esa-sceva/satcom-chunk-collection huggingface dataset:
# Shadow Removal for Enhanced Nighttime Driving Scene Generation
## 4 Experiment and Discussion
### Qualitative Results
#### 4.1.1 Shadow Removal Results  
We applied our method to several state-of-the-art single-image shadow removal models, including DHAN [10], DC-ShadowNet [55], and SpA-Former [58]. As shown in Figure 6, the use of the partitioned shadow removal approach enhances shadow removal performance in driving scenes containing multiple depths, regardless of the shadow removal model applied. Additionally, by partitioning the regions, the misprediction of non-shadow regions as shadow regions is reduced, thereby minimizing the overall brightening noise effect in the images.  
Figure 4: Examples of proposed application of shadow removal to nighttime image translation (the highlighted parts refer to the novelty of the paper).  
Figure 5: Examples of CoMoGAN nighttime image transformations with varying \(\phi\) values.  
For the shadow-removed images that are used as input for the I2I translation model, we ultimately employed the combination of DHAN [10] and Partitioned Shadow Removal, as it demonstrated the best performance. An example is shown in Figure 7, where the improvement in shadow removal is particularly evident in the central regions of images with depth variation.
# The BER Performance of the LDPC-Coded MPPM over Turbulence UWOC Channels
## 5 Results and Discussion
Log-normal distribution underestimates the fading, resulting in the best BER performance among the four-channel fading models. The LDPC code with simplified LLRs always improves the BER performance of MPPM for the UWOC systems and shows identical BER performance, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Channel Condition** & \(\mathbf{a}\) & \(\mathbf{d}\) & \(\mathbf{p}\) & \(\sigma_{I}^{2}\) \\ \hline Salinity random variations & 7.882 \(\times\) 10\({}^{-6}\) & 15.32 & 0.3288 & 0.2073 \\ \hline Temperature random variations & & & & \\ mixed presence of air bubbles & 0.64 & 1.6668 & 1.038 & 0.5782 \\ \hline Random presence of air bubbles & 1.407 \(\times\) 10\({}^{-7}\) & 3.764 & 0.1942 & 2.0399 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The parameters of GG-distributed fading channels, adopted from [7]. Figure 4: BERs of the (5, 2) MPPM UWOC system with/without LDPC code in the GG-distributed fading channel with different scintillation indexes. irrespective of turbulence-induced fading distribution. For the four distributions, coding gains are 10.7 dB, 12.4 dB, 13.6 dB and 11.8 dB at the BER of \(10^{-4}\), respectively. Figure 6 shows the comparison of BERs of uncoded and coded OOK, PPM and MPPM UWOC systems over a weak turbulence channel model by GG distribution. The initial LLR of OOK is given by \(\left(2 yI-I^{2}\right)/N_{0}\), where \(I\) is a constant corresponding to the fixed threshold value. PPM is a special case of \(\left(M,\ w\right)\) MPPM (i.e., \(w=1\)), whose initial LLR is obtained by (17). As reported in other work, the uncoded OOK with a fixed threshold shows a high error floor in the turbulence channel [45]. An adaptive threshold, that depends on the turbulence strength, is required for optimum OOK performance. The LDPC coding significantly improves the OOK performance, but the performance of coded OOK is inferior to MPPM. That is because LDPC decoding depends on initial LLRs (i.e., the received signal, which is significantly deteriorated by turbulence). By comparison, (6, 3), (5, 2) and (4, 1) MPPM offer improved BER performance with coding gains of 11.8 dB, 12.1 dB and 12.3 dB at the BER of \(10^{-4}\), respectively. Though the BER performance improves and coding gains get higher for (6, 3), (5, 2) and (4, 1) MPPM, the spectral efficiencies given by \(\eta=\left\lfloor\log_{2}\left( \begin{array}{c}M\\ w\end{array} \right)\right\rfloor/M\) decrease accordingly. This is expected, as MPPM always has a tradeoff between spectral efficiency and power efficiency [27]. Figure 5: BERs of the (6, 3) MPPM UWOC system with/without LDPC code in the same weak turbulence channel modeled by different statistical distributions. Figure 6: BERs of the UWOC systems with different modulation schemes in the GG fading channel with a scintillation index of \(\sigma_{7}^{2}=0.2073\).
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 4 Societal Benefit
### Forestry  
Traditional applications of forestry include the mapping of forest area and health of the canopy. These mainly depend on multi-spectral observation since forest canopies respond to visible and infrared light differently. The observed canopy spectral properties can then be used to derive several types of vegetation indices (VI), or leaf area index (LAI) with the aid of radiative transfer calculations and vegetation classification map [103]. Different values of these indices may indicate barren, sparse, or dense canopy, thus it is natural to extend the application to monitoring land use and land change, deforestation (planned or illegal), forest seasonality in Amazon region [104; 105], and response to extended drought in Congo region [63]. With long-term climate data, our understanding of the forest could be more consistent [106].  
Since the forest is the major storage of biomass on land area, its carbon exchange with atmosphere through photosynthesis and respiration becomes extremely important to understanding climate change. For this reason, forest management has a role to play in climate change policy and the development of the global carbon markets [107]. This duty can be performed through the synergy of satellite observation, ecological and economic modeling.  
On the other hand, fires are inevitable companions of forests across the world, which can be detected from visible, thermal infrared, and active microwave, as well as a combination of these three bands [108]. In time, wildfire hazard monitoring may serve as a crucial part of the fire response system, and the time series of a wildfire hazard CDR may serve in better prevention.
# MiFT: A Moment-Based Local Feature Extraction Algorithm
## 3 Proposed MIFT Algorithm
### Stage 1  
We use MDGHM based scale space to detect extrema, rather than the Gaussian pyramid and DoG used for conventional SIFT, extracting more distinctive features. The input image is down-sampled by a factor of 2 to create an octave, and MDGHM is applied to create scale images according to selected parameters for derivative order, sigma, and mask size.  
Let \((i_{a},j_{a},s)\) be an arbitrary pixel in a scale \(s\), then the local moment at \((i_{a},j_{a},s)\) can be expressed using MDGHM as:  
\[\hat{\hat{\eta}}_{p,q}(i_{a},j_{a},s)=\frac{4}{(M-1)(N-1)}\sum\limits_{j=0}^{k_ {M}-1}\sum\limits_{p=0}^{N-1}l(i_{a}+(m_{M}u-\frac{M}{2}+1),j_{a}+(m_{N}v-\frac{ N}{2}+1),s)\hat{\hat{H}}_{p}(x,\sigma)\hat{\hat{H}}_{q}(y,\sigma) \tag{5}\]  
Figure 1: Modified discrete Gaussian-Hermite moment examples with derivative orders \(p=1\), \(3\), \(5\); \(q=0\): (**a**) three-dimensional view and (**b**) two-dimensional view.  
Figure 2: Scale invariant feature transform (SIFT) and proposed MIFT methods.  
and the vertical, \(\overset{\wedge}{\eta}_{p,0}(i_{x},j_{x},s)\) and horizontal, \(\overset{\wedge}{\eta}_{0,q}(i_{a},j_{a},s)\), moments can be obtained for each keypoint. Therefore, we can calculate the scale space moment by summing the vertical and horizontal components:  
\[\overset{\wedge}{L}(i_{a},j_{a},s)=\overset{\wedge}{\eta}_{p,0}(i_{a},j_{a},s) +\overset{\wedge}{\eta}_{0,q}(i_{a},j_{a},s) \tag{6}\]  
and more distinctive keypoint candidates can be detected using MDGHM pyramid extrema than the DoG pyramid.  
Figure 3 compares and contrasts DoG and MDGHM pyramids and feature detection methods between conventional SIFT and proposed MIFT methods, and Figure 4 provides example scale space using DoG and MDGHM. Since MDGHM can include more distinctive feature information, the MDGHM pyramid can represent feature information in more detail than conventional SIFT, and building the MDGHM scale space is also somewhat simpler.  
The vertical and horizontal axes in Figure 4 represent octave and scale in scale space, respectively. MDGHM scale space images extract more distinctive feature information. Points with maximum or minimum MDGHM compared with its 26 neighbors at the consecutive three scales are regarded as keypoint candidates.
# Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks
## 5 Simulation Results
### Comparison of AmBC Systems: TD-AmBC and MSC-AmBC  
We evaluate the performance of our proposed MSC-AmBC system along with the conventional TD-AmBC system in OFDM networks. To do so, we summarize the AmBC system models as depicted in Figure 8 and Table 1. In general, these systems are integration of modulation, coding and detection blocks whose choices will be an important design factor for implementing high-rate and long-range AmBC system. However, to the best of the authors' knowledge, the integrated system design is still an open and challenging area, and only low-rate or short-range AmBC systems in WLAN are available in the literature [1; 2; 3; 5; 11; 12; 17; 31; 13; 12]. In these systems, for efficient suppression of the DLI and MAL, TDMA-based orthogonal transmission schemes [3; 10; 32] with \(M\)-ary PSK/QAM modulations [3; 17; 18; 19; 20; 21; 22] and maximal-ratio combining (MRC) detections [3; 11; 25] are the most favorable choice [3], compared to binary OOK/2-PSK modulations followed by energy detectors [1; 2; 3; 5; 11; 12; 32], which are susceptible to interferences and only support one-bit transmissions. Hence, the most general and practical system model representing previous works based on TDMA is denoted by the 'TD-AmBC' system which is the baseline of numerical evaluation for comparison with our proposed MSC-AmBC system. It is noteworthy that the MSC-AmBC is more cost-efficient (i.e., only \(\bar{M}=2\) load impedances) and more energy/spectral-efficient (i.e., extended duty cycle \(D=\frac{K_{1}}{K}\)) than the TD-AmBC as described in Figure 8.
# New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
## 2 Methodology
### Finite-Element Model of the Laminated Composite Beam
#### 2.1.1 Strains and Electrical Field  
Using first-order shear deformation theory, the displacement field equations can be expressed as:  
\[\mathbf{u}=\left\{\begin{aligned} & u_{1}(x,y,z,t)\\ & u_{2}(x,y,z,t)\\ & u_{3}(x,y,z,t)\\ \end{aligned}\right\}=\left\{\begin{aligned} & u_{0}(x,t)\\ & 0\\ \end{aligned}\right\}+z\left\{\begin{aligned} & \psi_{x}(x,t)\\ & 0\\ \end{aligned}\right\} \tag{1}\]  
where \(u_{1},\ u_{2}\) and \(u_{3}\) denote the components of the displacement vector, \(t\) is the time, \(u_{0}\) is axial displacement, \(w_{0}\) is the transverse displacement of the beam's midplane and \(\psi_{x}\) is the rotation of the beam cross-section about the positive \(y\)-axis.  
The strain-displacement relation, assuming a small deformation, may be written as:  
\[\mathbf{\varepsilon}=\left\{\begin{aligned} &\varepsilon_{x}\\ &\gamma_{xz}\\ \end{aligned}\right\}=\left\{\begin{aligned} &\varepsilon_{0}^{0}\\ &\gamma_{xz}^{0}\\ \end{aligned}\right\}+z\left\{\begin{aligned} & \kappa_{x}\\ & 0\\ \end{aligned}\right\} \tag{2}\]  
where  
\[\varepsilon_{x}^{0}=\frac{\partial u_{0}}{\partial x},\ \kappa_{x}=\frac{ \partial\psi_{x}}{\partial x},\ \gamma_{xz}^{0}=\frac{\partial w_{0}}{\partial x}+\psi_{x} \tag{3}\]  
Under the assumptions made, the electrical potential may be thought of as being constant throughout a layer and varying linearly with piezoelectric patch thickness. The component of the electric field that is dominant for a thin piezoelectric patch is in the thickness direction. As a result, only in the thickness direction can the electric field be adequately represented by a non-zero component:  
\[E_{z}=-\frac{v}{h_{p}}\equiv B_{v}v \tag{4}\]  
where \(v\) is the electric potential difference between the electrodes covering the piezoelectric layer's surface, and \(h_{p}\) denotes the thickness of the piezoelectric layer.  
Figure 1: A cantilever beam with a piezoelectric patch connected to a shunt-damping network.
## 4 Discussion
### Limitations and Future Directions  
This study has specific limitations that should be considered when interpreting the findings. First, the small convenience sample may limit the generalizability of findings to the broader stroke population. Second, the cross-sectional design prevents causal inferences regarding the relationship between observed differences and PC impairments. Longitudinal studies are needed to examine the progression of these impairments and the effects of targeted rehabilitation interventions.  
The MSE analysis, while methodologically robust, was limited to two scales due to constraints in data length (66 samples per trial at 100 Hz). This approach allowed a reliable estimation of entropy while maintaining data integrity, but may have missed broader multiscale dynamics. Future studies with higher sampling rates and longer time series should explore higher scales to provide a more comprehensive analysis of system complexity over longer time intervals.  
Although both linear and non-linear measures were used, the absence of neurophysiological data (e.g., muscle activation or cortical activity) limits the understanding of the mechanisms underlying motor control strategies. This study focused on biomechanical complexity and variability, and while this provides crucial insights, future research integrating neurophysiological data (e.g., electromyography and electroencephalography) could offer a more holistic perspective on postural control dysfunctions and neuromuscular adaptations.  
Additionally, although clinical assessments such as the Mini-BESTest were included for participant characterization, correlation analyses between clinical measures and biomechanical data were not performed due to the relatively small sample size. Future research should explore these relationships to strengthen the translational impact of biomechanical findings.  
Future directions include conducting longitudinal studies to assess the progression of impairments and the effectiveness of interventions, expanding analyses to other functional tasks (e.g., sit-to-stand), and exploring dynamic conditions such as dual tasks or unpredictable environments. Multivariate analyses could further clarify how kinematic variables influence CoP variability, while correlating clinical assessments with biomechanical measures could enhance translational applications. Additionally, principal component analysis (PCA) could be useful for simplifying complex datasets and identifying compensatory mechanisms in post-stroke individuals.
# Modeling of Fatigue-Strength Development
### The Maturity Method
#### 1.2.4 Development of Fatigue Maturity Functions for Cold-Mix Asphalts  
The study presented in this paper modified the hyperbolic strength-maturity functions, Equations (6) and (7), to yield fatigue-maturity functions presented as Equations (8) and (9), respectively. The modifications reflect the inverse relationship between stiffness and fatigue resistance in asphalt mixtures [52; 53]. In the modification, stiffness development of the cold-mix asphalt was assumed to start immediately after compaction, so that Mo = 0 at a datum temperature of 0 \({}^{\circ}\)C.  
\[\text{F}=\text{F}_{\text{u}}\frac{\text{kM}}{1+\text{kM}} \tag{8}\]  
\[\text{F}=\text{F}_{\text{u}}\frac{\sqrt{\text{kM}}}{1+\sqrt{\text{kM}}} \tag{9}\]  
where,\(\mathrm{N_{f}}\) = Fatigue strength at age t  
\(\mathrm{N_{fu}}\) = Limiting fatigue-strength at infinite age  
\(\mathrm{F}\) = Modified fatigue strength at age t, given by \(\mathrm{(N_{f}}^{-1})\)  
\(\mathrm{F_{u}}\) = Modified limiting fatigue strength at infinite age, given by \(\mathrm{(N_{fu}}^{-1})\)  
\(\mathrm{k}\) = Rate constant, the initial slope of the strength-maturity curve (1/days)  
\(\mathrm{M}\) = Maturity index (\({}^{\circ}\)C\(\cdot\)Days)  
Interpretation of results from the modeling exercise should always keep in mind that, inasmuch as they exhibit positive growth with time, Equations (8) and (9) technically represent a reduction in fatigue strength with time. Previous studies have shown that at isothermal curing conditions, the hyperbolic maturity functions, Equations (8) and (9) can be modified to provide a good fit for strength-age plots [47]. Maturity, M, in Equations (8) and (9) was substituted with chronological time, t, to yield modified fatigue-age functions presented as Equations (10) and (11).  
\[\mathrm{F}=\mathrm{F_{u}}\frac{\mathrm{kt}}{1+\mathrm{kt}} \tag{10}\]  
\[\mathrm{F}=\mathrm{F_{u}}\frac{\sqrt{\mathrm{kt}}}{1+\sqrt{\mathrm{kt}}} \tag{11}\]  
In Equations (10) and (11), t denotes the cure duration in days while the rest of the symbols remain as defined for Equations (8) and (9). The actual fatigue-age functions, Equations (12) and (13), were obtained by substituting natural logarithmic values of \(\mathrm{N_{f}}\) and \(\mathrm{N_{fu}}\) in Equations (10) and (11) and by making \(\mathrm{N_{f}}\) the subject of the Equations. Expression of fatigue-strength in natural logarithmic form is based on the assumption of logarithmic linearity between asphalt fatigue-strength and load strain levels [54].  
\[\mathrm{N_{f}}=\mathrm{N_{fu}}^{(\frac{1+\mathrm{kt}}{\mathrm{kt}})} \tag{12}\]  
\[\mathrm{N_{f}}=\mathrm{N_{fu}}^{(\frac{1+\sqrt{\mathrm{kt}}}{\mathrm{kt}})} \tag{13}\]
# A Hybrid CNN-GRU-LSTM Algorithm with SHAP-Based Interpretability for EEG-Based ADHD Diagnosis
## 1 Introduction
Combination with SHAP-based interpretability for increased transparency and clinical trust, revealing EEG channels of most diagnostic importance. 3. Application of a stringent preprocessing pipeline--comprising ICA-based artifact removal, z-score normalization, and event-related segmentation--specifically designed for pediatric EEG recordings obtained during cognitively engaging tasks. The rest of this paper is structured as follows: Section 2 surveys recent advances in EEG-based ADHD classification, with special emphasis on deep learning architectures and new directions in model interpretability. Section 3 outlines the materials and methods of this research, including the pediatric EEG dataset, preprocessing pipeline, and proposed CNN-GRU-LSTM, transformer-based, and hybrid architectures. Section 4 reports the experimental results, including performance metrics based on five-fold cross-validation confusion matrices and SHAP-based interpretability analysis. Section 5 offers a detailed discussion of the results, limitations of the present approach, and possible avenues for future research. Finally, Section 6 concludes the paper by summarizing the main contributions and outlining the wider implications of this research for interpretable AI in neurodevelopmental diagnostics.
## 3 Results
### Backscatter Characteristics
The combination of short- and longwave SAR facilitated the separation of the WI, BM and BW samples, and the features of X-/C- and L-Band showed no linear correlation (\(R^{2}\) less than 0.1) (Figure 6k,l). In summary, the X-, C- and L-Band data exhibit distinct scattering characteristics for the different land cover classes. All PolSAR data were sensitive to the OL, BS and BM coverage; additionally, L-Band data were most sensitive to the BW. The X- and C-Band features were suited to pronounce differences in WI and WT, and HT and ST coverage via the features HV, VOL3, DBL3, K0 and K3. Figure 6: Scatterplots of the land cover classes OL (Permanent Water Bodies), BS (Sand), BM (Mixed Sediment), BW (Driftwood Accumulation), HT (Herb Dominated Tundra), ST (Shrub Dominated Tundra), WI (Inundated Low Lying Tundra) and WT (Wetland) (see Table 2) for: (**a–c**) Kennaugh elements K0/K3/K4 of HHVV-polarized X-Band (TSX); (**d–f**) Kennaugh elements K0/K3/K4 of quad-polarized C-Band (R-2); (**g–i**) Kennaugh elements K0/K3/K4 of quad-polarized L-Band (ALOS-2); (**j–l**) Kennaugh element K0 of TSX, R-2 and ALOS-2. In the figure title the Root-Means-Square-Error (RMSE) and the squared Linear Pearson Correlation Coefficient (\(R^{2}\)) are drawn.
## 3 Research Results
### Analysis of Power Supply and Demand at Each Facility
#### 3.1.3 Development of Power Generation Pattern for Waste Power Generation  
Regarding waste power generation, as mentioned above, three facilities are operating in RC. Steam turbine \(\alpha\) and Steam turbine \(\beta\) have a constant amount of power generation because the amount of steam is always in surplus, and power is generated up to the upper limit, as shown in Figure 4. Equations (2) calculate the amount of power generated by Steam turbine \(\alpha\) and Steam turbine \(\beta\).  
\[P_{s}=\overline{P_{s}} \tag{2}\]  
where, \(P_{s}\) is the waste power generation in kW, and \(\overline{P_{s}}\) is the constant power generation value.  
Each incineration facility periodically undergoes repair work, and a one-sided furnace operation is carried out for dozens of days throughout the year. Since the amount of steam generated decreases during single-fired operation, the amount of power generated by Steam turbine \(\alpha\) and Steam turbine \(\beta\) differs from that during regular operation. The amount of power generated during single-fired operation is shown in Table 5.  
Figure 11: Load pattern of continuous operation line (Roasting pretreatment facility).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Line Name** & \begin{tabular}{c} **Basic Power,** \\ **kW** \\ \end{tabular} & \begin{tabular}{c} **Nighttime** \\ **Power, kW** \\ \end{tabular} & \
\begin{tabular}{c} **Holiday Power,** \\ **kW** \\ \end{tabular} &
\begin{tabular}{c} **Prediction Error,** \\ **\%** \\ \end{tabular} \\ \hline Roaster & 449 & 411 & 411 & 5.16 \\ Roaster & 155 & 114 & - & 12.61 \\ pretreatment & 128 & 100 & - & 11.33 \\ Water treatment & 350 & 270 & - & 10.10 \\ Office & 105 & 53 & 53 & 13.35 \\ Incinerator B & & & & \\ (operation & 885 & - & - & 1.84 \\ power) & & & & \\ (operation & 2190 & 2040 & 2040 & 3.08 \\ power) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Power demand and error rate of always-online.  
In incineration facility B, the amount of power generated fluctuates due to the variable amount of steam generated. In addition, the soot bluer operates by utilizing the generated steam to remove soot and dust, adhering to the electric heating surface of the boiler. Therefore, as shown in Figure 12, the amount of power generated during operation fluctuates regularly. The amount of power generated by incineration facility B is shown in Table 6.