esa-sceva/satcom-chunk-collection huggingface dataset:
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 6 Graph-Based RA in CR Networks  
The CR devices take advantage of SDR-empowered transceivers that enable DSA for mutually beneficial SSh between themselves and the primary users (PUs) of the spectrum, i.e., those which are incumbent to it by a license. Protecting their communications while providing better spectrum utilization in time, frequency, and space, is the fundamental problem of CR networks (their nodes are referred to as secondary users, or SUs) [74]. The SSh between primary and CR networks is achieved either by an autonomous operation (interweave mode) of the latter from the former, or through cooperation (underlay, overlay, and hybrid modes) for their mutual benefit. SUs usually need to first determine which channels are available through spectrum sensing, and afterwards distribute these frequency resources among themselves. Depending on their mode of operation, they may be allowed to transmit on channels used by the PUs under an interference constraint. These design considerations have made the application of graph theory somewhat limited in current literature, but the available works reveal these algorithms' potency for solving diverse SSh and RA problems in CR networks, especially in the underlay mode. The scenario of CR networks is illustrated in Figure 6. It also includes a generalized illustration of the most common graph model for RA problems, and a summary of their subcategories in this Section. A tabular summary of the main design characteristics of the algorithms reviewed in this Section, and the assumptions they are developed under are given in Table 8.  
Figure 6: Scenario of CR networks and summary of methods for graph-based RA.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Application/Assumptions**} & \multirow{2}{*}{**Graph Model Type**} & \multicolumn{2}{c}{**Tasks**} & \multirow{2}{*}{**Graph Formulation**} & \multirow{2}{*}{**Optimization**} & \multirow{2}{*}{**Performance**} \\  & & & **Solved via Graphs** & & & **Method** & **Assessment** \\ \hline \multirow{4}{*}{[75]} & RA in an underlay CR & Undirected/ & \multirow{4}{*}{Channel} & Vertices—SUs/sets of & & \(R_{T}\) up to \\  & network/CAP-BS & conflict & & links; & 5 kb/s for \\  & coordination (realistic) & graph & & edges—between & \(N_{SU}=45\), \\  & & graph & & \(N_{PU}=20\) and \\  & & graph & & weights—SINR & & \(N_{c}=16\) \\ \hline \multirow{4}{*}{[76]} & RA in an underlay CR & \multirow{4}{*}{Bipartite} & Vertices—SUs and & & Channel \\  & network/Channel & & channels; & & allocation \\  & selection functionality & graph & & edges—between & \(N_{PU}=20\) and \\  & & graph & &
## 4 Materials and Methods
### Differential Scanning Fluorimetry (DSF)  
4.6.1. Protein Thermal Stability Screen  
The protein thermal stability of wild-type hNudt16 and all tested mutants in the presence of increasing Mg\({}^{2+}\) ion concentration was measured using DSF with SYPRO orange dye on CFX RT-PCR (Bio-Rad, Hercules, CA, USA). This method is based on the change in fluorescence of the SYPRO orange dye in an aqueous buffer, which is strongly quenched, while its binding to the exposed hydrophobic regions of thermally unfolded protein results in an increased fluorescence [50]. The optimal concentrations of protein, SYPRO orange dye, and buffer composition, in particular ion content, were determined. The samples were prepared and measured in clear 96-well multiplate PCR plates for RT-PCR (Bio-Rad, Hercules, CA, USA). The final conditions in the measured wells were 5 uM protein, 4\(\times\) SYPRO orange dye, and an increasing MgCl\({}_{2}\) concentration, all in a buffer consisting of 10 mM HEPES pH 7.9, 150 mM NaCl, 5% glycerol, and 1 mM TCEP. The individual components of the solution were added in equal volumes to each well, with a final sample volume of 25 uL/well. The samples were gently mixed, and the 96-well plate was covered with optically clear PCR sealing tape (Bio-Rad, Hercules, CA, USA) to prevent evaporation. Samples were equilibrated at 25 \({}^{\circ}\)C for 5 min, and then the thermal unfolding of the protein was monitored using a temperature ramp of 1 \({}^{\circ}\)C/min in the range of 25-95 \({}^{\circ}\)C (fluorescent reads were taken every 30 s). The data processing procedure was performed using Bio-Rad CFX Manager (version 1.6.541.1028), and melting point temperatures were calculated as the minimum of the first derivative plot.
# Estimation of Gross Primary Productivity (GPP)
###### Abstract  
This study aimed to understand which vegetation indices (VIs) are an ideal proxy for describing phenology and interannual variability of Gross Primary Productivity (GPP) in short-rotation coppice (SRC) plantations. Canopy structure- and chlorophyll-sensitive VIs derived from Sentinel-2 images were used to estimate the start and end of the growing season (SOS and EOS, respectively) during the period 2016-2018, for an SRC poplar (_Populus_ spp.) plantation in Lochristi (Belgium). Three different filtering methods (Savitzky-Golay (SavGol), polynomial (Polyfit) and Harmonic Analysis of Time Series (HANTS)) and five SOS- and EOS threshold methods (first derivative function, 10% and 20% percentages and 10% and 20% percentiles) were applied to identify the optimal methods for the determination of phenophases. Our results showed that the MEdium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) had the best fit with GPP phenology, as derived from eddy covariance measurements, in identifying SOS- and EOS-dates. For SOS, the performance was only slightly better than for several other indices, whereas for EOS, MTCI performed markedly better. The relationship between SOS/EOS derived from GPP and VIs varied interannual. MTCI described best the seasonal pattern of the SRC plantation's GPP (R\({}^{2}\) = 0.52 when combining all three years). However, during the extreme dry year 2018, the Chlorophyll Red Edge Index performed slightly better in reproducing growing season GPP variability than MTCI (R\({}^{2}\) = 0.59; R\({}^{2}\) = 0.49, respectively). Regarding smoothing functions, Polyfit and HANTS methods showed the best (and very similar) performances. We further found that defining SOS as the date at which the 10% or 20% percentile occurred, yielded the best agreement between the VIs and the GPP; while for EOS the dates of the 10% percentile threshold came out as the best.  
greenness; structure- and chlorophyll-sensitive indices; start of season (SOS); end of season (EOS); filtering functions; threshold methods; eddy covariance; _Populus_ spp.; coppice; management
## 3 Problem Formulation
### Multi-Radio Switching
```
Notation & Description
```  
N & Represent Nodes x & The number of sending nodes to sink C & Represent Channels n & The number of non-overlapping channels R & Represent Radio i & The number of radios L & Represent Links & \(l\) & The number of links G & The graph V & The vertex of the graph E & The edge of the graph I & The number available edges ```
**Algorithm 1** Relationship between two communication links using \(G=(V,\,E)\). ```  Integer E1; /*E one of EI*/ Integer E2; /*E two of EI*/ Integer Communication_Link_Active_Status; Integer Communication_Active_Link; Integer Communication_Link_Not_Active_Status = 0; ```  
**Algorithm 2** Relationship between two communication links using \(G=(V,\,E)\). **Algorithm 3** Relationship between two communication links using \(G=(V,\,E)\). **Algorithm 4** Relationship between two communication links using \(G=(V,\,E)\). **Algorithm 5** Relationship between two communication links using \(G=(V,\,E)\). **Algorithm 6** Relationship between two communication links using \(G=(V,\,E)\  
\[ \begin{array}{ll}\text{L}_{(\text{\boldmath{$y-1$}})}\text{C}+\text{n}=&\text{L}_{ (\text{\boldmath{$x-1$}})}\text{C}+\text{n},&\text{\boldmath{$\forall$}}\,\text{n}= 1\,...,\text{C}\\ \\ &\Rightarrow BV=0\end{array}  \tag{3}\]  
For each row in matrix \(B\), two of the entries are equal to 1 and \(-1\) respectively, and all other entries are equal to 0. The dimension of \(B\) depends on the number of linked pairs that share a common radio interface. The vector definition in (1) and the equality constraints in (2) and (3), together form the following non-empty feasible set:  
\[ \begin{array}{ll}\text{\boldmath{$\varphi$}}\ =\{\text{L}:1\ \in\ \{\text{0,1}\}\ \text{\cap}\,\text{AL}=1\ \text{\cap}\,\text{BL}=0\}\end{array}  \tag{4}\]  
where any of \(\varphi\) represent one feasible link assignment to a radio interface on same channel allocation. Let's consider any two arbitrary links d and e, and their associated elements in vector \(V\). We define two \(C\) x 1 vectors as follows:  
\[ \begin{array}{ll}V_{d}=\left[L_{(d-1)}\text{C}+1\ L_{(d-1)}\text{C}+2\,...\, L_{d}\text{C}\right]^{T}\\ V_{e}=\left[L_{(e-1)}\text{C}+1\ L_{(e-1)}\text{C}+2\,...\,L_{e}\text{C} \right]^{T}\end{array}  \tag{5}\]  
We define \(Ri\times i\) as the radio matrix at the sink. The element RAD \(\in\) [0,1] represents the radio interface portion between nodes A and D to switch on the same channel \(Cn\). R is a symmetric matrix and its diagonal elements all equal to 1. If node A and D are assigned to links d and e respectively we have:  
\[ \begin{array}{ll}V_{d}^{T}R\ V_{g}=\tau_{AD}\end{array}  \tag{6}\]  
For example, using the three non-overlapping channels _i.e._, \(C=3\). \(R\) becomes a \(3\,\times 3\) unitary matrix. If two arbitrary links \(d\) and \(e\) are assigned the same channel, then \(V_{d}^{T}R\ V_{e}=1\). otherwise, the product will equal to zero.
## 1 Introduction
### Contributions  
In this paper, we have proposed a fine-tuned pretrained deep framework model to increase the performance of existing indoor scene recognition tasks. In this work, we have worked on an NYU depth dataset consisting of different classes of indoor scenes. The images are preprocessed using fuzzy colour stacking techniques. These preprocessed images are segmented and then fed to the pretrained DenseNet201 model for feature extraction. A feature selection process using adaptive World Cup optimisation selects the most superior features and feeds them to a deep classifier known as an LSM (liquid state machine).  
Some of the major contributions of our work can be summarised as follows:  
* Implementation of fuzzy colour stacking for preprocessing improves the foreground quality of the images by filtering the background noise;
* Unlike outdoor scene recognition that utilises global spatial information, indoor scene recognition is possible using the objects in the scene. Therefore, for both local and global features, semantic information is needed. Hence, the segmentation provides an ROI (region of interest) to detect the objects;
* Pretrained DenseNet201 improves feature extraction due to a lack of vanishing gradient problems;
* An attention module to select the best features using the World Cup optimisation algorithm improves the robust nature of the model towards indoor scene recognition;
* Classification using the deep LSM model utilising the winner-take-all layer improves the overall accuracy of the indoor scene classification.  
We have structured the paper as follows: In Section 2, some of the existing works and a literature survey of indoor scene recognition are discussed. In Section 3, we have explained the block diagram and workflow of our proposed approach. In Section 4, we have given details on the dataset and some sample indoor scenes. Section 5 shows the experimental results and discusses the performance matrices of our classifier model, and, in Section 6, we compare the performance of our model with existing indoor scene recognition works. Section 7 includes our concluding remarks with the future scope.
However, most discrete element analyses focus on either the materials with high cohesive strength, such as asphalt and cement, or materials with nearly nil cohesive strength, such as soil [22; 23; 24; 25]. Similar materials simulating weak surrounding rock in geo-mechanical model tests are weakly cohesive materials, which fall between these two categories, high cohesive strength and nil cohesive strength. There are limited studies on discrete element simulation of weakly cohesive, similar materials. In summary, the drawbacks of the current studies are as follows: (1) further investigation is needed to discuss the influence of aggregate proportioning on the mechanical properties of similar materials; (2) as repetitive proportioning tests require a significant investment of time and financial resources, most empirical equations often only apply to specific sets of components and lack generality; and (3) most current studies using discrete element analysis focus on either highly cohesive materials or non-cohesive materials, ignoring weakly cohesive materials such as similar materials simulating weak surrounding rock. In order to overcome these drawbacks, in this study, several specimens with different RFCAs were created to simulate weak surrounding rock. Uniaxial compression tests (UCTs) were conducted on these specimens, which were simulated by discrete element analysis. Some key parameters were calibrated by comparing the results from the discrete element analysis and the compression tests. Based on the calibrated parameters, the effect of RFCAs on the mechanical properties of similar materials was investigated. The mathematical relationships between the RFCAs and the mechanical properties, such as elastic modulus, cohesion, and internal friction angle, were proposed. The variations in particle contact forces and crack propagation patterns in similar materials with different RFCAs were discussed from a microscopic perspective. The purpose of this study is to provide an optimum RFCA for similar materials simulating weak surrounding rock and to present a convenient and efficient numerical method to determine this optimum RFCA in geochemical model tests. This study is intended to be a preliminary investigation into the effects of RFCA on the mechanical properties of similar materials. The findings presented here should be considered as foundational and indicative of potential trends, rather than definitive conclusions.
## 3 Methods
### Blind Signatures
Namely, in the security game, the Adversary can interact with \(\mathcal{S}\) and receive valid signatures \(\sigma_{i}\) for \(i\in\{1,\ldots,n\}\) on messages \(\mathsf{m}_{i}\) of their choice. The Adversary wins if they can present a valid signature \(\sigma_{*}\
ot\in\{\sigma_{1},\ldots,\sigma_{n}\}\). The scheme satisfies one-more unforgeability property if the probability of winning the game by the Adversary is negligible. * **Blindness:** This property captures the fact that \(\mathcal{S}\) cannot link the signatures with the sessions executed with the User. It is expressed by the following game: first, the Adversary chooses messages \(\mathsf{m}_{0}\), \(\mathsf{m}_{1}\), generates signing keys \((\mathsf{sk},\mathsf{pk})\), and sends \(\mathsf{m}_{0}\) and \(\mathsf{m}_{1}\) to the User. Next, the User chooses a bit \(b\) at random. Then, two instances of blind signing procedures are started (and possibly run concurrently) with the Adversary being the Signer. In the first instance, the User obtains a valid signature of \(\mathsf{m}_{b}\) signed with \(\mathsf{sk}\); in the second, it obtains a valid signature for \(\mathsf{m}_{1-b}\) signed with \(\mathsf{sk}\). The resulting valid unblinded signatures \(\sigma_{0},\sigma_{1}\) are presented to the Adversary. The Adversary presents a bit \(b^{\prime}\) and wins the game if \(b=b^{\prime}\). The scheme satisfies the blindness property if the probability of winning the game by the Adversary is \(\frac{1}{2}+\epsilon\), where \(\epsilon\) is a negligibly small value. In a standard use case, the User is authenticated before starting the signature creation procedure by the Signer \(\mathcal{S}\), since typically, signing blindly is a service offered only to entitled Users. Hence, by presenting a valid signature \(\sigma\) of \(\mathcal{S}\), the User implicitly proves that \(\sigma\) has been obtained by an eligible party.
## 5 Conclusions  
The feasibility of eddy current pulsed thermography (ECPT) on surface crack detection for polycrystalline diamond compact (PDC) bit is investigated in this paper. A successive scanning detection mode is proposed to make ECPT with low heating excitation power and small-size coil suitable for surface crack detection on the tungsten carbide matrix of a PDC bit. Experiments using ECPT were conducted for detecting two cross surface cracks on the tungsten carbide matrix of a PDC bit. Based on the gray contrast calculated from IR images, the optimum excitation duration is determined as 500 ms for the used heating excitation system and the PDC bit. With the coil successively moving from one position to another, all the segments of each are detected. The principal component analysis (PCA) is then introduced to enhance the features of cracks in IR images, which is demonstrated as a useful approach to improve the detectability of ECPT for surface crack detection on the matrix of the PDC bit. Finally, by integrating the reconstructed images from the second principle component that contain the information of varied segments of the crack, the entire shape of the crack can be approximately restored, which further demonstrates the feasibility of ECPT to detect surface cracks on the matrix of PDC bits in a successive scanning mode. Consequently, ECPT provides a promising technique to detect surface cracks on the matrix of PDC bits. It is noted that the latest development of a novel pulse-compression technique would be very useful for further improvement of the proposed detection technique [23]. In the future, we will conduct a further investigation on improving the detectability of ECPT for the surface defect detection of PDC bits, which mainly focuses on numerical simulation to reveal the optimization mechanisms of detection parameters, designing a new coil with special geometry, and the integration with the novel pulse-compression techniques.  
This work was financially supported by the National Key Research and Development Program (No. 2016 YFC0303803), the Fundamental Research Funds for the Central Universities of China (No. 14 CX02077A) and the Shandong Provincial Natural Science Foundation of China (No. ZR2013 EEQ028).  
[PERSON] developed the original concept and wrote the paper. [PERSON] performed experiments. [PERSON] conducted experimental data analysis. [PERSON] provided advices for results discussion.  
The authors declare no conflict of interest.  
Figure 9: Crack reconstruction using (**a**) raw IR images and (**b**) a reconstructed image by the second principal component.
## 3 Monopulse Ratio Curve Improvement
### General Monopulse Rate Curve Improvement Method Based on the Presented Tile  
The most representative method for improving the linear characteristics of the monopulse ratio curve is by expanding the beam width of the antenna. In the presented antenna system, the method employed to expand the beam width of a single-tile antenna involves disabling some of the radiating elements within a single tile. In this paper, for the radiating elements within a single tile, the beam width was expanded for the cases that involved using either six or four azimuth-based radiating elements. Figure 7 shows the status of the radiating element operating configuration used to expand the beam width based on the above method.  
Figure 8 shows the radiation characteristics (sum and difference patterns) according to each operating state. The HPBW of the sum pattern for six azimuth-based radiating elements is \(13.0^{\circ}\), while the figure for four azimuth-based radiating elements is \(19.8^{\circ}\).  
Table 2 shows the optimum \(c_{r}\) and the location estimation accuracy, which are calculated based on Equations (7) and (8) for each radiating element's operating state.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \(\phi_{c}\) & \(4.0^{\circ}\) & \(4.5^{\circ}\) & \(5.0^{\circ}\) & \(5.5^{\circ}\) & \(6.0^{\circ}\) & \(6.5^{\circ}\) \\ \cline{2-7}
6 elements & \(c_{r}^{opt}\) & 8.854 & 8.756 & 8.648 & 8.524 & 8.388 & 8.236 \\ MAE mean & 0.035 & 0.049 & 0.069 & 0.094 & 0.124 & 0.16 \\ MAE max & 0.103 & 0.148 & 0.209 & 0.284 & 0.379 & 0.496 \\ \hline \hline \end{tabular}
\end{table}
Table 2: For general method, estimation accuracy for monopulse ratio curves according to \(\phi_{c}\).  
Figure 6: Monopulse ratio curves in (**a**) \(\phi_{c}=3.6^{\circ}\) and (**b**) \(\phi_{c}=6.0^{\circ}\).  
Figure 7: Radiating element status with (**a**) six azimuth-based elements and (**b**) four azimuth-based elements.  
Figure 9 shows the monopulse ratio curves for six azimuth-based radiating elements for which \(\phi_{c}=4.5^{\circ}\) and \(\phi_{c}=6.5^{\circ}\), as well as four azimuth-based radiating elements for which \(\phi_{c}=6.0^{\circ}\) and \(\phi_{c}=8.0^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{3}{*}{4 elements} & \(\phi_{c}\) & 4.0\({}^{\circ}\) & 5.0\({}^{\circ}\) & 6.0\({}^{\circ}\) & 7.0\({}^{\circ}\) & 8.0\({}^{\circ}\) & 8.5\({}^{\circ}\) \\ \cline{2-9}  & \(c_{c}^{opt}\) & 13.586 & 13.456 & 13.294 & 2013.1.2 & 12.874 & 12.612 \\  & MAE mean & 0.015 & 0.029 & 0.051 & 0.08 & 0.124 & 0.179 \\  & MAE max & 0.043 & 0.087 & 0.153 & 0.249 & 0.382 & 0.562 \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 8: Sum and difference patterns in (**a**) six elements with polar plot and (**b**) rectangular plot and (**c**) four elements with polar plot and (**d**) rectangular plot.  
The results show that the linear region of the monopulse ratio curve expands as the beam width expands. Moreover, as shown in Figure 6, when the location estimation range is expanded, the accuracy of the estimation of the location decreases.