esa-sceva/satcom-chunk-collection huggingface dataset:
# Chinese Cabbage Changes Its Release of Volatiles to Defend against _Spodoptera litura_
## 3 Results
### Quantities of 16 Specific Compounds Released
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Linear Regression Equation** & \(R^{2}\) & \(P\) \\ \hline \multirow{2}{*}{Green leaf volatiles} & \(y_{1}=0.0122\)x \(-\) 0.0576 & 0.3775 & 0.6542 \\ \cline{2-4}  & \(y_{2}=0.0031\)x + 0.6681 & 0.1609 & 0.5782 \\ \hline \multirow{2}{*}{Esters} & \(y_{1}=-0.1427\)x + 0.6413 & 0.3946 & 0.5084 \\ \cline{2-4}  & \(y_{2}=0.0148\)x + 0.2220 & 0.8530 & 0.0382 \\ \hline \multirow{2}{*}{Aldehydes} & \(y_{1}=0.0963\)x + 0.1238 & 0.0587 & 0.1475 \\ \cline{2-4}  & \(y_{2}=-0.1305\)x + 0.9298 & 0.8681 & 0.0259 \\ \hline \multirow{2}{*}{Terpenoids} & \(y_{1}=0.2537\)x + 0.1194 & 0.7103 & 0.0443 \\ \cline{2-4}  & \(y_{2}=-0.1545\)x + 0.8538 & 0.4286 & 0.3579 \\ \hline \multirow{2}{*}{Ketones} & \(y_{1}=-0.0035\)x + 0.3651 & 0.0326 & 0.2442 \\ \cline{2-4}  & \(y_{2}=-0.0091\)x + 0.8029 & 0.6722 & 0.0182 \\ \hline \multirow{2}{*}{Alkanes} & \(y_{1}=0.0037\)x + 0.2313 & 0.0372 & 0.4786 \\ \cline{2-4}  & \(y_{2}=-0.0045\)x + 0.7619 & 0.4698 & 0.3374 \\ \hline \hline \end{tabular} Pearson correlation coefficient (\(R^{2}\)) between different classes of volatiles released and selection rate of _M. similis_ and _S. litura_. \(\kappa^{\prime}\)x in the equations represents the number of different classes of volatiles released. \(\gamma\)y\({}_{1}\)” represents the selection rate of _S. litura_ as affected by treatments, significantly correlation at \(p<0.05\).  
\end{table} 
Table 1: Linear relationships between quantities of classes of volatiles emitted by Chinese cabbage plants under different treatments and the selection rates of _M. similis_ and _S. litura_. Figure 4: Quantities of specific volatiles released by Chinese cabbage plants in response to six treatments. HP: healthy plants; MDP: mechanically damaged plants; RTP: regurgitate-treated plants; SIP: plants infested with _S. litura_ for 12, 24, or 48 h. Values are means + SE. Bars labeled with different letters are significantly different by [PERSON]’s HSD test (\(p<0.05\); one-way ANOVA, \(\alpha=0.05\)). According to linear regression, the selection rate of _S. litura_ was positively related with the release of (_E_)-2-hexenal, 1-Butene, 4-isothiocyanato, and decanal but was negatively related with the release of limonene, nonanal, hexadecane, heptadecane, and octadecane (_p_ < 0.05, Table 2).
# The Effect of Land Cover on the Nectar Collection by Honeybee Colonies in Urban and Rural Areas
## 2 Materials and Methods
### Landscape Structure Analysis  
The landscape characteristics surrounding each apiary were assessed using remote sensing data. For each location, land cover maps were generated within a 1.5 km and 3 km radius. The analysis was based on the national land cover map S2 GLC-PL National Satellite Information System [40] derived from Sentinel-2 satellite imagery with a spatial resolution of 10 m (European Space Agency (ESA) under the Copernicus Programme of the European Union). Photographs of land within a 3 km and 1.5 km radius of the apiaries were processed using Image Colour Summarizer software v0.80 (Canada's Michael Smith Genome Sciences Centre (GSC), Vancouver, BC, Canada). Using these maps, pixels were classified into the following land cover and land use categories:  
**Anthropogenic--**Built-up areas, industrial zones, roads, and paved surfaces (non-vegetated);  
**Agricultural--**Arable land and seasonal crops (excluding permanent grasslands);  
**Broad-leaved forest--**Forrested areas dominated by deciduous trees;  
**Coniferous forest--**Forrested areas dominated by coniferous trees;  
**Grassland--**Meadows, pastures, lawns (including urban green lawns), and other herbaceous vegetation;  
**Shrubs and scrubland--**Areas covered with dense shrub vegetation;  
**Wetlands--**Marshes and other water-saturated areas;  
**Water bodies--**Rivers, lakes, and other surface water features.  
For each area (1.5 km and 3 km), the percentage share of each land cover category was calculated. It was established that the total land cover was constant at 100% per location. It is important to note that the 'anthropogenic' category includes only impervious, non-vegetated surfaces (e.g., buildings and roads), while green areas in urban settings (such as parks) were classified as forests, grasslands, or shrublands, depending on the dominant vegetation type. This classification system enabled a comparison of the landscape composition between urban and rural apiaries and its relation to nectar foraging activity.
# Surface Defect Detection of Preform Based on Improved YOLOv5
## 4 Results and Discussion
### Deployment of Models  
The trained model has been deployed to the MXE-5501 industrial computer (with a CPU of i7-6820 EQ, 8 GB of memory, and a solid-state drive capacity of 480 G), with the following steps:  
1. Install the CPU version of PyTorch. According to the processor architecture of MXE-5501, install the corresponding PyTorch version. Due to the x86 CPU of this device, it is necessary to install the CPU version of PyTorch;
2. Install Python and OpenCV. Install Python 3.9 and OpenCV 4.1.1 on MXE-5501;
3. Clone Yolov5 code. Clone the required code onto MXE-5501;
4. Modify the yolo.py and common.py files and add interface files for detection. Modify the yolo.py and common.py files to adapt to the environment on MXE-5501, and develop interface files for detection using PyQt5, as shown in the Figure 12.  
After deployment on industrial control computers, online detection of surface defects on preforms can be achieved.  
The detection model deployed on an industrial computer consists of three steps in the workflow: camera image detection, folder image detection, and video detection. The linear array camera captures a complete image every second and saves it in a fixed folder. Open the Yolov5 detection interface, select the function of the detection folder, select the device as CPU, and select the corresponding model. The program will determine if there are any new files in the folder. If there are any new files, the detection will continue. If there are no new files, the previous detection results will remain.  
We used a visual platform as a simulation platform to obtain images and serve as a preliminary validation for online testing. According to current testing data, the time for online shooting and defect identification before improvement was 2236 mm; after improvement, the online shooting and defect recognition time was 1965 ms, and the recognition time was reduced by 331 ms, resulting in a significant improvement in detection speed.
# On the Performance of a UAV-Aided Wireless Network Based on NB-IoT
## 3 The Narrowband-IoT Technology  
The NB-IoT technology is intended to address the needs of mMTC, and has been standardized by 3 GPP initially on Rel. 13 in 2016, with new functionalities introduced in the subsequent releases. It will be briefly described in this section, taking as references [5; 27]. Particular emphasis is given to the uplink, which is the scenario of interest for this paper. The NB-IoT technical solution originates from the Long Term Evolution (LTE) technology, which has been substantially simplified to reduce the overheads, minimizing complexity, cost and consumption, and, at the same time, it keeps its usual mechanisms, such as synchronization, radio access and resource assignment. In addition, the NB-IoT technology features substantial flexibility allowing to deploy NB-IoTs cells by running a software update on already existing LTE cells. Indeed, three deployment options are available: Standalone, to reuse 200 kHz GSM carriers; guard-band, to exploit the guard band of two adjacent LTE carriers; and in-band, where one LTE Physical Resource Block (PRB) is reserved for NB-IoT within an LTE carrier bigger than 1.4 MHz. NB-IoT implements several mMTC-oriented enhancements, such as narrowband transmission and enhanced power saving techniques to increase battery life of UEs. In addition, up to 2048 and 128 repetitions in DL and UL, respectively, can be used to exploit the time variation of the radio channel, so that each replica can be decoded separately, or multiple replicas can be combined to further increase the reception probability. Furthermore, NB-IoT's coverage enhancement is provided by defining three coverage classes: Normal, robust, and extreme. Classes are differentiated through thresholds based on Reference Signals Received Power (RSRP), defined to introduce three levels of coverage extension. Such thresholds depend on the cell deployment, the propagation environment (i.e., outdoor, indoor, deep-indoor, underground), and the spatial distribution of devices. Network parameters as the number of repetitions can be tuned separately for each class.  
Before Rel. 15 introducing time division duplex (TDD) operation mode, the frequency division duplex (FDD) mode was the only option for NB-IoT. In this paper we consider the latter, since it is the primary mode used in most commercial networks and enables the maximum performance. The FDD mode implies different frequency bands to be used for UL and DL transmissions. The channel bandwidth of NB-IoT is 180 kHz, that corresponds to one LTE PRB. In UL, subcarrier spacing of either 15 or 3.75 kHz are possible, thus providing either 12 or 48 possible subcarriers within a 180 kHz resource block. The 15 kHz spacing allows transmission of either single or multitone (over up to 12 carriers) signals, while only single-carrier transmissions are possible for 3.75 kHz grid. On the contrary, in DL, only the 15 kHz resource grid is used.
# Generating DNA Origami Nanostructures through Shape Annealing
## 3 Applications
### Coating Application  
Due to its complexity and rigidity, DNA nanostructures can be used as a template to assemble inorganic materials in defined patterns [40]. [PERSON] et al. illustrated the design of DNA into mechanically stiff molds to cast and synthesize metallic nanoparticles into desired 3D configurations [20]. These 3D molds, originally designed through caDNAno, encapsulate a nucleating gold seed that grows to fill up the entire volume, thus reproducing its 3D shape. Although the wireframe design tools automate the design of rigid wireframe-based DNA nanostructures, current approaches have limited options for controlling the mechanical properties of automated structures. A multilayer DNA origami approach, as noted by [PERSON] et al., provides broad control over the mechanical properties of the mold, which are critical to casting applications [20]. However, even though our framework was developed to promote filling, it can be applied to coat the surface of arbitrary geometries with a scaffold of custom length to create solid DNA molds; thus, providing the automated generation of multilayer DNA origami based on 3D polyhedral meshes. The optimization problem in this example minimizes the distance of helical segments to the outer surface of the input mesh. The scaffold, in 7 bp segments, is assembled based on the shape grammars where no segments can overlap, and the shapes are generated to imitate continuous scaffold length growth. The coating application presents the case of objective minimization (Section 2.2). The objective function is:  
\[\mathrm{f(x)}\ =\ 1.19^{\mathrm{x}}, \tag{4}\]  
where x is the Euclidean distance from the outer surface of the mesh to the top of each helical segment. The objective function finds the minimum perpendicular distance to the surface of the mesh. This objective function was selected because it gradually decreases distances closer to the outer surface of the polyhedral mesh. An objective function with a base value much closer to one generates designs that are not as tightly coated. However, an objective function with a base value close to two generates designs that tightly coats the surface of the mesh. Thus, 1.19 is an appropriate base value for this function. As a demonstration of the method's capability to coat the surface of input meshes, we present three examples of the coating application using a tetrahedron, a snub cube, and a version of the Stanford bunny (Table 1).  
Figure 5: Input triangular mesh with a scale bar of 10 nm in length: (**a**) Tetrahedron mesh with dimensions for the filling application of the axis aligned bounding box of 47.6 nm in length, 55.0 nm in width, and 45.9 nm in height. This mesh has a volume of 19,600 nm\({}^{3}\); (**b**) Snub cuboctahedron mesh with the axis aligned bounding box of 35.1 nm in length, 36.0 nm in width, and 35.8 nm in height. This mesh has a volume of 20,000 nm\({}^{3}\); (**c**) Stanford bunny mesh with the axis aligned bounding box of 54.1 nm in length, 43.3 nm in width, and 53.6 nm in height. This mesh has a volume of 34,200 nm\({}^{3}\).
# An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO
## 2 Constraints on DNA Codes  
To store data in DNA more reliably, different constraints must be imposed on the sequences used in a DNA-coding set. These constraints can enhance the robustness of the sequence for better data storage. In order to design a DNA coding with No-runlength constraint and constant GC-content, constraint encoding is considered here, represented by q (\(n,M,d\)), where n represents the length of a sequence, M represents a symbol, and d represents the Hamming distance. Under these constraints, we tried to build as many constraint code sets as possible of different lengths as follows:  
* No-runlength constraint  
For q (\(n,M,d\)), a No-runlength constraint [26] means that two adjacent base elements at any position in the sequence are different. For example, in the base sequence _CTAACG_, the \(A\) base appears consecutively multiple times, violating this constraint, which can result in an increase in the bit error rate during decoding. The existence of a No-runlength constraint is beneficial to avoid the occurrence of homopolymers, so as to avoid the formation of secondary structure and prevent file corruption leading to decoding failure. For a DNA sequence containing n bases (\(b_{0}\), \(b_{1}\), \(b_{2}\)... \(b_{n}\)), this constraint is defined as follows by Equation (1):  
\[b_{i}\
eq b_{i-1}i\in[1,n] \tag{1}\]  
* GC-content constraint  
The GC-content constraint [32] in a q (\(n,M,d\)) set typically requires that the total content of bases G or C be 50% of the code length n, as a constant GC-content maintains the stability of a DNA sequence.  
For a sequence s, the GC-content is defined as GC(s), and here it is equal to 50%. We used the following formula by Equation (2) to calculate the GC-content:  
\[GC(s)=\frac{G+[C]}{[s]} \tag{2}\]  
* Hamming distance constraint Assuming there is a pair of sequences \(x\) and \(y\) of the same length (\(x!=y\)) in the q (\(n\), \(M\), \(d\)) set, the sum of the number of different base elements at the same position is called the Hamming distance [33], and is expressed by H (\(x,y\)). Satisfying H (\(x,y\)) \(\geq d\), \(d\) is a defined threshold.  
The Hamming distance is considered because it can discriminate between two sequences, and sequences with too much similarity lead to nonspecific hybridization reactions. At the same time, these can correct substitution errors [33] and play a crucial role in data resilience [26]. The larger the Hamming distance, the smaller the similarity between two sequences and the more stable the sequences are. The Hamming distance is calculated as follows by Equation (3):  
\[H(x,y)=\sum_{i=1}^{n}h(x_{i},y_{i}),h(x_{i},y_{i})=\left\{\begin{array}{l}0, x_{i}=y_{i}\\ 1,x_{i}\
eq y_{i}\end{array}\right. \tag{3}\]  
In this paper, we only considered a quaternary code, namely \"A-0, G-1, C-2, T-3\". A quaternary code has better storage capacity and coding rate as compared with a ternary code. Then, the proposed NOL-HHO algorithm was used to find more sequences that satisfy the No-runlength constraint, the GC-content, and have a large Hamming distance.
# Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm
### Literature Review on Available Solutions
#### 1.1.1 Unplanned Downtime (UPDT)
Consequently, the planned downtime (DT_planned) can be calculated using the following equation:  
\[\text{DT}_{\text{planned}}=\sum_{i,j}W_{ij}\,E_{i}P_{i}[T_{\text{si}}P_{i}+\ T_{ \text{ft}}(1-P_{i})] \tag{7}\]  
where  
\[\text{DT}_{\text{planned}}=\text{planned downtime for the system;}\] \[\text{UPDT Rate}=\frac{\text{Unplanned Downtime}}{\text{Planned Operating Time}} \tag{3}\]0 \[\text{UPDT Rate}=\frac{\text{Unplanned Downtime}}{\text{Planned Operating Time}} \tag{3}\]1 \[\text{UPDT Rate}=\frac{\text{Unplanned Downtime}}{\text{Planned Operating Time}} \tag{3}\]2 \[\text{UPDT Rate}=\frac{\text{Unplanned Downtime}}{\text{Planned Operating Time}} \tag{3}\]3  
\[\text{UPDT Rate}=\frac{\text{Unplanned Downtime}}{\text{Planned Operating Time}} \tag{3}\]4 Planned versus unplanned downtime. \(p_{i}\) = probability (percentage of events offered) that customers take event type \"\(i\)\"; \(P_{i}\) = success probability of event type \"\(i\)\"; \(1-P_{i}\) = probability of a failure during event type \"\(i\)\"; \(T_{si}\) = average time to successfully perform event type \"\(i\)\"; \(T_{ft}\) = average time to perform event type \"\(i\)\" when a failure occurs. The aforementioned method involves calculating UPDT and calculating DT\({}_{\text{planed}}\) through reverse computation from Unsuccessful Planned Events. Currently, various technologies are being applied to address these issues, such as the study of minimizing equipment downtime in manufacturing, particularly in the paper industry, to maintain high productivity. It proposes a novel framework using artificial neural networks (ANNs) to model downtime length probability distributions, focusing on parameter estimation by using the potential of advanced data-driven techniques to enhance maintenance practices and decision-making in industrial settings [10]. The study explores over 20 fault detection models using machine learning (ML), deep learning (DL), and deep hybrid learning (DHL) to predict machine failures and reduce downtime. Tested on a synthetic dataset, deep forest and gradient boosting algorithms achieved over 90% accuracy. The research highlights the potential of these models in optimizing maintenance and minimizing reliability risks [11]. In this research, the authors present an alternative approach to addressing the UPDT issue by employing SSS to identify maintenance stoppage patterns. This method aims to mitigate the occurrence of UPDT by leveraging systematic analysis and strategic planning.
## 4 Micro-Cracking Behaviors of Shale under Different Bedding Parameters
### Effects of Bedding Strength
#### 4.1.3 Micro-Cracking Morphology  
The final fracture morphology of shale is the accumulation of different micro-cracking modes. Figure 10 presents the variations of fracture morphology of shale specimens with the JSR. These figures show that both dip angle and bedding strength have significant impacts on the final fracture morphology. When the dip angle is 0\({}^{\circ}\), most of the micro-cracks occur in the rock matrix. The matrix cracks initiate from the top of the shale specimen and then cross the beddings along the loading direction. These matrix cracks coalesce into main fractures and then divert to one side of the specimen. The final fracture morphology is consistent with the laboratory observations in Figure 6a. On the micro-scale, the damage of the rock matrix results in the splitting failure of the shale specimen, while the bedding strength only has a local influence in the sub-fractures near the damaged beddings. When the dip angle is 30\({}^{\circ}\), a lot of beddings are in shear-slip failure. This limits the extension of matrix cracks from the top of the specimen. At this time, the shear cracks of beddings decrease more significantly for larger bedding strength, and the matrix cracks extend outward more evidently. This is a combined splitting and bedding sliding failure. When the dip angle is 60\({}^{\circ}\), the failure pattern of the shale specimen transforms from the combined splitting and bedding sliding failure to a pure sliding failure. Only a few matrix cracks emerge near the damaged beddings. As the JSR increases from 0.8 to 1.2, some matrix tensile cracks (Pbt) increase slightly near the sliding beddings. At the dip angle of 90\({}^{\circ}\), a combined tensile-sliding failure of beddings occurs if the JSR is 0.8. As the JSR increases from 1.0 to 1.2, the tensile crack of beddings (Sjt) decreases. The shale specimen fails gradually due to the rock matrix.  
The influence of the JSR on micro-cracking morphology is quantitatively analyzed by calculating the average of the crack fractal dimension at different dip angles. As the JSR increases from 0.8 to 1.2, the average fractal dimension of the matrix crack (Pbt+Pbs) is 1.207, 1.268 and 1.281 and the average fractal dimension of the tensile crack (Pbt+Sit) is 1.225, 1.293 and 1.291, respectively. The growth rate of the fractal dimension decreases gradually with the increase of the JSR. The above analysis shows that the dip angle controls the morphology of the main fracture, while the bedding strength could change the distribution of sub-fractures. To some extent, the increase of bedding strength can enhance the complexity of the sub-fracture distribution.
# Design Optimization of the Magnet-Free Synchronous Homopolar Motor of a Subway Train
## 5 Voltage Limit  
Kinetic energies of the train in operation points 2 and 3 (see Figure 4) equal to \(J\cdot\Omega_{\text{max}}{}^{2}/2\) and \(J\cdot\Omega_{\text{g}}{}^{2}/2\), respectively. That is the train loses \(1-\Omega_{\text{g}}{}^{2}/\Omega_{\text{max}}{}^{2}=1-n_{g}{}^{2}/n_{max}{}^{2}\) part of its kinetic energy when braking from point 2 to 3. This energy is converted from the motion to the charge of the supercapacitor. The ratio between the energy accumulated in the supercapacitor during segment 2-3 and the total energy being accumulated at both segments 2-3 and 3-4 is \((V_{3}{}^{2}-V_{DC\,\,n\!nd}{}^{2})/(V_{max}{}^{2}-V_{DC\,\,n\!nd}{}^{2})\), where \(V_{3}\) is the DC voltage in operating point 3; \(V_{DC\,\,n\!nd}\) is the rated voltage of the DC catenary; \(V_{max}\) is the maximum DC voltage. Therefore, the expected level of the DC link or supercapacitor voltage in the point 3 can be evaluated according to (8):  
\[V_{3}=\sqrt{V_{DC\,\,rated}^{2}+(V_{3}^{2}-V_{DC\,\,rated}^{2})(1-\frac{n_{ 8}^{2}}{n_{max}^{2}})}=929\ \text{V}. \tag{8}\]  
Thus, the desired motor phase-to-phase voltage amplitude \(V_{i}\) at operating points \(i\) = 1,2,3,4,5 can distinguish from \(V_{DC\,\,rated}\) = 750 V. Table 1 shows the main parameters of the motor in the operation points \(i\) = 1,2,3,4,5, including weight coefficients being evaluated using (6) and normalized in addition to coefficient \(k_{i}=V_{i}/V_{DC\,\,rated}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Operating Point, \(i\)** & **Operating Point Name** & **Speed, rpm** & **Torque, N\(\cdot\)m** & \(w_{i}\) & \(k_{i}\) \\ \hline
1 & Driving mode; maximum speed & 4380 & 413.4 & 0.363 & 0.97 \\ \hline
2 & Generating mode; maximum speed & 4380 & 826.9 & 0.091 & 0.99 \\ \hline
3 & Generating mode; transition from constant power to constant torque & 2854 & 1240 & 0.182 & 1.1 \\  & operation modes & & & & \\ \hline
4 & Zero speed & 0 & 1240 & 0.182 & 0.97 \\ \hline
5 & Driving mode; transition from constant torque to constant power & 1427 & 1240 & 0.182 & 0.97 \\  & operation modes & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operating points of the traction motor considered during optimization.  
The expected DC voltage in the operation point 3 is 100%-(929 \(-\) 750)/750 = 24% higher than \(V_{DC\ metal}\). Coefficient \(k_{3}\) = 1.1 is selected with a large margin, and it is expected that the magnitude of the phase-to-phase voltage is higher than the rated voltage of the grid by 10% in the operating point 3. Moreover, a 3% margin for \(V_{DC\ metal}\) is selected as the expected voltage drop across the power semiconducting switches in the driving mode, and a 1% margin for \(V_{DC\ metal}\) is selected at operating point 2.