esa-sceva/satcom-chunk-collection huggingface dataset:
# DCS-TransUperNet: Road Segmentation Network Based on CSwin Transformer with Dual Resolution
## 3 Method
### Encoder  
We adopt a similar UPerNet structure in our model on the whole. For the encoder section, we apply CSwin Transformer as the backbone for feature representation. As shown in Figure 2, many overlapping patches are obtained by cutting the input image. Here, the length of patches is \((W/s)\times(H/s)\), where s means the patch size, and each encoded patch is called a \"token\". Through the linear embedding layer, the patch is projected onto dimension C. Specifically, the convolution operation generates the overlapping patches. The convolution kernel is 7, the stride is 4, and the number of output channels is C. After adding position information, the tokens are fed to CSwin Transformer for feature extraction, which goes through four stages. Among the stages, it is composed of some CSwin Transformer blocks. In order to increase the receptive field of the attention mechanism, convolution is introduced to reduce the resolution of the features with the Transformer blocks getting deeper. Except for the last stage, the number of channels of the feature will also increase exponentially. Specifically, the feature resolution is reduced to half, and the number of channels is doubled after going through the convolution layer. So, the output resolutions of the four stages are \((W/s)\times(H/s)\), \((W/2s)\times(H/2s)\), \((W/4s)\times(H/4s)\), \((W/8s)\times(H/8s)\) and the numbers of channels are C, 2C, 4C, and 8C.  
Figure 2: Illustration of the designed DCS-TransUperNet. We obtain overlapping patches at different scales by splitting the remote sensing image and then feed these patches into dual branches of the CSwin Transformer decoder. Next, to fully utilize these features, we propose a specific FFM to fuse the multi-scale features from the two encoder subnetworks. Finally, the fused feature representations are restored to the same resolution as the input image by up-sampling based on the feature pyramid. Therefore, we obtain the final mask predictions.
## 3 Results and Discussion
### Spectroscopic Properties of SGB Glass and Glass Fibers
Analogously, we carried out the investigation of luminescent properties SGB_01 Ag02 Eu glass fiber under 394 nm laser excitation. The luminescence spectra of SGB_01 Ag02 Eu glass fiber are presented in Figure 4. It is worth noting that the obtained results for fiber are in good accordance with glass; hence, developed glass is characterized by high thermal stability and presents no structural changes during fiber drawing. In order to confirm our results, we used europium ions as a spectral probe. It is known that, among the \({}^{5}\)D\({}_{0}\)\(\rightarrow\)\({}^{7}\)F\({}_{\text{J}}\) transitions, the emission line at 594 nm (\({}^{5}\)D\({}_{0}\)\(\rightarrow\)\({}^{7}\)F\({}_{1}\)) is a magnetic dipole transition (MD), while the emission line at 616 nm (\({}^{5}\)D\({}_{0}\)\(\rightarrow\)\({}^{7}\)F\({}_{2}\)) is an electric dipole transition (ED). The integrated intensity ratio of (\({}^{5}\)D\({}_{0}\)\(\rightarrow\)\({}^{7}\)F\({}_{2}\))/(\({}^{5}\)D\({}_{0}\)\(\rightarrow\)\({}^{7}\)F\({}_{1}\)) transition gives a factor of the asymmetry of the local environment of the Eu ions. This value is less than 1.0 for symmetric, and is higher than 1.0 for non-centrosymmetric, surroundings [36]. In our experiment, the asymmetry ratio of SGB_01 Ag02 Eu glass is around 2.1 and is almost independent of annealing time (Table 1). In the case of the SGB_01 Ag02 Eu glass fiber used as reference sample, the asymmetry ratio is higher (R = 2.45). Thus, the drawing process leads to a slight structural distortion of glass lattice geometry around the Eu\({}^{3+}\) ions. After the annealing process, the R ratio slightly reduces and is close to the value obtained in glass samples. Moreover, the analysis of the intensity changes at 616 nm shows almost the same value of luminescence for 1 and 2 h annealing processes (inset Figure 4). This effect is connected with the increase of Ag\({}^{0}\) nanoparticles as a result of additional heat treatment process during the fiber drawing. Figure 3: Influence of annealing time on the luminescence spectra of SGB_01 Ag02 Eu glass.
###### Abstract  
As urbanization accelerates, the management of urban solid waste poses increasingly intricate challenges. Traditional urban metrics, such as GDP and per capita consumption rates, have become inadequate for accurately reflecting the realities of waste generation; moreover, the linear correlation between these metrics and waste production is progressively diminishing. Consequently, this study introduces a novel methodology leveraging nighttime light remote sensing data to enhance the precision of urban solid waste production forecasts. By processing remote sensing data to mitigate noise and integrating it with conventional urban datasets, an innovative index system and predictive model were developed. Using Beijing as a case study, the gradient boosting regression algorithm yielded a prediction accuracy of 92%. Furthermore, in light of the substantial costs associated with waste recovery route planning and site selection for treatment facilities, this research further devised a location and distribution framework for waste treatment centers based on high-precision predictions of waste production while employing multi-objective evolutionary algorithms (MOEAs) alongside the non-dominated sorting genetic algorithm II (NSGA-II) for optimization. Distinct from prior studies, this study suggests that service point waste quantities are not fixed values but rather adhere to a normal distribution within specified ranges and thus provides a more realistic simulation of fluctuations in waste production while enhancing both the robustness and predictive accuracy of the model. In conclusion, by incorporating nighttime light remote sensing data along with advanced machine learning techniques, this study markedly improves forecasting accuracy for waste production while offering effective optimization strategies for site selection and recovery route planning--thereby establishing a robust data foundation aimed at refining urban solid waste management systems.  
waste management; remote sensing lighting data; machine learning; multi-target optimization; site selection and allocation; Beijing +
Footnote †: journal: Applied sciences  
19 September 2024  
1
## 2 Related Work  
In recent decades, shadow detection and compensation techniques in hyperspectral images have been widely researched. Shadow detection methods are categorized into property-based, model-based, and machine-learning methods. Shadow compensation mainly employs methods based on physical models and deep learning methods.
## 3 Results and Discussion
### HS-SPME/GC-MS Analysis Results  
A total of 33 different aroma compounds were detected. According to the relative content of each group of aroma components determined, the components were arranged in order from highest to lowest: aldehydes, ketones, esters, lactones, alcohols, acids, alkenes, furfurrans and pyrazines (Figures 2 and 3). The relative content of aldehydes was the highest among the nine types of aroma compounds, accounting for approximately 29.7% of the total flavor components of each coconut jam sample.  
Figure 1: Flavor profiles for the coconut jams (0, 4, 8, 12, 16, 20 min, and after sterilization). The treatments were evaluated in triplicate by 10 panelists (\(n=30\)). Asterisks and “ns” indicate significant (\({}^{*}p\leq 0.05\)) differences and no significant differences of means, respectively.  
Figure 2: Relative contents of ester, lactone, alcohol and ketone aroma compounds for the coconut jam obtained by GC-MS. Values identified with different letters represent significant differences for each class of compound.
# Soil Moisture Influence on the FTIR Spectrum of Salt-Affected Soils
## 1 Introduction
Spectral signatures of pure halite crystals (NaCl) do not show absorbance in the visible, infrared and thermal regions [29], and absorbances near the 1400, 1900 and 2250 nm were attributed to moisture associated with the salt [30; 31; 32; 33]. The continuous spectra of halite (NaCl) and cinnite (KCI) show an absorbance at 1440 and 1933 nm, while soil samples treated with MgCl\({}_{2}\) (Bischofite) showed absorbance at 1190 and 1824 nm [33; 33]. Overall, an increased absorbance was associated with an increased salt content in soils [32; 33]. The presence of water darkens the soil [34], mainly due to a change in the refractive index (n) of the environment [35]. This reduces the contrast of soil particles and increases scatter and spectral absorbance [36]. Soil moisture and salt, depending on type, have similar effects on the soil reflectance spectrum, and may therefore cause erroneous salinity predictions from remote sensing data and increase the difficulty of monitoring saline soils over time [4; 22; 37; 38; 39]. Although hyperspectral remote sensing has been widely applied, only a few studies have concentrated on the effects of soil moisture in estimating salt from spectral reflectance [40; 41; 42; 43]. Moisture resistance estimation methods are used for the early detection and rapid monitoring of large-scale salinity intrusion, and observing moisture changes in space and time [39; 44]. Detection of soil salinity with remote sensing offers possibilities for soil salinity monitoring, and an exploration beyond the near-infrared spectrum may offer possibilities for monitoring with newly planned long-wave and thermal infrared satellite sensors [45]. Estuarine and coastal areas suffer from increased salinity risks due to sea level rise, while drought prone areas may suffer from salinity due to high evapotranspiration rates in combination with the use of saline irrigation water [12,46]. Therefore, the development of methods to monitor salinity is important. The advent of novel thermal satellite sensor technologies will enable new applications and monitoring of the difficult to access terrains. We hypothesized that salinity can be detected without interference from soil moisture in the spectral range of 2.5 to 15.4 \(\upmu\)m. The main objective was to find spectra in which salt contamination can be detected without the influence of soil moisture, to estimate salt content from absorbance peaks and to explore possibilities for monitoring saline and sodic soils in areas at risk, that also have a high soil moisture content and/or are difficult to access when taking soil samples. We used Fourier-Transform InfraRed (FTIR) spectroscopy to build a spectral database of saline soils with salinity levels, based on salt contamination with NaCl, Na\({}_{2}\)SO\({}_{4}\) and Na\({}_{2}\)CO\({}_{3}\). The corresponding electrical conductivity of the 1:5 soil to water extract was assessed in combination with changes in soil moisture levels from dry to fully-saturated.
## 3 Ship Target Detection based on Morphological Reconstruction and Multi-feature Analysis
### Proposed Small Ship Target Detection in TIR Images  
Currently, studies mostly focus on bright infrared ship target detection. However, in real cases, dark ship targets whose infrared radiation is lower than surroundings also exist in the backlighting infrared images. To detect dark infrared ship targets, Equations (4), (9), (8), and (10) in the Steps 1-5 of Algorithm 1 can be replaced by Equations (5), (12), (11), and (13), respectively, to compute normalized IFSM of dark ship image. Meanwhile, Equations (5), (12), (11), and (14) in the Steps 6-10 of Algorithm 1 can be replaced by Equations (4), (9), (8), and (15), respectively, to compute normalized BCSM of dark ship image. Then, the binary segmentation result of the final saliency map of dark ship image is obtained according to the Steps 11-13 of Algorithm 1. Finally, the two-step ship verification strategy according to Algorithm 2 for TIR ship image is adopted to distinguish true ship targets from non-ship targets, and the output detected dark ship target map and target position are reliably acquired.  
In the real-world maritime scenes, the local-contrast brightness of ship targets is unknown, so the small ship target detection for both bright and dark ones in infrared image remains to be worthy of further investigation [5; 42]. As discussed above, the bright and dark infrared ship target maps can be separately detected by parallel processing algorithms on both sides. The final detected ship target map is obtained by directly adding both bright and dark infrared ship target maps, and the flow chart of the proposed small ship target detection method is illustrated in Figure 1. Therefore, the proposed small ship target detection method has the potential ability for real-time application due to the highly parallel computing properties of multichannel image-processing and mathematical morphology in multicore hardware systems [43; 44].
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 2 Recent Research Direction for D2D Communications
### Resource Allocation and Power Control  
RA and power control (PC) gained considerable attention in the design of the in-band D2D networks, where the D2D users share the same resources with the CUs [7]. A variety of interference mitigation techniques exist in literature to address this problem, which can be divided into interference avoidance, interference coordination and interference cancellation. A limited interference area (LIA) surrounding the D2D users was introduced to prevent any CU from transmitting within the LIA as a way of interference avoidance. For interference coordination, optimal RA and PC were investigated between D2D users and CUs. The genetic algorithm, game theory and the optimization theory were utilized as efficient mathematical tools for coordinating the interference and controlling the power among CUs and D2D users [8; 9]. A new research direction is to use the social activities of the users in interference mitigation among D2D users and CUs. For interference cancellation, techniques based on successive interference cancellation (SIC), coordinated multi point (CoMP) and full-duplex (FD)-based self-interference cancellation were investigated in the literature to cancel the interference occurs between D2D users and CUs. Fortunately, for the out-of-band D2D networks, including mmWave D2D, the problem of interference between CUs and D2D users does not exist. A low complex and bandwidth efficient transceiver design for superimposed waveform is provided in [10]. A new RA technique based on NOMA called superimposed multi-user shared access (MUSA) that supports much users than conventional techniques was proposed in [11].
## 3 Experimental  
### Instrumentation  
All morphological characterisation was carried out using a Hitachi S-3400N scanning electron microscope (Hitachi, Maidenhead, UK) with Image J software. All samples were gold sputtered using a Quorum Technologies 750T sputter coater (Quorum Technologies, Sussex, UK). Surface area measurements were obtained using a Micromerities Gemini 2360 surface area analyser (Micromerities, Norcross, GA, USA). The balance used was a Sartorius Extend (Sartorius, Goettingen, Germany) and the sonication bath used was from Branson Ultrasonics Corporation (Danbury, CT, USA). A Knauer Smartline 100 V 5010 pump (Knauer, Berlin, Germany) was used for washing the prepared monoliths within silcosteel or PEEK tubing and an Ultimate 3000 capillary chromatography system (Thermo Fisher Scientific, Sunnyvale, CA, USA) was used to establish backpressure profiles for swelling studies. All van Deemter profiles and chromatographic data were acquired using an Agilent 1200 Series LC system (Agilent Technologies, Santa Clara, CA, USA). The dimensions of polyHIPE columns in silicosteel tubing was 100 mm x 1.0 mm i.d., and the mobile phase was 50:50 acetonitrile/water. The flow rate was 0.04 mL/min with an injection volume of 1 \(\upmu\)L, column temperature was 25 \({}^{\circ}\)C, and UV detection was at 214 nm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Chromatographic**} & \multirow{2}{*}{**Toluene**} & \multirow{2}{*}{**Ethylbenzene**} & \multirow{2}{*}{**Propylbenzene**} & \multirow{2}{*}{**Butylbenzene**} & \multirow{2}{*}{**Pentylbenzene**} \\ \cline{1-1} \multicolumn{1}{c}{**Performance Indicator**} & & & & & \\ \hline Retention factor (\(k\)) & 0.97 & 1.56 & 2.56 & 4.25 & 6.95 \\  & (1.03) & (1.63) & (2.65) & (4.38) & (7.13) \\ Selectivity factor (\(\alpha\)) & 1.6 & 1.6 & 1.7 & 1.6 & – \\  & (1.6) & (1.6) & (1.7) & 1.6 & – \\ Resolution (\(R_{s}\)) & – & 0.96 & 1.10 & 1.21 & 1.25 \\  & – & (0.98) & (1.12) & (1.25) & (1.27) \\ Efficiency (N/m) & 2470 & 2332 & 1808 & 1565 & 1810 \\  & (3087) & (2591) & (2008) & (1597) & (1907) \\ \hline \hline \end{tabular} Values are for Column #1, with values in parentheses representing Column #2.  
\end{table}
Table 1: Chromatographic performance criteria.
# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
## 3 GNSS-R Measurements
### GNSS-R Measurement Time Averaging  
GNSS-R measurements are obtained at a daily rate over the area under study, but in order to compare the results of the classification algorithm to the reference, we performed averaging according to the reference. In this study, we used the PALSAR-2 derived inundation classifications as reference datasets, training, and validation, within the MDTR algorithm scheme to generate the GNSS-R derived  
Figure 3: Sensitivity to inundation: (**a**) Global Forest Watch (GFW) Amazon Basin pantropical map of aboveground live woody biomass density (MgHa\({}^{-1}\)) at 30-m resolution for the year 2000 [41]; and (**b**) CYGNSS peak SNR (dB) averaged over one month (June 2017) and gridded at 7 km \(\times\) 7 km. Most of the areas with high peak SNR (in blue) are characterized as inundated. This comparison shows the sensitivity of the CYGNSS signal to inundation in areas with different levels of vegetation biomass.  
inundation classifications. About 80% of the Pacaya-Samiria National Reserve inundates seasonally, with significant portions remaining inundated throughout the year. Maximum inundation occurs during the wet season (November-March). In July and August of 2017 (during the dry season), we conducted an expedition to Pacaya-Samiria to conduct field surveys and collect ground reference data [10]. Detailed vegetation biometry (structure) data were collected for circular plots and along transects. Inundation status was noted and inundation history was inferred based on high water marks remaining on the tree trunks from prior flood seasons. These data were used to generate and validate inundation maps of the entire area using ALOS-2 PALSAR-2 ScanSAR imaging radar mosaics [10]. PALSAR-2 operates at 1.27 GHz (L-band) in wide-swath ScanSAR mode in 14 day cycles, observing our entire study area approximately every \(\sim\)1-2 months, based on the PALSAR-2 data acquisition plan ([[http://eorc.jaxa.jp](http://eorc.jaxa.jp)]([http://eorc.jaxa.jp](http://eorc.jaxa.jp))).  
The GPS constellation operates at 1.57542 GHz. The similar L-band frequency between CYGNSS and PALSAR-2 ensures that the results are comparable as opposed to for example, using a reference dataset derived from C-band SAR. The associated CYGNSS observables, defined in the following sub-section, were therefore temporally composited and averaged over the respective PALSAR-2 acquisition periods to match the 14-day cycles.
## 6 Results and Discussion
### Overview of Performance Measurement
#### 6.2.1 Precision  
Precision calculates the ratio of true positive predictions to the total number of positive predictions, including both true positives and false positives. It assesses the model's ability to avoid misclassifying negative instances as positive. The formula for precision can be expressed as follows:  
\[Precision=\frac{True\ Positives}{True\ Positives+False\ Positives}\]  
Figure 24: Scatter plot illustrating the classification of 215 EMG samples using the PCA-SVM model.  
Figure 25: Scatter plot illustrating the classification of 215 EMG samples using the LDA-SVM model.
# Observations on the Computation of Eigenvalue and Eigenvector Jacobians +
## 6 Simplified Cases
### Real Diagonal Parent Matrix
#### 6.2.2 Perturbation to the Eigenspace of a Diagonal Matrix  
With the simplified relationships in hand, it is possible to make some interesting observations on the perturbation of the eigenspace. (Again, note that it is only assumed that the parent matrix is diagonal. The perturbation matrix is not constrained to be diagonal.) The first observation is that to perturb the \(i^{th}\) eigenvalue, one must perturb the \(i^{th}\) diagonal element of the diagonal parent matrix, at least to first order. Furthermore, the perturbation to the eigenvalue in this case is exactly the perturbation to the parent matrix. While this observation should be trivial (since the diagonal elements are the eigenvalues themselves), it leads to a more interesting observation for the general case as will be discussed later. This observation can be expressed mathematically as  
\[\Delta\lambda_{i}=\delta_{i} \tag{70}\]  
when  
\[\Delta\mathbf{A}=\delta_{i}\mathbf{e}_{i}\mathbf{e}_{i}^{T}=\delta_{i}\mathbf{ v}_{i}\mathbf{v}_{i}^{T}. \tag{71}\]  
The next observation is that the eigenvector is only perturbed when the \(i^{th}\) column of the parent matrix is perturbed. Furthermore, there is an analytic relationship between the change to the eigenvector, the eigenvalues, and the perturbation itself. Mathematically, this is expressed as  
\[\Delta\mathbf{v}_{i}=-\sum_{k=1,k\
eq i}^{n}\frac{\delta_{k}}{\lambda_{k}- \lambda_{i}}\mathbf{e}_{k}=-\sum_{k=1,k\
eq i}^{n}\frac{\delta_{k}}{\lambda_{k }-\lambda_{i}}\mathbf{v}_{k} \tag{72}\]  
when  
\[\Delta\mathbf{A}=\sum_{k=1,k\
eq i}^{n}\delta_{k}\mathbf{e}_{k}\mathbf{e}_{i}^ {T}=\sum_{k=1,k\
eq i}^{n}\delta_{k}\mathbf{v}_{k}\mathbf{v}_{i}^{T}. \tag{73}\]These relations show that the eigenvector derivative for a diagonal matrix can be expressed as a modal expansion of the other eigenvectors if the coefficients \(\delta_{k}\) can be calculated (which is quite simple for a diagonal matrix since the eigenvectors are the standard basis).
## 5 Examples
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \(\boldsymbol{\sigma^{2}}\) & \(\boldsymbol{k}\) & \(\boldsymbol{a_{11}}\) & \(\boldsymbol{a_{12}}\) & \(\boldsymbol{b_{11}}\) & \(\boldsymbol{b_{12}}\) & \(\boldsymbol{a_{21}}\) & \(\boldsymbol{a_{22}}\) & \(\boldsymbol{b_{21}}\) & \(\boldsymbol{b_{22}}\) & \(\boldsymbol{c_{1}}\) & \(\delta\) (\%) \\ \hline \(0.20^{2}\) & 1 & \(-0.02217\) & \(-0.02741\) & \(0.00000\) & \(0.00000\) & \(0.00000\) & \(0.00000\) & \(0.00000\) & \(-0.04161\) & 100.72791 \\  & 2 & 0.00000 & 0.00000 & 0.97952 & \(-0.91091\) & \(0.00000\) & \(0.00000\) & \(-0.49792\) & \(-0.86478\) & 0.15858 & 29.65842 \\  & 5 & 0.15601 & 0.25797 & 0.99892 & \(-0.770708\) & \(-0.09937\) & 0.34653 & \(-0.49427\) & \(-0.79873\) & 0.19811 & 0.92811 \\  & 10 & 0.15519 & 0.25727 & 0.98927 & \(-0.77191\) & \(-0.09993\) & 0.34719 & \(-0.49479\) & \(-0.79810\) & 0.20207 & 0.83049 \\ \hline \(0.60^{2}\) & 1 & \(-0.02217\) & \(-0.02741\) & \(0.00000\) & \(0.00000\) & \(0.00000\) & \(0.00000\) & \(0.00000\) & \(-0.04161\) & 100.72791 \\  & 2 & 0.00000 & 0.00000 & 0.97808 & \(-0.90354\) & 0.00000 & \(0.00000\) & \(-0.48514\) & \(-0.86083\) & 0.16440 & 29.49952 \\  & 5 & 0.16653 & 0.27248 & 0.98778 & \(-0.75450\) & \(-0.09914\) & 0.34084 & \(-0.48385\) & \(-0.79505\) & 0.20121 & 2.56878 \\  & 10 & 0.16596 & 0.27194 & 0.98782 & \(-0.75539\) & \(-0.09986\) & 0.34146 & \(-0.48438\) & \(-0.79426\) & 0.20209 & 2.49317 \\ \hline True values & 0.15000 & 0.25000 & 0.99000 & \(-0.78000\) & \(-0.10000\) & 0.35000 & \(-0.50000\) & \(-0.80000\) & 0.20000 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The F-LSI parameter estimates and errors for Example 1 (\(L=2000\)). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(\boldsymbol{\sigma^{2}}\) & \(\boldsymbol{k}\) & \(\boldsymbol{a_{11}}\) & \(\boldsymbol{a_{12}}\) & \(\boldsymbol{b_{11}}\) & \(\boldsymbol{b_{12}}\) & \(\boldsymbol{a_{21}}\) & \(\boldsymbol{a_{2