esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Data
#### 2.2.1 Weibo Dataset  
Selecting \"Wuhan University\" as a keyword, we searched photos on Weibo related to the hashtag of Wuhan University on 23 March 2019. Using automated API requests with Python, the data were downloaded from the Weibo server. Both the actual photo and the meta-data (geo-tag, upload date and time, user name, and the number of likes, comments, and forwards) were downloaded. Only landscape photographs were preserved through manual recognition, while photographs of people, other buildings, or vehicles were excluded. Based on the text content embedded in the data, photographs were classified into 6 groups, which represented 6 scenic spots. Sorted by the number of likes, we chose the top 100 photographs of each spot as a dataset. The Weibo dataset contained 600 photos in total, which were used as research materials. According to the Sina Weibo certification list of the dataset, there were 385 personal users and 215 official accounts, which can show public preferences for the core landscape area. The ratio of male users to female users was about 1.5. Considering sex has no significant influence on landscape preference in prior studies, this paper did not explore the gender differences on landscape evaluation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of the Spot** & **Search Volume** & **The Number of Likes** \\ \hline Wuhan University Library & 17,841 & 105,400 \\ The Old Dormitory & 21,521 & 125,000 \\ Neo-confucianism Building & 7581 & 37,500 \\ Yifu Building & 6576 & 27,500 \\ College of Engineering & 16,034 & 97,580 \\ Songqing Gymnasium & 8156 & 69,500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The search volume and the number of likes of each scenic spot on Weibo within one day.
# Torsion Test vs. Other Methods to Obtain the Shear Strength of Elastic-Plastic Adhesives
## 2 Materials and Methods
### Tth--Torsion Hourglass  
Torsion of a circular section creates a state of pure shear stress, as stated previously, with its peak value in the periphery. Once the maximum shear stress was achieved, the yield limit \(\tau_{el}\) and the applied torsional moment was increased and a plastic annular zone was formed in the outer part of the cross section from radius \(R^{*}\) to radius \(R_{e}\), whilst a circular zone (or annular, if the section was hollow with inner radius \(R_{i}\)) in the central part of the cross section remained elastic. Figure 4 depicts the situation.  
The total torsional moment applied in such a condition could be described as the sum of two contributions: an elastic term \(M_{d}\) corresponding to the elastic shear stress distribution in the inner part of the cross section, and a plastic term \(M_{pl}\) corresponding to the constant shear stress in the outer part of the cross section:  
\[M_{tot}=M_{el}+M_{pl}=\tau_{el}\frac{\pi\big{(}R^{*4}-R_{i}^{4}\big{)}}{2R^{*} }+\tau_{el}\frac{2\pi}{3}\Big{(}R_{e}^{3}-R^{*3}\Big{)} \tag{2}\]  
Figure 3: Fixtures to avoid bending and rotation of the adherends in the SLO test.  
From Equation (2) it could be easily noticed that when \(R^{*}=R_{e}\) plasticity occurred at the outer edge and yielding started, whilst when \(R^{*}=R_{i}\) the ultimate value of the torsional moment was reached, and the section was completely yielded.  
The twist gradient \(\theta^{\prime}\) is governed by the elastic zone only, according to the known equation:  
\[\theta^{\prime}=\frac{M_{el}}{GJ^{*}}=\frac{\tau_{el}}{GR^{*}} \tag{3}\]  
In the case of a non-hollow section, \(R_{i}=0\), the ratio of the first yield moment \(M_{y}\) to the ultimate moment \(M_{max}\) read  
\[\frac{M_{y}}{M_{max}}=\frac{\tau_{el}\pi R_{e}^{4}/2R_{e}}{\tau_{el}2\pi R_{e} ^{3}/3}=\frac{3}{4} \tag{4}\]  
which corresponded to the classical relationship by [PERSON] (1931) recalled in [2].
## 4 The Proposed Algorithm  
### Markov Decision Process Modeling  
The optimization problem is formulated as a Markov Decision Process (MDP), which comprises three core components: states, actions, and rewards. In the multi-UAV MEC system, at each time slot \(t\), the UAVs interact with the environment, execute actions \(A_{t}\) based on the current state \(S_{t}\), receive rewards \(R_{t}\), and transition to the next state \(S_{t}^{\prime}\). Below, we detail the design of the state space, action space, and reward function for the multi-UAV MEC system.
# Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction
## 4 Results and Discussions
### Accuracy for Determined Principal Routes  
Various routes over the top, around the sides, or reflected are tested to find the accuracy of algorithm.
# A Review: Sample Preparation and Chromatographic Technologies for Detection of Aflatoxins in Foods
## 3 Chromatography
### Thin Layer Chromatography
With the advent of commercial UHPLC, there are some applications of UHPLC-FLD for aflatoxin analysis. Due to the use of columns packed with submicron (<2.0 \(\upmu\)m) particles, rapid and efficient separations could be achieved. The speed and high resolution of UHPLC made it an attractive tool for analyzing mycotoxins in foods [233; 234]. [PERSON] et al. [235] developed an UHPLC-FLD method to simultaneously determine aflat, OTA and zearalenone in barley. Photochemical derivatization was performed to enhance signal intensity of aflat without impacting ochratoxin A and zearalenone. Iodination or bromination, however, is not suited for multimycotoxin analyses as ochratoxin A and zearalenone are unable to survive the derivatization process. The author also mentioned that the impact of the void volume (0.25 mL) introduced by the photochemical coil on chromatography should be monitored, though it is not an issue for conventional columns with bigger particle sizes. [PERSON] et al. [236] used UHPLC-FLD to screen aflat in food samples collected from local markets. Without any pre- or post-column derivatization, the method could detect individual aflat as low as 0.02 ppb. An isocratic program was used to separate aflat on a C18 column (2.1 mm \(\times\) 100 mm, 1.7 \(\upmu\)m). Their results suggest there were significant retention time shifts of aflat in solvent and sample matrices. Interestingly, the method seems more sensitive for aflatoxin B1 and B2 than aflatoxin G1 and G2. [PERSON] and his colleagues [41; 237; 238] extended UHPLC-FLD-based aflatoxin analysis to both food and feed matrices. Using a FLD flowcell (13 \(\upmu\)L) and a large injection volume (10 \(\upmu\)L), these methods achieve the detection of aflat below 1 ppb even in feed matrices without performing conventional derivatization. Due to its sensitivity capabilities and ease of use, these newly developed LC-FLD methods continue to attract mycotoxin labs to adopt them for routine sample analysis.
## 3 Results and Discussion
### Free Radical Scavenging Ability as Estimated by DPPH and ABTS
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{8}{c}{**Flavonoid Standards**} \\ \cline{2-9}  & **RT** & **PHR** & **MC** & **QE** & **NG** & **PHT** & **AP** & **GLG** \\ \hline S\({}_{\text{A}}\) & ND & ND & ND & ND & 25.45 \(\pm\) 1.05b & ND & 0.96 \(\pm\) 0.03b & 5.83 \(\pm\) 1.09b \\ S\({}_{\text{B}}\) & ND & ND & ND & ND & 30.66 \(\pm\) 2.96a & ND & 1.31 \(\pm\) 0.08a & 8.69 \(\pm\) 1.72a \\ X\({}_{\text{A}}\) & ND & ND & ND & ND & ND & ND & ND & 2.91 \(\pm\) 0.05d \\ X\({}_{\text{B}}\) & ND & ND & ND & ND & ND & ND & ND & 3.58 \(\pm\) 0.25c \\ \hline RP & 4.4 & 6.77 & 7.57 & 10.5 & 12.25 & 13.18 & 14.63 & 22.0 \\ \hline \hline \end{tabular}  
* Letters in each column indicate statistically significant differences; S\({}_{\text{A}}\): unsterilized dragon’s blood rhizome extract; S\({}_{\text{B}}\): autoclave-sterilized dragon’s blood rhizome extract; X\({}_{\text{A}}\): unsterilized xoconostle fruit extract; X\({}_{\text{B}}\): autoclave-sterilized xoconostle fruit extract; RP: retention period; RT: nutin; PHR: phloriidin; MC: myrietin; QE: quercetin; NG: naringren; PHT: phlorietin; AP: apigenin; GLG: galangin; ND: Not detected.  
\end{table} 
Table 5: Flavonoid standard content (ug/mL) in dragon’s blood rhizome and xoconostle fruit extracts, as detected by HPLC. The difference between the results observed by the ABTS method with respect to DPPH could be due to the affinity of both techniques; quantification by the ABTS method tends towards hydrophilic and lipophilic compounds, while the DPPH method tends towards slightly polar compounds [52]. With regard to the antioxidant activity by the ABTS method on xoconostle extracts, there was a decrease of 18.3% in X\({}_{\mathrm{B}}\) with respect to X\({}_{\mathrm{A}}\) (Table 3). The decrease in antioxidant activity in the autoclaved extracts could be attributed to the loss of thermolabile compounds such as betalain and ascorbic acid, which are present in xoconostle fruit extracts [7]. The behaviors of compounds from extracts with complex compositions cannot be predicted when they are affected by severe thermal treatments, given that the antioxidant properties can decrease or increase, due to factors such as exposure time to the thermal treatment, the temperature, the pH of the medium, the water activity, or the composition of the extract and its interactions, when its structure is modified [40].
## 2 Determining Vehicle Position
### Position from Wheels and IMU  
The current relative position of the mobile robot in two-dimensional real space was determined by a vector of two coordinates \(\mathbf{p}_{w}=[x,y]\). Those coordinates were determined according to Equation (1)  
\[x=\sin\theta\int v_{x}dt+x_{0},\quad y=\cos\theta\int v_{y}dt+y_{0}, \tag{1}\]  
where: \(\theta\)--current vehicle yaw angle relative to the axis \(Y\), \(v_{x},v_{y}\)--vehicle speed, \(x_{0},y_{0}\)--initial vehicle position. In order to determine the current relative position of the mobile robot, information about the current speed and angle relative to the \(Y\)-axis (azimuth) in some coordinate system is necessary. For this purpose, each of the robot wheels is equipped with a DC motor with an incremental encoder and also for each wheel there a PID controller algorithm was implemented to control the velocity. The control system, at each calculation step, maps the current linear vehicle speed from known angular velocities of individual wheels and geometrical dimensions. At the same time, azimuth is read from an inertial navigation system equipped with an accelerometer, gyroscope and magnetometer.
## 3 Model Development
### Ad Doc Data Process
For instance, when traffic constraints outside the boundaries of the LP simulation result in a spillback of congestion into the region being modeled this spillback will not be reflected in the model. As they have no knowledge of downstream traffic condition, vehicles on the LP simulation may exit the link at free flow speed, providing inaccurate traffic estimates. As discussed later, to address this situation, outbound link speed is controlled to meter the outflow rate from the LP simulation model Thus, upstream internal link behavior will reflect the spillback due to a bottleneck outside the modeled area however the outbound link itself is being artificially manipulated to capture this impact, resulting in its data not being suitable for the global aggregation. More details are discussed in later section on how to represent these intermitted capacity bottlenecks on outbound links and the link speed selection process to meter vehicles. The server keeps track of all available estimates. \(G_{j,k,l}^{m}\) represents global state on link j at simulation time k at wall-clock time l with data type m. Two attributes of the ad hoc distributed system estimate are considered (1) length of prediction horizon, i.e., how far in advance of the current wall-clock time the system provides estimates and (2) how accurate the estimates are at specific prediction horizon, i.e., how accurate is the estimate. For example, the following analysis would be available. Suppose at 7:00 AM wall-clock time the system was able to predict until 7:30 AM simulation time and its estimates regarding 10 min period between 7:20 AM simulation time and 7:30 AM simulation time over-estimated by 15%. However, at 7:10 AM wall-clock time with more updated information the system was able to provide the same time period estimates (7:20 AM-7:30 AM simulation time) with better accuracy (5% difference). Lastly, global-to-local transition function \(\left\{f^{-1}(\cdot)\right\}\) is called when any _LPs_ need to roll back in order to revise its estimates with updated information. This function \(\left\{f^{-1}(\cdot)\right\}\) converts the global object instances to local state, i.e.,  
\[L_{i,j,k}^{m,p}=f^{-1}(G_{j,k}^{m}) \tag{4}\]  
Whenever this function is called, \(G_{j,k,l}^{m}\), aggregated value from the estimates of the _LPs_ on link \(j\) at simulation time \(k\), is converted to the local instance for \(L_{i}\). Then, \(L_{i,j,k}^{m,p}\) is used as new input to revise its estimates.
# Research into Image Point Positioning Compensation of a High-Orbit Thermal Infrared Staring Camera
## 3 Experimental Results and Analysis
### Internal and External Parameter Error Compensation Experiment
The difference between the two error compensation schemes, namely, the internal first and then the external, and the external first and then the internal, is not significant. This shows that after multiple iterations, the internal and external parameters tend to be stable, but the scheme in which the internal compensation is carried out first, followed by the external, is still relatively better than the one in which the external takes place before the internal. This is due to the different methods of solving the compensation error, and it can be concluded that it is better to finally conduct the external parameter compensation scheme. In addition, the error compensation effect of the external scheme alone is much better than that of the internal scheme alone, and this indicates that there is still a large external parameter error after the internal parameter compensation, and the positioning error does not decrease but increases. This is because when the internal and external errors exist, there is a correlation, which will offset some of the errors. After the internal parameter compensation, most of the positioning errors caused by distortion are eliminated. On the other hand, the error caused by the external parameters is more obvious, so the external parameters need to be compensated. However, after the external parameter compensation, it can be seen that the external parameter compensation will absorb some of the internal parameter errors; that is, there will be partial substitution between the internal and external parameters.
# Cyber-Security Threats and Side-Channel Attacks for Digital Agriculture
## 5 Research Challenges and Future Directions
### Vulnerability and Threat Analysis  
DigAg devices and IT requirements are different for various applications. Hardware and software from multiple vendors are integrated into one particular solution, which increases the attack surface. Before integrating the devices, a thorough vulnerability and threat analysis should be performed, including the side-channel attacks, which are difficult to analyse and typically not covered in the cyber-security frameworks. Each hardware system should be analysed in the context of its use and threats, whether physical, hardware, or software-related.
# Cubic \(q\)-Bezier Triangular Patch for Scattered Data Interpolation and Its Algorithm
## 4 Results and Discussion  
In this study, we implemented the proposed scheme using MATLAB 2023a to conduct the experiment. The MATLAB coding was developed based on Algorithm 1, and the processor used was 1.6 GHz Dual-Core Intel Core i5 (Intel, Santa Clara, CA, USA). The error norms were computed using a \(33\times 33\) uniform rectangular grid of evaluation points in the unit square. The error measurements used were the (a) coefficient of determination, \(R^{2}\); (b) root mean square error (RMSE); and (c) central processing unit (CPU) time.  
```
1:Input: Scattered data points \(\{(x_{i},y_{i},z_{i})\}_{i=1}^{n}\)
2:Output: Interpolated surface, \(R^{2}\), RMSE, and CPU time
3:
4:Step 1: Estimate Partial Derivatives
5:
6:
7: Compute the partial derivatives \(\frac{\partial f}{\partial x_{1}}\), \(\frac{\partial f}{\partial x_{2}}\) and \(\frac{\partial f}{\partial x_{3}}\) at each data point using Equation (8).
8:MATLAB functions: diff, gradient
9:
10:Step 2: Perform Delaunay Triangulation
11:
12:
13: Use a Delaunay triangulation algorithm to create a mesh from the scattered data points.
14:MATLAB functions: delaunay, triplot
15:
16:Step 3: Calculate Boundary Control Points
17:
18: Determine the boundary control points for each triangle in the triangulated mesh.
19:MATLAB functions: convhull, boundary
20:
21:
22:
23:Step 4: Calculate Inner Control Points
24:
25:
26:
27:
28:
29:
30:
```  
**Algorithm 1**\(q\)-Bezier Triangular Patches for Scattered Data Interpolation  
The computational complexity of our proposed scattered data interpolation scheme for a single triangular patch involves 38 multiplications, 13 additions, and 1 division. This complexity is comparable to that of classical methods, such as those in [16], which require a total of 52 operations.  
The overall algorithm used in this study with descriptions is below.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 4 Comparison of the Prestress Loss with Steel Strands
### Results for Steel Strands
#### 4.2.2 Prestress Loss during the In-Service Stage  
Similar to BFRP tendons, the prestress loss of steel strands during the in-service stage did not vary with the slab width and distribution angle of steel strands either. The prestress loss of steel strands during the in-service stage is listed in Table 12. The prestress loss caused by concrete shrinkage and creep (\(\sigma_{15}\)) was 6.9%, almost twice of that caused due to the relaxation of the prestressed steel strands (\(\sigma_{14}\)). As expected, the prestress loss of steel strands during the in-service stage was significantly lower than that in the prestressing stage.
## 2 Results
### Identification of AlGGPPS for \(\beta\)-Carotene Biosynthesis  
The constructions of pEBIY, pBIY and pGBIY were verified by PCR amplification with primer pairs P1/P2, P1/P3 and P4/P3, respectively (Figures 4 and S2). As mentioned, three stains bearing expression plasmids of pEBIY, pBIY and pGBIY were successively constructed for positive control, negative control and functional identification, respectively. Results showed that all 3 stains exhibited similar OD\({}_{600}\) values at 12 h (Figure 5a). \(\beta\)-carotene was not detected in the BIV stain with \(Cr\)\(\epsilon\) deletion by HPLC analysis (Figure 6b), and the strain harboring the pEBIY or pGBIY plasmid accumulated \(\beta\)-carotene with an orange pigmented colony (Figures 5b and 6a), indicating that the \(Cr\)\(\epsilon\) gene was crucial for \(\beta\)-carotene production. Furthermore, _AlGGPPS_ could compensate for the lack of the original \(Cr\)\(\epsilon\) gene and replace its function in \(\beta\)-carotene biosynthesis.  
Figure 1: Transmembrane prediction of AlGGPPS (**a**) and AIIDI (**b**).  
Figure 3: (**a**) Structure overlay of AIIDI model and its template hIPPI (gray; PDB ID 2i6k). (**b**) Molecular docking of AIIDI with substrate IPP (red). (**c**) Amino acid residues (K57, S108, K132, E189) hydrogen-bonded to IPP.  
Figure 2: (**a**) Structure overlay of dimeric AlGGPPS model and its template hGGPPS mutant (Y246D; gray; PDB ID 6C56). (**b**) Molecular docking of AlGGPPS with substrates IPP (red) and FPP (green). (**c**) Amino acid residues (R95, Q300, K327) hydrogen-bonded to FPP. (**d**) Amino acid residues (N141, R143, H172) hydrogen-bonded to IPP.  
Figure 4: Schematic presentation of plasmid construction. The red arrows represent primer pairs.  
Figure 5: Physiological index measurement of stains. OD\({}_{600}\) (**a**) and \(\beta\)-carotene yield (**b**) of stain EBIY, BIY and GBIY. OD\({}_{600}\) (**c**) and \(\beta\)-carotene yield (**d**) of stain GBIY, GBIY-EclDI and GBIY-AIIDI.  
As for the \(\beta\)-carotene content, the GBIY stain harboring the _AIGGPPS-CrtB-Crtl-CrtY_ gene cluster performed better in \(\beta\)-carotene accumulation than the initial EBIY stain during cultivation. The GBIY strain produced \(5.14\,\mathrm{mg/L}\) of \(\beta\)-carotene in 12 h, which was a 39.7% increase in production compared with strain EBIY. In other words, in situ replacement of the original _CrtE_ with _AIGGPPS_ enhanced \(\beta\)-carotene accumulation with a 1.40-fold production of the initial EBIY strain. In summary, the result of genetic complementation expression indicated that _AIGGPPS_ functioned in \(\beta\)-carotene biosynthesis and performed better than the original _CrtE_ gene of _Pantoa ananatis_, with higher \(\beta\)-carotene yield, implying a superior catalytic property. Thus, the GBIY stain harboring the _AIGGPPS-CrtB-Crtl-CrtY_ gene cluster was chosen for further genetic manipulation and engineering improvement.
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 3 Improved D* Lite
### Multi-Goal Path Planning in Unknown Environment  
In practical applications, sensors on the USV can continuously sense the local surrounding environment during autonomous navigation. Thus, it can only plan a local path based on the local environment information obtained by sensors each time. Furthermore, USVs may need to arrive at several special positions for water quality monitoring or water sampling. Therefore, there are several goal nodes along the planned path. Thus, the D* Lite must be expanded to support multi-goal path planning. Figure 7 shows the main procedure of multi-goal path planning in unknown environments.  
The main steps of multi-goal path planning in unknown environments are as follows:  
1. Initialize the start and goal nodes. Assume that the whole region from the start node to the goal node is a free space (i.e., no obstacles). Then, plan a path from the goal node to the start node with D* Lite.
2. The USV detects environmental information with its sensors, starting from the current node, and verifies whether the goal node is within the detected area. If so, the USV proceeds to the goal node according to the path planned with D* Lite and the path planning procedure is finished. Otherwise, go to step (3).
3. Use D* Lite to plan routes within the detected area. When changes to the environmental information within the detection range are detected, start dynamic route planning until travelling to the boundary of the detected area. Then, the local path planned within the detected area is finished. Set the current node as the start node and plan a new local path within the detected area, and go to step (2).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Operation**} & \multicolumn{3}{c}{**Time Complexity**} \\ \cline{2-3}  & \multicolumn{2}{c}{**Conventional Queue**} & \multicolumn{2}{c}{**Minimum Binary Heap**} \\ \hline Pop up & \(O(n)\) & \(O(1)\) \\ Push in & \(O(1)\) & \(O(log_{n})\) \\ Search & \(O(n)\) & \(O(log_{n})\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time complexity for conventional priority queue and minimum binary heap.  
Figure 6: Data storage of binary heap.