esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Methods
#### 2.2.3 Binning  
To reduce the dominant role of radiation on the responses of GPP and SIF to the environment, we used the binning method to exclude the interaction of multiple environmental variables to some degree. This method aims to spilt an environmental variable into several bins and assumes that there is no large variation in this variable in each bin. Then the responses of GPP and SIF to other environmental variables in each bin for the above variables are analyzed [45; 46]. In this study, we investigated the responses of GPP and SIF to radiation (PAR/SW) under different EF and Ta bins, and to EF and Ta under different radiation (PAR/SW) bins, respectively.  
For the responses of GPP and SIF to radiation under different EF and Ta bins, the process was as follows: Based on the standardized environmental variables, we first sorted PAR(SW), Ta, and EF into 8 bins with a 0.5 interval without changing the temporal match of all data. Next, within each EF or Ta bin (i = 1, 2, 3,..., 8), data were further ranked according to PAR(SW) bins from minimum bin to maximum bin. Finally, the averages of GPP and SIF in the corresponding PAR(SW) bins in each Ta or EF bin were used to quantify the responses of GPP and SIF to the radiation in each Ta or EF bin. For the responses of GPP and SIF to EF and Ta under different radiation bins, the process was similar to the above-mentioned method, which is just needed to replace the parameter EF or Ta with PAR(SW) and replace PAR(SW) with EF or Ta in the second step, respectively. We aimed to explore the response of SIF to PAR under different EF bins, and so we carried out the following based on the standardized data:  
1. Dividing EF into 8 bins (i = 1, 2, 3,..., 8) and ranking the bins of EF from minimum to maximum;
2. In each EF bin, further dividing PAR into 8 bins (j = 1, 2, 3,..., 8) and ranking the bins of PAR from minimum to maximum;3. Calculating the mean values of SIF within each PAR bin under different EF bins to characterize the responses of SIF to PAR under different EF intervals. The response of SIF to PAR under different EF intervals can be described as follows: \[SIF_{PAR_{ij}|EF_{i}}=\frac{1}{n}\times\sum_{1}^{n}SIF_{n}\] (5) where \(i\) is the index number of \(i\)th EF bins, \(j\) is the index number of \(j\)th PAR bins in \(i\)th EF bins. \(n\) is the number of SIF within \(j\)th PAR bins.
## 2 Antenna Modelling  
A trapezoidal monopole has been designed on the substrate material FR4 with dimensions of 31 \(\times\) 42 \(\times\) 1.6 mm. Coplanar wave guide feeding has been used with an impedance of 50 ohms in the current design.  
Complex antenna design, modelling, and analysis can be difficult. Engineers and researchers face these issues and use various methods to overcome them. There are common issues and possible solutions: Designing antennas for certain frequency ranges or bandwidths can limit broad-frequency coverage. To overcome frequency restrictions, multi-band or reconfigurable antennas might be constructed. These antennas can operate at many frequencies or dynamically modify their resonance frequency. Small form factors can make it difficult to build effective antennas, especially at lower frequencies. As a solution, meandered structures or metamaterials can minimise antenna size without compromising performance. Designing antennas with complicated shapes can be computationally intensive and difficult to precisely model. As a solution, advanced numerical methods like FEA or MoM can simulate antenna performances with complex geometry.
# A 100-Gb/s PAM-4 DSP in 28-nm CMOS for Sredes Receiver
## 3 Crucial Techniques
### Influence of Input Data Quantization  
The bit number of the input data quantization is a critical parameter for both the ADC design and the DSP power optimization. Although a high resolution can improve the equalization accuracy, it not only increases the design difficulty and complexity in the analog-front ADC but also increases the hardware and power consumption in the DSP. To explore the impact of input data precision, Figure 10 shows the eye-opening of different input data quantization bits (i.e., 5, 6, 7, and 8 bits). It can be seen that the eye-opening effect increases significantly with the increase of quantization bits.  
To further quantitatively investigate this effect, VEC and VEOR defined in IEEE 802.3 [12] are also calculated utilizing different channels (see Figure 11). The VEC is defined as 20 log10 (AVupp/Vupp)max, where the AVupp is the amplitude of the eye and the Vupp is the eye height. And the VEOR is defined as \(-20\)log10((v \(-\) 1)/v), where the v = \(10^{VEC/20}\). As can be seen, a higher quantization bit always helps to optimize the VEC and VEOR. When the channel loss is below 14 dB, the VEC and VEOR can separately maintain below 4 dB and above 8 dB. When the channel loss increases to 23 dB, the VEC and VORE performance will degrade dramatically as the quantization bit decreases from 8 bit to 5 bit. To maintain the VEC and VEOR below 5 dB and above 7 dB, at least 7 quantization bit is required. This analysis result keeps consistency with the existing PAM4 transceivers that usually utilize 6-8 bit quantization. In this design, we adopt 8 bits to quantize the received signal.  
Figure 10: The equalization effect for different data digitalization bits in a \(-\)14 dB channel loss at 64 Gb/s. (**a**) 5 bits. (**b**) 6 bits. (**c**) 7 bits. (**d**) 8 bits.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Order** & **Technology** & **Channel** & **Clock (Hz)** & **No.Adders** & **Power** \\ \hline Moving average & 4 & 28 nm & 64 & 1 G & 36,140 & 43 mW \\ Sigma-delta & 1 & 28 nm & 64 & 1 G & 10,407 & 14 mW \\ \hline \hline \end{tabular}
\end{table}
Table 3: 4 th-order moving average filter versus 1 st-order sigma-delta.
# Mapping Subtidal Marine Forests in the Mediterranean Sea Using Copernicus Contributing Mission
## 4 Discussion
### Limitations in Mapping Cystoseira s.l. Forests and the Role of Copernicus Sentinel-2  
Identifying suitable sites for mapping _Cystoseira_ s.l. forests is increasingly challenging due to ongoing habitat degradation. Many historically rich sites have transitioned to barren reefs dominated by filamentous or encrusting algae. Based on this study's experience, we propose the following site adequacy criteria for effectively mapping _Cystoseira_ forests via satellite imaging:  
1. Dominance of _Cystoseira_ s.l. canopy algae.
2. More than 80% cover at the pixel scale of PlanetLabs imagery.
3. Minimal interference from other species.
4. Homogenous patches of at least 3 \(\times\) 3 pixels (\(\sim\)9 \(\times\) 9 m)--the central pixel to be used for training to avoid adjacency effects.
5. Avoidance of steep slopes (>30\({}^{\circ}\) within a pixel) and areas where landscape features cast shadows.
6. At least 20 pure pixels for independent validation, avoiding mixed-habitat edges.  
The 10 m resolution of Sentinel-2 limits its utility for mapping _Cystoseira_ forests, as these habitats often span \(<\) 10 m width. Sentinel-2 can effectively map binary categories in clearly separable habitats (e.g., _Seagrass_ vs. sand) but is inadequate for detailed mapping of _Cystoseira_. Future approaches should combine UAVs with high-resolution commercial satellites (1.2-3 m) for precise delineation. Accurate habitat extent mapping is essential for the implementation of environmental EU policies, including the Habitats Directive, the Marine Strategy Framework Directive, and the Nature Restoration Regulation. Satellite-based monitoring, integrated with robust field data, provides a scalable, transparent, and cost-effective solution for meeting reporting obligations for these legislative instruments and establishing baselines for conservation and restoration planning.
# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## 6 Wrap-up Discussion on New Research Challenges
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Semantic Approach** & \multicolumn{4}{c}{**Evaluation Dimensions**} \\ \hline
**5. Geospatial Data** & & & & \\
**Integration** & **Precision-Recall** & & & \\
**Knowledge** & & & & \\
**Discovery** & & & & \\
**Ontology mapping** & & P & P \\ approaches & & & & \\ \hline
**6. Smart Geospatial** & & & & \\
**Fusion of** & **Vector** & & & \\
**Heterogeneous** & **Sources** & & & \\
**Vector-Raster Data** & & & & \\ Ad-hoc approaches & P & P & P & P \\ Vector feature fusion & Y & N & N & P \\ \hline
**7. Semantic** & & & & \\
**Federated Querying** & **Vector** & & & \\
**over Vector and** & **sources** & & & \\
**Raster Datasets** & & & & \\ SPARQL & N & N & N & N \\ GeoSPARQL & Y & N & N & N \\ Implementations & Y & N & N & N \\ SciSPARQL & N & N & N & N \\ \hline
**8. Keyword-Based** & **Leverage** & & & & \\
**Search over** & **Geo** & & & \\
**Geospatial Data** & **Metadata** & & & \\
**Sources** & & & & \\ SPARQL Full Text & & P & N & N \\ Search & & & & \\ OGC Catalog Service & Y & N & N & N \\ (CSW) & & & & \\ GeoDCAT-AP & Y & N & N & N \\ Relational & & & & \\ Keyword-based & N & Y & N & N \\ Search & & & & \\ Keywordterm & P & P & P & N \\ \hline
**9. Interactive** & & & & \\
**Exploratory Analysis** & **Vector** & & & \\
**of Very Large** & **Features** & & & \\
**Geospatial Datasets** & & & & \\ OGC Map Service & P & P & N & P \\ (WMS) & & & & \\ GeoSPARQL & Y & N & N & N \\ Implementations & & & & \\ Approximate & N & N & P & P \\ Processing & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._purpose multidimensional data, however, they do not incorporate geospatial data representation capabilities. GeoSPARQL [12] supports only vector features and SciSPARQL [53] supports only raster coverages.
## 3 Modeling Methodology
### Cases Conditions  
The combustion and NO\({}_{x}\) production characteristics under various semi-coke blending ratios, injection positions of semi-coke, blending method, excess air ratios in primary combustion zone, and OFA arrangements in a 600 MW supercritical wall-fired boiler were further studied. In this paper, lean coal and semi-coke are selected in simulations and the ultimate analysis and proximate analysis of the fuels are described in Table 1. The subscript \"ar\" means \"as received basis\". This is the combination in which the intact fuels at the state as received are taken to calculate the proportion of each constituent. The acronyms C, H, O, N, S in the ultimate analysis, respectively, mean carbon, hydrogen, oxygen, nitrogen and sulfur. The acronyms M, A, V, and FC in the proximate analysis, respectively, mean moisture, ash, volatile, and fixed carbon in coals. The low calorific value of semi-coke is 25.35 MJ/kg and that of lean coal is 24.36 MJ/kg. Table 2 depicts simulation cases with specific parameters. \"Out-furnace\" corresponds to the cases when the semi-coke and coal are premixed before the furnace, while \"In-furnace\" corresponds to the cases when the semi-coke and coal are entered into the furnace from different burners. When the blending ratio of semi-coke is 45%, \"up\" means that semi-coke is injected from all nozzles of upper burners and four medial nozzles of the middle burners, and \"down\" implies that semi-coke is injected from all nozzles of the lower burners and four external nozzles of the middle burners. The OFA arrangement is represented by \"S+ number\". The number 1 refers to the original OFA nozzles of the boiler at the height of 31 m, and the numbers 2 and 3, respectively, refer to the OFA nozzles at the height of 34 m and 39 m. Case T1 is the numerical calculation of the wall-fired boiler at the condition of the boiler's maximum continuous evaporation (BMRC) as the basis condition. The physical parameters and boundary conditions of the basis condition were set in accordance with the data of the in-situ boiler. The temperature of the primary air is 363 K and the temperature of the secondary air is 608 K. The primary air contains the moisture in fuels, and the total excess air coefficient remains unchanged at 1.14. The excess air ratio in the primary combustion zone is defined as \(\alpha_{1}\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{4}{c}{**Ultimate Analysis (wt \%)**} & \multicolumn{4}{c}{**Proximate Analysis (wt \%)**} \\ \cline{2-10}  & **C\({}_{ar}\)** & H\({}_{ar}\) & O\({}_{ar}\) & N\({}_{ar}\) & S\({}_{ar}\) & M\({}_{ar}\) & A\({}_{ar}\) & V\({}_{ar}\) & FC\({}_{ar}\) \\ \hline Lean coal & 64.24 & 3.55 & 2.62 & 1.15 & 0.33 & 5.88 & 22.22 & 10.38 & 61.52 \\ Semi-coke & 74.42 & 1.40 & 1.97 & 0.91 & 0.40 & 8.10 & 12.80 & 8.22 & 70.88 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The ultimate and proximate analysis of the fuels.
## 2 Materials and Methods
### Determining the DC of RBCs with Spectroscopic Analysis  
The DC of RBCs was determined using a Fourier-transform infrared (FTIR) spectrometer (Mattson 1000$ FTIR, CA, USA). Non-polymerized and polymerized samples were prepared in each group for each surface (n = 3). For this purpose, 50 \(\upmu\)g of non-polymerized (in dough form) and polymerized RBCs (as ground powder of RBCs scraped from the cutting and the bottom surfaces) were ground into 5 mg of KBr fine powder (100 mg Merck's spectroscopically pure potassium bromide, chemical formula weight 119.01 g/mol, 0.15% water, Merck KgaA, Darmstadt, Germany) and mixed with a mortar and pestle. After this, these mixtures were placed into a pelleting device and pressed under hydraulic pressure with a load of 10 t over 1 min by a Carver hand press (Graesby & Specac, Orpington, UK) to prepare RBC-KBr pellets (\(\upsigma\) = 6 mm). Then, these assemblies were placed into the transmission holder of the FTIR spectrometer. A total of 20 scans were measured at a resolution of 0.2 cm\({}^{-1}\). The range from 4000 to 400 cm\({}^{-1}\) was scanned, and then the range from 1560 to 1670 cm\({}^{-1}\) was expanded. The absorption of the aromatic C=C stretching band serves as an internal standard, as it remains constant during the polymerization reaction. The absorption peaks were recorded by measuring the absorbances of samples against the wavelength number. The spectra were recorded initially by subtracting the background as the transmission mode at room temperature, then converted to the absorbance mode by the microprocessor of the spectrometer. The DC of each sample surface was calculated with [PERSON]'s standard baseline technique by comparing the ratio of the aliphatic C=C absorption peak at 1638 cm\({}^{-1}\) to the aromatic C=C peak at 1609 cm\({}^{-1}\) between polymerized and unpolymerized samples [31]:  
\[\begin{split}\text{DC}\%=1-\frac{\left[\text{abs}(\text{aliphatic C=C})/\text{abs}(\text{aromatic C=C})\right]\text{ of polymer}}{\left[\text{abs}(\text{aliphatic C=C})/\text{abs}(\text{aromatic C=C})\right]\text{ of monomer}}\times 100\\ \text{or}\qquad\text{DC}\%=\left[1-\left(\text{R}_{\text{ polymerized}}/\text{R}_{\text{unpolymerized}}\right)\right]\times 100\end{split} \tag{1}\]  
R is the ratio between peak heights at 1638 cm\({}^{-1}\) and 1609 cm\({}^{-1}\).  
A total of 6 measurements were made for each RBC sample placed with the standard technique from the top and bottom surfaces, and 9 measurements were made for the RBC samples placed with the bulk and incremental techniques from the top, middle, and bottom surfaces. For statistical evaluation, 30 measurements were made for each RBC sample placed with the standard technique, and 45 measurements were made for each RBC sample placed with the bulk and incremental techniques. While the DCs of RBCs placed with the standard technique were assessed at 0.5 and 2 mm depths, the DCs of RBCs placed with the bulk and incremental techniques were determined at 0.5, 4, and 8 mm depths, respectively.
## 5 Challenges and Opportunities
### Shift towards Zero-Trust Network Protocol Paradigm  
With OneNet protocol [40], we can see that marine network protocols are shifting their design toward enhancing throughput and security. In the past, the protocols were constructed just for data transmission. Most marine communications assume that the network is not compromised as demonstrated by our analysis of the various attack vectors. Instead, the presence of multiple devices on vessels requires us to treat the network and, in effect, the network protocols as organizations that need have an established intrusion detection, as well as incident response strategy in place. We recommend that manufacturers change their networking approach from using proprietary or specialized protocols to incorporating flexible, multi-purpose ones. In addition, increasing the bandwidth and transfer speeds for data enables seafaring vessels to implement security measures that work with on-land networking protocols, such as firewalls, intrusion detection systems, and honeypots. For example, a study has used wireless sensors to establish physical intrusion detection on boats [46]. It is only natural that such physical controls can be extended on the technical aspects of marine systems. Finally, recent advances to improve security in Internet of Things devices moves close to a zero-trust paradigm. One such work attempts to document the device types and communication behaviors of Internet of Things devices connected to a network [47].
## 2 Materials and Methods
### Method
#### 2.6.3 Model Accuracy Evaluation Method  
A range of statistical indicators may be used to evaluate the efficacy of landslide hazard zonation models. To assess the performance of the prediction model, many validation model evaluation approaches were employed in this study, including sensitivity, receiver operating characteristics (ROC), specificity, area under curve (AUC), and accuracy. The effectiveness of landslide susceptibility prediction has recently been extensively studied using the ROC_AUC technique [26; 66]. The inputs used to create the ROC curve were true positive, which refers to a landslide that was correctly anticipated on the axis-\"X\", and false positive, which refers to a landslide that was not correctly predicted on the axis-\"Y\". The models were statistically compared using AUC, a measure of the inclusive effectiveness of the models. ROC-AUC's accuracy is assessed as poor when the value is between 0.5 and 0.6, moderate when the value is between 0.6 and 0.7, high when the value is between 0.7 and 0.8, and exceptional when the value is between 0.8 and 0.9 [67]. Consequently, AUC values may be used to evaluate the accuracy of a prediction model.  
The receiver operating characteristics (ROC) curve was used to assess the map and the model's precision [38; 68; 69; 70]. The ROC curve was shown concerning the axis-\"X\" and axis-\"Y\", where the axis-\"X\" is the false positive rate (1-specificity) and the \"Y\" axis is the true positive rate (sensitivity) [25; 31; 70; 71].  
\[\text{X}-\text{axis}=\frac{\text{TN}}{\text{TN}+\text{FP}}=1-\text{Specificity} \tag{6}\]  
\[\text{Y}-\text{axis}=\frac{\text{TP}}{\text{TP}+\text{FN}}=\text{Sensitivity} \tag{7}\]  
where TP = true positive, FP = false positive, TN = true negative, FN = false negative. A ROC curve was used for the assessment of the hazard map.
###### Abstract  
Feature tracking in image collections significantly affects the efficiency and accuracy of Structure from Motion (SFM). Insufficient correspondences may result in disconnected structures and incomplete components, while the redundant correspondences containing incorrect ones may yield to folded and superimposed structures. In this paper, we present a Superpixel-based feature tracking method for structure from motion. In the proposed method, we first propose to use a joint approach to detect local keypoints and compute descriptors. Second, the superpixel-based approach is used to generate labels for the input image. Third, we combine the Speed Up Robust Feature and binary test in the generated label regions to produce a set of combined descriptors for the detected keypoints. Fourth, the locality-sensitive hash (LSH)-based k nearest neighboring matching (KNN) is utilized to produce feature correspondences, and then the ratio test approach is used to remove outliers from the previous matching collection. Finally, we conduct comprehensive experiments on several challenging benchmarking datasets including highly ambiguous and duplicated scenes. Experimental results show that the proposed method gets better performances with respect to the state of the art methods.  
Keywords:feature tracking; superpixel; structure from motion; three-dimensional reconstruction; local feature; multi-view stereo +
Footnote †: journal: Applied
# An Effective Multi-Label Feature Selection Model Towards Eliminating Noisy Features
###### Abstract  
Feature selection has devoted a consistently great amount of effort to dimension reduction for various machine learning tasks. Existing feature selection models focus on selecting the most discriminative features for learning targets. However, this strategy is weak in handling two kinds of features, that is, the irrelevant and redundant ones, which are collectively referred to as noisy features. These features may hamper the construction of optimal low-dimensional subspaces and compromise the learning performance of downstream tasks. In this study, we propose a novel multi-label feature selection approach by embedding label correlations (dubbed ELC) to address these issues. Particularly, we extract label correlations for reliable label space structures and employ them to steer feature selection. In this way, label and feature spaces can be expected to be consistent and noisy features can be effectively eliminated. An extensive experimental evaluation on public benchmarks validated the superiority of ELC.  
Keywords:feature selection; noise elimination; space consistency; label correlations +
Footnote †: journal: Applied Sciences
# Integrating Visual Cryptography for Efficient and Secure Image Sharing on Social Networks
## 4 Experimental Results
### Security Analysis  
Our proposed scheme ensures the confidentiality of images through an SS algorithm during the construction of shares. It is impossible to access the secret from shared images, as a single share or less than the threshold will not reveal any information about the secret image. This property is inherent to the VCS and has been formally proven by [PERSON] and [PERSON] [11]. The preprocessing operations, such as downsampling and error diffusion, are performed on the cover images and do not involve the secret image. Therefore, the preprocessing step does not disclose any part of the secret image. The encryption process relies on the existing VCS mechanism, which has been proven to be secure. Even if an adversary intercepts the encrypted images, they cannot obtain the secret image without access to the required number of shares. Our method is computationally efficient, as it leverages the existing VCS mechanism and performs preprocessing operations only on the cover images. This makes our method suitable for real-time applications.