esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of the System  
The proposed multi-sensor fusion SLAM framework is shown in Figure 1, which is tightly coupled with LiDAR, camera, and IMU, and consists of the VIO subsystem and the LIO subsystem. The LIO subsystem receives the point cloud data from the LiDAR and the measurement data of the IMU. The high-frequency IMU data are used to eliminate the distorted point cloud, and then calculate the curvature of each point, and extract edge features and plane features according to different curvatures. New point cloud features are matched against previously generated local maps in a sliding window, LiDAR odometry is performed, and used to compute IMU bias. The pose of the current frame is optimized by constructing a factor graph with LiDAR odometry and IMU preintegration between two keyframes. If loop closure are detected, constraints are constructed between loop closure frames and loop closure factors are added to the factor graph. The VIO subsystem receives the image information detected by camera and the measurement data of IMU, uses the preintegration to integrate the information of the IMU between the keyframes of the image, and tracks the image by minimizing the direct photometric error and the measurement value of the IMU to perform visual odometry. If the LIO subsystem is available, it will send estimated state to the VIO system for initialization. Conversely, the VIO system can also help the LIO system to perform initial state estimation. The entire system combines visual odometry, LiDAR odometry, IMU preintegration, and loop closure constraints to achieve global pose optimization in the form of a factor graph.
# Polyhydroxyalkanoates (PHAs): Biopolymers for Biofuel and Biorefineies
## 2 Sources of PHAs
### Production of PHAs  
In the 1980s, numerous companies tried to produce PHAs on an industrial scale based on the expectations that petroleum prices would increase over time. People would prefer to use eco-friendly biodegradable plastics or green plastics instead. Moreover, industries such as Chemie Linz AG, Austria; ICI, UK; and TianAn, China, successfully produced poly-(\(R\))-3-hydroxybutyrate (P3 HB) and several copolymers such as P3 HBHHx of (\(R\))-3-hydroxybutyrate (3 HB), (\(R\))-3-hydroxyhexanoate (3 HHx), P3 HBV of (\(R\))-3-hydroxybutyrate (3 HB), and (\(R\))-3-hydroxyValerate (3 HV) [1]. However, up until the year 2000, petroleum prices did not increase as significantly as expected and many PHA production-related projects were discontinued. Surprisingly, after 2001 a sharp increase in the petroleum price was observed and in 2008, a barrel of oil reached $140, which reignited the search for petroleum-free plastics (Figure 3 shows the production of PHAs). Industry focused on two polyesters: polylactic acid (PLA) and PHAs, where PLA is economical and available in bulk but PHAs are expensive. However, the applications and properties of PHAs can be tailored by varying the co-monomer and the content of the copolyesters [26; 27; 28; 29]. The advantages and disadvantages of both PLA and PHAs are compiled in Table 1.  
\begin{table}
\begin{tabular}{c c c}
**Properties** & **PHA** & **PLA** \\ \hline Structure & Over 150 monomer units & D- and L-lactic acids (LA) monomer. \\ Synthesis method & Biosynthesisized as intracellular polyester & Biological synthesis of LA and Chemical synthesis \\ Cost & Twice than PLA & of PLA \\ Material properties & Brittle (could be controlled by structural modifications) & Comparable with conventional plastics. \\ Technology advancement & Over 10 companies Producing PHA up to & Could be varied by adjusting D- and L- ratios. \\  & 2000 ton/year by fermentation of microbes. & NatureWorks, WeforYou, Evonik, and Total-Corbion \\ Applications & Almost all areas of conventional plastic industry. & Packaging, medical implants, printing and coatings. \\ \end{tabular}
\end{table}
Table 1: Comparison of polyhydroxyalkanoates (PHA) and polylactic acid (PLA).  
Figure 2: Biosynthesis of PHAs in bacteria. Reproduced with permission from [25]. Copyright 2015 Elsevier Ltd.
## 6 Experimental Results and Discussion
### Energy Dissipation  
The history of normalized cumulative hysteretic energy (NHE) with respect to the drift level is presented in Figure 11. The NHE at a given cycle was computed as the summation of the dissipated hysteretic energies (HE) of the current and preceding cycles normalized  
Figure 15: Peak strength deterioration (**left**) and stiffness deterioration (**right**).  
by twice the elastic strain energy (computed at the effective yield point), where HEi was determined as the area enclosed by the ith hysteretic loop.  
\[\text{NHE}_{\text{i}}=\frac{\sum_{\text{j=1}}^{\text{j=i}}\text{HE}_{\text{j}}}{2 \left(\frac{1}{2}\text{F}_{\text{y,eff}}\Delta_{\text{y,eff}}\right)}=\frac{ \text{HE}_{\text{1}}+\text{HE}_{\text{2}}+\cdots+\text{HE}_{\text{i}}}{2\left( \frac{1}{2}\text{F}_{\text{y,eff}}\Delta_{\text{y,eff}}\right)} \tag{2}\]  
Figure 16 reveals an exponential growth trend of the NHE. The NHE dissipated at peak capacity, which was reached at around 3% drift for all three specimens, accumulated to 24.9 for S1A, 35.7 for specimens S1B, and 51.6 for S5, which represents a 108% energy dissipation increase achieved with the full-wrap strengthening scheme with respect to the unanchored FRP U-wrap (S1A) and a 45% increase with respect to the FRP U-wrap with traditional embedded anchors (S1B).
## 2 System Synthesis
### Navi Group  
The Navi Group is the set of subsystems necessary for making the SailREC System navigable and controllable and to keep it in a stable state of motion in all degrees of freedom.  
All the components needed to accelerate the SailREC System in the water make up the **Propulsion Subsystem**. This facilitates location change, course correction during operation, avoidance of collisions with other vessels, staying away from the coast during storms, and more. It is not a requirement for the Propulsion Subsystem to have both Active and Passive Propulsion Components, however, it can be an advantage. _Active Propulsion Components_ generate a force by themselves and not through an external potential. Their operation would necessitate the use of a power source, which could be stored renewable fuel or electric power. _Passive Propulsion Components_, on the other hand, generate a force through an external potential applied to the SailREC System. This could be a sail or a Flettner rotor. The Wind Turbine Subsystem itself creates a high thrust force and, hence, can also be used as a Passive Propulsion Component.  
Movement of the SailREC System in wind direction decreases the resultant wind velocity on the wind turbine, which results in reduced power output. Thus, the **Anti-Wind Force Subsystem** compensates or reduces any force that is acting downwind on the system. Not all potential solutions are able to compensate for the wind turbine thrust, but only reduce it. While a water parachute could reduce the thrust, a keel may be able to completely compensate for the thrust in wind direction if the SailREC System would sail perpendicularly to the wind, similarly to a sailing ship.  
The **Control Surfaces Subsystem** comprises surfaces that have fluid flowing over them. These are movable to control the state of motion by changing the angle of attack. The SailREC System can not only have control surfaces in the water but as well in the air. A control surface that likely needs to be implemented is a rudder. How many and which control surfaces will be used depends on the overall design of the SailREC System.  
The **Floatation Subsystem** must be able to withstand the loads to which the SailREC System is subjected at sea, e.g., waves, currents, and overturning moments of the wind turbine. This is realised by the following components: The _Buoyancy Component_, which could simply be a ship hull, generates buoyancy for the whole system and, hence, compensates for the gravitational force. The _Counter Torque Component_ compensates for torques, such as the torque generated by the thrust on the wind turbine and tower, or the torque applied to the rotor axis of the wind turbine. The _Damping Component_ dampens the SailREC System's angular oscillations and ensures a stable frequency response to external excitation, bringing the system back to an equilibrium state.
# Dynamic Stiffness Effect of Mechanical Components on Gear Mesh Misalignment
## 4 Gear Design Process Considering the Dynamic Stiffness  
Here, we introduce a numerical gear design procedure for considering dynamic stiffness. In Step 1, using static analysis, the reaction force, \(\mathbf{f}_{i}\), at the bearing center is computed using the gear macro- and micro-geometries. In Step 2, \(\mathbf{f}_{i}\), is loaded onto the housing structure at the bearing center \(\mathbf{q}_{i}\). We can then compute the housing deformation using the dynamic stiffness in Equation (21). We should compute the angular frequency, \(\omega\) (rad/s), first in Equation (21), which can be defined by the critical speed of the certain gearbox as:  
\[\omega=\omega_{G}=2\pi f_{G},f_{G}=N_{G}f_{C} \tag{23}\]  
in which \(\omega_{G}\) is the gear mesh angular frequency (rad/s), \(f_{G}\) is the gear mesh frequency (Hz), \(f_{C}\) is the gear rotating frequency (Hz), \(N_{G}\) denotes the number of gear teeth, and the subscript \(G\) indicates a term associated with the gear. The dynamic stiffness is then defined without the unknown variable as:  
\[\overline{\mathbf{K}}_{DS}=\overline{\mathbf{K}}-\omega_{G}^{2}\overline{ \mathbf{M}}\pm j\omega_{G}\overline{\mathbf{C}} \tag{24}\]  
Using the dynamic stiffness, except for the damping term in Equation (24), the displacement of the bearing center can be computed as:  
\[\overline{\mathbf{u}}_{i+1}=\overline{\mathbf{K}}_{DS}^{-1}\overline{\mathbf{f} }_{i},\ \overline{\mathbf{u}}_{0}=\mathbf{0},\ \overline{\mathbf{f}}_{0}=\mathbf{f}_{0} \tag{25}\]  
and the bearing center is also updated as:  
\[\mathbf{q}_{i+1}=\mathbf{q}_{i}+\overline{\mathbf{u}}_{i+1} \tag{26}\]Using the new bearing center, \(\mathbf{q}_{i+1}\), the updated reaction force, \(\mathbf{f}_{i+1}\), is computed again. This iteration procedure can stop when the following criteria are satisfied:  
\[\mathbf{q}_{i+1}-\mathbf{q}_{i}=\mathbf{0},\ \mathbf{f}_{i+1}-\mathbf{f}_{i}= \mathbf{0} \tag{27}\]  
It implies that the bearing center and reaction force converge at certain values. Based on the results in Equation (27), the gear mesh misalignment, \(F_{\beta X}\), in Equation (1) and the face load factor, \(K_{H\beta}\), in Equation (3) can be computed and used in the gear design process.  
A similar iteration process has been implemented in several commercialized software designs, including RomaxDesigner and KISSsoft [5; 18; 19]. However, these implementations apply only the static stiffness, \(\overline{\mathbf{K}}\), instead of the dynamic stiffness, \(\overline{\mathbf{K}}_{DS}\). This simple idea, using the dynamic stiffness, \(\overline{\mathbf{K}}_{DS}\), can expose important differences in the gear mesh misalignment compared to the results obtained with static stiffness, as discussed in the next section.
## 2 Literature Review
### Nighttime Travel Behavior  
Nighttime mobility is increasingly recognized as a critical dimension of urban travel behavior, particularly due to challenges related to safety and service availability. PT continues to be a vital mode during nighttime hours, although with specific limitations. In a study based on semi-structured interviews with 167 PT users in Karlsruhe (Germany), [PERSON] [19] found that, although nighttime PT is generally positively perceived, issues of safety and cleanliness were frequently mentioned as areas for improvement. Using a hybrid choice modeling approach, [PERSON] et al. [20] demonstrated that young drivers are more likely to switch to PT when safe and affordable options are available. Similarly, [PERSON] and [PERSON] [21] showed in a GIS-based analysis that bus users tend to avoid poorly lit stops, underscoring the role of physical infrastructure in nighttime mobility. In the context of bike-sharing systems, [PERSON] et al. [22] found that nighttime demand is more dispersed and often associated with night bus routes in Beijing, while [PERSON] et al. [23] observed significantly reduced late-night demand in Barcelona and Seville using a mixed linear model. At the same time, private car usage tends to increase at night, largely due to safety concerns and limited PT availability. Analyzing MiD 2017 data via a discrete choice model, [PERSON] [24] found that suburban commuters are especially prone to using private cars at night. Furthermore, [PERSON] et al. [25] noted that concerns about the security of parking facilities play a significant role in the decision to use private cars at night. In a similar vein, [PERSON] et al. [26] demonstrated that unreliable nighttime transport services, specifically delays exceeding 30 min, can prompt students to walk home alone, even when this option is perceived as unsafe. Moreover, gender-specific safety perceptions have emerged as a critical determinant of nighttime mobility behavior. Based on a commuter survey, [PERSON] [27] showed that women perceive nighttime travel as significantly less safe, which directly affects their mode choice. Similarly, [PERSON] et al. [25] found that women report substantially higher levels of fear when waiting at bus stops or in parking garages during nighttime hours. [PERSON] et al. [28] also observed that female bicycle commuters in the Netherlands are less likely to cycle at night, predominantly due to safety concerns. These findings underscore the importance of incorporating gender-sensitive perspectives into nighttime mobility planning, particularly when aiming to foster equitable and inclusive access to transportation across all user groups.
# Distributed Graph Diameter Approximation
## 7 Experiments
### Comparison with the State of the Art  
Figure 4 reports the comparison between ClusterDiameter and DeltaStepping on the benchmark datasets. The left plot shows running times, the right plot shows the diameter.  
Considering the running times, we observe that on sk-2005 and twitter-2010 the running times are comparable, with ClusterDiameter being approximately twice as fast. Recall that DeltaStepping performs a number of parallel rounds linear in the unweighted diameter of the graph, whereas ClusterDiameter employs a number of rounds typically sublinear in this metric. However, on these two graphs the unweighted diameter is too small to make the difference in performance evident. On the other hand, USA has a very large unweighted diameter, and, as expected ClusterDiameter is much faster than DeltaStepping on this instance.  
As for the diameter returned by the two algorithms, we have that both yield similar results, with ClusterDiameter reporting a slightly larger diameter on twitter-2010 and sk-2005. Note that DeltaStepping provides a 2-approximation, whereas ClusterDiameter provides a polylog approximation in theory. The fact that the two algorithms actually give similar results shows that ClusterDiameter is in practice much better than what is predicted by the theory. We notice that,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Nodes** & **Edges** & **Max. Weight** & **Avg. Weight** \\ \hline sk-2005 & 50 066 547 & 1 806 103 236 & 50 066 547 & 25 033 273 \\ twitter-2010 & 41 582 394 & 1 200 658 436 & 41 582 394 & 20 791 197 \\ USA & 23 947 347 & 28 854 312 & 368 855 & 2 950 \\ USA-x5 & 119 736 735 & 240 060 948 & 368 855 & 2 950 \\ USA-x10 & 239 473 470 & 504 069 243 & 368 855 & 2 950 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets used in the experimental evaluation.  
Figure 4: Comparison between DeltaStepping and ClusterDiameter: running time (**left**) and diameter (**right**). On USA-x10 DeltaStepping timed out after five hours.  
since the very slow execution of DeltaStepping on USA-x10 was stopped after 5 hours, no diameter approximation was obtained in this case.
# Intelligent Control System to Irrigate Orchids Based on Visual Recognition and 3D Positioning
## 2 System Design and Methodology
### Objecti Detection System  
In order to accurately identify the location of each seedling to be watered and to distinguish the characteristics of each seedling from overlook view, this study frames the junction between the stem and leaf of the seedling, and collects hundreds of seedling over-view images to train the learning model of YOLOv3.  
Firstly, this work manually frames the image datasets for junctions between stems and leaves of all seedlings via the software _Labellmg_. Secondly, the image datasets are divided into a high ratio of training, accompanied with a low ratio of validation and testing sets. Finally, the tactic of cross validation is selected to train the YOLOv3 model. The training and validation sets are used, respectively, to training the model and to prove the prediction accuracy of the trained model, which is followed eventually by the step that the use of the testing set is to provide an objective evaluation on a final model fitting the training set.
## 2 Related Work
### Detail of Spatial Sparse Convolution  
Spatial sparse convolution involves using a spatial mask to distinguish between 'important' and 'unimportant' regions in the feature maps, assigning a value of 0 to the latter and 1 to the former [31]. The view of UAVs is large, and the foreground containing the target occupies a small part of the entire image. UAV images are inherently a type of spatially non-intensive data, and spatial sparse convolution has been shown to improve the efficiency of processing such data [32]. Given a feature map of size \(C\times H\times W\), SSC performs convolution using a shared kernel with dimensions of \(C\times 3\times 3\). The convolution result is a matrix with dimensions of \(1\times H\times W\) which can then be further normalized by a mask matrix. Only those positions with value 1 in the mask matrix will be convolved in the next step. Nevertheless, the mask matrix is derived from a mask ratio \(r\in[0,1]\) according to Gumbel-Softmax [33]. Therefore, the value of the mask ratio determines the efficiency of SSC. Context-enhanced adaptive sparse convolution (CEASC) [34] proposes an adaptive multilayer masking scheme to introduce a mask network for each head, replacing the convolution layer with an SC layer. These methods typically perform inference on one feature map multiple times, limiting their applications on UAV platforms.
# Automobile Fine-Grained Detection Algorithm Based on Multi-Improved YOLOv3 in Smart Streetlights
## 3 Methodology
### Improvement Based on the Position Regression Loss Function
#### 3.4.2 Improvement of Loss Function Based on GIOU  
IoU has scale invariability and can be used as the index to measure the distance between objects; however, when IoU is used as distance, if the two objects are completely disjoint, no matter whether  
Figure 6: Schematic diagram of Mixup.  
they are far from or close to each other, the distance value is fixed, which is 1. At this time, the gradient cannot be returned, and the model may be unable to learn the position of part of true-value bounding box during training, which has lowered the final precision of the model. In this section, GIOU will be used as the distance to improve the YOLO position loss.  
GIOU is a new index used to measure the overlapping degree between two bounding boxes on the basis of IoU, which considers the non-overlapping area not considered by IoU, and it can reflect the overlapping method between A and B. Assume the coordinate of the predicted bounding box is \(B^{p}=\left(x_{1}^{p},y_{1}^{p},x_{2}^{p},y_{2}^{p}\right)\), and the coordinate of true-value bounding box is \(B^{g}=\left(x_{1}^{g},y_{1}^{g},x_{2}^{g},y_{2}^{g}\right)\). GIOU and the computation process using GIOU as the loss function is as follows:  
1. In order to ensure the coordinates of the predicted box satisfy \(x_{2}^{p}>x_{1}^{p}\) and \(y_{2}^{p}>y_{1}^{p}\), conduct data manipulation as in Formulas (12)-(15): \[\hat{x}_{1}^{p} =\min\left(x_{1}^{p},x_{2}^{p}\right)\] (12) \[\hat{x}_{2}^{p} =\max\left(x_{1}^{p},x_{2}^{p}\right)\] (13) \[\hat{y}_{1}^{p} =\min\left(y_{1}^{p},y_{2}^{p}\right)\] (14) \[\hat{y}_{2}^{p} =\max\left(y_{1}^{p},y_{2}^{p}\right)\] (15) 2. Calculate the area of \(B^{g}\): \[A^{g} =\left(x_{2}^{g}-x_{1}^{g}\right)\times\left(y_{2}^{g}-y_{1}^{g}\right)\] (16) 3. Calculate the area of \(B^{p}\): \[A^{p} =\left(x_{2}^{p}-\hat{x}_{1}^{p}\right)\times\left(\hat{y}_{2}^{p}-\hat{y}_{ 1}^{p}\right)\] (17) 4. Calculate the intersection between \(B^{p}\) and \(B^{g}\): \[x_{1}^{I} =\max\left(\hat{x}_{1}^{p},x_{1}^{g}\right)\] (18) \[x_{2}^{I} =\min\left(\hat{x}_{2}^{p},x_{2}^{g}\right)\] (19) \[y_{1}^{I} =\max\left(\hat{y}_{1}^{p},y_{1}^{g}\right)\] (20) \[y_{2}^{I} =\min\left(\hat{y}_{2}^{p},y_{2}^{g}\right)\] (21) \[I =\left\{\begin{array}{ll}(x_{2}^{I}-x_{1}^{I})\times(y_{2}^{I}-y_{1}^{I}) &x_{2}^{I}>x_{1}^{I},y_{2}^{I}>y_{1}^{I}\\ 0&\text{else}\end{array}\right.\] (22) (5) Find the smallest rectangular box \(B^{c}\) enclosing \(B^{p}\) and \(B^{g}\): \[x_{1}^{c} =\min\left(\hat{x}_{1}^{p},x_{1}^{g}\right)\] (23) \[y_{1}^{c} =\min\left(\hat{y}_{1}^{p},y_{1}^{g}\right)\] (24) \[y_{2}^{c} =\max\left(\hat{y}_{2}^{p},y_{2}^{g}\right)\] (25) (6) Calculate the area of \(B^{c}\): \[A^{c} =\left(x_{2}^{c}-x_{1}^{c}\right)\times\left(y_{2}^{c}-y_{1}^{c}\right)\] (26) (7) Calculate IoU and GIOU: \[IoU =\frac{I}{A^{p}+A^{g}-I}\] (27)\[GloU=IoU-\frac{A^{c}-(A^{p}+A^{S}-I)}{A^{c}} \tag{28}\]  
Obtain the loss function based on GIOU:  
\[\mathcal{L}_{GloU}=1-GloU \tag{29}\]
## 4 Discussion
### Frozen  
The second objective of this study was to monitor the persistence of the chemical odor signatures of the SLF eggs and crickets over a 6-week longevity period under frozen conditions. A total of eight primary compounds were detected in the egg samples: anisole, tridecane, dodecane, 2-nonanone, 3-octanone, D-limonene, 2,3-butanediol, and phenylethylal alcohol. A total of thirteen primary compounds were detected in the cricket samples: dodecane, 1-octen-3-ol, 6-methyl-2-heptanone, 5-ethyldihydro-2(3H)-Furanone, 2-decanone,benzaldehyde, 2-butylhept-2-enal, 2-butyl-2-ocrenal, 2-heptanone, 3,5-octalien-2-ol, octanal, 2-pentyl-pyridine, and p-xylene. Dodecane is the only compound shared between the SLF eggs and crickets at one sample time point (week 2). This could indicate dodecane as a potential VOC marker for SLF eggs, and it has also been previously identified as an attractive compound in other pests, such as the grain beetle _Oryzaophilus surinamensis_[24]. Among the cricket samples, many compounds were found to be aldehydes, including benzaldehyde, octanal, 2-butylhept-2-enal, 2-butyl-2-ocrenal, and hexanal. Aldehydes are produced by insects, and butterflies and moths in the order Lepidoptera have been known to react to benzaldehyde and octanal [25]. Regarding the overall persistence of each specimen type, both the SLF eggs and crickets depicted a weekly change in the reported VOC profile. PCA analysis corroborated that the primary odor set of compounds did not cluster together as a function of the week, indicating continuous flux over time.  
There was a drop in VOC accumulation in the SLF eggs from week 3 to week 4; however, the abundance went back up into week 5. This change could be a result of chemical degradation within the sample yielding new volatiles and/or changes in the abundance of original volatiles reported. The thaw/freeze cycle is also an important variable to consider, and future studies should evaluate the optimal thaw time to ensure odor availability is reached after the sample is frozen. The crickets had a higher VOC abundance overall, but neither specimen type had a complete depletion of odor accumulation over the sampling period. Thus, the ability of this method to detect odor volatiles even after 6 weeks of frozen storage suggests that cold storage safeguards volatile detection; however, it does not equate to volatile reproducibility. Other factors that could be explored in future studies include evaluating storage in amber vials to protect samples from light degradation and/or refrigerating at \(-80\)\({}^{\circ}\)C conditions.