esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this study, a numerical model of wedge-ice-water interaction is constructed based on the commercial software LS-DYNA to simulate the process of wedge breaking through ice into water. The ALE method is used to track the gas-liquid interface and model the wedge breaking through ice and entering water. This study also discusses the effects of different impact velocities and wedge masses on the icebreaking process. The following conclusions are drawn:  
1. Numerical simulations of wedge ingress are carried out to simulate pool ingress tests. The feasibility of the ALE method for simulating structural ingress is verified by comparing acceleration data.
2. Numerical simulations of a rigid plate impacting spherical ice are carried out. The feasibility of the numerical simulation method in structure-ice collisions is verified by comparison with P-A theoretical curves.
3. The simulation shows that the destruction rate of ice is slower in the presence of water than in the absence of water, thus demonstrating that water can delay the destruction of ice.
4. When the wedge mass is 23.55 kg and the impact velocity is increased from 10 to 30 m/s, greater impact velocities (e.g., 30 m/s) cause more pronounced ice destruction and greater damage acceleration.
5. When the water entry speed is 10 m/s and the mass is 43.55 kg, the peak of acceleration is 1520 m/s2. It can be seen that the larger the mass of the wedge, the smaller the collision acceleration, though the effect on ice destruction is minimal.
6. When the impact velocity is 10 m/s, the mass is 33.55 kg, and the ice thickness is 3 cm, the peak acceleration reaches 3202.06 m/s2. The acceleration of the wedge increases as the ice thickness increases.  
Conceptualization, Y.J. and F.W.; methodology, Y.J., F.W. and B.Q.; software, Z.Z., Y.L. and B.Q.; validation, Y.L. and Z.Z.; formal analysis, Y.J., F.W., L.M. and X.W.; investigation, Y.J., Z.Z., L.M. and X.W.; resources, Y.J. and F.W.; data curation, Y.J., F.W., Y.L., Z.Z. and B.Q.; writing--original draft preparation, Y.J. and B.Q.; writing--review and editing, Y.J. and Y.L.; visualization, Y.L., Z.Z. and B.Q.; supervision, Y.J. and F.W.; project administration, Y.J.; funding acquisition, Y.J. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Key Research and Development program of Shandong Province (2022 CXGC020509), the Program of Yantai Science and Technology Innovation Development (2023 XDRH018), and the program of KY10100230078 and 62602010322.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding authors upon request.  
Figure 23: Calculation of the final state of different objects when they break through the ice over time. (**a**) Wedge. (**b**) Cube. (**c**) Sphere.  
Conflicts of Interest: Author [PERSON] was employed by the Yantai Hongyuan Oxygen Industrial company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 3 Results
### RGB Results  
Figure 12 shows the results for the three experiments that used 2-D RGB digital imagery. The best results were obtained in experiment one (Figure 12a), which produced an r\({}^{2}\) of 0.889 and an RMSE of  
Figure 11: Experiment one’s results, for the 21 individual bunches, illustrating the volume estimation error by the Kinect sensor.  
Figure 10: Point-cloud reconstruction testing the alpha value for the _alphashade3d_ package. The original point cloud before reconstruction—represented as white points for a visual purpose (**a**), and after reconstruction (**b–d**).  
17.978 g. The level of accuracy achieved in experiment one can be attributed to the controlled laboratory conditions and supports the proposed methodology for yield estimation. Applying this methodology to in-situ bunches produced less accurate results, as seen in experiment two. Experiment two's FC treatment (Figure 12b) produced the lowest yield estimation results for 2-D RGB imagery, with an r\({}^{2}\) of 0.625 and an RMSE of 27.738 g. The LR treatment's (Figure 12c) r\({}^{2}\) and RMSE values were 0.742 and 25.066 g, respectively. The lesser FC values were directly attributed to the canopy coverage present, as the bunches were partially occluded from the sensor's view. The effect of the canopy treatment was evident when comparing the results. At plant-level (experiment three), the same pattern was present between the canopy treatments. The FC (Figure 12d) treatment of experiment three produced an r\({}^{2}\) of 0.779, while the LR (Figure 12e) treatment produced an even higher r\({}^{2}\) of 0.877. The respective RMSE values were 559.357 g and 443.235 g. The success of yield estimation in experiment three, specifically the LR treatment, supports the methodology's capability for 2-D RGB yield estimation. The in-situ yield estimation was preferable at plant-level, which may be attributed to the lighting conditions and the success of the colour thresholding for bunch segmentation at plant-level.
## 3 Rice Phytoalexin Biosynthesis
### Biosynthesis of Rice Diterpenol Phytoaleoxins
#### 3.1.3 Biosynthesis of Oryza  
Despite their names, oryzaekins A-F are distinguished from oryzaekin S both structurally and biosynthetically (Figures 3 and 6). Oryzalexins A-F are _ent_-sandaracopimaradiene-type compounds derived from _ent_-sandaracopimara-8(14),15-diene, while oryzaekin S is a stemarane-type compound derived from _syn_-stemar-13-ene.  
As shown in Figure 10, hydroxylation of _ent_-sandaracopimara-8(14),15-diene catalyzed by CYP701A8 results in the formation of 3\(\alpha\)-hydroxy-_ent_-sandaracopimaradiene, the putative precursor of oryzaelexins A-E [124]. This precursor is then converted to oryzaelexins D and E by the P450 enzymes CYP76M8 and CYP76M6, respectively [123]. Several short-chain oxidoreductaseus oxidase oryzaelain D at different positions to form oryzaelexins A-C [135].  
Figure 9: Proposed pathway for the biosynthesis of phytocassanes. The green highlight indicates enzymes encoded by genes belong to the biosynthetic gene cluster on chromosome 2 (c2 BGC). Enzymes encoded by genes located on chromosomes 6 and in both 2 and 6 (c6 and c2–c6) are highlighted in blue and grey, respectively. GGPP: geranyl geranyl diphosphate; OsKSLT; OsDTC1): _ent_-cassa-12,15-diene synthase (EC 4.2.3.28); OsCSP2 (OsCyc2): _ent_-copalyl diphosphate synthase (EC 5.5.1.13); CYP76M7: _ent_-cassadiene C11-alpha-hydroxylase 1 (EC 1.14.14.112); CYP76M8: oryalexin D synthase (EC 1.14.14.112; 1.14.14.123); CYP701A8: _ent_-sandaracopimaradiene 3-hydroxylase (EC 1.14.14.70); C71Z7: _ent_-cassadiene hydroxylase (EC 1.14.14.69) (adapted from [138]).  
In contrast to their _ent_-sandaracopimaradiene-derived counterparts, little information is available on the biosynthesis of oryzaekin S. The biosynthetic pathway of this compound branches when GGPP is converted into _syn_-CDP by OsCPS4 (Figure 6). At that point, OsKS8 converts it into _syn_-stemar-13-ene [24; 26; 108]. To date, the last steps concerning the addition of the hydroxyl groups to C2\(\beta\) and to the methyl substituent in C4\(\beta\), which characterize oryzaekin S, have not been resolved.
###### Abstract  
This paper presents an efficient real-coefficient fitting both Green's function and its gradient with Fast Fourier Transform (RFGG-FG-FFT) with cube polynomial inter/extrapolation method (CPIE), which is established for the analysis of scattering from antenna over a wide frequency. To improve the computation efficiency, the CPIE is utilized. In order to reduce memory requirements and accelerate matrix vector multiplication, the RFGG-FG-FFT is adopted. The accuracy, correctness and efficiency of the new method are researched on some examples. Compared with the direct method, the examples show that the new method is superior in broadband without loss of accuracy.  
impedance matrix; MoM; RFG-FFT; cube polynomial inter/extrapolation method (CPIE) +
Footnote †: journal: Applied
## 4 Discussion
### Linking Hg Contamination, Climate Change Stressors, and Potential Impacts on Seafood Safety
Hence, it was interesting to note in the present study, for instance, that acidification alone enhanced CAT activity in fish muscle, but this effect was counteracted by the co-exposure to Hg, probably due to an excessive formation of substrate (H\({}_{2}\)O\({}_{2}\)) that impaired the activity of this enzyme. The same argument may also be applied to the increased CAT activity in the brain of fish exposed to Hg and acidification, which was attenuated when warming was also added to the equation. In contrast, SOD activity in fish muscle and liver was significantly inhibited by warmer temperatures and/or acidification, but this trend was somewhat reversed by the co-exposure to Hg, probably because this contaminant has a pro-oxidative effect [22], thereby triggering the activity of this primary antioxidant scavenger. Despite changes in CAT and SOD activities elicited by Hg exposure, LPO levels were not significantly affected by this contaminant in muscle and liver, but rather by warming and acidification. This suggests that abiotic stressors played a preponderant negative role in these tissues, resulting in tissue damage that CAT and SOD by themselves were unable to prevent. In contrast, effects of Hg exposure seemed to have prevailed in the brain, translating into increased tissue LPO, once again reinforcing the neurotoxicological attributes of this contaminant [11; 16]. Such an increase was partially counteracted by the co-exposure to abiotic stressors, probably due to an enhancement of fish metabolic rates (especially at warmer temperatures) and, thus, of enzymatic activity [80]. As for AchE activity, this enzyme is extensively used as a biomarker of xenobiotic exposure, particularly those that are neurotoxic (such as metals [22] and organophosphate pesticides), and changes in its activity can lead to alterations in behavior and neuromuscular activity [42]. AchE is a common target for many chemical compounds and, thus, the exposure to xenobiotics is frequently associated with AchE activity inhibition [86; 87]. Nevertheless, an activation of this enzyme may also occur, although less commonly, as a result of (i) brain cell apoptosis (e.g., References [80; 88; 89]), (ii) enhanced synthesis of AchE splicing variants (e.g., AChE-R) due to stress [90], or (iii) xenobiotics' modulation of the fish cholinergic system [89; 91; 92]. Such was the case for _S. senegalensis_ exposed to Hg alone or combined with warming and acidification, which exhibited significantly higher AchE activity than non-contaminated fish reared under control temperature and pH conditions. Finally, it is worth mentioning that, despite the neuro-oxidative responses elicited by the three studied stressors, no correlation was found between these variables and the overall animal condition (i.e., K), once more pointing out that Hg concentration, \(\Delta\)temperature, and \(\Delta\)pH selected in the present study were within the thresholds of physiological tolerance of _S. senegalensis_.
## 2 Related Work
### Pneumatically Controlled Multi-Mode Haptic Feedback  
There have been attempts to produce different kinds of feedback with a single actuator. [PERSON] et al. [24] proposed a pneumatically controlled complex haptic device with a dual-layered air structure to control both the reaction force and 3D concave deformation. They used a solid elevating part between two elastic stretchable layers to form the actuator. The finger deformation was controlled by the upper air layer, and the reaction force applied to the finger was controlled by the bottom air layer. The device was capable of rendering the realistic hardness and softness of soft surfaces. [PERSON] et al. [9] proposed a pneumatic ring-shaped finger-worn actuator that provided multi-mode haptic feedback consecutively in a VR or AR environment. They utilized a single air chamber between the stretchable and non-stretchable membrane to render haptic feedback. The actuator generated three distinctive haptic effects, such as static pressure, high-frequency vibration, and impact. Similarly, [PERSON] et al. [25] designed a pneumatically controlled haptic mouse that also rendered static pressure, high-frequency vibration, and impact response separately. The haptic mouse consisted of two air housings. The inside housing was non-stretchable to hold the shape of the mouse, while the outside housing was stretchable to deliver the haptic feedback directly on the user's hand. [PERSON] et al. [26] developed a pneumatic control arm-worn band that rendered different types of tactile feedback at different times. However, they focused on tapping, holding, and tracing sensations to generate different haptic cues using five air chambers. Tapping was rendered by inflating and deflating one air chamber; holding occurred when some or all the air chambers inflated at the same time. The tracing sensation was simulated by inflating an array of air chambers in sequence. [PERSON] et al. [27] designed a soft pneumatic actuator incorporated into a prosthetic limb. They utilized a sheet of nylon ripstop fabric between two silicone layer to make the air chamber. The actuator rendered vibration and tapping feedback separately according to its application. On the other hand, [PERSON] et al. [28] proposed a soft actuator incorporating a dielectric elastomer actuator covered by a silicone elastomer membrane. The actuator was capable of rendering pressure and vibration feedback separately.  
Although the single actuator approach improves practicality, existing works have been unable to simultaneously generate multi-mode feedback, resulting in reduced realism.  
The common factor among all the aforementioned systems is that either they used multiple actuators (end effectors) for delivering different modalities of haptic feedback or delivered feedback iteratively. Our proposed system can deliver multiple haptic feedback simultaneously using the same actuator.
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 3 Proposed Ship Classification Method
### Feature Extraction Based on HOG and LBP  
CNN automatically learns features through layer-by-layer propagation, but it may lose some important low-level information. Therefore, low-level features extracted by HOG and LBP are used to supplement contour, edge features, and spatial texture feature. The combination of three features can more accurately represent the features of ship images.
# Binary PSO with Classification Trees Algorithm for Enhancing Power Efficiency in 5G Networks
## 4 Proposed Algorithms
### SC Sub-Band Allotment Using Classification Trees (CTs)
Since the computational complexity of the DTs is \(O(1)\)[61], as no multiplication process is done and only a sequence of branching operations are performed, then computational complexity of the CTs is also \(O(1)\) for every SC. Thus, the computational complexity of the system is \(S\) x \(O(1)\), since only a sequence of branching operations are performed on moving along the CT according to the binary indicator (\(\tau_{m}\)) and the distance between the SC and the closest nonadjacent SC (\(D_{Non-adf}\))/farthest adjoining SC (\(D_{Ad_{q}}\)) using the sub-band in its edge region. Hence, applying SFR after the SC switching greatly reduces the computational complexity as SFR will be applied to the active SCs only (smaller \(S\)), while the complexity of the BPSO algorithm \(O(Z_{max}\). \(P)\) depends on the maximum number of iterations and the population size (\(P\)) [62; 63]. On the other hand, the average computational time of the proposed algorithm is less than one minute (54.857 s). In modern HetNets, the traffic load of the network is monitored for longer time periods in the order of 5-15 min [64; 65; 66; 64], while the on-off switching decisions of the SCs are usually taken every 15-60 min [64; 66]. Thus, the proposed algorithm is suitable for practical implementation in real time. Figure 3: (**a**) A real example demonstrating a SC (the purple SC) with the center region (the grey region); (**b**) the seven used sub-bands.
# Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager
## 4 Discussion
### Limitations and Prospects for Future Work  
In this research, SDGSAT-1 TIS data with the highest resolution (30 m) in the civilian field and a three-band advantage were first introduced to detect IHSPAs. Our results on the study area estimated that the proposed method can improve the granularity and accuracy of IHS detection. To ensure the consistency of IHSPA products, new features based on land surface temperature (LST) data of the SDGSAT-1 TIS will be constructed to detect IHSPAs easily and depict the difference between different IHSs clearly. Additionally, a new automatically constructed IHS model using IHSPAs will be built and used in this study, as opposed to the manual method.  
It is worth noting that SDGSAT-1 TIS data, especially nighttime TIS data, are inevitably impacted by cloud contamination. Future studies should also focus on removing clouds from nighttime SDGSAT-1 TIS data or on finding more cloudless SDGSAT-1 TIS data to detect IHSPAs on a large scale in different seasons with complex topography using SDGSAT-1 TIS and Landsat-8 OLI data. We also intend to research the connection between IHSPAs or IHSs and economic development and to keep working to determine more about the relationship between climate change and carbon neutrality. These are also important issues in the Paris Agreement [57, 58].
## 3 Results and Discussion
### Leading Results  
The construction materials must not pose a hazard to the safety, health, or hygiene, of occupants, neighbors, or workers. Moreover, they must not have a high impact on the environment during their useful life. Although, there are no specific regulations on the emissions of heavy metals in hydraulic road binder-containing wastes in Europe. Italy [39] and Portugal [40] have national regulations and Spain presents some regional regulations (Catalonia [41] and Basque Country [42]) for the recycling of the wastes in construction materials according to the European standard leaching test EN12457-4, the same one the European Union uses to limit the emissions of leaching heavy metals in waste landfills [38]. All use the same test but establish different limits for the same elements.  
The European Directive defines three waste categories: hazardous, non-hazardous, and inert wastes. Table 4 compares leaching data from the three wastes and Portland cement to the European Directive limits [38]. Regarding [38], OR can be considered a non-hazardous waste due to its As and Pb content being higher than the inert limit and lower than the non-hazardous limit. Scallop and eggshells can be considered inert wastes. In Portugal, waste recycling is allowed by the Portuguese Environment Agency [39] when limits for the inert waste of [38] are not exceeded in construction materials. Both shells could be reused, but not biomass fly ash.  
On the other hand, Italian Ministerial Decree 5 [40] establishes the maximum limits that waste must leach if it wants to be used in construction according to EN12457-4. Regarding the Italian Decree [40], Table 4 shows that OR (and CP) cannot be used in construction materials in Italy due to high values of Se, Pb, Ni, Mo, Cu, and As. Scallop and eggshells meet all the requirements in Italian legislation.  
In Spain, there are no established national limits about the leaching emissions of hazardous heavy metals of construction materials containing wastes. However, for example in the case of Spain, there are regional laws that allow, based on the results of EN 12457-4, the establishment of whether waste can be used in specific construction applications (pavements, roads), such as the Autonomous Government of Catalonia and Basque Country. These legislations establish different limits for the same compounds (and are different from the Italian and Portuguese norms). They even establish limits for compounds that are not limited in others (i.e., Hg, Pb, Sb, As, and Cu are limited in Catalonia and not in Basque Country and, V is limited in Basque Country and not in Catalonia). These limits can be observed in previous studies [58]. According to these limits. scallop and eggshells can be used but biomass fly ash exceeds the limits regarding Se, Mo, and Ni in both regulations and Pb, As, and Cu in Catalonia limits.  
Figure 4: Specimens (**a**) before, (**b**) after acid attack, and (**c**) CP-20-OR-80 after acid attack and compressive strength testing.
## 2 Materials and Methods
### Finite Element Modeling  
This methodology involves the FEA of CBF followed by design. The pre-processing stage in the simulation involves CAD modelling, meshing and applying loads and boundary conditions. The solution stage involves element matrix formulations, and the assemblage of global stiffness elements followed by inversions and multiplications. The final stage is post-processing, which involves viewing and editing the results.  
The CAD model of the CBF was created in the SolidWorks referring to the frame drawing and body attachment holes of a single cab, as shown in Figure 1. This model is simulated using the ANSYS software, V18.1 version. The overall view orientation is shown in Figure 2. Limitations are set for the length (L), width (W), height (H) and weight of the parts mounted according to the particular base vehicle. Any restrictions which may be imposed in each country should be complied with. The simulation used in this research is the modal analysis for model validation to compare with the EMA, after which this CAD model is used to evaluate the effect of static structural analysis.  
The physical and mechanical properties are shown in Table 1. The software is used to determine the dynamic characteristics such as natural frequencies and the corresponding  
Figure 1: Frame drawing of single cab and chassis dimensions [3]: (a) holes; (b) long wheel base.  
Figure 2: View orientation of CBT and CBF: (a) perspective views of commercial vehicle; (b) CAD modelling.  
mode shapes of the CBF. The hot rolled structural steel (SS400) material is defined in the property of the static structural and modal modules in ANSYS software. The following in Table 1 shows the Thai Industrial Standard (TIS) of SS400 material mechanical properties, such as the yield point or yield strength (yield stress), tensile strength and elongation. Convergence studies about element size were preformed and optimum element sizes were selected as 10 mm. In the finite element model of the CBF, 552,400 elements of hybrid grid type were used. Hybrid grids combine the advantages of structured (quadrilateral) and unstructured (triangular) elements, enabling efficient meshing in areas where structured grids are suitable, as well as handling complex regions with unstructured elements. They are particularly useful for the intricate or highly distorted geometries of the CBF. The initial testing was subjected to a free-free boundary condition, where it was supported by a chain hoist. This condition was then modeled in Ansys Workbench, simulating a free-free condition without any imposed restrictions on the boundary conditions. Specifically, none of the DOFs under the body attachment holes of the CBF were constrained or selected.