esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Establishment of Geological Hazard Risk Assessment Model
### Evaluation Model  
#### 3.3.1 Information Volume Model  
The information value method was originally proposed by [PERSON] [32] and has been widely applied in regional geological hazard assessment, landslide stability prediction, and other related studies. The information value model primarily utilizes the frequency or density of geological hazard occurrences to convert the influence of evaluation factors (\(x_{i}\)) on geological disaster events (\(\gamma\)) into information entropy. A higher information entropy indicates a greater influence of the factor. The model spatially overlays the information entropy of each factor to assess regional landslide hazard risk [33]. The information content \(I(x_{i},\gamma)\) provided by the \(i\)-th influencing factor (\(x_{i}\)) in relation to the occurrence of a geological hazard event (\(\gamma\)) can be expressed as follows:  
\[I(x_{i},Y)=ln\frac{N_{i}/N}{S_{i}/S} \tag{2}\]  
In the formula, \(x_{i}\) represents the \(i\)-th geological hazard influencing factor within the evaluation unit; \(I(x_{i},Y)\) represents the information content contributed by factor \(x_{i}\) to the geological hazard; \(S\) denotes the total number of grids in the evaluation unit; \(S_{i}\) indicates the number of grids containing factor \(x_{i}\) within the study area; \(N\) represents the total number of grids containing geological hazards in the study area; and \(N_{i}\) refers to the number of grids with geological hazards in a specific category associated with factor \(x_{i}\).  
According to the information, the content of a single indicator factor can be calculated for the whole evaluation unit within the study area. A larger information content value indicates a higher probability of geological hazard occurrence within the evaluation unit [34]. The expression for the total information content of the evaluation unit is as follows:  
\[I=\sum_{i=1}^{n}I(x_{i},Y)=\sum_{i=1}^{n}ln\frac{N_{i}/N}{S_{i}/S} \tag{3}\]  
In the formula, \(I\) represents the total information content provided by all factors within the evaluation unit, reflecting the degree of susceptibility to geological hazards; \(n\) represents the number of influencing factors.
# Analysis of the Failure Area of the Slope Using the Slip Line Method
## 3 Analysis of Failure Area
### Analysis of the Failure Area of a Single Ground Slope  
Figures 10-13 show the failure area using the slip line method and the strength reduction method according to the analysis conditions of a single ground. Table 2 shows the failure area scale and internal stress analysis results of a single ground.  
Figure 10: Failure area analysis result of slip line method and finite element limit analysis (alluvium): **(a)** failure area (slope angle \(10^{\circ}\)); **(b)** failure area (slope angle \(20^{\circ}\)); **(c)** failure area (slope angle \(30^{\circ}\)); **(d)** plastic multiplier (slope angle \(10^{\circ}\)); **(e)** plastic multiplier (slope angle \(20^{\circ}\)); **(f)** plastic multiplier (slope angle \(30^{\circ}\)).  
Figure 9: Ground diagram applied to heterogeneous ground analysis: **(a)** Case 1; **(b)** Case 2; **(c)** Case 3; **(d)** Case 4; **(e)** Case 5; **(f)** Case 6.  
Figure 11: Failure area analysis result of slip line method and finite element limit analysis (sedimentary soil): (**a**) failure area (slope angle \(10^{\circ}\)); (**b**) failure area (slope angle \(20^{\circ}\)); (**c**) failure area (slope angle \(30^{\circ}\)); (**d**) plastic multiplier (slope angle \(10^{\circ}\)); (**e**) plastic multiplier (slope angle \(20^{\circ}\)); (**f**) plastic multiplier (slope angle \(30^{\circ}\)).  
Figure 12: Failure area analysis result of slip line method and finite element limit analysis (weathered rock): (**a**) failure area (slope angle \(10^{\circ}\)); (**b**) failure area (slope angle \(20^{\circ}\)); (**c**) failure area (slope angle \(30^{\circ}\)); (**d**) plastic multiplier (slope angle \(10^{\circ}\)); (**e**) plastic multiplier (slope angle \(20^{\circ}\)); (**f**) plastic multiplier (slope angle \(30^{\circ}\)).  
As a result of the slip line method, the depth of the failure area increases as the slope increases (see Figures 10-13, Table 2). The maximum internal stress appears at the point where the virtual failure surface begins (virtual singularity), decreases as the slope increases, and increases as the ground strength increases (see Table 2).  
As a result of the finite element limit analysis, the plastic area was analyzed as larger than the slip line method in all analysis conditions, and there was no significant difference in the scale of the plastic area according to the ground properties. It can be seen that the plastic area is formed at the start and end points of the slope, which are the stress singularity points.
# Instance and Data Generation for the Offline Nanosatellite Task Scheduling Problem
## 2 Problem Formulation
### Power Generation and Management
#### 2.2.1 Orbit Model
Using Equation (8) [16], we can test whether the nanosatellite is in the shadow (\(\xi=0\)) or not (\(\xi=1\)) based on its position and the sun's location:  
\[\xi=\left\{ \begin{array}{ll}0,&\cos^{-1}\left(\frac{R_{E}}{|\mathbf{r}_{X}| }\right)+\frac{\pi}{2}\leq\cos^{-1}\left(\frac{\mathbf{r}_{X}\mathbf{r}_{ \text{sun}}}{|\mathbf{r}_{X}||\mathbf{r}_{\text{sun}}|}\right)\\ 1,&\text{otherwise}\end{array} \right. \tag{8}\]  
where \(R_{E}\) is a constant value for the radius of the earth (6378 km). The estimation of the sun's position only requires information about the date (day, month, and year) [16], as will be shown later.
## 3 Data Interoperability Provisioning with Web of Objects  
### Web of Objects Model  
The Web-of-Objects (WoO) reference model [24] supports the development of interoperable applications using data-driven features in combination with knowledge-driven approaches. Web-of-Objects provides the mechanism for semantic virtualization of data and resources. It provides a unified model of digital representation of heterogeneous information generated from diverse data sources including sensing, actuating, and other information sources. Incorporation of WoO features in heterogeneous healthcare applications could foster data interoperability provision.  
The Web-of-Objects is highly significant for the development of interoperable healthcare applications. Its importance can be realized through the layered reference model (WoO Specification [ITU-T Y.4458] [24]) for the management and processing of data. Its virtual level features Virtual Objects (VOs) which are necessary to provide a semantic representation of heterogeneous healthcare data. Composite virtual level provides enhanced congolomeration and orchestration of VOs to reflect logical constraints on data with respect to healthcare domain. To develop interoperable service capabilities over the healthcare data model, it supports features for distributed applications with semantic data management.
## 4 Discussion
### Caveats and Implications  
Considering the computational capacity of the model and the long assimilation period (36 years), we use the coarse model resolution (\(8^{\circ}\times 10^{\circ}\)) to perform the assimilation experiment. We were able to show and quantify the additional value of the ESA-CCI soil moisture observation as a significant constraint on the terrestrial carbon cycle and could capture the response of GPP to drought at this coarse resolution. Previous studies showed that drought has cumulative effects [100; 102; 105] and lagged effects on vegetation [99; 100; 106; 107]. SPEI at different time scales calculates the difference between precipitation and PET during the current and previous months [99]. SPEI at different time scales could therefore measure the cumulative effect of drought on vegetation. However, the SPEI with a time scale of 9 months in this study causes difficulty in identifying the lagged effect of droughts on GPP [104]. In addition, it would take a high resolution to accurately capture the lagged effects of drought on GPP [99; 104; 106], so the coarse spatial resolution in this study makes the analysis of the legacy effects of drought on GPP more challenging. In the future research, the resolution of the assimilation system needs to improve to investigate the lagged effect of specific drought events on GPP at a higher spatial resolution. It is expected that the assimilation of satellite-derived soil moisture observation data would be a promising approach to analyze the cumulative and lagged effects of drought on GPP on finer scales.  
It is necessary to further develop the terrestrial biosphere model. For instance, it may be crucial to include more precise processes relating to soil water and heat transfer in soil profiles to the model to improve the model's response to extremes such as drought. Another option for future research is to continually (i.e., simultaneously assimilation) add new datasets to the model assimilation in order to constrain the processes of the ecosystem model, which are now largely unconstrained. Potential candidate observations include VOD (Vegetation Optical Depth) to provide a constraint on the total amount of vegetation water content and COS (Carbonyl Sulfide) to include a more direct constraint on photosynthesis.
## 3 Results and Discussion
### Tensile Test  
The mechanical features of all the specimens were examined with tensile testing. Figure 4 shows the stress/strain curves of all the polymer-filler composites. These curves were found to be unique for each sample and were established by recording the extension or deformation (strain) at different intervals of standard force (tensile) loading.  
The detailed tensile properties of the TPU composites at different compositions are represented in Table 2. The tensile strength and Young's Modulus of the pure TPU were 4.234 MPa and 147.348 MPa, which increased to 6.105 MPa and 161.811 MPa, respectively, with the addition of 3% Benzofllex (TPU-2). This plasticizer has nothing to do with flame retardance, but it greatly improves the mechanical strength. The next nanocomposite sample (TPU-3) containing 3% antimony trioxide also showed improved tensile strength as compared to the pure TPU (TPU-1). A low quantity of inorganic filler (such as antimony trioxide) improves the strength of composites because of its strong mechanical features [27, 28]. However, this improvement was less than that of the TPU-2 because this sample lacked the plasticizer, the main cause of the improvement in the mechanical strength of the polymer. With the addition of both the plasticizer and the primary flame retardant Sb\({}_{2}\)O\({}_{3}\) (3% of both), the mechanical strength further improved. The Young's Modulus of the sample TPU-5 with the previous nanofillers plus 0.2% of the secondary flame retardant organo-clay o-MMT was exceptionally improved to 244.568 MPa. This improvement was credited to the polymer-clay tethering and hydrogen bonding among the composite [29]. However, the mechanical strength of the sample of interest (TPU-6) with 0.5% o-MMT decreased slightly. With the increase in nanofillers, agglomeration occurred resulting in poor compatibility between the nanofillers and the TPU molecular chains [3, 30]. However, based on the mechanical properties, it can be concluded that the mechanical strength of the sample of interest (TPU-6) was high enough to meet the needs of the commercial standards.  
Figure 2: FTIR spectra of the (**a**) organo-modified montmorillonite clay (O-MMT) and (**b**) the TPU -fillers nanocomposites: (1) TPU-1 and (2) TPU-6.  
Figure 4: Stress/strain curves of the TPU Nanocomposites (a) TPU-1, (b) TPU-2, (c) TPU-3, (d) TPU-4, (e) TPU-5, and (f) TPU-6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Young’s Modulus** & **Tensile Strength** & **Stress at Break** & **Strain at Break** \\  & **(MPa)** & **(MPa)** & **(MPa)** & **(\%)** \\ \hline TPU-1 & 147.348 & 4.234 & 422.04 & 4.40 \\ TPU-2 & 161.811 & 6.105 & 612.56 & 6.99 \\ TPU-3 & 147.641 & 5.194 & 517.56 & 6.24 \\ TPU-4 & 195.053 & 6.848 & 647.86 & 8.12 \\ TPU-5 & 244.568 & 6.488 & 684.72 & 8.35 \\ TPU-6 & 180.989 & 6.408 & 638.52 & 7.86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical features of the TPU-filler composites.  
Figure 3: SEM images of (**a**) pure TPU, (**b**) TPU 3 (50\(\mu\)m), (**c**) TPU3 (100 \(\mu\)m), (**d**) TPU-6 (50 \(\mu\)m), and (**e**) TPU-6 (100 \(\mu\)m).
## 6 Conclusions  
In order to achieve straight-line path tracking control with high robustness for an unmanned tractor-trailer, a fuzzy sliding mode controller was developed in this study. The FSMC was created using the power-reaching law, which solved the chattering issue of the traditional SMC. The Lyapunov stability theory was used to analyze the system's stability. Combining with the fuzzy controller, the controller parameters were adjusted, which improved the online speed and the steady-state tracking accuracy of the vehicle path tracking. A real experimental platform was used to validate the effectiveness of the suggested control approach. According to experimental results, the suggested control technique may increase the trailer's steady-state tracking accuracy by about 10.5-36.8% and the tractor's steady-state tracking accuracy by about 11.1-50% when compared to the traditional SMC.  
Conceptualization, [PERSON] and X.J.; methodology, [PERSON] and X.J.; software, [PERSON] and Y.W.; validation, X.W.; formal analysis, [PERSON] and X.J.; investigation, [PERSON] and X.J.; resources, X.W.; data curation, [PERSON] and X.J.; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON] and Y.W.; supervision, [PERSON]; project administration, [PERSON] All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Controller**} & \multicolumn{3}{c}{**Trailer’s Lateral Error (m)**} \\ \cline{2-4}  & **Max** & **Min** & **Ave** \\ \hline \multirow{3}{*}{SMC} & 0.75 & \(-\)0.46 & 0.14 \\ \cline{2-4}  & 0.44 & \(-\)1.51 & 0.19 \\ \cline{2-4}  & 0.75 & \(-\)0.45 & 0.19 \\ \hline \multirow{3}{*}{FSMC} & 0.35 & \(-\)0.23 & 0.09 \\ \cline{2-4}  & 0.45 & \(-\)1.35 & 0.12 \\ \cline{1-1} \cline{2-4}  & 0.6 & \(-\)0.29 & 0.17 \\ \hline \end{tabular}
\end{table}
Table 4: Statistics of trailer path tracking error after going online.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Controller**} & \multicolumn{3}{c}{**Tractor’s Lateral Error (m)**} \\ \cline{2-4}  & **Max** & **Min** & **Ave** \\ \hline \multirow{3}{*}{SMC} & 0.73 & \(-\)0.35 & 0.08 \\ \cline{2-4}  & 0.38 & \(-\)1.48 & 0.18 \\ \cline{2-4}  & 0.74 & \(-\)0.9 & 0.18 \\ \hline \multirow{3}{*}{FSMC} & 0.39 & \(-\)0.17 & 0.04 \\ \cline{2-4}  & 0.42 & \(-\)0.6 & 0.11 \\ \cline{2-4}  & 0.53 & \(-\)0.29 & 0.16 \\ \hline \end{tabular}
\end{table}
Table 3: Statistics of tractor path tracking error after going online.  
**Funding:** This research was funded by the National Key Research and Development Program (grant Nos. 2019 YFB1312302), the Jiangsu Agricultural Science and Technology Innovation Fund (grant Nos. CX(22)2040), and the Key Research and Development Program of Jiangsu Province (grant Nos. BE2021313).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All research data supporting this study are included in the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# RNase L Induces Expression of A Novel Serine/Threonine Protein Kinase, DRAK1, to Promote Apoptosis
## 2 Results
### DRAK1 Localizes to The Nucleus and Induces Apoptosis  
Previous studies demonstrated that DRAK1 is localized in the nucleus and induces apoptosis when overexpressed [19]. Our results show that DRAK1 is transcriptionally induced by RNase L activation and IFN signaling. We next investigated if overexpression of DRAK1 could induce apoptosis in the absence of RNase L activation. PC3 cells transfected with Green Fluorescent Protein (GFP)-DRAK1 or GFP alone as control were visualized microscopically at various time points. At 16 h post transfection, GFP-DRAK1 localized to the nucleus compared to diffuse cytosolic staining of GFP expressing cells and 24 h later GFP-DRAK1 expressing cells displayed morphological changes like membrane blebbing characteristic of apoptotic cells (Figure 2A). To further confirm that DRAK1 overexpressing cells show increased apoptosis, cell viability was determined in PC3 cells at various time points after DRAK1 transfection. Cell viability decreased over time and only 23% cells remained viable 36 h post transfection (Figure 2B). Expression of DRAK1 resulted in increase in apoptotic cells with sub-G1 DNA content as determined by analyzing DNA content with fluorescence-activated cell sorting (FACS) and trypan blue exclusion (Figure 2C,D). To determine the physiological levels of DRAK1 that induces apoptosis, PC3 cells were transfected with increasing amounts of DRAK1 plasmid and mRNA levels were compared to 2-5A transfected cells (Supplementary Figure S1A). Apoptosis induced was determined by trypan blue exclusion assay and compared to levels of DRAK1 (Supplementary Figure S1B). To further demonstrate that DRAK1 is involved in RNase L-induced apoptosis, DRAK1 levels were reduced in PC3 cells transfected with DRAK1 short interfering RNA (siRNA) and compared to scrambled control siRNA. siRNA targeting DRAK1 reduced protein levels as no induction was observed following Poly I:C transfection (Figure 2E). To rule out the contribution of DRAK2, siRNA DRAK2 was transfected and cell viability was compared after 2-5A transfection. Cell viability increased 15% when DRAK1 levels were knocked down whereas decreasing DRAK2 levels did not affect cell viability after RNase L activation by 2-5A (Figure 2E-H). Thus, increase in DRAK1 levels is sufficient to induce apoptosis and suggests that DRAK1 induction by RNase L can be an important component of RNase L-induced apoptosis in cells.
# Role of Protein Glycoslation in Host-Pathogen Interaction
## 3 Glycosylated Proteins of Hosts Act as Barriers to Defense Pathogens
### Physical Barrier  
In animals, the physical contact of the pathogen to epithelial cells can be prevented by a rigid or visco-elastic gel formed by secretory oligomerized mucins [80]. Heavily \(O\)-glycosylated mucins forming a dense mucus layer can prevent the attachment of parasitic nematodes (e.g., _Nippostrongylus brasiliensis_) [81]. In contrast, after the mucus layer was removed by mustard oil, cysteine protease or papain, the intestinal colonization of _N. brasiliensis_ was increased [80; 82]. Likewise, the study of _Trichuris muris_ infection in mice suggests that expression of the mucins was exclusively induced in _T. muris_resistant mice. These mucins promoted _T. muris_ to be expelled from intestinal tracts, and accordingly, a knock-out of the mucin gene abolished protective functions in the host [15]. A similar protective phenomenon was also observed during the infection of _Trichostropsylus calubriformis_, _Trichiella spiralis_, _Helicobacter pylori_ and _Hymenoptera diminishta_[16; 83; 84; 85]. In addition, cell surface mucins like MUC1 contribute to the formation of the apical cell glycocalyx in wet-surfaced epithelia, inhibiting the infection of _Corynebacteria_ and coagulase-negative _Staphylococci_[86]. It's worthy to note that mucins, especially secretory mucins also involve in promoting pathogen infection via adhesions on the pathogen, as discussed in Section 7 (lectin, a class of protein entangled with glycoprotein, affects host-pathogen interactions).  
In plants, hydroxyproline-rich glycoproteins (HRGPs) which are characterized by a rigid polyproline type II (PPII) conformation and extensive \(O\)-glycosylation of 4 (R)-hydroxy-l-proline residues [87], can physically inhibit pathogen penetration [88]. HRGPs play a leading role in plant defense, e.g., HRGPs were induced in incompatible plant-pathogen interactions [89; 18] and rapidly deposited in the cell wall after pathogen infection [90; 91]. Such a defense ability is a result of the intra- and inter-molecular cross-linking in plant cell walls after HRGPs become insolubilized [88; 92]. In this process, the interaction between insoluble HRGPs and the polysaccharide reduces the hydration and pectin mobility of the cell wall thus leading to the increase of rigidity and resistance of cell wall against pathogen invasion [92]. Furthermore, it was found that intra- and inter-molecular cross-linking took place in glycosylated HRGPs under the catalysis of the peroxidase. However, the deglycosylated HRGPs could not be cross-linked [18], suggesting that glycosylation contributes to the HRGPs network formation.
# Computer-Vision-Based Product Quality Inspection and Novel Counting System
## 3 Methods and Result
### Product Counting Algorithm Using Camera-Based Skeleton Tracking and Body Part Detection  
#### 3.2.1 Classification of Counting Classes Based on Body Part Detection  
In this study, we developed a product counting system that applies the YOLOv8 Pose algorithm to detect the human body and correct errors that occur when a person steps onto the load cell [44; 45]. The core of the research lies in using a camera and algorithm to detect human body parts in real time, distinguishing factors that affect weight data measured by the load cell and reducing counting errors. Body parts that influence load cell weight measurements were categorized into four classes, and the load cell's weight measurement actions were controlled according to each class. As shown in Figure 7, the control images illustrate the situations of full upper body detection, partial upper body detection, lower body detection, and no detection.  
* Full Upper Body Detection: When the full upper body is detected within the load cell area, weight measurement is temporarily paused. This is because the structure, in which the camera views the load cell from above, may result in the lower body or other body parts not being detected. When the full upper body is detected, weight measurement is paused to eliminate the influence of the body on the load cell, and the measurement resumes once the upper body moves away from the load cell.
* Partial Upper Body Detection: In partial upper body detection, only a part of the upper body is detected within the load cell area. During this time, the load cell continuously reads weight data in real time, and only when the weight change meets the stabilization value is it considered valid. Weight changes less than 0.5 kg are regarded as insignificant fluctuations and are not included in the count. Therefore, when partial upper body detection occurs, the load cell measurement continues, but small weight changes are ignored, and only meaningful changes are reflected in the count.  
* Lower Body Detection: When the lower body is detected within the load cell area, weight measurement is paused. The weight of the lower body directly affects the load cell, so weight changes are not measured while the lower body is detected. Measurement resumes when the lower body moves away from the load cell area.
* No Detection: If the camera does not detect any part of the body over the load cell, the load cell continuously measures weight changes in real time and counts the product based on the weight variations. In the no detection state, the load cell counting process proceeds normally, and the measured weight changes are used to calculate the product count.