esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Methodology and Procedures
### Building the LoRa Research Environment: GNU Radio Software and Hardware Setup (SDR)
#### 3.2.1 GNU Radio Interface  
As shown in Figures 3 and 4, the transmitter and receiver chains also perform some additional processing. Before chirp modulation, the LoRa transmitter chain performs whitening, Hamming coding, interleaving, and gray mapping [30]. The receiver performs de-interlacing, Hamming decoding, de-whitening, and gray de-mapping.  
Figure 3: GNU Radio interface for the transmitter.  
The basic whitening matrix we discovered is the one corresponding to the coding rate, CR = 4/8. The rightmost one and two columns of the basic whitening matrix are eliminated for CR = 4/7 and CR = 4/6, respectively. The last column in the whitening matrix is different from the fifth column in the basic whitening matrix when CR = 4/5. This is because, as will be shown later, a single parity check is used instead of a Hamming code for CR = 4/5. The Hamming code is error-correcting, so it should be able to correct errors by including extra words in each code word. The Hamming coding described in the patent can only be a variant of the regular Hamming coding because it can be done for coding rates of 4/8 and 4/7, but not for the others. As long as the exact composition of each code word remained unknown, the bit position within each code word was unknown.  
A symbol can be distorted during transmission by noise or fading, resulting in many bit errors after demodulation. All the errors are caused by the same symbol, so they are all closely related. The problem with this is that error correction codes--which are designed to repair random errors in code words--do not deal with them effectively. The idea is to break the link between the wrong bit and the right bit by spreading the errors over many code words. This is achieved by spreading the bits of a code word over many LoRa symbols.  
Any mapping of a symbol in a numerical representation to a binary sequence is known as gray encoding. The unique feature of the resulting binary sequence is that successive symbols with the same gray representation in the original format differ by only one bit. Gray codes are particularly useful in wireless communications modulation, where it is more likely that signals from nearby sources will be confused with those from random sources.
# Opportunistic Multi-Technology Cooperative Scheme and UAV Relaying for Network Disaster Recovery
## 3 Multi-Technology Cooperative Communication and Drone Data Relaying
### System Model  
The system includes the set \(\mathcal{S}=\{s_{1},s_{2},\ldots,s_{M}\}\) of \(M=|\mathcal{S}|\) nodes representing survivors at their locations. The node density is relatively sparse, i.e., nodes might not all be connected; the cluster formation among multiple nodes is not guaranteed and clusters may consist of a single node. Moreover, each node is characterized by an initial available (battery) energy level \(e_{s_{m}}\). The system also comprises the set \(\mathcal{N}=\{n_{u},1\leq u\leq U\}\) of communication interfaces, with \(U\) the number of available interfaces. Furthermore, the communication interfaces are ordered based on their energy consumption \(c\) as \(c_{n_{1}}<c_{n_{l}}<c_{n_{l}}\), and transmission range \(r\) as \(r_{n_{1}}<r_{n_{l}}<r_{n_{l}}\). This assumption will be investigated in next section 4 which evaluates the performances of smartphones in terms of energy consumption and transmission range. Mobile devices are carried by survivors who move slowly if at all. This assumption is justified by the high chance that the survivors are unable to walk fast or run, due to possible injuries and the many obstacles that the natural disaster causes.  
The drone collects data from survivors and make them available to search and rescue teams [18; 20; 21]. The drone operates in two phases: it first identifies the location of the nodes in the area affected by the disaster and plans a path that visits all the discovered nodes; it then flies around the area according to such a path. The drone moves with a fixed speed between the intended locations, where it stops for a certain amount of time. This can easily be accomplished, for instance, by using a rotary-winged drone. More details about drone data relaying are provided in Section 5.
# Analysis and Applications of GlobeLand30: A Review
## 4 Application Analysis  
GlobeLand30 provides useful information with higher spatial resolution for different research fields. These include quantifying land use for each watershed in Panamanian drainage basins [55]; identifying types of sampling sites based on the hydrology and land-use characteristics to monitor contaminants in river sediments [46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57], discriminating fire types from MODIS active fire products, such as forest fire, grassland fire, agricultural burning and so on [58]; assessing flooded arable land of a major flood in Myanmar [46]; selecting eddy-covariance flux towers with relatively homogenous land cover in the light use efficiency models to simulate GPP [59]; analyzing habitat of bats in Lao PDR and Cambodia [60; 61; 62]; and providing validation sources to evaluate the classification performance of the water body extraction from MODIS eight-day products [62]. In particular, GlobeLand30 data has been used to derive useful information about the status and change of land cover, to examine their causes and consequence analysis, and to explore future development scenarios.
# Cooperative Jamming-Based Physical-Layer Group Secret and Private Key Generation
## 4 Performance Analysis
### SK/PK Lower Bound  
Based on (11), (13), and the findings in [10], an achievable SK or PK rate is given by  
\[R_{s} =\min\bigl{\{}I\bigl{(}\tilde{Y}_{A};\hat{G}_{1,B}\bigr{)},I \bigl{(}\tilde{Y}_{A};\hat{G}_{1,C}\bigr{)}\bigr{\}}, \tag{14}\] \[R_{p} =\Bigl{[}\min\bigl{\{}I\bigl{(}\tilde{Y}_{A};\hat{G}_{1,B}\bigr{)},I\bigl{(}\tilde{Y}_{A};\hat{G}_{1,C}\bigr{)}\bigr{\}}-I\Bigl{(}\tilde{Y}_{A} ;\tilde{Y}_{R}^{(1)},\tilde{Y}_{R}^{(2)}\Bigr{)}\Bigr{]}^{+}, \tag{15}\]  
which represents a lower bound on the SK or PK capacity, respectively. Note that \(I\Bigl{(}\tilde{Y}_{A};\tilde{Y}_{R}^{(1)},\tilde{Y}_{R}^{(2)}\Bigr{)}\) denotes the information leaked to the relay.
# A New BDS-2 Satellite Clock Bias Prediction Algorithm with an Improved Exponential Smoothing Method
## 2 Theory of Improved Exponential Smoothing Method Prediction  
The modeling principles of the basic one exponential smoothing (ES1) model, two exponential smoothing (ES2) model, and three exponential smoothing (ES3) model are introduced first. Then, the principle and process of searching for the optimal smoothing coefficient using the TNTF principle is described in detail. Meanwhile, the prediction principles of the ES model based on the SW idea are introduced. Finally, the forecasting principles of the combined prediction method of the ES model and the GM model based on the SW idea are presented.
## 6 Conclusions  
In this paper, we propose a routing strategy based on GA assisted by ground access optimization for LEO satellite constellations. The proposed algorithm aims to optimize the routing strategy for LSN systems with ISLs, which can reduce the RTT and improve the packet reception rate. The experimental results show that the proposed algorithm outperforms the traditional satellite routing algorithm in terms of these metrics. Moreover, the proposed algorithm can adapt to different network topologies and traffic patterns, which makes it suitable for various LSN systems.  
In summary, the proposed GAGR routing strategy provides a promising solution for the optimization of the routing strategy in LSN systems with ISLs. Although our proposed GAGR routing strategy demonstrates significant performance improvements in our experiments, it still inherently suffers from the drawbacks of heuristic algorithms, making it prone to falling into local optima. Future work will consider payload reconfiguration, introduce onboard processing techniques [36], incorporate machine learning techniques for predictive routing optimization to further improve the routing algorithm performance, strengthen the load balancing effect, increase the realism of experiments, and extend the proposal to other LEO satellite constellations.  
Conceptualization, P.Z. and G.X.; methodology, C.L. and H.W.; software, L.T.; validation, G.X., P.Z. and C.L.; formal analysis, H.W.; investigation, K.K.L; resources, L.T. and P.Z.; data curation, K.K.I; writing--original draft preparation, C.L. and G.X.; writing--review and editing, P.Z. and C.L.; visualization, H.W.; supervision, P.Z.; project administration, G.X.; funding acquisition, P.Z. and L.T. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the Natural Science Foundation of Shandong Province under Grants ZR2023 LZH017, ZR2022 LZH015, and 2023 QR025; partially supported by the Industry-University Research Innovation Foundation of the Ministry of Education of China under Grant 2021 FNA01001; partially supported by the Open Foundation of the State Key Laboratory of Integrated Service Networks (Oxidian University) under Grant ISN23-09; partially supported by the Integrated Innovation of Science, Education and Industry of Qilu University of Technology (Shandong Academy of Sciences) under Grant 2023 PX057; partially supported by the Talent Project of Qilu University of Technology (Shandong Academy of Sciences) under Grant 2023 RCKY141; and partially supported by the RSF project under Grant 22-71-10095.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Specie Variability between Tuber borchii and Tuber mesentericum
mesentericum_, 3-methylanisole was the most abundant VOC, responsible for a floral aroma (Table 2). This compound has been identified as one of the most abundant VOCs in black truffles [26]. Consistently with our results, [PERSON] and [PERSON] [25] observed a higher quantity of volatile aromatic compounds in black truffles bringing typical floral, spicy, and nutty odours. On the other hand, 8-carbon chain alcohols prevailed in _T. borchii_, characterised by a mushroom and earthy odour, with 1-octen-3-ol as the most abundant one. It is noteworthy to mention that volatile phenols qualitatively characterised _T. mesentericum_. These compounds, including meta-cresol (m-cresol), which constitutes >99% of the phenolics detected in the headspace of _T. mesentericum_, have low odour thresholds and contribute to the distinctive smoky/burnt and phenolic aroma of black truffles [25]. A paper describing the odour thresholds in the air of 30 volatile phenols and their isomers found that compounds with monoalkyl groups in the meta position lowered thresholds substantially, imparting very low odour detection thresholds of <1 ng/L air [27]. Another important difference was in the sulphur compounds, which were more representative of the \"bianchetto\" headspace (Figure 2), according to [PERSON], [PERSON], [PERSON], and [PERSON] [26], who identified them as more abundant compounds in _T. borchii_ compared to black truffles such as _T. melanosporum_ and _T. indicum_. [PERSON] and [PERSON] [25] found, among the most odour-active compounds (FD \(\geq\) 4), greater quantities of bis(methylthio)methane, 3-(methylthio)propanal, etc., in white Alba truffle compared to black Burgundy truffle, which provide a greater sulphury, garlic, and cooked cabbage-like aroma, as confirmed by sensory analysis. Moreover, in agreement with our findings, 2-methyl-4,5-dihydrothe is exclusively found in _T. borchii_ and contributes to the aroma perceived by humans in this kind of truffle [16; 28]. The bacteria that colonise _T. borchii_ have exhibited an important role in contributing to the formation of these thiophene derivatives [7]. Overall, these differences in volatile compounds have led to sensory evaluations describing white truffles as having a garlicky, mushroom, and cheesy aroma, with subtle methane notes, while black truffles are described as having a \"wet forest\" aroma, with hazelnut and phenolic notes [29].
## 1 Introduction
[32] utilized the self-attention mechanism module to consider the relationship between signal input segmentation, which can effectively capture the dependencies in the signal data. [PERSON] et al. [33] suggested a bearing FD model based on one-dimensional convolutional neural networks for the fusion of frequency-domain and time-domain features driven by simulation data. The simulation data was obtained through dynamic modeling. [PERSON] et al. [34] established a transfer learning model. The simulation signals were utilized as source-domain data for transfer learning FD by creating a blend of fault pulses and measured normal cardinal line data. [PERSON] et al. [35] established a small-sample diagnosis method driven by simulation and test data through bearing dynamics modeling, which was applied to the bearings of high-speed rail axle boxes. However, when actually collecting the bearing's vibration signals, it will be affected by environmental noise, and the collection of experimental data is very difficult. These problems will all affect the diagnostic results of the model. In response to the above problems, this paper proposes an FD method of a convolution cross-attention mechanism with dual channels in the frequency domain and time domain. Firstly, the bearing's dynamic model is established. It is solved using the Runge-Kutta theory. And frequency-domain and time-domain data of the simulation are generated. Then, a convolutional neural network is used to extract the global and local information, and it can be input into the cross-attention mechanism model for feature fusion. Finally, the model's accuracy is verified through experiments to provide technical support for FD and identification.
# Cracking and Toughening Mechanisms in Nanoscale Metallic Multilayer Films: A Brief Review
## 4 Other Fracture Properties  
### Fracture Toughness  
#### 4.1.1 Length-Scale-Dependent of Fracture Behavior  
The fracture mode of NMMFs was directly associated with the strengthening mechanism. [PERSON] et al. [111] proposed a dislocation mechanism transition from Orowan bowing to interface crossing to interpret the limited ductility and fracture toughness. [PERSON] et al. [112] found that the Cu/Ta multilayer with thickness of 5 nm exhibited shear-mode fracture while the Cu/Ta multilayer with thickness of 125 nm fractured in an opening mode. According to the theoretical analyses, it is pronounced that the opening-mode and shear-mode fracture was relevant to interface crossing and Orowan bowing deformation mechanism, respectively. It further implies that the interface structure player a key role in the fracture behavior of metallic multilayers, as discussed in Section 4.1.2. Additionally, the deformation and fracture behavior also display a strong length scale effect. When decreasing \(h\), both the ductility and fracture toughness of NMMFs increases until a critical \(h_{\text{cri}}\). Below \(h_{\text{cri}}\) the ductility and fracture toughness decrease with reducing \(h\)[8; 48; 52]. The size-dependent toughness shown here indicates that the failure mechanisms for NMMFs are complex and the controlling mechanisms are completely different in the two regions above and below \(h_{\text{cri}}\). The fracture behavior in A/B NMMFs, which consists of alternating ductile (A) and brittle (B) layers, can be characterized by a micromechanical model [8; 55; 113]. The deformation capability of the ductile layers can be quantitatively obtained by an equilibrium number of dislocations accumulated in the A layer. Once the crack nucleated in the brittle B layer, it will propagate easily throughout the entire B layer and arrested by the interface, accompanied with high stress concentration around the crack tips. Crack propagation is caused by the variation in applied stress intensity, crack-tip  
Figure 13: Flow stress versus maximum engineering strain at failure for various NMMFs with equal layer thickness. Circle indicates tensile data; remaining data are from micropillar compression tests. For all the cases, the individual layer thickness \(h\) is smaller than 100 nm. shielding induced by the plastic deformation, and the capacity to accommodate the activity of crack-tip dislocation [8]. Dislocations may emit from the microcrack tip with two effects: firstly, the crack tip is blunted by the emitted dislocations and the stress concentration around crack tip is reduced (Figure 14a). Consequently, it is difficult to achieve the cohesive strength of A layers and the fracture progress is improved; Secondly, once dislocations hindered by the interface (Figure 14a), they send a stress back to the crack tip. Further dislocation emission will be impeded by the back stress and fracture progress is reduced.
# Influence of Angiopoietin Treatment with Hypoxia and
## 2 Materials and Methods
### Quantitative Polymerase Chain Reaction (qPCR)  
To study the relative gene expression, cDNA (2 uL) was mixed with the PCR reaction solution (2X SYBR green master mix, #B21202; Bimake) containing 0.25 uM specific primers (Table 2). A real-time quantitative polymerase chain reaction (qPCR) was performed using the CFX96 TM Real-Time System (#185-5096 C1000 Touch Thermal Cycler; Bio-Rad Laboratories) under standard thermal conditions. The qPCR program consisted of a two-step cycling protocol: an initial denaturation at 95 \({}^{\circ}\)C for 5 min, followed by 39 PCR cycles at 95 \({}^{\circ}\)C for 10 sec, and an annealing temperature of 61 \({}^{\circ}\)C for 30 sec. To control the specificity of the amplification products, a melting curve analysis was carried out for each reaction. The results were expressed relative to gene expression levels on day 0. Gene expression was calculated by the 2\({}^{-\Delta\text{AC}}\) method [27], and the results were normalized to the reference genes glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and 18S ribosomal RNA (18S) expression. All reactions were performed in triplicate.  
We first assessed the effect of Ang-1/2 at different concentrations in hypoxia and normoxia on the relative expression of two genes that are associated with ECM production in the IVD, namely aggrecan (ACAN). We also examined the HIF1A gene, which is the crucial mediator of the adaptive response of cells to hypoxia. Furthermore, to estimate the expression of the Tie2 receptor, regarding the effect of the Ang-1/2 proteins and the microenvironments (normoxia vs. hypoxia) in the NP cells mixed population, and the NP Tie2\({}^{-}\) and NP Tie2\({}^{+}\) cell populations, we analyzed the TEK gene.  
\begin{table}
\begin{tabular}{c c c} \hline
**Gene** & **Full Gene Name** & **Primer Nucleotide Sequence from 5\({}^{\prime}\) to 3\({}^{\prime}\)** \\ \hline GAPDH & Glyceraldehyde-3-Phosphate Dehydrogenase & F—ATC CTC CAG GAG CGA GAT \\  & & R—GGA GGC ATT GCT GAT GAT GAT \\ \hline
18S & 18S ribosomal RNA & F—CGA TGC GGC GGC GTT ATT \\  & & R—TCT GTC AAT CCT GTC GCT GCT GCT C C \\ \hline TEK & TEK Receptor Tyrosine Kinase & F—TTA GCC ATG CTA GTT CTC TGT GG \\  & & R—AGC ATC AGA TAC AAG AGG TAG GG \\ \hline COL2 & Collagen Type II & F—AGC AGC AAG ACC AAG GAG AA \\  & & R—GTA GGA AGG TCA TCT GGA \\ \hline HIF1A & Hypoxia Inducible Factor 1 Subunit Alpha & F—GTC GCT TCG GCC AGT GTG \\  & & R—GGA AAG GCA AGT CCA GAG GTG \\ \hline ACAN & Aggrecan & F—CAT CAC TGC AGC TGT CAC \\  & & R—AGC AGC ACT ACC TCC TCC TCC \\ \hline \end{tabular}
\end{table}
Table 2: Overview of human genes used for qPCR using glyceraldehyde-3-phosphate dehydrogenase (_GAPDH_) and _18S_ ribosomal RNA (_18S_) as a reference gene. Genes analyzed for the NPCs: receptor tyrosine kinase (_TEK_), collagen type II (_COL2_), hypoxia-inducible factor-1\(\alpha\) (_HIF1A_), aggrecan (_ACAN_). (F = forward, R = reverse).
# MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template
###### Abstract
To solve this problem, we propose a multiple partial point cloud registration network using a global template named MPCR-Net. MPCR-Net was inspired by PointNet [10]. It uses deep neural networks to extract and fuse the learnable features of partial point clouds and the global template point cloud. It then trains the rigid body transformation matrix for partial point clouds to register the correspondence partial point cloud to the global template point cloud and finally forms a complete point cloud of the workpiece. In MPCR-Net, the partial point cloud has the characteristics of a local geometric structure of the workpiece and is sampled by the measuring device; the global template point cloud has the complete geometric structure of the workpiece and is converted from the CAD model of the workpiece. The key contributions of our work are listed as follows:  
1. A multiple partial point cloud registration method based on a global template is proposed. Each partial point cloud is gradually registered to the global template in patches, which can effectively improve the accuracy of the point cloud registration. 2. A clipping network for the global template point cloud, TPCC-Net (clipping network for template point cloud), was designed. In TPCC-Net, the features of partial point clouds and the global template point cloud are extracted and fused through a neural network, and the correspondence points of each partial point cloud are cut out from the global template point cloud. Compared to the existing registration algorithm based on deep learning, this method can reduce the correspondence point estimation error and improve registration efficiency. A parameter estimation network for rigid body transformation, TMPE-Net (parameter estimating network for transformation matrix), was designed. The learnable features of a partial point cloud and its correspondence points generated through the TPCC-NET were extracted through a neural network, and the parameters of the rigid body transformation matrix were estimated to minimize the learnable feature gap between the partial point cloud and the global template point cloud.
## 2 Experimental Methods
### FT-IR and NMR Analysis  
The synthesized graft copolymer (_i.e._, PVDF-g-PHEA additive) was analyzed using FT-IR and NMR method to figure out if PVDF was synthesized with HEA correctly. An Excalibur series FT-IR spectrometer (Spectrum 100, Perkin-Elmer, Waltham, MA, USA) was used to express the structure of the graft copolymer. The range of FT-IR spectra was from 4000 cm\({}^{-1}\) to 1000 cm\({}^{-1}\). NMR spectrometer (Avance-600 Bruker, Rheinstetten, Germany) was used to investigate the grafting ratio of the graft copolymer. The range of NMR spectra was from 0 to 7 ppm.
## 4 Computational Experiments and Analyses
### Algorithm Comparison Experiment in TDGVRPSTW Model
It is noted that both the proposed method and HGA search for a better solution by changing the neighborhood structure of the solution. The difference is that the proposed method includes multiple different domain structures while HGA only contains one neighborhood structure. The diversity of solution space of the proposed method is better than that of HGA. This proves that the systematic change of neighborhood structure proposed in the literature [26] can improve the search efficiency of the solution space. To sum up, in the TDGVRPSTW model, the approach proposed in this paper can obtain better solutions than AGA and HGA for all types of data, with fewer iterations. The different distribution of customers will also affect the result and efficiency of the solution. The proposed approach is more suitable for clients with C-type and RC-type distribution. The proposed approach is better and more efficient than the other two algorithms. The diversity of neighborhood structure also improves the optimization performance of the algorithm.