the CFD model is feasible for modeling airflow characteristics in the naturally ventilated building as airflow in and outside the scale model followed similar trends in simulation as BLWT experimental results. Therefore, further simulations were conducted for all the cases following the similar set-up and model in CFD.  
Figure 6: Grid distribution in the computational domain and at the building surfaces. (**a**) iso-metric view of the whole computational domain and (**b**) iso-metric view closed to the model.  
Figure 7: A grid independency study with three density grids. (**a**) vertical profile, and (**b**) horizontal profile inside middle of the NVD barn with SM opening conditions.
## 3 Results  
### Reflectance Conversion  
The mean color values of the blue, green, and red bands were calculated for DRSPs with 1.2%, 5%, 10%, and 25% reflectance. Figure 5 shows a strong positive correlation between these mean color values and their corresponding standard reflectance values, with an \(R^{2}\) value exceeding 0.99 for an exponential fit.  
Figure 5: Relationship between color value and reflectance for different spectral bands.  
However, the ratio of the mean color values corresponding to the four DRSPs deviated from the theoretical reflectance ratio of 1.2:5:10:25, indicating a non-linear relationship between color values and surface reflectance. This observation aligns with prior research findings [34]. This deviation is likely due to the camera's sensitivity to light intensity, highlighting the inherent limitations of consumer-grade cameras compared to high-performance spectral measurement instruments. Nevertheless, this limitation can be partially addressed by calibrating the imagery using DRSPs with known and varying reflectance levels. These results emphasize the importance of employing DRSPs with a range of reflectance values to enable a more accurate conversion of pixel-level color values to reflectance, thereby improving the reliability of image-based quantitative analyses.
# Enhancing Neural Network Interpretability Through Deep Prior-Guided Expected Gradients
## 2 Related Work
### Integrated Gradients  
Integrated gradients improves upon traditional gradient methods by computing feature importance through path integrals between baseline and input. For each feature \(x_{i}\), its attribution is calculated via Equation (3)\[\text{IntegratedGrads}_{i}(x)=\int_{\alpha=0}^{1}\frac{\partial F(\gamma(\alpha))}{ \partial\gamma_{i}(\alpha)}\frac{\partial\gamma_{i}(\alpha)}{\partial\alpha}d\alpha \tag{3}\]  
where \(\gamma(\alpha)\) represents the interpolation path from the baseline to the actual input, typically chosen as a linear interpolation \(\gamma(\alpha)=\alpha x\). The attribution property of integrated gradients ensures that the summation of importance scores satisfies Equation (4)  
\[\sum_{i=1}^{n}\text{IntegratedGrads}_{i}(x)=F(x)-F(\gamma(0)) \tag{4}\]  
with \(\gamma(0)\) being the baseline input state.  
Integrated gradients (IG) provides a robust measure of feature contributions by accumulating gradients along an input-baseline path, effectively addressing vanishing gradients while maintaining computational efficiency. However, its application in high-dimensional settings can be resource-intensive due to repeated gradient calculations, and the choice of interpolation path may introduce attribution variability. The method has demonstrated strong performance across domains: in image classification it generates interpretable heatmaps highlighting salient regions [19], in text analysis it identifies influential words or phrases [33], and in healthcare it explains diagnostic predictions to enhance trust in clinical applications [34].
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 5 Results
### Quality Assessment of Received Images  
After reassembling the received images into the JPEG file format, we calculated the values of _PSNR_, _MSE_, and SSIM for the different transmission conditions using MATLAB, for image quality assessment. The results of these calculations are summarised in the following table.  
We observed from Table 6, that the values of _MSE_ and SSIM were 0 and 1, respectively, at communication distances between 0 and 3 km when _SF_ was set to 7. These values indicate that the original and the received images were identical in these transmission conditions. The same values were calculated for images transmitted with SFs of 10, 11, and 12 at a distance of 4 km from the transmission node. The following table (Table 7) summarizes the _SF_ settings where the original and transmitted image are identical.
## 3 Optical Indices
### Wavelength Coverage and F Number  
In order to more effectively detect a target signal, wavelength coverage with strong radiation for target planetary systems and weak radiation for background should be considered. The black-body radiations of observed objects appear more intensely in the range of 0.5-8 \(\upmu\)m [19]. The zodiacal emission emerges as a peak in 0.35-0.55 \(\upmu\)m, and significantly decreases in the near-infrared band above 2 \(\upmu\)m, though it slightly increases at a larger wavelength under the impact of a thermal-emission component, as shown in Figure 2[20].  
Furthermore, the main atmospheric species, including H\({}_{2}\)O, CO\({}_{2}\), and CH\({}_{4}\), have obvious absorption characteristics at 2-8 \(\upmu\)m, while the molecular curve at shorter wavelength is relatively smooth [21]. Additionally, broadband coverage is a beneficial for breaking the degeneracy of the spectra of different species, which ensures the retrieval of molecular abundance [22]. Consequently, wavelength coverage of 2-8 \(\upmu\)m for the spectrometer and 0.5-2 \(\upmu\)m for the pointing system were selected.  
Limited by the optical materials and detector indicators, the spectral coverage of 2-8 \(\upmu\)m was too wide to meet the detection requirements in a single channel. Considering that a wavelength around 4 \(\upmu\)m is not the absorption of a certain atmosphere, the spectrometer was split into two wavelength channels with 2-4 \(\upmu\)m and 4-8 \(\upmu\)m, named CH1 and CH2, respectively. Because the full width at half-maximum (FWHM) of point-spread function (PSF) is required to cover at least two pixels at each channel, the relationship between F number and pixel size \(l_{pix}\) is shown as Equation (10). The F numbers of two channels were set to \(F_{CH1}=14.754\) and \(F_{CH2}=7.337\) in \(l_{pix}=18\)\(\upmu\)m.  
\[1.22\lambda_{\text{min}}F=2l_{pix} \tag{10}\]
# Estimating the Performance of MIMO SC-FDE Systems Using SISO Measurements
## 5 Performance Results
However, we must be aware that MIMO systems have simultaneous data streams covering the different antenna sectors from each Base Station, as opposed to SISO systems. Therefore, and although there is a slightly smaller global coverage area for MIMO systems, the increase in the number of data streams allows for an increase in the data rate, which improves the QoS for users and the overall capacity, when compared to a SISO system. Despite the results achieved, it should be noted that with the change from a SISO system to a MIMO system the complexity associated with the receiver also increases. The methodology presented is independent of the geographical area where it is applied as this factor only influences the signal estimation through the propagation models. If a propagation model is influenced by terrain morphology, the values obtained for MIMO systems will also take this fact into account, if we use this methodology.