esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Spectral-Spatial Classification Method for Hyperspectral Image at Superpixel Level
## 3 Experimental Results
### Hyperspectral Datasets  
The main features and parameters of three benchmark hyperspectral datasets are described as follows.  
A: Indian Pines dataset. This dataset was acquired by an airborne visible/infrared imaging spectrometer (AVIRIS) sensor over Indian Pines test site in the northwest of Indian. It consists of \(145\times 145\) pixels, 200 spectral bands, and 16 different classes. This HSI has a spatial resolution of 20 m. With the background removed, 10,249 pixels take part in the classification. Figure 1 shows the false-color image of Indian Pines dataset and the corresponding reference data.  
B: Salinas dataset. This image was also captured by the AVIRIS sensor over Salinas Valley in California. This image is of size \(512\times 217\times 204\) (20 water absorption bands were discarded), which has a spatial resolution of 3.7 m. The provided data are divided into 16 classes. The false-color image of this dataset and the corresponding reference data are shown in Figure 2.  
Figure 1: Indian Pines dataset. (**a**) False color image. (**b**) Reference image.  
C: University of Pavia dataset. This image was collected by the reflective optics system imaging spectrometer (ROSIS) over University of Pavia in Italy. It contains nine classes and 103 bands after removing 12 noise bands. Each band size in this dataset is \(610\times 340\) with a spatial resolution of 1.3 m. The false-color image of this dataset and the corresponding reference data are shown in Figure 3.  
Figure 3: University of Pavia dataset. (**a**) False color image. (**b**) Reference image.  
Figure 2: Salinas dataset. (**a**) False color image. (**b**) Reference image.
# Acoustic Emission Analysis of Fracture and Size Effect in Cementitious Mortars
###### Abstract  
The size effect is a phenomenon where the strength and the ductility of a material depend on the size of the structure. Investigating size effects and related crack formation in brittle materials requires advanced monitoring methods. The aim of this research is to experimentally investigate the impact of size effect with the acoustic emission (AE) technique. Brazilian splitting tests with AE monitoring were performed on cement-based mortar cylinders of three sizes. It was found that in addition to the size, the boundary condition affects the final strength. When adopting similar boundary conditions in samples with different sizes, the larger samples had the lowest tensile splitting strength. For the larger samples, initially, there were fewer AE activities. However, there was a surge of high-amplitude AE events near the peak load. This indicates that as size increases, there is a lack of micro-cracking before macro-crack propagation, and the material fails in a more brittle manner. The width of the fracture process zone was quantified with AE and increased with sample size. A further analysis of the AE amplitude distribution demonstrated a change in the distribution in the pre-peak phase for the larger samples and for the smaller samples in the post-peak phase, signifying the brittle to ductile failure transition that occurs as size decreases.  
Keywords:size effect; Brazilian splitting test; acoustic emission; AE source localization; fracture process zone; \(b\)-value; mortars +
Footnote †: journal: _Sci._
# On the Performance of Underlay Device-to-Device Communications
## 4 Performance Analysis  
In the present paper, we investigate the performance of both the cellular networks and D2D networks under three distinguished power allocation schemes. In particular, we derive the outage probability, the average rate, and the amount of fading of the D2D receiver. Regarding the cellular networks, we derive the coverage probability under the impact of the aggregate interference from all D2D transmissions. Before going to derive these metrics, we first present Theorem 1, which is useful for later computation.  
**Theorem 1**.: _Let us denote \(X_{1},X_{2},\ldots,X_{N}\) as a set of independent non-identical distributed (i.n.i.d) exponential random variables (RVs) with corresponding parameters \(\Omega_{1},\Omega_{2},\ldots,\Omega_{N}\), the moment generating function (MGF) of sum of \(N\) RVs, i.e., \(Y=\sum\limits_{i=1}^{N}X_{i}\), denoted by \(M_{Y}(s)\) is calculated as [54]_  
\[M_{Y}(s)=\prod\limits_{i=1}^{N}\frac{1}{1+s\Omega_{i}}. \tag{10}\]  
Proof of Theorem 1.: Let us denote the joint probability density function (PDF) of \(N\) exponential RVs as \(f_{X_{1},\ldots,X_{N}}(x_{1},...,x_{N})\), the MGF of the sum of \(N\) RVs, e.g., \(Y=\sum\limits_{k=1}^{N}X_{k}\) is then computed as [14]  
\[M_{Y}(s)=\mathbb{E}\{\exp(-sy)\}=\int\limits_{x_{1}=0}^{\infty} \cdots\int\limits_{x_{N}=0}^{\infty}\exp\left(-s\sum\limits_{k=1}^{N}x_{k} \right)f_{X_{1},\ldots,X_{N}}(x_{1},\ldots,x_{N})dx_{1}...dx_{N}\] \[\overset{(a)}{=}\prod\limits_{k=1}^{N}\int\limits_{x_{k}=0}^{ \infty}\exp(-sx_{k})f_{X_{k}}(x_{k})dx_{k}\] \[\overset{(b)}{=}\prod\limits_{k=1}^{N}\frac{1}{\Omega_{k}}\int \limits_{x_{k}=0}^{\infty}\exp\left(-sx_{k}-\frac{x_{k}}{\Omega_{k}}\right)dx _{k}\] \[=\prod\limits_{i=1}^{N}\frac{1}{1+s\Omega_{i}}, \tag{11}\]  
where \((a)\) is held due to the independent property of these RVs; substituting the PDF of the exponential RV we obtain \((b)\). Q.E.D.  
Having obtained the MGF of the sum of the arbitrary exponential RVs, we are now ready to compute the outage probability.
# Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms
## 2 Materials and Methods
### Models
Convolutional neural networks, commonly referred to as ConvNeXt, perform significantly advanced image analysis tasks by effectively capturing spatial features through their hierarchical convolutional layers. Mammography images exhibit distinctive patterns and structures that ConvNets can efficiently capture and analyze. Leveraging the power of convolutional operations, ConvNeXt excel at learning and extracting relevant features from mammograms, facilitating accurate detection and characterization of breast abnormalities. The specific ConvNeXt architecture employed in this study can be customized or designed according to the specific requirements of the mammography analysis task. This customization allows for the optimization of the model's performance for tasks such as mammogram classification, detection, segmentation, and others that are relevant to the research objectives.
# Sub-Milliwatt Transceiver IC for Transcutaneous Communication of an Intracortical Visual Prosthesis
## 4 Measurement Setup  
### Test IC  
The IR-UWB transmitter and the BPSK receiver were fabricated on the same die; 1.66 mm by 1.66 mm of the CMOS 180 nm semiconductor technology process. It is a mature and robust technology which is important for medical applications. Figure 9 shows the silicon die. The active areas of the IR-UWB transmitter and BPSK receiver are 220 \(\upmu\)m by 80 \(\upmu\)m and 170 \(\upmu\)m by 160 \(\upmu\)m, respectively. Decoupling capacitors were placed to fill the empty area in the die and to improve the signal performance. The die was package in a QFN32 5 mm by 5 mm package. Care was taken to minimize the bond-wire distance for the IR-UWB transmitter by moving it closer to the package pins.
## 3 Results and Discussion
### Potentials for Tilia Seed oil Use  
In terms of nutritional value, _Tilia_ seed oil may not be considered a beneficial dietary source of fatty acids, as the oil predominantly contains an omega-6 linoleic acid and no omega-3 fatty acids. In addition, _Tilia_ seed oil is not rich in phytosterols when compared, for example, to corn oil containing 809-1557 mg phytosterols per 100 g or sunflower oil containing 374-725 mg phytosterols per 100 g. Corn oil and original, low-oleic sunflower oil are linoleic acid-based oils. In terms of the phytosterol content, palm oil with 71-117 mg phytosterols per 100 g is similar to _Tilia_ seed oil [27].  
However, the high content of linoleic acid and the presence of phytosterols and squalene as the main compounds of unsaponifiable matter make the potential of _Tilia_ seed oil of great importance in cosmetics. Linoleic acid was shown to activate a specific receptor, i.e., the peroxisome proliferatorator-activated receptor alpha (PPAR-\(\alpha\)), in keratinocytes, which is involved in the regulation of keratinocyte proliferation, inflammation and the maintenance of skin homeostasis. Linoleic acid contributes to reduced transepidermal water loss and thus improves the skin's hydration [10; 28]. Squalene and tocopherols have antioxidant properties and protect the skin against lipid peroxidation [17]. Research shows that phytosterols protect the skin from aging. In vitro human keratinocyte assays have demonstrated that phytosterols inhibit the UV-induced expression of the matrix metalloproteinase-1 (MMP-1) enzyme that is responsible for the proteolysis of collagen degradation and the reduction of COL1A1 and COL1A2 genes responsible for collagen synthesis. Reduced phytosterols in the skin may therefore lead to increased susceptibility of the skin to UV damage. In addition, phytosterols act as emollients [17].
## 3 Results and Discussion
### NO\({}_{2}\) Absorption Pathway  
According to the above-mentioned analyses, a possible NO\({}_{2}\) absorption pathway can be proposed, as depicted in Figure 9.  
The main reactants in the gas phase include NO\({}_{2}\), SO\({}_{2}\), and O\({}_{2}\). The sole N source in the inlet flue gas is NO\({}_{2}\). When NO\({}_{2}\) laden gas contacts with sulfite solution, the transfer of the NO\({}_{2}\) into absorption solution occurs, meanwhile, reactions take place in the liquid phase, and the main products of NO\({}_{2}\) absorption in flue gas are NO\({}_{3}\)\({}^{-}\) and NO\({}_{2}\)\({}^{-}\), especially NO\({}_{3}\)\({}^{-}\) in absorption solution dramatically increases due to the absorption of NO\({}_{2}\) and the oxidation of NO\({}_{2}\)\({}^{-}\). HNO\({}_{3}\) is considered to involve gas and liquid states existing in reaction system, and the gaseous HNO\({}_{3}\) in outlet flue gas is from both HNO\({}_{3}\) escape in absorption solution and the reaction between NO\({}_{2}\) and H\({}_{2}\)O in the gas phase. HNO\({}_{2}\) may be produced by NO\({}_{2}\)\({}^{-}\) combining with H\({}^{+}\), and HNO\({}_{2}\) is unstable in liquid phase and easily decomposes, which leads to HNO\({}_{3}\) formation and NO release. As NO increasing, a homogeneous reaction of NO and NO\({}_{2}\) results in an increase of the N\({}_{2}\)O\({}_{3}\) to a smaller extent, the NO or N\({}_{2}\)O\({}_{3}\) can react with SO\({}_{3}\)\({}^{2-}\) to form hydroxylamine-disulfonate (HADS), which is a precursor of numerous N-S compounds. In addition, the existence of SO\({}_{2}\) in the gas phase is in favor of the sulfite and bisulfate formation, and HNO\({}_{2}\) can also react with HNO\({}_{3}\)\({}^{-}\) to form HADS. The intermediate products of N-S compounds finally decompose to N\({}_{2}\)O\({}_{5}\), N\({}_{2}\)O, and N\({}_{2}\) in gas phase.  
Figure 8: The nitrogen balance of NO\({}_{2}\) absorption in sulfite solution.  
Figure 9: A possible NO\({}_{2}\) absorption pathway.
# QuoTa: An Online Quality-Aware Incentive Mechanism for Fast Federated Learning
## 5 Evaluation
### Comparison of Data Owner Selection Schemes  
The QuoTa framework can prioritize a winning data owner set that has a large local data volume for a single worker and a large total amount of data in the federated system and directly set a computing capability threshold for the selection of data owners. We experimentally demonstrate that this selection scheme performs well in terms of model accuracy and convergence time. We mainly compare the three algorithms, SCORE, GREEDY, and RRAFL-Variant. Except for different data owner selection indicators, SCORE and GREEDY adopt the same model quality detection, reputation value calculation, and computing capability estimation.  
From Figure 5a,b, we can see that QuoTa can converge to a higher accuracy faster with the same number of communication rounds and has a significant advantage in convergence time. The SCORE algorithm calculates the amount of data, computing capability, and the bid price into a single score, which allows for a more effective balance between model accuracy and convergence time. But, if the bid price of a data owner with the average model quality is low enough, it still has a higher probability of being selected, and thus it is inferior to QuoTa algorithm in terms of final global model performance. Similarly, if a data owner's bid price is low enough, the RRAFL-variant algorithm may still select them to participate in the model training even if its reputation value is low. Additionally, the RRAFL-Variant does not take training time into account, so it performs poorly in terms of model convergence. The GREEDY algorithm prioritizes data owners with the lower bid price. As data owners with average model quality may have a low bid price, the GREEDY algorithm performs moderately in terms of the final model accuracy. In consideration of  
Figure 4: The impact of different numbers of malicious data owners on global accuracy.  
shorter convergence time than the RRAFL-variant algorithm, the reason lies in the fact that many data owners with less data volume have lower bid prices, thus producing shorter training time. Consequently, QuoTa performs better than other baseline algorithms in terms of global model accuracy and convergence time.
## 3 Simulation Results
The residual energy in percentage of the SU node is generated randomly with a uniform distribution. Finally, each observation (a vector containing three features extracted from PU signal concatenated with residual energy) was labeled based on the \(P_{d}\) of the signal and the residual energy of node. After labeling, the capacity of channel is obtained for each label of the observation. In each iteration, the residual energy of SU is updated by deducting the energy consumed for transmission and adding the harvested energy with a Poisson distribution. The correct class labels of observations are used to obtain the theoretical results whereas the output labels of SVM are used in simulation results. Consequently, there is a difference between the theoretical and simulation results, as illustrated in the figure. The difference between the theoretical and simulation results of proposed scheme is higher for \(\gamma_{p}=-15\) dB as compared to \(\gamma_{p}=-5\) dB. The reason to this statement is that the accuracy of SVM is comparatively lower for  
Figure 5: Average throughput versus battery capacity of SU node. \(\gamma_{p}=-15\) dB. The higher number of miss-classifications when \(\gamma_{p}=-15\) dB lead to a higher difference from theoretical results as compared to \(\gamma_{p}=-5\) dB. A comparison of average throughput versus \(\gamma_{p}\) is presented in Figure 6. All scenarios follow a similar trend with a slight increase in throughput from \(-20\) to \(-15\) dB for \(\gamma_{p}\). Further increases in \(\gamma_{p}\) also increase the slope of the graph. As shown in the figure, the throughput achieved in the proposed scheme is much higher than _Max Tx Power_ and _Min Tx Power_ scenarios for all ranges of \(\gamma_{p}\). The total battery capacity, \(B\), was kept at 100 packets to obtain these results. The relation of average throughput to the reference energy consumption is demonstrated in Figure 7. The reference energy is the maximum energy consumed for maximum power transmission under the proposed scheme. For instance, the transmission energy consumed for \(C_{1}\), \(C_{2}\), \(C_{3}\), and \(C_{4}\) was 5, 10, 15, and 20 packets, respectively, when the reference energy was 20 packets, whereas the transmission energies were 20 and 5 packets for _Max Tx Power_ and _Min Tx Power_, respectively. The details on reference consumption energy to the energy consumed in different scenarios and classes can be understood from Table 5.  
The results shown in the figure strengthen the claim that the proposed scheme achieves better performance, compared to _Max Tx Power_ and _Min Tx Power_ scenarios. With the battery capacity at 100 packets, the average throughput decreases with an increase in the reference consumption energy. Figure 6: Average throughput versus PU SNR for battery capacity, \(B=100\) of SU node. Figure 7: Average throughput versus reference consumption energy with battery capacity, \(B=100\) of SU node. However, the proposed scheme shows improved results compared to _Max Tx Power_ and _Min Tx Power_ for both \(\gamma_{p}=-15\) dB and \(\gamma_{p}=-5\) dB.
# A Two-Step Surface Reconstruction Method Using Signed Marching Cubes
## 2 Method  
### Radial Basis Functions Interpolation  
The RBF interpolation method is widely used in surface modeling as it is well suited to scattered data interpolation problems. In this paper, taking the RBF interpolation method as an example, we analyzed the two-step reconstruction method. The RBF interpolant \(s(\mathbf{x})\) is a function of the following form [35]  
\[s(\mathbf{x})=\sum_{j=1}^{N}\omega_{i}\Phi\big{(}\mathbf{x},\mathbf{x}_{j}\big{)}+p(\mathbf{x}) \tag{1}\]  
where \(\{\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{N}\}\) is a set of different interpolation points, and \(\omega_{i}\) is the weight coefficient to be determined. \(\Phi(\mathbf{x},\mathbf{x}_{j})\) is a radial basis kernel function. \(p(\mathbf{x})\) is the polynomial part. For example, the first-degree polynomial is a function of the following form  
\[p(\mathbf{x})=c_{1}+c_{2}x+c_{3}y+c_{4}z \tag{2}\]where \(x,y,z\) are the coordinate components of the evaluation point \(\mathbf{x}\) and \(c_{1},c_{2},c_{3},c_{4}\) are the weight coefficients to be determined. In general, the polynomial part should satisfy the side conditions of the form  
\[\sum_{i=1}^{N}\omega_{i}=\sum_{i=1}^{N}\omega_{i}x_{i}=\sum_{i=1}^{N}\omega_{i} y_{i}=\sum_{i=1}^{N}\omega_{i}z_{i}=0 \tag{3}\]  
where \(x_{i},y_{i},z_{i}\) are the coordinate components of point \(\mathbf{x}_{i}\).  
To interpolate the implicit function \(s(\mathbf{x})\), the geological sampling data should be converted into the interpolation constraints of the form  
\[s(\mathbf{x}_{i})=f_{i},\ i=1,2,\ldots N \tag{4}\]  
where \(f_{i}\) is the function value at the interpolation point \(\mathbf{x}_{i}\).  
The interpolation constraints and the side conditions are combined to form a linear system of the following form  
\[\left[\begin{array}{cccccccc}\[PERSON]_{11}&\[PERSON]_{12}&\cdots&\[PERSON]_{1N}&1&x_{1}&y_ {1}&z_{1}\\ \[PERSON]_{21}&\[PERSON]_{22}&\cdots&\[PERSON]_{2N}&1&x_{2}&y_{2}&z_{2}\\ \vdots&\vdots&\ddots&\vdots&\vdots&\vdots&\vdots&\vdots\\ \[PERSON]_{N1}&\[PERSON]&\cdots&\[PERSON]_{NN}&1&x_{N}&y_{N}&z_{N}\\ 1&1&\cdots&1&0&0&0&0\\ x_{1}&x_{2}&\cdots&x_{N}&0&0&0&0\\ y_{1}&y_{2}&\cdots&y_{N}&0&0&0&0\\ z_{1}&z_{2}&\cdots&z_{N}&0&0&0&0\end{array}\right]\left[\begin{array}{c} \omega_{1}\\ \omega_{2}\\ \vdots\\ \omega_{N}\\ c_{1}\\ c_{2}\\ c_{3}\\ c_{4}\end{array}\right]=\left[\begin{array}{c}f_{1}\\ f_{2}\\ \vdots\\ f_{N}\\ 0\\ 0\\ 0\\ 0\end{array}\right] \tag{5}\]  
where  
\[\Phi_{ij}=\Phi\big{(}\mathbf{x}_{i},\mathbf{x}_{j}\big{)} \tag{6}\]  
The unknown coefficients of \(s(\mathbf{x})\) can be calculated by solving the above linear system.
## 3 Results and Discussion
### Proximate Analysis and Higher Heating Values (HHV) of Pellets  
Table 2 shows the results of the experiment's proximity analysis and higher heating values (HHV). The moisture content of the samples ranged from 5.38 to 11.27%. Compared to the RDF samples (65.17 to 68.17%), WPP had the highest volatile matter of 72.11%. Meanwhile, the fixed carbon of pellets decreased when RDF was added. Furthermore, the ash content of the pellets was unsatisfactory in this experiment, with the control pellet  
Figure 2: The SEM images of (**a**) cross-section of wood pellet, (**b**) longitudinal section of wood pellet, (**c**) cross-section of wood/RDF pellet, and (**d**) longitudinal section of wood/RDF pellet.  
(2.54%) having less ash content (9.85-13.86%), which was 3.9-5.4 times lower than that of RDF samples. However, RDF reported an ash content of 11-13.4% [28]. The increase in the RDF ratio increased the volatile matter and ash content of the pellets while decreasing the fixed carbon content. [PERSON] et al. [28] conducted a similar study using pinewood and non-recyclable textile materials. On the other hand, the HHV of the pellets produced in this study ranged between 19.40 and 22.09 MJ/kg, whereas the HHV of typical RDF values were reported at 13-20 MJ/kg [29]. The results showed that adding RDF to the pellet significantly increased its HHV. The highest higher heating values of 22.09 MJ/kg were observed in the pellet with 50% RDF, while values of 19.40, 21.19, and 21.83 MJ/kg resulted in 100/0, 70/30, and 60/40 ratios, respectively. A previous investigation established that wood pellets made from mixed wood species had HVV of 19.85-20.45 MJ/kg [30], whereas an ultimate analysis of RDF in Europe reported an average HVV of 20.79 MJ/kg [31]. The highest HHV found in 50% RDF samples might be due to their lower MC and higher volatile matter and ash content. [PERSON] et al. [32] demonstrated that the HHV of RDF pellets were inversely proportional to the MC but positively proportional to the volatile matter, ash content, and fixed carbon content.
# Which Local Search Operator Works Best for the Open-Loop TSP?
## 2 Local Search
### Two-Optimization (2-opt)  
[PERSON] [22] first introduced the 2-optimization method, which is a simple and very common operator [36]. The idea of 2-opt is to exchange the links between two pairs of subsequent nodes as shown in Figure 4. It has also been generalized to k-opt (L-K heuristics) [23] and implemented by [PERSON] [37]. [PERSON] and [PERSON] [21] explained thoroughly the 2-opt and 3-opt processes. In the case of 3-opt, the only difference from 2-opt is to exchange the links between three pairs of nodes. The method was originally developed for the closed-loop TSP, but it applies to the open-loop TSP as well. In addition, we also consider dummy nodes paired with one of the two terminal nodes. Such a way, terminal nodes can also change into an internal node.  
The 2-opt approach removes crossings in the path. [PERSON] and [PERSON] [38] highlighted this cross-removing effect in their work. However, 2-opt is more than the cross-removal method, as shown in Figure 4.  
In Figure 5, 2-opt is illustrated with two real examples from O-Mopsi. Here, AB and CD are the two links involved in the operation. In the modified tour, these links are replaced by AC and BD. In the first example, the original links cross, which is then deleted and the length of the overall tour is shortened from 1010 m to 710 m. The second example shows that 2-opt can also improve the tour, even when the original links do not cross.  
The 2-opt operator works with links. A tour consists of \(N\)-1 links, so there are (\(N\)-1)*(\(N\)-2) possible pairs of links. Thus, the size of the neighborhood is O(\(N^{2}\)).  
Figure 4: Three examples of how the 2-optimization method can improve the tour.