esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area  
Two counties located at Xinjiang Province, China, were selected as the study areas (Figure 1). One county is Bole located at the northwestern part of Xinjiang (44\({}^{\circ}\)36\({}^{\prime}\)-44\({}^{\circ}\)58\({}^{\prime}\)N and 82\({}^{\circ}\)05\({}^{\prime}\)-82\({}^{\circ}\)40\({}^{\prime}\)E). It is in a continental arid semi-desert and desert climate. The main land cover types are agriculture (cotton and corn) and deserts. Another county is Luntai (41\({}^{\circ}\)39\({}^{\prime}\)-41\({}^{\circ}\)56\({}^{\prime}\)N and 84\({}^{\circ}\)00\({}^{\prime}\)-84\({}^{\circ}\)21\({}^{\prime}\)E), located at Bayinguoleng Mongolian Autonomous Prefecture, between Southern Tianshan and north of the Tarim Basin. It has a warm temperate continental arid climate. Luntai also consists of plains covered by agricultural lands, cities, and deserts. The main crops of Luntai are cotton, corn, and winter wheat.
# Cyber Security Threat Modeling for Supply Chain Organizational Environments
## 3 Threat Modeling for Supply Chain Contexts
### Conceptual View
Due to the invincible nature of cyber attacks, the organization should establish a collaborative mechanism with all stakeholders on the supply chain to protect and secure the supply chain systems. **Cyber incident reports:** Incident report systems provide CSC attack victims the platform to report attacks and threats that have occurred, including their impact and the degree of severity. The purpose is to gather and analyze threat information that can assist organizations and stakeholders to achieve their security goals. Properties for cyber incident reports include type, date, source, and impact. They could serve as notification platforms and disseminators of threat information for training and awareness among third party collaborators, software developers, and security experts. **Threat information sharing:** Cyber threat information sharing is a platform that provides the information necessary to assist an organization in identifying, assessing, monitoring, and responding to cyber threats [30]. There are rules that govern and protect information sharing, such as information sensitivity and privacy, sharing designations, and tracking procedures [30; 31]. The meta-model shown in Figure 1 depicts the concepts and their relationships. A threat actor may want to attack the system and exploit vulnerable spots using TTP methods to manipulate the CSC system. CSC requirements are used to ensure the security goal, and constraints are achieved to meet the organizational goal. The attack entity is linked to the threat actor, TTP, risks, and threat, as the properties determine the nature of attacks and probable threats. The CSC requirements, risk, controls, and cyber incident report could have an effect on the organizational goal as well as the inbound and outbound supply chains. This interrelationship provides evidence of the degree of threat or cascading effect of how a particular risk could impact the CSC. The likelihood of an attack becoming a probable threat is determined by the threat intelligence gathered.
# Study on Deformation Characteristics of Low-Highway Subgrade under Traffic Load
## 5 Conclusions  
As the main bearing layer of road engineering, highway subgrade bears millions of traffic loads over the years, and its strength, stiffness, and long-term stability gradually decline. In this paper, the dynamic triaxial test is carried out to study the time evolution and spatial distribution of strain and pore pressure of highway-subgrade soil under the action of traffic load. The influence of vehicle-axle load on subgrade deformation is analyzed. The numerical calculation model of the subgrade is established. The deformation characteristics of subgrade under traffic load are analyzed. The main conclusions can be drawn as follows:  
Figure 23: Change curve of roadbed settlement under different traffic loads.  
Figure 22: Vertical deformation distribution on the top surface of subgrade.  
1. The cumulative strain and pore-water pressure of subgrade soil show a nonlinear growth trend under the action of traffic load. With the increase in loading times, the cumulative strain and pore pressure can be roughly divided into three stages: rapid growth, slow growth, and equilibrium.
2. The influence of traffic load on the cumulative strain and pore-water pressure of subgrade soil decreases rapidly with the increase in depth. The cumulative strain and pore-water-pressure ratio at a depth of 2.5 m is only one-seventh that of 0.5 m subgrade soil.
3. The amplitude of traffic load significantly influences the strain and pore pressure of subgrade soil, especially for shallow subgrade. We should pay more attention to the shallow subgrade of the expressway with a large axle load.
4. As the distance from the subgrade surface increases, the maximum deformation appears at the edge of the subgrade.  
Conceptualization, H.Q. and X.Z.; methodology, H.Q. and J.L.; software, G.L.; validation, J.L.; formal analysis, Q.X.; investigation, X.Z.; resources, Q.X.; data curation, G.L.; writing--original draft preparation, L.C.; writing--review and editing, L.C. and T.Z.; visualization, X.Z. and G.L.; supervision, J.L. and T.Z.; project administration, Q.X.; funding acquisition, H.Q. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Science and Technology Plan of Shandong Transportation Department (No. 2019B48), the Youth Foundation of Shandong Natural Science Foundation of China (ZR2021 QE279), the Shandong Key Research and Development Plan (2019 JZZY010429) and the Shandong Key Research and Development Plan (2019 GSF111040).  
Not applicable.  
The data used to support the findings of this study are included within the article. Any additional data related to the paper may be requested from the corresponding author.  
The authors declare no conflict of interest.
## 3 Results
### Principal Component Analysis (PCA) of Photosynthetic Characteristic Parameters and Yield Components  
The PCA diagram reflects the relationship between each treatment and each index with PC1 and PC2 (Figure 12). The contribution rate of PC1 was 33.3%, and the contribution rate of PC2 was 23.0%, indicating that the two principal components covered more than 56.6% of the 11 indicators. Rd, LCP, Pmax, LSP, dry weight per plant (Dwpp), bell number per plant (Bpp), lint percentage (Lp), and yield were positively correlated with PC1, indicating that these indicators had a positive contribution to PC1. AQE, LER, and single bell weight (Bw) were negatively correlated with PC1, indicating a negative contribution to PC1. The load coefficient of the Pmax index in PC1 was the largest, and the contribution to PC1 was the largest, followed by LCP, while the Lp contribution was the smallest. The forward projection of Rd in PC2 was the largest, followed by AQE. Similarly, Rd had the greatest positive contribution to PC2 and the highest degree of synergistic change, while AQE ranked second. LER had almost no contribution to PC2 but had a great negative contribution to PC1. The confidence circles of CM3 were crossed with those of the other treatments, indicating that CM3 performed differently from the other treatments in 11 indicators.  
A value of 90\({}^{\circ}\) between Pmax, Dwpp, LSP, Bpp, yield, and Lp indicates a negative correlation. The 90\({}^{\circ}\) angle between Rd and LSP showed that there was no correlation between the two parameters. There was a positive correlation with LCP, AQE, Pmax, and Dwpp, but a negative correlation with yield, Bpp, Lp, LER, and Bw. The PCA diagram also reflected the relationship between each configuration treatment and each index and principal component. The CK treatment was far away from the other treatments, and the difference was large, and it was positively correlated with PC1 and negatively correlated with PC2. This suggests that there was an overall positive impact on PC1 and a negative impact on PC2. There was little difference among the CM1, CM2, and CM3 treatments. CM1 had a positive response to PC1 and PC2, and CM3 had a negative effect on PC1 and PC2. It can be seen that PC1 was greatly affected by the CK treatment, and PC2 was most affected by the CM1 treatment.  
Figure 11: Effects of different configuration treatments on cotton yield and yield components. * represents significant difference at the \(p>0.05\) level.
## 6 Engineering Verification
### Parameter Calculation for Roof Self-Stability Control  
Xinjulong Coal Mine requires waste filling of 1.5 million t/a. The backfilling working face is arranged by gob-side entry retaining, and the surface structures exist above the working face. The core parameters such as the support working resistance and backfill ratio are designed to realize roof self-stability, which satisfies protection level I of structures. Roof self-stability characteristic refers to bending deformation rather than broken collapse, and the roof has a certain bearing capacity. Roof self-stability conditions are proposed on basis of this: (1) The tensile strength of the roof is not greater than the allowable tensile strength; (2) the roof subsidence is not greater than the allowable subsidence.  
The strength and backfill ratio of the backfilling hydraulic support are designed to keep Xinjulong Coal Mine in line with the roof self-stability conditions. The specific process is as follows.  
The support strength is calculated to be 0.71-0.93 MPa according to the overburden failure caused by the mining disturbance and overlying strata characteristics in Xinjulong Coal Mine. It is suggested that the support strength is greater than 0.93 MPa by considering roof lithology and working face safety.  
The top of the backfilling working face is mainly villages where the horizontal deformation of surface structures is generally no more than 2 mm/m. The maximum horizontal deformation is set to 1 mm/m to increase the safety factor. The probability integration method is used to predict the mining subsidence of waste filling based on the equivalent mining-height model. After that, a reasonable backfill ratio is obtained through inversion. The specific formula is as follows.  
\[\left\{\begin{array}{l}\varepsilon=1.52\dfrac{W_{0}}{r}\\ W_{0}=H_{\mathrm{z}}q\cos\alpha\\ H_{\mathrm{z}}=(1-\eta)\,M\\ r=\dfrac{H}{\tan\beta}\\ \varepsilon\leq[\varepsilon]\end{array}\right. \tag{24}\]  
where \(\varepsilon\) is the maximum horizontal deformation; \(b\) the horizontal movement coefficient; \(W_{0}\) the maximum subsidence of the surface, m; \(r\) the main influencing radius, m; \(H_{\mathrm{z}}\) the equivalent mining height, m; \(q\) the subsidence coefficient; \(\alpha\) the coal-seam inclination; \(H\) the buried depth, m; \(tan\beta\) the main influencing tangent angle; \(M\) the mining height, m; \(\eta\) the backfill ratio.  
When the backfill ratio is greater than 84.6%, the roof can form a self-stable structure according to the geological and mining conditions of the Xinjulong Coal Mine.  
Figure 11: Layout of working face 1304N-1#.
## 4 Proposed Method  
### Pre-Processing of Data  
High resolution images can not be processed in a single pass by CNN because of their large size. As the average tile size of the Vaihingen ISPRS dataset is 2493 \(\times\) 2063, while the majority of CNNs are limited to a 256 \(\times\) 256 pixel entry. Given the limitations of the amount of GPU memory, we divide the high-resolution images by slicing them out of a sliding window. In the case where the pitch of the sliding window is smaller than its size, the predictions are averaged over the pixels subject to recovery. This makes it possible to refine the predictions along the edges of the window and to smooth any discontinuities that may appear. Figure 1 shows flowchart of our proposed method.
## 4 Discussion  
In this study, we deployed a tower-based optical system [55] to collect a continuous time series of canopy SiF over an evergreen canopy alongside measurements of PAM fluorescence, GPP from eddy covariance and environmental variables, NDVI and PRI, with the aim of describing both the diurnal and seasonal patterns of SiF in connection with ecosystem GPP dynamics. We aimed at exploring the following questions: (1) How does SiF change over the growing season in a boreal evergreen ecosystem  
Figure 7: Diurnal relationship in SiF (-), GPP (solid circles), PPFD (open circles) and SZA (-) on select clear days throughout the growing season. (**A**) April 28 th, (**B**) May 10 th, (**C**) June 5 th, (**D**) July 29 th, (**E**) August 27 th, and (**F**) September 5 th.  
experiencing modest changes in APAR? (2) Is the SiF-GPP relationship as good as previously reported for crops and deciduous forests? (3) Is SiF able to capture changes in canopy physiological dynamics (LUE)? To the best of our knowledge, this is the first long-term time-series of SiF in an evergreen canopy from field-based instruments.  
While a strong SiF-GPP relationship has been repeatedly observed at the ecosystem level using ground based instruments in crop and deciduous canopy types, [18; 23; 24; 25; 29; 40; 41; 42; 45; 47; 50; 64; 65; 66; 67], as well as at landscape/regional level from satellite SiF retrievals [26; 27; 31; 33; 34; 68; 69; 70; 71; 72; 73], the factors that drive these relationships remain controversial. Biome dependent differences in canopy structure have been suggested to affect the slope of the relationship [35; 64; 68; 74], but recent work at the satellite pixel scale is suggestive of a universal relationship [31]. Similarly, the relative role that ecosystem APAR and LUE dynamics exert on linking SiF to GPP is also expected to be biome dependent, but its characterization is still limited. Observations across crops and deciduous forests suggest that APAR dynamics could be the main factor connecting SiF to GPP [25; 75; 73]. This is logical, since annual GPP dynamics for these vegetation types are dominated by APAR, which in turn controls both the SIF emission and photosynthetic CO\({}_{2}\) assimilation. The question remains as to how well SIF and GPP are related for an evergreen ecosystem, where only modest changes in APAR occur.
# An Identification Method of Feature Interpretation for Melanoma Using Machine Learning
## 2 Methodology
### Feature Extraction
#### 2.2.3 Shape Feature  
The shape feature is a visual description of the lesion area and includes various metrics such as area and perimeter [30]. The area feature (A) quantifies the total number of pixels within the lesion area, while the perimeter feature (P) quantifies the total number of contour pixels on the boundary of the lesion area. Based on these two parameters, other shape descriptors, such as dispersion, saturation, and roundness, can be derived, as shown in (10)-(12) [31]. In the shape characterization process, dispersion, saturation, and roundness were chosen as descriptors to fully present the shape characteristics of the lesion area.  
Dispersion characterizes the extent of spread within a region in an image. It provides information about how the elements within the region are distributed. Dispersion is the ratio of the square of the perimeter of the lesion area to the area and describes the process of regional dispersion. The calculation for dispersion is as follows:  
\[\text{Dispersity}=\frac{\text{P}^{2}}{\text{A}} \tag{10}\]  
Saturation, which can also be referred to as convexity, pertains to the shape of the region's boundary in an image. It describes how closely the region resembles a convex shape. Saturation is the ratio of area to perimeter of the lesion region. The calculation for saturation (or convexity) is as follows:  
\[\text{Saturation}=\frac{\text{A}}{\text{P}} \tag{11}\]  
Circularity evaluates how closely the shape of a region resembles a circle, indicating the level of proximity to a circular form. It provides insights into the compactness of the region. Roundness describes the similarity of the shape of the lesion region to a circle. The calculation for circularity is as follows:  
\[\text{Roundness}=\frac{(4*\Pi*\text{A})}{\text{P}^{2}} \tag{12}\]
## 2 Research Methods for Reservoir Fluid PVT High-Pressure Physical Property Analysis
### Reservoir Fluid PVT High-Pressure Physical Property Parameters  
The high-pressure physical property parameters of reservoir fluid PVT are crucial aspects that need to be analyzed. These parameters play a vital role in reservoir evaluation, reserve calculation, and dynamic analysis. As for what these parameters are specifically, these are listed in Table 1, which presents the inventory of the high-pressure physical property parameters of reservoir fluid PVT.  
The parameters presented in Table 1 were acquired by means of PVT (pressure-volume-temperature) experiments, and they are extremely important for the development and management of oil and gas reservoirs. The high-pressure physical property parameters of reservoir fluid PVT hold a significant position in the development and management of oil and gas reservoirs. Bubble point pressure and saturation pressure are vital indicators when it comes to evaluating the phase behavior changes of reservoir fluids and have a direct connection with both the production efficiency and safety of oil and gas reservoirs. The volume factor indicates the relationship between the volume of the underground fluid and the volume under standard surface conditions, and it is indispensable for the calculation of reserves. Crude oil viscosity decides the flow resistance of the fluid within the pipeline, and has an impact on the production efficiency as well as the cost of the oil field. The dissolved gas-oil ratio discloses the quantity of dissolved natural gas in a unit volume of crude oil, and it is highly significant for assessing the saturation of the oil and gas reservoir. Compressibility and crude oil density respectively describe how sensitive the fluid volume is to pressure changes and what the mass of a unit volume of crude oil is. These have a direct influence on reservoir dynamic prediction and productivity evaluation.  
All in all, these parameters are obtained through PVT experiments and play a foundational role in the development decisions made regarding oil and gas reservoirs.
# Laser Cooling and Trapping of Rare-Earth-Doped Particles
## 4 Conclusions  
Optical refrigeration based on anti-Stokes fluorescence has dramatically improved over last several years. Early in the development of laser cooling of RE-doped low-phonon solids, \(mm\)- and \(cm-\) sized samples were the focus of attention. These bulk samples are interesting for the development of an all-optical solid-state cryocooler, which is free from mechanical vibrations, moving parts, and liquids. The first all-solid-state optical cryocooler started its operation in 2018 [46]. Laser cooling with anti-Stokes fluorescence in bulk samples can also be used for heat mitigation in high-power lasers [47].  
At the moment, optical refrigeration of solids has moved to the area of small \(\mu m\)- and \(nm\)-sized RE-doped low-phonon particles. A hybrid cooling mechanism resulting in a decrease in translational temperature and in internal temperature of the sample has been considered in this paper. The theoretical background of optical refrigeration based on anti-Stokes fluorescence has been presented and discussed. It is shown that Mie resonances can enhance laser cooling in a number of properly designed samples. It has been shown that hybrid trapping can be used for the localization and internal cooling of RE-doped nanocrystals. This approach can be applied to the fundamental studies in biophysics, chemistry, and medicine.  
It has been shown that hybrid laser cooling can be realized using a single-beam optical trapping. This approach can be useful for physiological laser refrigeration and for future levitated hybrid opto-mechanics experiments, where the control of translational  
Figure 14: Hybrid trapping and deep laser cooling of a Yb\({}^{3+}\):CsF\({}_{2}\) nanocrystal with the path function of its optical cooling.  
and internal temperatures is important. For example, using this approach and Yb\({}^{3+}\):YLF nanocrystals, one can build a levitated nanocryostat. The birefringence of the particles can be used to control their space orientation. Laser cooling of RE-doped nanocrystals can also be used to mitigate heat generated in spherical microlasers [48].  
Laser hybrid cooling of micro- and nanoparticles is in its infancy. I believe that further miniaturization of the RE-doped samples that undergo hybrid laser cooling is in sight. In very small samples (several \(nm\)), quantization of phonon modes is very important. As the size of the nanocrystal decreases, its phonon cut-off frequency increases. This effect can deteriorate thermalization in very small particles [27]. Engineering of the host material can be a solution to this problem.  
This research received no external funding.  
Not applicable.  
The author declares no conflict of interest.
# Bell-X, An Opportunistic Time Synchronization Mechanism for Scheduled Wireless Sensor Networks
## 2 Timing for TSCH and RPL  
In the WSN research field, timing is of great importance for accomplishing deterministic communications, since it is necessary to form and maintain a precise synchronization between the different nodes forming the network, in order to manage the generation of messages, so nodes on the receiving end know when to have their radio enabled to detect these messages. The following subsections show an overview of the TSCH and RPL protocols, describing what timing mechanisms are used to generate the necessary advertising and control messages for each protocol. An in-depth explanation of these protocols can be found in [6] and in their respective standard and RFC documents.
# Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery
## 3 Results
### Quantitative Quality Assessment by Region
For preserving the Moran I and GearyC indices (see Figure 4), the CN method was best, followed by the mIHS method. The GS method was worst. For preserving the CC values between the four bands, the mIHS method was best, followed by the CN method (see Figure 4). The PCA method was worst. In general, the change of CC values of shrub and grassland, and forest regions by all pansharpening methods was less than that of urban, and farmland regions. The foregoing quantitative assessment results were consistent with the results of the visual analysis; i.e., the mIHS method exhibited the best performance. However, the quantitative assessment captured statistical features that the visual analysis could not perceive because of a weak color difference among the pansharpened images. The results indicated that the mIHS method was the best for maintaining the spectral information of the pansharpened images, and the CN method was the best for maintaining the spatial information of the pansharpened images.