esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sensors Database  
We collected data regarding the band center and full width half maximum (FWHM) from various sources, as shown in Table 1.  
Figure 2: The location of the study is in southern Israel. The extent of the Fenix hyperspectral image is shown in the red rectangular.  
The data were organized to form a sensor database (see DB2 in Figure 1), which consisted of 11 different sensors, namely: multi- and hyperspectral air- and spaceborne sensors. The chosen sensors are well-known in the literature and are frequently used in studies. In other words, these sensors have been proven to work in practice, and thus represent \"legitimate\" spectral and spatial configuration.  
The hyperspectral sensors obviously contain much more information than the multispectral, but in practice, this comes at the price of higher operational costs (for airborne), coarser GSD (for spaceborne), and more complex processing. Hence, multispectral sensors were also included in the data to examine whether they can deliver similar results to the hyperspectral ones. Three sensors in the database are currently not functioning, namely: ASTER, the high-resolution hyperspectral imager (Hyperion), and EnMap. The ASTER SWIR bands have been out of service since 2008, Hyperion was decommissioned at the beginning of 2017, and EnMap is not yet in orbit. Nevertheless, we decided to include them, as two of them were proven to function well in orbit, and EnMap is expected to be launched in the near future. An interesting choice was the Vegetation and Environment monitoring on a New Micro-Satellite (VEN\(\upmu\)S, hereafter termed VENUS; multispectral) and the Compact Airborne Spectrographic Imager (CASI; hyperspectral) sensors, because their spectral range is limited to ~400-1000 nm (visible-near-infrared--VNIR), as opposed to the other sensors (~400-2500 nm). Moreover, the diagnostic PHC-SFs are mainly found at ~1700 and ~2300 nm, which is not part of the spectral range of these sensors. This choice was made because of the nature of the machine learning approach taken here, which can find hidden connections between the spectra of the pixels to their class. In other words, as our method is based on a model that learns the spectral characteristics of each class without the use of any prior spectral knowledge, it was intriguing to see if it is possible to get good results with a limited spectral range. Additionally, we added a new sensor (Self; Table 2), which is a theoretical multi-spectral sensor with six bands in the SWIR spectral region. The band centers and FWHM of this sensor were defined according to the analysis of variance (ANOVA) results from [7], and are designed to overlap the PHC-SF
# Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things
## 4 Markov Decision Process
### State Space  
\(S\) is a state space of the mobile node. In the manufacturing cell, the mobile node connects to the relay node for the cellular link. The state space of the mobile node is defined as  
\[S=D\times M, \tag{2}\]  
where \(D\) is the distance between the mobile node and the relay node. \(M\) is the memory buffer state space of the mobile node, which can be represented as \(M=\{0,1,\ldots,M_{max}\}\). The mobile node moves around the manufacturing cell and collects data, which can be stored in the memory buffer \(M\).
## 4 Discussion
### Glutamate Carboxyeptidase II (FOLH1) Gene  
Glutamate carboxypeptidase II (FOLH1) gene encodes three distinct splice variants with different functional roles: N-acetylated-\(\alpha\)-linked acidic dipeptidase (NAALADase), folyl poly-\(\gamma\)-glutamate carboxypeptidase (FGCP), and prostate-specific membrane antigen (PSMA) [50]. The rs61886492 variant results in an amino acid substitution from histidine to tyrosine, changing a basic amino acid with an imidazole ring to an aromatic amino acid with a polar-charged group. This alteration in amino acid structure (from positive to neutral) modifies the protein's conformation, which in turn affects its binding capacity. Studies have shown that the H475Y polymorphism reduces the enzyme's activity, leading to lower plasma folate concentrations and higher total plasma homocysteine (tHcy) levels [28, 51, 52]. The role of this variant was analyzed by [PERSON] et al. [50] in a group of 1561 people with different disease conditions. They observed a protective role of the H475Y variant in cancers [breast cancer; OR = 0.81 (95%CI: 0.55-1.19), prostate cancer: OR = 0.00 (95%CI: 0.00-0.66)], and in autism (OR = 0.47 (95%CI: 0.21-1.03), whereas an inflated risk was observed in CAD OR = 1.69 (95%CI: 1.20-2.37) and miscarriages [maternal OR = 3.26 (95%CI: 2.11-5.04); paternal OR = 1.99 (95%CI: 1.23-3.21)]. In our study, the rare allele A of the rs61886492 FOLH1 variant increased the risk of IUFD 2.8 times compared to the control group, but unlike the previously discussed [PERSON] study, we did not observe a statistically significant association of this variant with recurrent miscarriages. We hypothesize that the rs61886492 variant of the FOLH1 gene will probably not have a significant impact on fetal development with appropriate folic acid supplementation. However, during rapid tissue growth occurring in the second and third trimester of pregnancy, the resulting reduced folate absorption may be one of the factors leading to intrauterine fetal death. Although several epidemiological studies have examined the impact of the His475 Tyr polymorphism, its precise role in folate uptake and metabolism remains uncertain. [PERSON] et al. [53] explored the molecular recognition of polyglutaminated folates by GCPII and aimed to understand how the His475 Tyr polymorphism influences the protein's structure and function. Their findings showed that the enzymatic activity of recombinant human GCPII with the His475 Tyr variant was similar to the wild-type enzyme. Additionally, X-ray crystallography of the protein's three-dimensional structure did not reveal any significant changes caused by the mutation. However, the authors observed subtle differences that did not fully account for the phenotypic effects associated with this polymorphism. They suggest that the His475 Tyr variant might influence folate levels through alternative mechanisms, such as altering the transport function of GCPII, its expression levels, or its interactions with other signaling molecules [53].
# DenseNet-Based Classification of EEG Abnormalities Using Spectrograms
## 3 Methodology
### Data Preprocessing  
To reduce artifacts and enhance signal quality, a Butterworth filter (an infinite impulse response filter) was applied to obtain the frequency band of interest (0.1-100 Hz; delta, theta, alpha, beta, and gamma wave) in the TUAB EEG recordings. Additionally, a notch filter was used to remove powerline interference at 60 Hz. For classification of normal and abnormal EEGs in the TUAB dataset, channels T5 and O1 were selected as input, providing a minimal yet informative representation while reducing data dimensionality.  
The EEG recordings used in this study are labelled at the file level as either normal or abnormal, without annotations indicating the precise timing or spatial location of abnormal events within each recording. This presents a fundamental challenge for training a classification model, as a label of \"abnormal\" does not imply that the entire EEG trace is abnormal; many abnormal-labelled EEGs contain long stretches of normal brain activity. Training on full-length recordings without accounting for this could introduce significant label noise and degrade model performance.  
To address this issue and better align the input data with the provided labels, we segmented each EEG recording into 5 min non-overlapping epochs. This epoch length reflects a strategic compromise: it is sufficiently long to capture a representative temporal context and allow for the manifestation of clinically relevant EEG patterns (e.g., seizure discharges, slowing, or asymmetries), yet short enough to minimize the inclusion of extensive normal segments in recordings labelled as abnormal. In practice, 5 min windows improve the likelihood that at least part of each segment in an \"abnormal\" file contains diagnostically useful activity, thereby reducing the risk of mislabeling during model training.  
Moreover, shorter segments would increase the number of training samples but might miss important temporal dependencies, while much longer segments could introduce redundant or irrelevant information, increase computational demands, and hinder real-time applicability. Each 5 min epoch was then transformed into three complementary representations: signal images, spectrograms, and scalograms--to capture time-domain, frequency-domain, and time-frequency characteristics, respectively, for robust classification.
## 2 Materials
### Dataset Used  
The experiments utilized two images with different resolutions covering the same urban area near Theftor Mines in Canada: a coarse-resolution thermal infrared hyperspectral image (Figure 1a) with a 1-m spatial resolution, and a fine-resolution visible image (Figure 1b) at a 0.2-m spatial resolution. The coarse-resolution image was acquired by Hyper-Cam, which is an airborne long wave infrared (LWIR) hyperspectral imagery. The imagery has a spatial dimension of 751 \(\times\) 874 pixels and has 84 spectral bands between 868 and 1280 cm\({}^{-1}\) (7.8 um and 11.5 um) at a spectral resolution of 6 cm\({}^{-1}\) (full-width half-maximum). The fine-resolution image was acquired using a digital color camera, mounted on the same airborne platform. This image is composed of a series of spatially disjoint sub-images, and red, green, and blue spectral channels, with a spatial size of 3769 \(\times\) 4386 pixels, which is the uncalibrated digital data. The two sensors were integrated on a gyro-stabilized platform in a fixed-wing aircraft. The images were acquired simultaneously on 21 May 2013, between 22:27:36 and 23:46:01 UTC at a height of 2650 ft (807 m). The training samples with seven land-cover classes are provided using one data subset, as shown in Figure 1c, and the test samples are shown in Figure 1d. Table 1 lists the number of training and test samples for each class.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Training Samples** & **Test Samples** \\ \hline Road & 112,457 & 809,098 \\ Tree & 27,700 & 100,749 \\ Red roof & 46,578 & 136,697 \\ Grey roof & 53,520 & 142,868 \\ Concrete roof & 97,826 & 109,539 \\ Glass & 185,329 & 103,583 \\ Bare soil & 44,738 & 49,212 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Class information for the datasets.  
Figure 1: Experimental datasets. (**a**) The visible image. (**b**) Gray-scale representation of the thermal infrared hyperspectral image. (**c**) The training samples. (**d**) The test samples.
# Amodal Segmentation Just Like Doing a Jigsaw
## 4 Experiments
### Baselines  
* **ORCNN**[6] adds a branch to the Mask R-CNN, and the two branches respectively predict amodal mask and visible mask. Subtract the visible mask from the amodal mask to obtain the occlusion mask, thereby completing the task of amodal segmentation.
* **VRS & SP**[5] firstly estimates a coarse visible mask and a coarse amodal mask. Then based on the coarse prediction, it infers the amodal mask by concentrating on the visible region and utilizing the shape prior in the memory.
## 4 Numerical Examples  
Numerical examples are presented in this section to illustrate the feasibility of the proposed method in handling large-scale truss optimization problems with size and topology design variables. First, its effectiveness and efficiency are verified by designing a classic small-scale truss structure, and a brief comparison with the literature is made. Subsequently, the structure of a large-scale cantilever truss is optimized to show the effectiveness of the LCS strategy on a large-scale problem by contrast with the scheme without the LCS strategy. Afterward, the efficacy of the improved scheme on large-scale truss optimization is verified by a large-scale Michell truss problem with mixed variables. Comparison of the same-scale problem with other literature results demonstrates its high efficiency with very few structural analyses.
## 4 Materials and Methods  
### Model Specification  
#### 4.1.1 Panel Fixed-Effects Model  
To assess the effect of GPP on urban carbon emissions intensity, we constructed a panel fixed-effects model. The model controls for both individual and time-fixed effects, thus addressing the potential endogeneity arising from unobserved heterogeneity across these dimensions. Logarithmic transformations were applied to the continuous variables to mitigate heteroskedasticity and multicollinearity concerns.  
\[CEI_{it}=\alpha_{0}+\alpha_{1}LnGPP_{it}+\sum\alpha_{n}Control_{it}+\mu_{it} \tag{1}\]  
where \(CEI_{it}\) denotes the carbon emissions intensity of city \(i\) in period \(t\); \(LnGPP_{it}\) represents the GPP of city \(i\) in period \(t\); \(Control_{it}\) refers to a vector of control variables, including economic development, industrial structure, urbanization rate, total retail sales, government intervention; \(\alpha_{1}\) captures the marginal effect of GPP on urban carbon emissions intensity; \(\mu_{it}\) is the idiosyncratic error term. A statistically significant \(\alpha_{1}<0\) would indicate that higher GPP correlates with lower urban carbon emissions intensity, thereby supporting H1.
## 4 Implementation Scenario and Conceptual Framework
### Classification and Training Latency of NTCA Using Different ML Models  
Training latency refers to the delay incurred in training a given ML model.  
The mean training latencies of 154.3, 154.9, 155.1, and 162.2 ms were measured for the DT-, K-NN-, NB-, and SVM-based NTCAs, respectively. It is worth mentioning that the DT-based NTCA design exhibits the lowest average training latency with 154.3 ms, as shown in Figure 15. We observe that NB, DT, and K-NN models train faster than the SVM-based NTCA model.  
Figure 15: Comparison of NTCA training latency  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(k_{th}\) Run of Algorithm} & \multicolumn{4}{c}{**Classifier Agent Designs**} \\ \cline{2-5}  & **K-NN** & **Decision** & **SVM** & **Naive** \\  & **(\%)** & **Tree (\%)** & **(\%)** & **Bayes (\%)** \\ \hline
1 & 98.95 & 99.6 & 94.77 & \(88.01\) \\ \hline
2 & 98.87 & 99.44 & 94.45 & 88.01 \\ \hline
3 & 98.79 & 99.52 & 94.53 & 89.14 \\ \hline
4 & 9  
Classification latency is how long it takes to classify new data. In Figure 16, we compare the NTCA classification accuracy, simulating over a one-hour simulation period.  
The average latencies of K-NN-, SVM-, NB-, and DT-based NTCAs are 28.01 us, 3.668 us, 2.773 us, and 1.593 us, respectively. Relatively, SVM, NB, and DT NTCAs impose the least mean latency, which is less than 4 us. Additionally, the DT-based NTCA has the least average latency, which is 1.593 us.
## 4 Results and Discussion
### Calculation of the Free Surface Profiles in the Staggered Pattern  
[PERSON] et al. [1] applied their relationship for estimating the drag coefficient (derived for the case in which the cylinders were arranged according to a linear distribution) to a test in which the cylinders were arranged according to a diagonal distribution. The obtained profile showed a satisfactory agreement between the measured and calculated results, such that [PERSON] et al. [1] argued that the model results were insensitive to the precise vegetation arrangements.  
Similarly, we tested Equation (28), derived in the case of a linear distribution, on the three tests T13, T14 and T15, which were carried out with a staggered pattern, with a lower density.  
The obtained results showed that the flow depths are significantly underestimated. Figure 12 shows the experimental and calculated free surface profiles with reference to two different flow rates \(Q=7.7\times 10^{-3}\) and \(17.4\times 10^{-3}\) m\({}^{3}\)/s (tests T13 and T15, respectively).  
Thus, Figure 12 shows that, according to [PERSON] et al. [67] for tests under uniform flow conditions, for similar vegetation densities and boundary conditions a staggered  
Figure 11: Comparison between two free surface profiles measured by [PERSON] et al. [1] and the profiles calculated using \(C_{D-UC}\): (**a**) \(\phi_{reg}=0.01\); (**b**) \(\phi_{reg}=0.073\). The black bold line indicates the flume bottom; the black line is the calculated free surface; the red symbols indicate the measured free surface.  
Figure 12: Comparison between the measured and the calculated free surface profiles obtained using \(C_{D-UC}\) in the case of staggered pattern: (**a**) T13; (**b**) T15. The black bold line indicates the flume bottom; the black line is the calculated free surface; the red symbols indicate the measured free surface.  
vegetation distribution results in a higher resistance, and thus a higher drag coefficient than that of a linear vegetation distribution. As also observed by [PERSON] et al. [67], this depends on the different structure of turbulent wakes.
# Research on Solidification Methods and Stabilization Mechanisms of Sulfate Saline Soils
## 4 Results and Analysis
### Microstructure Analysis of Stabilized Soils
#### 4.3.2 SEM Analysis
When subjected to 20 freeze-thaw cycles, the L3-stabilized soil showed microcracks at the locations designated by arrows in Figure 10a,b, but the bonding pattern remained unchanged. The area shown by the white circle in Figure 10c shows damage to the gel material on the surface with the formation of large cracks, suggesting a large change in particle morphology after 20 freeze-thaw cycles. As a result, after 20 freeze-thaw cycles, FA-stabilized soil significantly loses strength. In Figure 10d, after 20 freeze-thaw cycles, the film on the surface of soil particles almost disappeared, leading to the dispersion of soil particles and loss of strength in PAM-stabilized soil. Figure 10: SEM after 20 freeze–thaw cycles: **(a,b)** L3 FTs = 20; **(c)** F3 FTs = 20; **(d)** P3 FTs = 20. Figure 9: SEM of after 3 freeze–thaw cycles: **(a)** saline soils; **(b)** L3; **(c,d)** F3. The line stabilization of saline soils, after 28 d of curing, resulted in the production of CaSO4 and CaCO3 binding substances, which made the saline soils denser, with enhanced mechanical properties and frost resistance. FA hydration generates C\(-\)(A)\(-\)S\(-\)H gel material, which partially reacts with SO4\({}^{2-}\) to generate calcite, which covers the saline soil particles surface and enhances the saline soils' properties. The PAM film wraps around the soil particles and fills the pores, which enhances the mechanical properties of the saline soils [45]. The three materials provide different kinds of cementing substances so that the soil particles are connected in a way that changes them into face-face, side-face, and side-side, and become a whole with definite structural characteristics. This improves the mechanical properties and frost resistance of stabilized saline soils.
## 3 Results
### Main Effect of Variables
#### 3.1.3 Effect of Initial Turbidity  
According to Figure 5B, a greater reduction in turbidity is observed as the initial turbidity is less than 1130 NTU, this behavior could be explained because the amount of flocs formed is sufficient for their adsorption and thus quickly decrease turbidity. This trend is also deduced from [PERSON]'s law, which states that Al\({}^{3+}\) released to the solution for the same applied solution is constant [27].  
Figure 3: Turbidity reduction for separate processes such as pure ozone, electrocoagulation and coupled process of EC/O\({}_{3}\), operated at conditions of 10.0 volts, feed flow 360 L/h, O\({}_{3}\) flow 2400 mg/h, pH 6.89, and initial turbidity of 655 NTU.  
Figure 4: Variation of the pH in the time of treatment by electrocoagulation/O\({}_{3}\).  
Figure 5: Representation of the effect of operational variables on % reduction in turbidity, energy consumed in the electrolytic cell, and operational cost of the module. (**A**) Variation of the pH and voltage variables against % turbidity for fixed values of 300 L/h, 752.5 NTU, and 1650 mg/h. (**B**) Variability of pH and initial turbidity versus % turbidity for fixed values of 6.5 volts, 300 L/h and 1650 mg/h of O\({}_{3}\) flow. (**C**) pH and feed flow variability versus % turbidity for fixed values of 6.5 volts, 752.5 NTU, and 1650 mg/h O\({}_{3}\) flow. (**D**) Ozone and pH flow variability versus % turbidity for fixed values of 6.5 volts, 300 L/h, and 752.5 NTU. (**E**) Variability of initial turbidity and voltage versus energy consumption in the electrocoagulation cell for fixed values of 300 L/h, 7.4 pH, and 1650 mg/h of O\({}_{3}\) flow. (**F**) Variability of voltage and initial turbidity against the cost of the built module for fixed values of 300 L/h, 7.4 pH, and 1650 mg/h of O\({}_{3}\) flow.  
The proposed mechanism for the reduction of turbidity by means of the hybrid system of electrocoagulation and ozone is shown in Figure 6. This consists of destabilizing the colloidal particles and forming larger flocs, in which the contaminants are trapped and these flocs can be separated from the solution by flotation or sedimentation [28]. The dissolved air flotation mechanism is effective in reducing the organic load [29] and dissolved ozone flotation gives efficient results in the removal of suspended solids [30; 31]. For soluble contaminants, aluminum-based coagulants can act as catalysts for ozone and generate hydroxyl formation [22] and also oxidize surface functional groups of colloidal contaminants that promote colloid aggregation.
## 2 Principle of Simplified Second-Order Volterra-DFE Equalizer  
### Volterra Transmission Model for EML-IMDD Systems  
Considering the nonlinear damage of signals in short-range transmission systems, such as the nonlinear effect of optical fiber, the frequency fading effect caused by the interaction of laser chirp and dispersion, and the signal-signal beat noise (SSBN), the nonlinear equalizer with the Volterra structure has been widely used and studied by researchers. The traditional second-order Volterra equalizer in the discrete-time domain can be expressed as:  
\[y(n)=\sum_{l_{1}=0}^{L_{1}-1}h_{1}(l_{1})x(n-l_{1})+\sum_{l_{1}=0}^{L_{2}-1} \sum_{l_{2}=0}^{l_{1}}h_{2}(l_{1},l_{2})x(n-l_{1})x(n-l_{2}) \tag{1}\]  
where \(x(n)\) and \(y(n)\) denote input and output real-valued signals; \(h_{1}(l_{1})\) and \(h_{2}(l_{1},l_{2})\) represent the first and second Volterra kernel coefficients; and \(L_{1}\) and \(L_{2}\) are the corresponding memory length. It should be noted that the first and second terms in Equation (1) are dedicated to dealing with linear distortion and SSBN noise. The computational complexity is mainly determined by the second kernel, whose numbers are \(L_{2}\times(L_{2}+1)/2\). During the equalization process, the kernel coefficients can be obtained at the training stage by algorithm.  
The DFE equalizer can be represented as a transverse filter [17]. Unlike the FFE equalizer, the DFE equalizer combines the feedforward portion and the portion of the symbol value that has been decided together to produce an estimate of the current symbol. The use of a feedback equalizer makes the DFE equalizer nonlinear, which can effectively reduce the crosstalk of the previously sent symbol sequence to the currently decided symbols. The principle of the decision feedback equalizer can be expressed as:  
\[y(n)=\sum_{l=0}^{L-1}h(l)x(n-l)+\sum_{k=1}^{K}g(k)y^{\prime}(n-k) \tag{2}\]  
where \(x(n)\) and \(y(n)\) are the signal sequences before and after the equalization, and \(y^{\prime}(n)\) is the sequence that has been decided. \(h(l)\) and \(g(k)\) are the kernel coefficients of FFE and DFE, respectively, while \(L\) and \(K\) represent the number of taps of FFE and DFE, respectively.