esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Spatial-Temporal Self-Attention Network for Traffic Flow Prediction
## 2 Related Work  
### Graph Convolution Network  
Graph Convolutional Neural Networks (GCNs) [42; 43] are an extension of Convolutional Neural Networks (CNNs) applied to non-Euclidean spatial data represented by graph structures. They can be categorised into spectral domain graph convolution networks and spatial domain graph convolution networks [14; 44; 45; 46; 47; 48]. The spectral domain approach extends the Convolutional Neural Network algorithm to process graph-structured data by utilising the graph Laplacian matrix, which resembles the Fourier transform in signal processing. However, computing all the eigenvalues of the graph Laplacian matrix introduces high computational complexity. To address this, ChebNet [43] replaces the convolutional kernel in spectral domain graph convolution with Chebyshev polynomials, resulting in significant efficiency improvements. Building upon ChebNet, the classical GCN [42] further simplifies the model by employing a first-order Chebyshev polynomial as an approximation of the ChebNet filter. Consequently, the classical GCN is regarded as a first-order approximation model of ChebNet.  
Spatial domain graph convolution networks are defined based on the mechanism of information propagation [48]. For example, Message Passing Neural Networks (MPNNs) [49] utilise message passing functions to unify different variants in the spatial domain. In GraphSAGE [50] and Diffusion Graph Convolutional Networks (DGCs) [51], the graph convolution operation is modelled as a process of diffusion or direct information propagation from a node to its adjacent nodes with a certain probability. Graph Attention Networks (GATs) [52] employ attention mechanisms to learn the relative weights between adjacent nodes, and incorporate the attention mechanism into the information propagation process. Moreover, GAT utilises multi-head attention mechanisms to ensure stable learning.
# Integrated Radar and Communications Waveform Design Based on Multi-Symbol OFDM
## 3 IRC Waveform Design Objective
### Joint Optimization of PMEPR and \(\mathrm{PSLR}\)
\begin{split}&\min_{\mathbf{C}\in\mathbb{C}^{MN}}\widetilde{O}( \mathbf{C})\\ =&\frac{1}{p}\bigg{[}\alpha\log_{10}\bigg{(}\sum_{q=0 }^{MQ-1}|s(q)|^{2p}\bigg{)}+\log_{10}\bigg{(}\sum_{k\in\Omega}|R(k)|^{2p} \bigg{)}\bigg{]}-(\alpha+2)\log_{10}\Big{(}E[|s(q)|^{2}]\Big{)}\\ =&\frac{1}{p}\bigg{[}\alpha\log_{10}\bigg{(}\sum_{q=0 }^{MQ-1}|s(q)|^{2p}\bigg{)}+\log_{10}\bigg{(}\sum_{k\in\Omega}|R(k)|^{2p} \bigg{)}\bigg{]}-\\ &(\alpha+2)\log_{10}\bigg{(}P_{X}+\frac{1}{M}\sum_{m=0}^{M-1} \sum_{n\in\mathcal{R}}|C_{n,m}|^{2}\bigg{)}\end{split}  \tag{21}\]  
It is found from (21) that the objective function \(\widetilde{O}(\mathbf{C})\) is an analytical expression about \(\mathbf{C}\). This motivates us to consider a gradient analysis-based approach to this unconstrained optimization problem. Since the CGA [35; 38] is a classical algorithm for solving such unconstrained optimization problems, in the next section, we will develop a conjugate gradient-based iterative algorithm to minimize \(\widetilde{O}(\mathbf{C})\).
## 3 Experimental Results and Analysis
### Physiological Strain Index  
Figure 12 shows the variation of the mean PSI value with time under the four different heat stress levels. With the increase in exposure time and environmental thermal intensity, the PSI increased from 0 (no heat strain) to 4.1 (moderate heat strain). At 27-29 \({}^{\circ}\)C, the PSI index increased the most, which indicated that under the same exercise intensity, the greater the environmental stress index was, the more the physiological strain index increased.
## 4 Experimental Results
### Experimental Setup
#### 4.1.1 Simulation Environment  
The simulation experiments were primarily conducted using the OMNeT++ discrete event simulator, version 6.0.1, coupled with the INET framework, version 4.6. The INET framework's TSN functionalities, which were significantly enhanced starting from its version 4.4, provided the necessary modules for modeling TSN switches, end-stations, and protocols. This particular TSN model within INET was co-developed by the OMNeT++maintainers and automotive industry partners, ensuring its relevance and capability to simulate critical features pertinent to in-vehicle networking scenarios.  
A cornerstone of Time-Aware Shaping (TAS) is precise time synchronization across all network nodes, typically achieved via protocols like gPTP (IEEE 802.1 AS). This shared sense of time is fundamental to the offline GCL scheduling paradigm investigated in this paper, although alternative asynchronous approaches also exist [29]. The gPTP implementation within OMNeT++, whose modeling considerations are discussed in works like [30], demonstrated consistent nanosecond-level synchronization accuracy among simulated nodes throughout the experiments.  
All simulations were executed on a host machine equipped with an 11 th Generation Intel(R) Core(TM) i7-11800H processor (Intel Corporation, Santa Clara, CA, USA) and an NVIDIA RTX 3060 Laptop GPU (NVIDIA Corporation, Santa Clara, CA, USA), running a Windows 10 operating system. Unless specified otherwise for particular experiments, the simulated in-vehicle network architecture corresponded to the centralized model previously depicted in Figure 2, featuring a Centralized Network Configuration (CNC) entity responsible for GCL computation and distribution.
## 3 Cervical Cancer
ATR-FTIR spectroscopy involves the IR beam being directed through a high refractive index crystal, typically diamond, germanium or zinc, where it undergoes total internal reflection generating an evanescent waveform that interacts with the biological sample at the surface of the crystal. Interestingly, a recent ATR--FTIR study has shown that cervical pre-cancer is more accurately identified when histology rather than cytology is used as the gold standard to classify the samples [27]. ATR-FTIR spectroscopy has also been shown to be capable of distinguishing between high risk HPV infections in women with low grade and high grade dysplasia [28]. Interestingly, spectral differences were also found between women in their 20s and in their 30s in samples of the same cytological grade and HPV type, most likely because of a persistent HPV infection. [PERSON] _et al._[25] also showed that FTIR spectroscopy could discriminate cervical exfoliated cells based on their HPV status. In addition, a number of studies have used cervical cell lines as model systems to show the ability of vibrational spectroscopy (both IR and Raman) to differentiate cells based on HPV status [28; 29; 30; 31; 32; 33]. In contrast to the extensive work on exfoliated cervical cells using IR spectroscopy, there are relatively few studies using Raman spectroscopy, most likely because of the issues with confounding factors. [PERSON] _et al._[33] showed that Raman spectroscopy could classify HPV-positive and -negative cytology samples with an accuracy of 98.5%. A study by [PERSON] _et al._[34] used Raman spectroscopy to distinguish between normal and cervical cancer cytology samples. As the spectra of cervical cancer samples were dominated by blood features, cytology samples were treated with red blood cell lysis buffer prior to Raman acquisition. A classification accuracy of ~80% was obtained. Both of these studies used cell pellets rather than recording Raman spectra from individual cells and this probably resulted in the relatively low classification accuracy in the study by [PERSON] _et al._ due to sample heterogeneity. A recent study by [PERSON] _et al._[35] addressed many of the issues involved in recording Raman spectra from ThinPrep cervical cytology samples. A new method based on pre-treatment of the slides with hydrogen peroxide to clear blood residue contamination before Raman recording was shown to minimise variability within the data sets. This resulted in the collection of highly reproducible data with clear discrimination between negative cytology and CIN cytology. Importantly, all data was recorded on glass ThinPrep slides which are currently used for clinical cervical cytology rather than spectroscopic substrates such as MirrIR or calcium fluoride substrates which are commonly used for research purposes and, although they reduce the presence of confounding contributions of the substrate, are considerably more expensive.
# A Simulation of Contact Graph Routing for Mars-Earth Data Communication
## 2 Model of CGR
### Routing Management for the Combination of Rover and Satellite  
For \(i=1,2\) and \(j=3,\ldots,6\), the data will be sent from rover \(i\) to satellite \(j\). The choice of satellite \(j\) depends on the following conditions:  
(i) The contact window between \(i\) and \(j\) is open, or \(rs_{ij}(t)=1\);  
(ii) \(C_{j}(t)=\max\{C_{w}(t)\}\) for all \(w\) with \(rs_{iw}(t)=1\), and the storage \(C_{j}(t)\) is not full;  
(iii) There is any DSN-antenna \(k\), such that \(se_{jk}(t)=1\).  
Condition (ii) means we choose the satellite with the maximum current storage capacity among others. The satellite \(j\) may be selected from the second maximum if the first maximum's storage capacity is exhausted, or from the third maximum if the first and second ones are exhausted, and so on. If all satellites are exhausted, the data will be deleted. Those receiving the data on Earth would notice this sending failure, so they would transmit a new command to the sending rover to create the same data to be sent at a consecutive time. For time \(t=1,2,\ldots M\), if \(rs_{ij}(t)=1\) and \(\Sigma_{w=1}^{n_{d}(t)}dt_{w}(t)\leq C_{j}\), then \(x(1,r,d,t)=1\).  
If Condition (iii) cannot be satisfied or \(se_{jk}=0\) for all \(k\), the data are stored to satellite \(j\), satisfying Condition (ii). Sometimes, when there is antenna \(k\) and satellite \(j\), such that \(se_{jk}(t)=1\) but \(rs_{ij}(t)=0\); therefore, for all \(i=1,2\), so satellite \(j\) cannot be chosen.  
At minute \(t\), if there are data packets from \(v\)th-packet to \(n_{d}(t)\)th-packet stored in satellite \(j\), \(v\geq 1\), then the size of these data packets is written as \(storsats(j,t)\).  
\[storsats(j,t)=\Sigma_{w=0}^{n_{d}(t)}d_{w}(t). \tag{11}\]  
The indices of the non-zero entries of this variable identify the satellite indices that hold data while there is no contact window between satellites and DSN antennas, from the previous minute until time \(t\).  
For time \(t=1,2,\ldots M\) and data packets \(d=1,\ldots,n_{d}(t)\), if \(rs_{ij}(t)=1\), \(se_{jk}(t)=0\), and \(storsats(j,t)\leq C_{j}\), then \(x(1,r,d,t)=1\).  
Information of the storing satellite \(j\) is put into the variable \(r\), where  
\[r=Ids(i)+j-i, \tag{12}\]  
where index-\(i\) is 1 or 2, depending on the departure rover.
# Crack Propagation Behavior of Fused Silica during Cyclic Indentation under Incremental Loads
## 3 Results and Discussion
### Crack Generation/Propagation Mechanisms
Figure 17: Schematic cross-sectional view of various spalling patterns in early stage of incremental cyclic indentation. in the depth direction, resulting in semielliptical medial/radial cracks. In the early cycles of incremental-load cyclic indentation, the indentation load was very small, so the lateral cracks would form during only unloading. Hence, the shape of median/radial cracks around the indent edges should be quarter circular containing lateral cracks. As the indentation cycle increases, expansion of the plastic region and propagation of median/radial cracks progress during loading (Figure 19c). During unloading, in addition to the propagation of lateral cracks formed on earlier cycles, new lateral cracks initiate beneath the expanded plastic region (Figure 19d). The lateral cracks propagate, resulting spalling fractures. If the fragment of spalling is not removed, the subsequent loading will cause the lateral cracks to close, and thus the lateral compression from the indenter is transferred to the outer region via the fragment, forming an expanding core. A new lateral crack is then initiated and propagates in the outer region, forming the secondary spreading (Figure 19e). The clarification of cracking/spalling behavior during incremental-load cyclic indentation is very useful for understanding the mechanisms of abrasive machining, especially the grinding of hard brittle materials for high-precision optics fabrication [33; 34; 35]. The characteristic crack propagation and spalling fractures observed in this study provide direct reference for understanding the subsurface damage formation and material removal mechanism under incremental cyclic loads, as shown in Figure 1. The findings will close the gap in the understanding of microscopic fracture phenomena during real micro/nanoscale processing of optical glass and other brittle materials, as it is extremely difficult for in situ observation of these high-speed processes. If the correlation between cyclic nanoindentation and grinding is clearly established, it will greatly contribute to the process optimization of grinding technology for advanced optical components. Figure 18: Schematic top view of secondary spalling formation in later stage of incremental cyclic indentation.
## 6 Evaluation and Discussion
### Evaluation
Figure 11: Comparison of average throughput (**a**) and average latency (**b**) in the Check New Version step between direct firmware update and peer-to-peer firmware update. Figure 10: The duration of the Check New Version step: direct firmware update vs. peer-to-peer firmware update. The average throughput and average latency of ten groups of requests are shown in Figure 13. The peer-to-peer protocol can handle 49.1 req/s. That means that if there are 10,000 requests, the peer-to-peer firmware update protocol can finish this step in 203.42 s or 3.3 min. This value is smaller compared to the direct firmware update protocol that can handle 45.8 req/s. That means that if there are 10,000 requests, the direct firmware update protocol can finish this step in 218.21 s or 3.6 min. The direct firmware update takes 11 min to finish 10,000 requests of two primary steps, and peer-to-peer takes 9.6 min. The average latency result for direct firmware update was 202.73 ms. This value is higher compared to the peer-to-peer protocol, which got a value of 189.58 ms. Our evaluations of both protocols in the Check New Version step and Download Update step show that the peer-to-peer firmware update protocol has better time performance than the direct firmware update protocol. We also evaluated the computational complexity and data transmission size for direct firmware update and peer-to-peer firmware update. As shown in Table 6, compared to the direct firmware \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Number of Requests**} & \multicolumn{3}{c}{**Duration (s)**} \\ \cline{2-3}  & **Direct Firmware Update** & **Peer-to-Peer Firmware Update** \\ \hline
100 & 3.28 & 3.25 \\
200 & 5.30 & 4.25 \\
300 & 7.35 & 6.25 \\
400 & 9.20 & 8.28 \\
500 & 10.31 & 11.26 \\
600 & 12.28 & 12.24 \\
700 & 14.30 & 13.33 \\
800 & 16.28 & 15.34 \\
900 & 18.29 & 17.27 \\
1000 & 21.32 & 19.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Direct firmware update and peer-to-peer firmware update: the results of duration for the Download Update step. Figure 12: The duration of Download Update step: direct firmware update vs. peer-to-peer firmware update. Figure 13: Comparison of average throughput (**a**) and average latency (**b**) in the Download Update step between direct firmware update and peer-to-peer firmware update. update, a peer-to-peer firmware update requires the extra computation of one hashing, one digital signature, and one signature verification. Additionally, it requires 197 bytes of extra data transmission. One gateway will need an extra 7.85 KB of memory space to implement a peer-to-peer update. With this cost, the peer-to-peer firmware update can increase overall firmware update availability and disseminate the traffic burden from the manufacturer's server to the gateways.
# Application of Corrected Methods for High-Resolution XRF Core Scanning Elements in Lake Sediments
## 6 Conclusions  
The comparison of four recent XRF-scan calibration methods indicates that the NMS and LREC calibrations do not work well in sediments characterized by large changes in downcore water content. In contrast, the NPS method, which eliminates the influences of long-term changes in water content, exhibits a relatively good calibration. However, the NPS method fails to calibrate light element fractions (such as Al and Si) towards the core-top where water content increases markedly. Notably, we show that the MLC method is capable of efficiently converting almost all the detected XRF-scan intensities to fractions. For fine-grained sediments, grain size exhibits little impact on the XRF-scan data. Water content within the lake sediment was found to be the most important factor influencing the XRF-scan intensities, especially for the light elements (From Al to Fe). A significant decrease of XRF-scan accuracy may occur when the sediment water content is >47%. Therefore, we strongly recommend selecting a range of different samples throughout any given core, testing element fractions and applying the MLC method when performing high-resolution XRF-scan measurements in the determination of elemental fractions within lake sediments.  
Conceptualization, X.Z. and H.Z.; methodology, X.Z.; software, X.Z.; validation, X.Z., Y.Z., H.W., Q.L. and F.L.; formal analysis, X.Z.; investigation, X.Z., W.P. and L.D.; resources, F.C.; data curation, X.Z. and U.A.; writing--original draft preparation, X.Z. and H.Z.; project administration, H.Z.; funding acquisition, H.Z. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 41901104 and 41820104008, the Yunnan Provincial Government Leading Scientist Program, grant number 2015 HA024, the Yunnan Postdoctoral Science Foundation and China Postdoctoral Science Foundation, grant number 2019M663584.  
We thank [PERSON] and [PERSON] for his/her help on the software implementation. We also thank [PERSON] from University of Liverpool, United Kingdom, for improving the language. We appreciate the comments and suggestions by [PERSON].  
The authors declare no conflict of interest.
# Intersatellite and Downlink Agile Attitude Maneuvers
## 5 Attitude Maneuvering for Downlink Connection
### Command Attitude
The latter can be written as the sum of three contributions:  
\[\underset{c\gets N}{R}=\frac{\partial\underset{c\gets N}{R}}{\partial \xi}\dot{\xi}+\frac{\partial\underset{c\gets N}{R}}{\partial\theta_{t}} \dot{\theta}_{t}+\frac{\partial\underset{c\gets N}{R}}{\partial\theta_{* }}\dot{\theta}_{*} \tag{48}\]  
where  
\[\dot{\xi}=\dot{\theta}_{G}=\omega_{\oplus} \tag{49}\]  
whereas the time derivative of the argument of latitude is [32]  
\[\dot{\theta}_{t}=\dot{\theta}_{*}=\sqrt{\frac{\mu}{p^{3}}}\big{(}1+e\cos\theta _{*}\big{)}^{2} \tag{50}\]  
In Equation (50), \(\mu\) is the gravitational parameter of the Earth, while \(p\) is the semilatus rectum of the operational orbit. The preceding equation assumes Keplerian motion, i.e., negligibility of orbit perturbations, which is a reasonable and rather accurate approximation over the timescale of a single repetition period. As previously remarked, Equation (47) leads to obtaining the three components of \(\underline{\omega}_{c}^{(C)}\) in closed form. Their expressions are rather long and are not reported for the sake of conciseness. The last step consists of computing the time derivative of the commanded angular velocity,  
\[\hat{k}_{c} =\frac{\vec{r}_{T}-\vec{r}_{S}}{||\vec{r}_{T}-\vec{r}_{S}||} \tag{43}\]0  
It is worth noting that the preceding developments lead to finding the desired attitude and angular rate in tracking intervals. The desired attitude in reorientation arcs can be found by predicting the inertial position of the ground station at the time when it becomes visible. Because this is a slewing maneuver, the corresponding commanded angular velocity \(\underline{\omega}_{c}^{(C)}\) equals \(\underline{\underline{0}}\), as well as its time derivative. \[\hat{k}_{c} =\frac{\vec{r}_{T}-\vec{r}_{S}}{||\vec{r}_{T}-\vec{r}_{S}||} \tag{43}\]1 Geometry of the commanded attitude (downlink).
## 1 Introduction  
Although burning alternative biomass is generally a beneficial approach, there are certain concerns involved. Due to the large ash concentrations and low melting points, the most dangerous aspect of this practice is ash melting. Molten ash becomes sintered matter and agglomerates when it attaches to the combustion chamber's walls, smothering the flame. Alternative biomass is inferior to traditional fuels because of flame smothering, which results in poor combustion and excessive emissions [1]. Many researchers have already tried to solve these problems in several ways.  
Another option is to change the structural elements of the combustion equipment, particularly the burners designed to burn biomass at low ash-melting temperatures [2, 3, 4]. The direction of the fuel supply and combustion, which occurs in the vertical plane, is a significant drawback of standard retort burners used for the automated burning of bulk fuels. The drawback of this method is that, due to the high temperature and gravitational force, the heavier ash particles are trapped in the burner, where they cling to the walls and lead to clogging. By making it easier for fuel to tumble out of the burning chamber, more recent varieties of horizontal tube burners help to partially resolve this issue [5].  
One possible alternative energy resource is straw. Usage of straw as an energy resource for burners and furnaces is problematic because of ash melting and the covering of heat exchange surfaces with slag [6]. Straw may, however, be an efficient and environmentally sound choice, not only satisfying rising energy needs as the economy expands but also laying the groundwork for environmental conservation and societal sustainability [7]. The efficiency of straw in fluidized-bed combustion has been improved, and its performance is close to that of the traditional boiler, which uses coal, petroleum, and natural gas as fuels [8; 9]. Therefore, on the basis of available technologies, research on the direct use of straw in fluidized-bed combustion is one of a number of effective and large-scale approaches to straw utilization, yielding benefits in terms of both energy and the environment [8; 9].  
Several initiatives have used straw as a source of energy. Early in the 1990s, the IEA project was one of the most significant. In that study, authors compared the potential and actual use of straw in energy generation in five European countries: the United Kingdom, Denmark, Sweden, the Netherlands, and Austria [10]. The primary objective of this endeavor was to research the use of straw and agricultural byproducts for the manufacture of heat or power. The study revealed various obstacles that make the use of straw challenging, such as challenging delivery arrangements and minimal economic impact, but also some skepticism regarding straw and other resources of a similar nature [11; 12]. According to the project, 1 million tons of straw were utilized in Denmark to produce heat or electricity. Some power stations also co-fired straw with coal. In households, straw, straw pellets or other low-quality fuels are not used frequently [11; 13].
###### Abstract
The novelty of this paper is the use of Sentinel-1 (texture characteristics and backscatter coefficients) and Sentinel-2 (multispectral bands, vegetation indices, and biophysical variables) imagery in the mapping of forest AGB and AGB model development, as well as their comparison. This study attempted to contribute to the development of remote sensing-based predictive mapping techniques for forest AGB using freely accessible multi-source remote sensing data with a relatively high spatial resolution.