esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Selected Characteristics of SARS-CoV-2, SARS-CoV, and HCoV-NL63
### Morphology, Structure, and Replication of SARS-CoV-2
#### 1.3.1 Nonstructural Proteins  
SARS-CoV-2 encodes several unique group-specific open reading frames (ORFs) relative to other known coronaviruses. The replicase locus of SARS-CoV-2 contains two substantial ORFs, 1a and 1b, which are the largest part of the SARS-CoV-2 genome, spanning about 20 kb and two-thirds of the genome. It contains 16 non-structural proteins (nsps), termed nsp1 to nsp16. The replicase-locus-encoded nonstructural proteins (nsp1-nsp16) all have functions required for replication and transcription in the virus's life cycle. A smaller part of the genome encodes four structural proteins. Many nsps are involved in viral replication and represent potential targets for antiviral drugs. SARS-CoV-2 nsp12, the RNA-dependent RNA polymerase (RdRp), forms a large multiprotein complex with nsp7 and nsp8, is involved in RNA replication, and is part of a much larger replication and transcription complex (RTC). SARS-CoV-2 nsp10 exhibits close similarity with its SARS homologue. The ORF1a encodes a polyprotein (pp) 1a, whereas ORF1a and ORF1b together encode a C terminal-extended frameshift protein pp1 ab. The polyproteins pp1a and pp1 ab are first produced and then further processed via autoproteolysis by the viral-encoded polymerase in the virion into nsp1-nsp16 [21].
## 3 Proposed Security Model
### Cluster Assumption  
The mobile nodes are generally deployed in clusters [18] where each cluster has a cluster head (CH) [19]. In MWSNs, the operation of replica detection relies on the performance of cluster head, as cluster head is responsible to transmit data, communicate with other cluster head and arrange the information before transmission. Therefore, it is essential to pick a mobile node that has the capability to achieve the above target [20]. In this paper, the following features are assumed for the deployment of cluster:  
1. The maximum battery level should necessitate for cluster head to accomplish the responsibilities.
2. The high-level connectivity and coverage must have a chosen cluster head in MWSN.
3. To abate the transmission time, the selected cluster head should be placed near the BS (Base Station).
4. The cluster head is fixed over time for the predefined detection period.  
Figure 1 shows the clusters in MWSN where they are grouped together in circles with radius R. Basically, clustering algorithm is mostly used to enhance the network's life time in MWSN. It carries out self-organizing and re-clustering operations for cluster head selection in each detection period. Accordingly, one mobile node performs the tasks of CH and other mobile nodes take action as the member of the clusters. CH communicates with members of the mobile nodes and sends the collected information to the BS. It is dependent on two stages: First is the setup stage and second is the steady state stage. Formation of clustering and CH occurs in the first phase. Mobile nodes elect the cluster head independently. The chosen CH transmissions the information via Carrier Sense Multiple Access with Collision Avoidance Medium Access Control (CSMA/CA MAC) protocols [21]. Associate mobile nodes choose the cluster head (i.e., CH) by computing Received Signal Strength Indication (RSSI). After the CH captures the information and connects to the cluster, the cluster head (i.e., CH) creates a Time Division Multiple Access (TDMA) found list for its members and allots a time period slot for transmission for every node. The second stage is initiated after selecting the cluster and CH in MWSNs. In steady state, mobile nodes establish connection to CH through allots slots depending upon the period. Otherwise, mobile nodes lie in sleeping state. Once CH receives information from all associated mobile nodes, it would start the data transmission towards the BS [22].
## 4 Experimental Results and Analyses
### Ablation Experiment  
We divide the ablation experiment into three parts to verify the effectiveness of the main innovations: multi-level supervision, EEMA, and MSFA modules. It should be noted that when conducting ablation experiments on a certain innovation, the other network structures of MS-WFFNet remain fixed to ensure the accuracy and reliability of the experimental results. In addition, to ensure the experiment's fairness, we conduct ablation experiments on our self-built dataset DTH and the public dataset ShanghaiTech PartA.
# Geometric Characterization of Vines from 3D Point Clouds Obtained with Laser Scanner Systems
## 2 Materials and Methods
### Study Areas for Calibration Process and Application of the Proposed Methodology  
A calibration area where the VSAOs were scanned is located in a practice field at the University of Castilla, La Mancha, Albacete (Spain). This vineyard has experimental and teaching purposes, so its state and morphology are highly heterogeneous. However, it covers the typical physical characteristics of trellis systems with a drip irrigation system, where possible occlusions, slope changes, vegetation height, etc., occur. Two identical VSAOs were located in places were vines were missing in positions similar to those of actual vines. Figure 2 shows the location of the two VSAOs in the scanned area. Since the objective was to determine the volume occupied by the vine's trunk, measurements were obtained without leaves and prunes. This state is the most appropriate for scanning the evidence of crop vigor, because the accumulated vigor appears in the perennial parts of the plant and not in the deciduous parts, such as leaves or prunes.  
A real application of this methodology was implemented in a vineyard located in the southeast of Spain (38.728928\({}^{\circ}\), \(-1.470696^{\circ}\) EPSG:3857, Figure 3). The study area comprises an area inside of a 0.58 ha vineyard. Trellis driving is separated 3 m between strips and 1.5 m between vines. In this plot, different irrigation treatments have been performed since 2016, as described in Figure 3. These different  
Figure 1: General procedure workflow. TLS: terrestrial laser scanner. MMS: mobile mapping system. VSAO: vine-shaped artificial object.  
Figure 2: Vine-shaped artificial objects (VSAOs) in the test area of the scan.  
treatments will, in the future, drive new developments of this canopy. These features make this place an interesting application area due to their high variability. These treatments have been applied only during the last two years, so they have not yet resulted in noticeable differences in the trunk diameter. However, determining plant vigor using the proposed methodology can provide useful information about nutritional and irrigation requirements in the decision making process. Vines with higher vigor would demand more nutrients and water than those with lower vigor. Also of interest is the determination of carbon sequestration by plants, which only accounts for perennial wood and not for shoots or leaves that are removed every year. Thus, with the proposed methodology and data acquisition procedure, vigor maps can be obtained to help farmers better manage their vineyards.
# Dynamic Characteristics of the Bouc-Wen Nonlinear Isolation System
## 2 Mechanical Properties of the Bouc-Wen Model  
The Bouc-Wen model (the Bouc-Wen nonlinear isolation model with two degrees of freedom) is a hysteretic model widely used in the engineering and scientific research fields at present, and specifically widely used in the range of seismic isolation engineering. Moreover, the hysteretic restoring force and displacement diagram of the model is smooth and continuous, and the restoring force of the seismic isolation structure can be simulated by using the model, as shown in Figure 1.  
The force-displacement relationship of this model is expressed as follows:  
\[F(x)=\alpha k_{0}x+(1-\alpha)k_{0}z \tag{1}\]  
Figure 1: Schematic diagram of the Bouc–Wen hysteresis.  
In addition, the following equations, Equations (2) and (3), are satisfied:  
\[\dot{z}=\frac{1}{x_{y}}(A\dot{x}(t)-\beta\dot{x}(t)|z^{n}(t)|-\gamma\big{|}\dot{x }(t)\big{|}z^{n}(t))n=1,3,5\dots \tag{2}\]  
\[\dot{z}=\frac{1}{x_{y}}(A\dot{x}(t)-\beta\dot{x}(t)z^{n}(t)-\gamma\big{|}\dot{x }(t)\big{|}\Big{|}z^{n-1}(t)\Big{|}|z(t)|)n=2,4,6\dots \tag{3}\]  
where \(Z\) is the hysteretic displacement of the system, the area of the hysteretic loop controlled by the parameter \(A\); the shape of the hysteretic loop is controlled by parameters \(\beta\) and \(\gamma\); the smoothness of the hysteretic curve controlled by \(n\); and \(\alpha\) represents the ratio of the back-to-front stiffness of the yield value. When \(\alpha=1\), the system has ordinary linear elasticity.  
For different values of parameters \(\beta\) and \(\gamma\), the hysteretic characteristics of the structure will change in different forms. When \(\beta+\gamma>0\), as shown in Figure 2a,b, the structure presents soft characteristics, and the hysteretic restoring force of the system decreases as the displacement response increases. When \(\beta+\gamma=0\), the structure is linear in the loading stage. When \(\beta+\gamma<0\), the structure shows hardening properties, and the hysteretic restoring force of the system increases with the increase in displacement response. As we can be seen from Figure 2b,d, when the ratio of \(\beta/|\gamma|\) is relatively large, the enveloping graph of the system hysteretic restoring force curve is larger and the curve shape is relatively full, which proves that the structural system consumes more energy under this parameter. When the ratio is small, it can be seen that the overall enveloped area of Figure 2a,c is small, and the energy dissipation characteristics of the structural system are relatively weak compared with the others.  
Figure 2: Bouc–Wen hysteresis loops with different parameters. (The values of \(\beta\) in (**a–d**) are 0.15, 0.5, 0.15, 0.5; The values of \(\gamma\) in (**a–d**) are 0.85, 0.5, \(-0.85,-0.5\).)
## 2 Materials and Methods  
### Extraction and Isolation  
The dried aerial parts of _S. chinensis_ (1.2 kg) were finely ground using a blender. _S. chinensis_ powder was macerated in 3 L of methanol at 20 \({}^{\circ}\)C for 3 days and filtered (Whatman No. 2). This process was repeated three times using 3 L of methanol. The combined filtrates were evaporated using a 40 \({}^{\circ}\)C water bath to yield a dark-green residue (84.2 g) upon removal of the solvent using a rotary evaporator. The total extract was dissolved in 1 L water and then partitioned with \(n\)-hexane, ethyl acetate (EtOAc), and n-butanol, sequentially. The EtOAc-soluble extract (30.1 g) was subjected to reverse-phase column chromatography (Merck, Darmstadt, Germany, 40-63 \(\upmu\)m, 300 g) and eluted with a gradient consisting of methanol:water (2:8, 4:6, 6:4, 8:2, 10:0; 2 \(\times\) 500 mL each), producing 10 sub-fractions (Fr. 1-10). Among the n-hexane, ethyl acetate (EtOAc), and n-butanol fraction samples, the EtOAc fraction of _S. chinensis_ showed the lowest IC\({}_{50}\) value of 4.59 \(\mathrm{\SIUnitSymbolMicro mL}\) against CVB3 infection in Vero cells. Based on bioassay-guided fractionation, Fr. 7 and 10, which showed the most potent inhibitory effects, were subjected to column chromatography. Fr. 7 was purified by preparative high-performance liquid chromatography (HPLC) using a gradient of 30-100% acetonitrile in water (Shiseido-Capcell Pak C18 UG120, 250 \(\times\) 10 mm, 10 \(\mathrm{\SIUnitSymbolMicro m}\), Tokyo, Japan) to obtain the active compound (Figure S1).
## 1 Introduction
Unlike other countries, these transmission projects in China pass through high-altitude areas such as Lhasa in Tibet (with an atmospheric pressure of about 56 kPa) and Xining in Qinghai (with an atmospheric pressure of about 70 kPa). The low-pressure environment can affect the interaction processes between lasers and samples, as well as the light collection process in optical fibers, which can interfere with experimental data and impact the accuracy of LIBS detection. Therefore, studying the impact of atmospheric pressure on LIBS data and its mechanisms is particularly important. This research utilizes LIBS to collect spectral data from fireproof coating samples under different pressures (35-101 kPa) and analyzes how the spectral data vary with pressure. A confocal microscope is used to observe the ablation morphology of the samples, and a linear model is established to relate the number of laser pulses to the ablation depth of the samples. The thickness of the fireproof coating is characterized based on the number of laser pulses required to penetrate the coating. Additionally, by establishing a linear model under different pressures, the impact of pressure on the measurement of fireproof coating thickness is mitigated. This study facilitates the accurate characterization of fireproof coating thickness under varying pressure conditions, providing a reference for assessing the fireproof performance of these coatings.
# Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks
## 2 Related Concepts  
### Directed Graph Convolutional Networks  
The graph convolutional network (referred to as GCN) has been widely used for its excellent performance in processing graph result data. However, undirected graphs limit the application range of GCNs, and the directed graph convolutional networks (referred to as DGCNs) [46] are proposed for this problem. The graph convolution is extended to the directed graph by using the first-order and second-order degrees, which not only preserves the connected property of the directed graph, but also enlarges the receptive field of the convolution operation. Compared with a traditional GCN, DGCN has a stronger feature conversion ability. It can obtain simpler feature distribution than GCN, and thus facilitates the analysis of samples. DGCN takes the directed graph and item feature matrix as input in the preprocessing stage, constructs the first-order and second-order proximity, and embedsthem into the proximity convolutional layer. The node feature matrix of the first-order proximity nodes, second-order in-degree proximity, and second-order out-degree proximity after convolution operation are obtained. The node feature matrix is aggregated into the node feature map and input to the fully connected layer, and finally the model output is obtained through the softmax classifier.
###### Abstract  
Injecting carbon dioxide (CO\({}_{2}\)) into subsea water zones, where the in situ temperatures are below the hydrate-forming temperature of CO\({}_{2}\), has been recently proposed to lock CO\({}_{2}\) inside the water zones in a solid hydrate form. It is a common concern that CO\({}_{2}\) may form hydrates during the injection period, which would reduce well injectivity. CO\({}_{2}\) injection into sandstone cores under simulated subsea temperatures ranging from 0\({}^{\circ}\)C to 5\({}^{\circ}\)C was investigated in this study. Experimental results show that flowing CO\({}_{2}\) at Darcy velocity 0.033 cm/s begins to form hydrate in the sandstone core at dynamic pressures higher than the minimum required pressure under static conditions. At temperatures changing from 0 \({}^{\circ}\)C to 5\({}^{\circ}\)C, the observed hydrate-forming pressure changes from 1.87 to 2.5 times the pressures required for CO\({}_{2}\) hydrates under static conditions. The reason why the required minimum pressure for CO\({}_{2}\) to form hydrates in dynamic conditions is higher than that in static conditions is attributed to the shear rate effect of flowing fluids that should slow down the growth of hydrate crystals and/or break down formed hydrate films in the dynamic conditions. Therefore, higher pressure energy, or fugacity, is required to promote the growth of hydrate crystals and hydrate films in dynamic conditions. More rigorous investigations in this area are needed in the future.  
CO\({}_{2}\); subsea; storage; hydrate; dynamic formation; porous media +
Footnote †: journal: Applied Sciences
# Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images
## 3 Ore Rock Fragmentation Calculation Method
### Edge-Enhanced SDM Calculation  
In mineral rock images, when there is a small grayscale difference between catchment basins and watershed regions, the watershed algorithm is prone to flooding, resulting in under-segmentation issues. This, in turn, reduces the accuracy of rock mass calculation. To address this issue, an edge-enhancement technique is applied to the SDM based on the mineral rock edge map \(E\). This enhances the distinction between the watershed and catchment basins, thus preventing the submersion of critical edges if, and only if, the pixels \(E(i,j)\) on the edge map \(E\) and the corresponding pixels \(S(i,j)\) on the standard deviation map \(S\) satisfy the following conditions:  
\[\left\{\begin{aligned} & E(i,j)=255\\ & S(i,j)\geq s_{m}\end{aligned}\right., \tag{11}\]  
where \(s_{m}\) is the median value of all pixels on the standard deviation map \(S\); the pixel value \(S(i,j)\) on the standard deviation map is set as:  
\[S(i,j)=255. \tag{12}\]  
If the condition is not satisfied, the pixel value on the standard deviation map \(S\) remains unchanged.
# UVSQ-SAT, a Pathfinder CubeSat Mission for Observing Essential Climate Variables
## 3 Materials and Methods
### UVSQ-SAT Mission Concept of Operations  
There are four separate and distinct UVSQ-SAT mission Concepts of the Operation (ConOps) phases:  
* Operations before launch and for launch.
* Launch early operations and satellite platform in-orbit verification: deployment of UVSQ-SAT from the CubeSat deployer, automatic activation of the satellite by separation switches, automatic initialization of the onboard software a few seconds after the satellite separation, deployment of deployable structures (antenna), automatic satellite ADCS activation to perform autonomous detumbling of the spacecraft, verification of the link between the ground and satellite, restitution of the satellite orbit thanks to the first visibilities, check that all platform satellite services are running, payload switch-on, and check that all payload instruments are functional.
* Instrument in-orbit verification and operations: preliminary configuration, operational configuration of the satellite, Calibration/Validation (CalVal) of the payload instruments and comparisons with payload ground based calibration (ERS (responsivity, solar absorption (200 to 2500 nm), normal emittance (around 10 \(\upmu\)m), bidirectional reflectance distribution function for different angle of incidence, etc.), DEVINS (responsivity, slit function of the sensor, calibrations against national SI standards, etc.), etc.), and validation of the performances. In \"routine\", the CubeSat will observe the Earth and the Sun full time. Each month, a calibration will be done to characterize the angular responsivity of the sensors (ERS and DEVINS).
* End of life of the UVSQ-SAT CubeSat.  
Figure 5: UVSQ/LATMOS UHF/VHF station (one of the stations of the INSPIRE network). LATMOS hosts students from UVSQ and Paris Saclay universities to provide hands-on experience (satellite manufacturing, antenna realization, etc.).
# Hydraulic Expansion Techniques for Fracture-Cavity Carbonate Rock with Field Applications
## 2 Proposed Method for Increased Production Using Hydraulic Expansion
### Blocking and Expansion Composite Process  
Fracture-cavity carbonate reservoirs can have well-developed reservoirs with relatively high cumulative production but with a tightly developed periphery and poor permeability. After water injection, we found deep fractures in the backbone of some reservoirs, rendering injection ineffective. In these cases, the main fractures could be temporarily plugged to then perform hydraulic expansion, thereby restoring the vitality of reservoirs around the well [20].  
Expansion well 5 is located in the north of well block S94-1. The main fault of S94-1 and fault 501 surround the fracture zone. Secondary faults are developed around the well. The whole area is controlled by a river channel and faults. In the first round of high-pressure water injection, 14,000 m\({}^{3}\) was injected, but it remained flat after 3000 m\({}^{3}\), as shown in Figure 8. Water injection in this well likely formed a dominant fracture channel, and injection into deep fractures failed to expand the well's circumferential communication and achieve lateral replacement. To enhance water injection, the injected water should reach a wider area, achieve high-efficiency utilization, increase the bottom hole pressure, and make the reservoir around the well show vitality again. To this end, we conducted a pilot test of an integrated composite plugging and expansion process. During this process, 10 to 1-2 mm medium-density elastic particles was injected, and the cumulative water injection was 4100 m\({}^{3}\), reaching a maximum oil casing pressure of 42/38 MPa.  
A temporary plugging occurred during construction, obtaining a well-communicated reservoir and greatly improved remote flow coefficient. Thus, the design target was essentially achieved, and the well was simmered. The construction results showed that the carbonate reservoirs where the injected water seeped down along the large fault zone were temporarily plugged in the high-permeability zone to improve the efficiency of water injection. Therefore, the spreading volume could be increased on the plane and longitudinal directions through a composite blocking and expansion process. The process results are listed in Table 1.  
Figure 7: Production and water content during acidification and hydraulic expansion of expansion well 4.
# Water Pipeline Leakage Detection Based on Coherent \(\varphi\)-OTDR and Deep Learning Technology
## 2 Sensing Principle of \(\varphi\)-OTDR
The light output of the light source is linearly polarized; in the case of considering that the scattered light is also ideally linearly polarized, the expression for \(I\) can be obtained according to the interference principle:  
\[I=E_{0}{}^{2}+r^{2}E_{1}{}^{2}e^{-2\alpha z}\pm 2 rE_{0}E_{1}e^{-\alpha z} \gamma\cos\theta\cos(\Delta\omega t+\varphi) \tag{5}\]  
Here, the symbols represent the following parameters: \(\gamma\) denotes the spectral correlation factor, \(\theta\) represents the polarization angle, \(\Delta\omega\) signifies the angular frequency shift introduced by the modulator (\(\Delta\omega=\omega_{s}-\omega_{0}\)), and \(\varphi\) represents the system phase shift (\(\varphi=2\beta z+\varphi_{s}\), where \(2\beta z\) corresponds to the transmission phase shift and \(\varphi_{s}\) represents the Rayleigh scattering phase shift). When a water pipe experiences a leak, it generates acoustic waves at a certain frequency near the leakage point, which will introduce phase shift \(\eta_{\mathrm{s}}\) when applied to the sensing fiber. The schematic diagram of an optical pulse passing through external disturbances is shown in Figure 2. Here, point A represents the position before the external disturbance test point and point B represents the position after the test point. The phase of the optical pulse at point A is denoted as \(\eta_{0}\). After encountering the external disturbance, it acquires an additional phase shift \(\eta_{\mathrm{s}}\), resulting in a phase of \(\eta_{0}+\beta\Delta z+\eta_{\mathrm{s}}\) at point B. Upon the return of scattered light, it again encounters external disturbances, introducing an additional phase  
Figure 1: Framework diagram of COTDR. Abbreviations—NLL: narrow-linewidth laser; AOM: acousto-optic modulator; EDFA: erbium-doped fiber amplifier; BPD: balanced photodetector; DAQ: data acquisition card. shift \(\eta_{\text{s}}\), resulting in a phase of \(\eta_{0}+2\beta\Delta z+2\eta_{\text{s}}\) at point A. It should be noted that \(\Delta z\) is the distance between points A and B. Leveraging this, demodulating phase information enables the extraction of disturbance frequency information, facilitating subsequent event type recognition using deep learning techniques.