esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Compression and Fracture Tests  
Two different well-known quasi-brittle polymeric materials were chosen to fabricate the keyhole notched specimens: PMMA and GPPS. These two polymers exhibit brittle behavior with minor plastic deformations at room temperature during tensile loading. However, when the standard un-notched PMMA and GPPS specimens are tested under compression, significant plastic deformations are developed. One reason for the larger plastic deformation under compression is that the Poisson's ratio under compression is significantly different from the ratio under tension, leading to a change of the cross-sectional area under compression that may be considerably larger than that observed under tension. Consequently, the growth rate of the cross-sectional area under compression becomes considerably greater than the reduction rate of such area under tension.  
The determination of the compressive properties of the polymers chosen in this study has been performed following ASTM D695-10 [43]. Accordingly, the geometry of the specimens is a block of 12.7 \(\times\) 12.7 \(\times\) 25.4 mm\({}^{3}\) (\(\frac{1}{2}\times\frac{1}{2}\times 1\) in\({}^{3}\)). The compression tests, three per material, were performed under displacement-control conditions. The load was recorded by the load-cell, and by using the initial cross-sectional area, the engineering stresses were computed. Concerning the displacements, the travel of the plates was assumed to be the same as that occurring in the specimens. Consequently, the engineering strains were calculated by dividing the recorded displacements by the initial length of the specimen. This procedure is accurate within the elastic regime (used for the estimation of the elastic moduli) and does not affect the calculation of the ultimate tensile strength. Beyond the linear-elastic behavior, and especially beyond the peak point, where friction becomes significant and barreling starts happening, accurate measurement of the strains would require the use of an extensometer.  
Concerning the fracture tests under negative mode I (compression) loading, the geometry of the corresponding specimens is shown in Figure 1. It can be observed that it is a rectangular specimen weakened by a keyhole, with the hole radii varying from 0.5 mm up to 4 mm, and the thickness being 8 mm.  
The specimens were machined from PMMA and GPPS plates with the same thickness. In order to prevent the appearance of residual stresses around keyhole notches during the production process, a two-dimensional water-jet cutting machine was used. The total number of specimens is 24, combining the two materials, the four notch radii and three tests per combination of material and notch radius. Figure 2 shows two of the specimens.  
The displacement rate of the test machine is set as equal to 0.5 mm/min to provide quasi-static loading conditions.  
Figure 1: Geometry of the fracture specimens.
## 5 Conclusions  
The efficient utilization of the excess PV energy of prosumers can enhance the social welfare of the P2P community. P2P trading platforms using a specific trading mechanism ensures that the surplus PV energy can be shared in a cost-effective manner. This research proposed the comparative analysis of two trading strategies, i.e., the ZI method and GT approach, for a constrained P2P trading platform. The results were simulated over different intervals to show the effectiveness of the proposed trading methods. Additionally, the trading losses and network utilization fee were suggested for incorporating the network constraints. The results show that both of the trading methods give additional monetary advantages for the market participants, while they increased the average welfare by 17%.  
A reluctance-based sensitivity approach was also suggested to model the individual preferences of the prosumers under heavy distribution losses/network fees. The GT approach is more beneficial for the networks where there are high losses/a utilization fee as compared to the ZI method, wherein it improved the welfare of the market participants by about 75%. The future research involves adding battery energy storage and an aggregator demand response optimization framework to further enhance the monetary benefits of the market participants for the suggested P2P network.  
Conceptualization, S.L. and T.M.H.; methodology, S.L. and T.M.H.; validation, S.L., T.H., R.F. and F.A.K.; formal analysis, S.L., T.H., F.A.K. and B.C.; writing--original draft preparation, S.L., T.H. and F.A.K.; writing--review and editing, B.C., R.F. and T.M.H.; supervision, T.M.H. and R.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
Not applicable.  
The data presented in this study are available on reasonable request from the corresponding author.  
[PERSON] thanks the Higher Education Commission (HEC) of Pakistan and the Center of Power System Studies (CPSS) at SDSU for funding this research.  
The authors declare no conflict of interest.  
The following abbreviations and symbols are used in this manuscript:  
P2P Peer-To-Peer  
ZI Zero Intelligent  
GT Game Theory  
FIT Feed-In Tariff  
RTP Real Time Price  
EM Equilibrium Matching  
Figure 17: Prosumers’ welfare for different values of charge rate coefficient. (a) ZI strategy. (b) GT approach.
## 3 Results
### Tensile Test Results
Figure 3 also shows that the standard deviation in N sample is 3.2; even though this is a very good value, it is still higher than the stitching samples values of 1.49, 1.5, 0.81, and 1.01, respectively, which indicates that by adding the stitching, the properties are more stable. It has been reported that stitching reduces tensile strength because of the creation of stress concentrations at the stitch point, misalignment, crimping, and even breakage on fibers [30; 39; 40; 41]. But it has also been reported that stitching increases tensile strength [42; 43]. Figure 2: Stress–strain curves from tensile tests. (**a**) N specimens, (**b**) 0.5 specimens, (**c**) 1.0 specimens, (**d**) 1.5 specimens, and (**e**) 2.0 specimens. Figure 3b shows the average Young's modulus in all samples, in which 0.5 samples have increases of 99% over the N sample, and as the length separation of the stitching increases, [PERSON]'s modulus also decreases, making the 2.0 sample the lower value in comparison to all the other stitched samples, but there is still an increase of 31.41% over the N sample. It can be deduced that the smaller the length distance within the stitching, the more rigid the composite becomes. We can attribute this to the flexibility of nylon. In Figure 3c, a comparison of the average deformation on all samples is show; the N samples present the maximum value, 0.5 samples decrease the deformation by 41.66%, 1.0 samples decrease by 32.47%, 1.5 samples decrease by 23.85%, and 2.0 samples reduce their deformation by 16.99%. Therefore, the length distance of the stitching is inversely proportional to the deformation of the samples. Figure 3d shows the toughness results, where it is observed that the N sample has the maximum value. The 0.5 samples have the minimum value, decreasing by 36.89% over the N sample; by this, we can deduce that as the stitching length separation increases, toughness increases. From what can be shown, stitched strength slightly increases tensile strength and decreases toughness because they help stabilize the weave structure of the fibers within the composite.
## Data and Data Preparation
### Digital Elevation Models

Table 3: Co-registration details for the TanDEM-X and ASTER GDEM V2 relative to the reference DEM derived from stable terrain before and after the co-registration. The variables \(\Delta\)X, \(\Delta\)Y and \(\Delta\)Z are the shift vectors, STD is the standard deviation and \(\overline{d}\overline{h}\) is the mean difference. (STD): 14.8). We defined the SRTM DEM as the master and the other DEMs as the slave DEMs (Table 3). The co-registration method analyses elevation differences over stable terrain against terrain aspect and determines shift vectors by fitting (least squares matching) the parameters of a cosine function to the values.
# CA-CGNet: Component-Aware Capsule Graph Neural Network for Non-Rigid Shape Correspondence
## 2 Related Work  
### Non-Rigid Shape Correspondence  
Various work has been done in the direction of shape correspondence. Before deep learning methods were commonly applied to point descriptor learning, many scholars conducted in-depth research on handcrafted feature descriptors for characterizing the geometric information of shapes. [PERSON] et al. [14] proposed the signature of histograms of orientations (SHOT) descriptor with rotation and translation invariance by counting the topological features adjacent to points in the local coordinate system and saving them in the histogram. Average geodesic distance (AGD) [15] was the attitude insensitive descriptor, which could effectively measure the overall topological characteristics of multi-resolution models. [PERSON] et al. [16] extracted all key points in different proportions and combined them with the thermal diffusion equation to develop a heat kernel signature (HKS) with multi-scale characteristics. In [17], the wave kernel signature (WKS) used the Schrodinger equation to acquire the evolution process of particles on the shape surface to obtain the features of the vertex on the object surface. To explore posture more effectively, [PERSON] et al. [18] proposed to use graphical nodes corresponding to body parts so that these parts could be independently translated and rotated in 3D to represent different body shapes and to capture pose-dependent shape variations. This method was similar to our approach but only applied to human models.  
Recently, researchers have tried to use deep learning methods to further process shape descriptors to obtain high-quality geometric features of 3D non-rigid surfaces [19; 20]. Cyclic-FM [21] was based on a cyclic mapping between metric spaces for self-supervised dense correspondence mapping between non-isometric shapes. [PERSON] et al. [22] extracted stable landmarks over human bodies, relying entirely on the geometric properties in the spectral domain. The MGCN [23] advanced a multi-scale graph convolutional network, which transformed wavelet energy decomposition signature (WEDS) to a more discriminative descriptor. [PERSON] et al. [24] made use of deep residual neural networks to calculate minimizing paths between deformations and, thus, between shapes for the alignment of 3D shapes under complex topology-preserving transformations. Although these methods have achieved good results, they did not make full use of local pose information to maintain the geometrical relationship of the manifold. In addition, when the model resolution decreases, the discriminative power of the network decreases significantly.
# Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA
## 2 Problem Formulation
### Multi-Stage Dynamic TNEP (MSDTNEP) Model  
The formulation of the MSDTNEP model to minimize investment cost and amount of energy not supplied was computed using Equations (7)-(14) [4; 5; 6]. The objective function (Equation (7)) corresponds to the addition of the investment cost in each period adequately transferred to the initial period using an interest rate \(\lambda\). To maintain the transmission system's reliability, North America Electrical Reliability Corporation (NERC) has published a series of standards in which all the balancing authorities within North American interconnection must comply [27]. N-1 security constraint is widely embedded in TNEP models to improve transmission capacity and security of power grids and is presented in Equation (14) [5; 27].  
\[min.\left(\sum_{yr=1}^{N_{y}}\sum_{i=1,j=1,i\
eq j}^{i=n,j=n,i\
eq j}\frac{C_{ ij}X_{ij}+C_{r}\;r_{i}}{(1+\lambda)^{yr-1}}\right) \tag{7}\]  
which is subject to:  
\[P_{Gi}-\left(P_{di}-P_{sh,i}\right)=\sum_{j=1}^{n,i\
eq j}P_{ij} \tag{8}\]  
\[X_{ij}^{min}\leq X_{ij}\leq X_{ij}^{max},\ i=1,2,\ldots,n,\ j=1,2,\ldots,n,\ i\
eq j \tag{9}\]  
\[P_{Gi}^{min}\leq P_{Gi}\leq P_{Gi}^{max} \tag{10}\]  
\[P_{di}^{min}\leq P_{di}\leq P_{di}^{max} \tag{11}\]  
\[o\leq P_{sh,i}\leq\alpha P_{di}\,\ and \tag{12}\]  
\[\theta_{i}^{min}\leq\theta_{i}\leq\theta_{i}^{max} \tag{13}\]  
The N-1 security constraint was modeled using Equation (14):  
\[-P_{ij}^{max}\leq\beta_{ij}\Big{(}X_{ij}^{o}+X_{ij}-1\Big{)}\left(\theta_{i} -\theta_{j}\right)\leq P_{ij}^{max} \tag{14}\]  
where the adequate values for \(C_{r}\) in real networks can vary between 0.5 and 1 million USD/MW.
## 2 Materials and Methods
### Formulas and Calculations
#### 2.5.5 TOPSIS Optimal Solution Analysis Method  
The TOPSIS method is a famous classic multi-criteria decision analysis method that can make full use of research target attributes and information to rank different schemes, and it does not require preference independence. According to the maximum distance from the positive ideal solution to the negative ideal solution, the alternative solutions are prioritized. If the evaluation object is closest to the optimal solution and farthest from the worst solution, it is the best; otherwise, it is not the best [25]. The main calculation steps are as follows:  
Weighted normalized decision matrix construction:  
The weighted normalized decision matrix \(Z=\left(Z_{ij}\right)_{m\times n}\) can be obtained by multiplying the index standardization matrix \(A^{\prime}\) with weight \(w_{i}\):  
\[Z_{ij}=A_{ij}tw_{i}(i=1,\ 2,\ \cdots,\ \text{m;}\ \text{j}=1,\ 2,\ \cdots,\ \text{n}) \tag{12}\]  
Determine the positive ideal solution \(X_{i}^{+}\) and the negative ideal solution \(X_{i}^{-}\):  
\[X_{i}^{+} =(X_{1}^{+},\ X_{2}^{+},\ \cdots,\ X_{m}^{+}) \tag{13}\] \[X_{i}^{-} =(X_{1}^{-},\ X_{2}^{-},\ \cdots,\ X_{m}^{-}) \tag{14}\]The formula for calculating the positive and negative ideal solutions of the positive indicators is as follows: \(\left\{\begin{array}{l}X_{i}^{+}=\text{max}Z_{ij}\\ X_{i}^{-}=\text{min}Z_{ij}\end{array}\right..\) The formula for calculating the ideal solution of the negative indicators is \(\left\{\begin{array}{l}X_{i}^{+}=\text{min}Z_{ij}\\ X_{i}^{-}=\text{max}Z_{ij}\end{array}\right..\)  
The Euclidean distances, \(S_{j}^{+}\) and \(S_{j}^{-}\), of each evaluation object to positive and negative ideal solutions are calculated separately.  
The Euclidean distance formula is used to calculate the distance from the standardized vector of each evaluation index to the optimal solution and the worst solution, respectively:  
\[S_{j}^{+}=\sqrt{\sum_{i=1}^{m}\left(Z_{ij}-X_{i}^{+}\right)^{2}} \tag{15}\]  
\[S_{j}^{-}=\sqrt{\sum_{i=1}^{m}\left(Z_{ij}-X_{i}^{-}\right)^{2}} \tag{16}\]  
Calculate the relative proximity between each evaluation object and the optimal scheme:  
\[D_{j}=\frac{S_{j}^{-}}{S_{j}^{+}+S_{j}^{-}} \tag{17}\]  
where \(D_{j}\) is the comprehensive evaluation score of each evaluation object. The larger value indicates that the evaluated object is closer to the ideal solution and the benefit level is better; a smaller value indicates that the evaluated object is farther from the ideal solution and the benefit level is worse.
# Recent Advances in the Targeting of Head and Neck Cancer Stem Cells
## 3 Identification of CSC
### Sphere-Forming Ability  
Since epithelial cells typically rely on substrate attachment for their survival, the capacity to grow under anchorage-independent conditions, avoiding anoikis and differentiation, is considered to be a hallmark of stem cells. Therefore, one of the methods for CSC char acterization and enrichment relies on their functional property to form non-adherent 3D structures called tumor spheres when grown in ultra-low attachment plates in a serum-free medium or 3D matrices such as soft agar or dishes coated with fibronectin or matrigel. Such sphere-forming cells retain their stem cell characteristics over several generations and the formation of secondary spheres following the dissociation of primary spheres can serve as evidence of their self-renewal ability. Additionally, phenotypic markers are employed for their characterization.  
Several studies showed that a sphere-forming assay might be useful in the enrichment of CSCs from HNSCC cell lines or primary tumor samples [96; 97; 98]. Furthermore, CSCs enriched through the sphere-forming assay from cell lines and HNSCC primary cultures display an increased expression of CSC markers such as CD133, CD44, Sox2, Nanog, and Nestin in comparison to the parental counterparts, as well as enhanced tumorigenic ability in vivo [97; 98; 99]. However, spheroid-forming capacity does not always reflect tumorigenic ability in vivo [100], which indicates the need for in vitro assays that closely replicate the physiological microenvironment (niche) of CSCs, allowing for the more accurate observation of their functional properties.  
Besides being useful in evaluating CSCs' function, the sphere-forming assay represents an attractive and reliable model for the evaluation of treatment response in CSCs [86]. For example, a study by [PERSON] and colleagues showed that HNSCC tumorspheres, besides the expression of common CSC markers, exhibit increased levels of ABCG2 transporter and were resistant to several chemotherapeutics such as cisplatin, 5-fluorouracil(FU), paclitaxel, and docetaxel [97].
# GPU Algorithms for Structured Sparse Matrix Multiplication with Diagonal Storage Schemes +
## 3 Storage Schemes
### Diagonal Storage Scheme  
The diagonal storage scheme proposed by [PERSON] and [PERSON] [20] (called the HM scheme) for banded matrices is free from the aforementioned extraneous padded storage. It also supports a uniform representation of different kinds of structured matrices (e.g., diagonal, banded, triangular, symmetric, and fully dense matrices) and is independent of any orientation. In this section, we describe the diagonal storage scheme presented in [20]. From now on, we will use the terms _\"diagonal storage\"_ and _\"HM\"_ interchangeably to denote the storage scheme presented in [20].  
For a dense matrix \(A\), the HM diagonal storage scheme uses a one-dimensional array of size \(n(k_{u}+k_{l}+1)-\frac{k_{u}(k_{u}+1)}{2}-\frac{k_{l}(k_{l}+1)}{2}\). Note that, for a dense matrix \(A\), \(k_{l}=k_{u}=(n-1)\). Likewise, for an upper triangular matrix, we have \(k_{l}=0\) and \(k_{u}=n-1\), whereas for a lower triangular matrix, \(k_{u}=0\) and \(k_{l}=n-1\). The nonzero elements are ordered by diagonals as follows: \((A_{0};\overline{A_{1}};\overline{A_{2}};\ldots\overline{A_{k_{u}}};\underline {A_{1}};\underline{A_{2}};\ldots\underline{A_{k_{l}}})\). For example, considering this storage scheme, the elements of the matrix in Figure 2a can be stored as  
\[(a_{00},a_{11},a_{22},a_{33}\mid a_{01},a_{12},a_{23}\mid a_{02},a_{13}\mid a_{ 10},a_{21},a_{32})\]  
where the symbol \(\mid\) is used to denote the boundary of the diagonals. According to the authors of [20], some elementary arithmetic calculations are required to access the elements of a specific diagonal in the HM scheme. For example, the index of the first element of the \(k\)th superdiagonal is calculated as \(x+nk-\frac{k(k-1)}{2}\), where \(x\) is the starting index of the first element of the main diagonal \(A_{0}\).  
The indices of the diagonals that we follow in this paper are as follows. The index of the principle diagonal \(A_{0}\) is \(0\). \(\overline{A_{i}}\), a superdiagonal, and \(\underline{A_{j}}\), a subdiagonal, are indexed as \(i\) and \(-j\), respectively, where \(i,j<n\).
# An Optimized Terpene Profile for a New Medical Cannabis Oil
## 4 Discussion
In particular, they were deprived of oxidized terpen, thus free of secondary thermal oxidation'sproducts as aldehydes and ketones [30; 38]. The formation of these compounds could significantly affect the quality of a product being a marker of aging [38]. For example, the presence of oxidized terpenes in essential oils has been associated with shifting colors, unpleasant smell, unpalatable taste, and consistency alterations [39]. Terpene oxidation may produce skin sensitizers causing hazardous allergic reactions in sensitive individuals [40; 41; 42]. Generally, the toxicity of terpenes and their oxidized derivatives is attributed to their pro-oxidant power originating reactive oxygen species (ROS) formation via lipid peroxidation [43]. For example, limonene acute toxicity is reported associated with its oxidized product that is limonene hydroperoxide [44]. Thus, the absence of oxidized components in MCOs is an important requisite both for the quality control of the product and the safety of the patients [38]. Finally, evidence results on the synergic role of Cannabis terpenes and cannabinoids to date were conflicting [45], but no studies investigated the effects of medical Cannabis preparations rich in hydrocarbon terpenes with no oxidant compounds, potentially harmful constituents [46; 47]. In our opinion, the new extract is very promising for both the biological activity of the individual terpenes and the related entourage effect [48]. In fact, the most important Cannabis terpenes (limonene, pinene, myrcene, \(\beta\)-caryophyllene and linalool) were reported to be individually responsible for the anti-inflammatory, analgesic, antinociceptive, anxiolytic, and multi-target effects [49]. Concurrently, the synergistic effect between the phytocannabinoids [49; 50] and terpenes and phytocannabinoids [51] was shown. In this context, a differential cannabinoid receptor activity [52] and the inhibition of p-glycoprotein [52; 53] by terpenes were investigated. Moreover, several studies showed terpene enhancer activity on the absorption of active substances [54; 55], as in the case of analgesics and anti-inflammatory [56; 57]. For example, limonene and bisabolol were studied as enhancers in the percutaneous absorption of sumatriptan [58] and naproxen skin absorption was increased by linalool [59]. Furthermore, a cannabis preparation containing the most important terpenes could exert a multi-target synergy [60]. In fact, each component of the phyto-complex could have low therapeutic potency but their simultaneous pharmacological action was often shown to be highly effective with significantly low toxicity [61]. Thus, it is important to obtain concentrated cannabis oil preparations and maintain their terpene content.