esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Importance of Photovoltaic Installation Calculations and Solar Radiation Prediction  
The correct design and calculation of photovoltaic installations is essential to maximize energy efficiency and minimize the associated costs. According to studies published in the IEEE Journal of Photovoltaics [19], optimizing various aspects of the system, such as the location, orientation, and angle of the solar panels, is crucial to ensure the optimal performance of PV systems. This optimization not only improves energy efficiency, but also significantly reduces installation and operating costs in the long run. A thorough analysis can make a big difference to the overall performance of the system. Therefore, it is vital to also take into account factors such as climatic and geographical conditions during the design phase, as they directly influence the amount of energy a system can generate.  
Accurate solar radiation prediction is essential for the planning and optimization of photovoltaic installations. Several prediction methods coexist today, including models based on historical data and advanced machine learning techniques. These methods make it possible to anticipate the amount of solar radiation that a given area will receive, which is vital for adjusting the design and improving the efficiency of PV systems [20].  
In addition, it is also relevant to highlight the importance of maximum power point tracking (MPPT) algorithms, which rely on accurate predictions of solar radiation to continuously optimize system performance. MPPT algorithms adjust system operation in real time to ensure that the maximum possible amount of energy is always being generated, which is crucial to maximize the efficiency and profitability of solar installations.  
Despite advancements in technology and optimization, photovoltaic installations face several challenges. These includes the variability in the availability of solar radiation, the need for efficient energy storage, and integration with existing electrical grids. The mentioned studies also address these challenges, proposing solutions such as the use of integrated energy storage systems and improvements in control algorithms to better manage energy generation and distribution.
## 3 Results and Discussion
### Parameter Study of the Adsorption of DR-81 onto the Cockleshell Powder
#### 3.2.3 Effect of the Temperature  
The effect of temperature is of paramount importance; since it affects the adsorption capacity and the percentage of removal of the DR-81 dye in the bio-adsorption process. The adsorption of DR-81 in an aqueous solution was studied in the range of 22 \({}^{\circ}\)C to 61 \({}^{\circ}\)C. The effect of temperature on the adsorption process is presented in Figure 10.  
Figure 9: Effects of initial concentration and contact time on DR-81 removal efficiency (C\({}_{0}\) = 10 mg/L, m = 0.5 g, pH = 7.09, T = 22 \({}^{\circ}\)C, v = 300 rpm).  
From the data in Figure 10, the decrease in the removal efficiency of DR-81 from 88 to 67%, and in the capacity of adsorption from 0.88 to 0.74 mg/g, with a rising temperature in the range 22-61 \({}^{\circ}\)C, can be explained by weaker adsorption forces between the bio-adsorbent and the DR-81 molecules. This implies that when the temperature increases, either the active sites on the bio-adsorbent surface are damaged [42] or the Brownian movement of the DR-81 molecules increases as the temperature rises.
## 2 Data and Method  
### Sentinel-1  
Sentinel-1 (S-1) is a part of the European Space Agency's (ESA) Copernicus program, dedicated to Earth observation. Sentinel-1 carries an advanced SAR system that provides high-resolution radar imagery regardless of weather conditions or daylight. It is composed of two satellites (A and B, launched in 2014 and 2016, respectively. The Sentinel-1B mission ended in 2022), both operating at C band with a radar frequency of 5.405 GHz. S-1 is designed to operate at four imaging modes: interferometric wide swath (IW), extra wide swath, stripmap, and wave mode. IW acquisitions are mostly located along the coastal regions, with a swath of 250 km at the spatial slant-range spacing of 2.5 m by 15 m (range by azimuth) for the original single-look images.  
In this study, the level-2 ocean (OCN) products of IW mode are included to jointly retrieve the sea surface wind and surface current velocity. This type of product is formatted into three components: ocean wind (OWI), radial velocity (RVL), and ocean swell [19]. Specifically, radar NRCS from the OWI component at a spatial resolution of 1 km \(\times\) 1 km is used in the following analysis. The SAR-estimated Doppler centroid annotated in the RVL group is also included, available at 1 km \(\times\) 1 km resolution. The products acquired by both satellites between January 2018 and December 2023 over the coast of North America are explored, resulting in the acquisition of 407 images. Figure 1 presents the spatial coverage of the S-1 IW products used in this study. Due to the revisit planning of S-1, all products are acquired over the overlapping coverage as shown. It is of note that all products are used for wind inversion and validation, while only SAR images over the Gulf Stream are included for current validation.
## 2 Data Used for Typhoons Hagibis and Bualoi
### Super Typhoon Bualoi  
The predecessor of _Bualoi_ (No. 1921) was the tropical generated in the Northwest Pacific on 15 October, 2019. It was upgraded to a tropical storm and numbered 1921 by the JMA at approximately 13:00 UTC on 19 October. Since that time, Typhoon _Bualoi_ steadily strengthened. The JMA and CMA upgraded it to a strong tropical storm at 00:00 UTC and 09:00 UTC on 27 October, respectively, and to a super typhoon on 22 October. _Bualoi_ then began to weaken and gradually transformed into an extratropical cyclone. At 12:00 UTC on 25 October, the CMA discontinued forecasting on _Bualoi_.
# G&G Attack: General and Geometry-Aware Adversarial Attack on the Point Cloud
## 4 Experiments Settings and Results  
### Experimental Setup  
The ModelNet40 [54] consists of 12,311 CAD models across 40 common categories that are suitable for benchmark training (9843) and testing (2468) in classification and segmentation tasks. We normalize each point cloud surface to a unit sphere to standardize and validate our method. Preprocessing steps such as rotation, translation, and scaling are applied to the data, as described in [27]. To ensure the validity of black-box transfer query attacks, the targeted classifiers default to applying pre-trained parameters on the down-sampled dataset based on the original content.  
We compare the G&G attack with various outstanding methods, including gradient-based point-move methods, such as PGD [18], SI-Adv [20], and I-FGSM [11], gradient-based point-drop and point-add methods, such as Saliency Map [24] and Add [27], and constraint-based optimization methods, like AOF [44] and L3A-attack [55]. All methods adopt the best parameters provided by the original papers. In our method, for the global attack, the step size is set to 0.02 with 150 iterations. The dropout parameter for the autoencoder is 0.4. After training, some parameters are randomly frozen to improve the network's performance. We use LeakyReLU as the activation function in the backbone. For local attacks, the step size is 0.16, the batch size is 16, and the number of iterations is 100. To normalize, all adversarial examples are subject to an L-infinity norm (with a radius of 0.07), and the attacks are non-targeted. We conduct comprehensive evaluations of the Attack Success Rate (ASR), Chamfer Distance (CD), Hausdorff Distance (HD), \(L_{2}\)-norm, and Attack Time (AT) under the same experimental environment (NVIDIA GeForce RTX 4090 and Intel(R) Xeon(R) Gold 6326 CPU @ 2.90 GHz).
## 5 Simulation Verification
### Comparative Experiment
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**t\({}_{i}\)** & **Random Path** & **Uniform Path** & **Method In Ihis Paper** \\ \hline
1 & 0.000 & 0.000 & 0.000 \\
2 & 1.755 & 1.766 & 1.325 \\
3 & 2.534 & 2.316 & 1.802 \\
4 & 3.709 & 2.825 & 2.271 \\
5 & 5.112 & 3.441 & 2.720 \\
6 & 6.396 & 4.093 & 3.171 \\
7 & 7.336 & 4.656 & 3.645 \\
8 & 8.396 & 5.224 & 4.127 \\
9 & 9.121 & 5.811 & 4.598 \\
10 & 10.970 & 7.306 & 5.899 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of experimental results. Figure 7: The figure of three trajectory planning. Figure 8: Random path trajectory planning curves charts: (**a**) angular displacement chart; (**b**) angular velocity chart; (**c**) angular acceleration chart; (**d**) angular jerk chart. Figure 9: Uniform path trajectory planning curves charts: (**a**) angular displacement chart; (**b**) angular velocity chart; (**c**) angular acceleration chart; (**d**) angular jerk chart. Figure 10: Angular displacement curve figures of different algorithms: (**a**) NSGA-II; (**b**) MOPSO; (**c**) MOEA/D; (**d**) SPEA2; (**e**) KNSGA-II; (**f**) AONSGA-II. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{t_{i}}\) (s) & **NSGA-II** & **MOPSO** & **MOEA/D** & **SPEA2** & **KNSGA-II** & **AONSGA-II** \\ \hline \(\mathbf{t_{1}}\) & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ \(\mathbf{t_{2}}\) & 3.077 & 2.748 & 4.927 & 3.814 & 2. Figure 11: Angular velocity curve figures of different algorithms: (**a**) NSGA-II; (**b**) MOPSO; (**c**) MOEA/D; (**d**) SPEA2; (**e**) KNSGA-II; (**f**) AONSGA-II. Figure 12: Angular acceleration curve figures of different algorithms: (**a**) NSGA-II; (**b**) MOPSO; (**c**) MOEA/D; (**d**) SPEA2; (**e**) KNSGA-II; (**f**) AONSGA-II. Based on the data in Table 10 and the trajectory curve figures in Figures 10-13, all algorithms satisfy the kinematic constraints in Table 7, validating the constraining effect of the penalty function on the results. By comparing the results, it can be observed that the traditional NSGA-II achieves shorter time durations compared to other optimization algorithms, demonstrating that NSGA-II is more suitable for solving the problem presented in this paper. KNSGA-II and AONSGA-II exhibit better search capabilities than the traditional NSGA-II. Figure 13: Angular jerk curve figures of different algorithms: (**a**) NSGA-II; (**b**) MOPSO; (**c**) MOEA/D; (**d**) SPEA2; (**e**) KNSGA-II; (**f**) AONSGA-II. tional NSGA-II, resulting in shorter time durations. However, when comparing with the improved NSGA-II proposed in this paper, the total runtime is optimized to 5.899 s, which is 38.4% and 54.4% shorter than KNSGA-II (9.591 s) and AONSGA-II (12.939 s), respectively. This further confirms that the improved NSGA-II possesses superior search capabilities.
# Improved Sea Ice Image Segmentation Using U\({}^{2}\)-Net and Dataset Augmentation
## 5 Conclusions  
Based on the U\({}^{2}\)-Net semantic segmentation network, this study expanded the training set using a data augmentation method and investigated the effect of data augmentation on the accuracy and generalization ability of U\({}^{2}\)-Net. In the case of poor segmentation of some sea ice images, the SC-U\({}^{2}\)-Net network was constructed by adding a multiscale, inflation convolution and multilayer CBAM attention mechanism on top of U\({}^{2}\)-Net, and its accuracy was compared with that of the U\({}^{2}\)-Net network. The study concluded the following: (1) U\({}^{2}\)-Net could segment the original test set images well, but the model generalization was poor. (2) The multilevel Gaussian noise data enhancement scheme designed in this study improved the noise interference resistance of the network, considered the generalization performance and accuracy of the model, and achieved more accurate segmentation of images with different degrees of noise pollution. (3) In SC-U\({}^{2}\)-Net, the residual structure reduced the loss of features during downsampling, multiscale inflation convolution increased the perceptual field of deep convolution, and the multilayer CBAM attention mechanism improved the recognition ability of the network for local features. SC-U\({}^{2}\)-Net had a higher average IoU, average F1-score, and average recall rate than U\({}^{2}\)-Net for each test set, especially for fragmented sea ice regions.  
The limitations of the experiments were as follows: (1) From the experimental data, the amount of training and test data were relatively small, which affects the reliability of the network training effect and test accuracy. (2) In the experimental setup, only U\({}^{2}\)-Net and SC-U\({}^{2}\)-Net were compared, and the other networks were not used as references in the accuracy assessment. (3) The experimental results indicated that, although both data augmentation and network improvement could improve the accuracy of semantic segmentation, the improvement was not substantial enough.  
Conceptualization, Y.L., H.L. and S.J.; methodology, Y.L.; software, S.J.; validation, Z.L. and D.F.; formal analysis, S.J.; investigation, S.J.; resources, H.L.; data curation, H.L.; writing--original draft preparation, Y.L.; writing--review and editing, S.J.; visualization, Y.L.; supervision, S.J.; project administration, S.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Natural Science Foundation of China (No. 41971427). Songshan Laboratory Project (No. 221100211000-5). National Science Foundation of Henan Province under Grant (No. 222300420592). The High Resolution Remote Sensing, Surveying and Mapping Application Demonstration System (Phase II) (No. 42-Y30B04-9001-19/21).  
Not applicable.  
The authors declare no conflict of interest.
# Robust Burst Detection Algorithm for Distributed Unique Word TDMA Signal
## 1 Introduction
### Related Work  
At present, there are mainly the following burst detection algorithms for TDMA signal. The energy based detection algorithm [6; 7; 8] can determine the existence of burst signal by detecting the energy within the window. This kind of method has good anti-frequency offset performance; however, it is sensitive to noise and is not suitable for detection under low SNR conditions. The frequency domain based detection method [9; 10] leverages the frequency domain features of signal to detect the start position of burst signal. This method has good anti-noise performance; unfortunately, the realization complexity is impractical. The unique word (UW) based detection algorithm [11; 12; 13] can achieve high accuracy by detecting the burst of received signal through the self-correlation property of UW in the TDMA signal under the assumption that the receiver has the perfect prior knowledge of UW. However, current signal detection algorithms based on UW mainly consider the distribution of UW in signal frame head [14; 15; 16; 17], and we have not seen much research on DUW, except Hou\({}_{1}\) and Hou\({}_{2}\) algorithms [18]. They have advantages of good anti-frequency offset, but do not behave well at lower SNR and are greatly influenced by the structure of UW. To sum up, in order to improve detection accuracy and robustness, burst detection based on DUW needs further research.
## 3 Results and Discussion
### 3.3. USeNPs Cytotoxicity  
#### 3.3.1 USeNPs Cytotoxic Activities against Normal and Cancer Cell Lines  
Vero and HepG2 cell lines were treated with varying doses of USeNPs (3.9-500 ug/mL), and the cytotoxicity (illustrated in Figures 9-11: control1,2, A1,2, B1,2 and C1,2) occurred in a concentration-dependent manner, with CC\({}_{50}\) = 331.76 \(\pm\) 4.79 ug/mL, and IC\({}_{50}\) = 103.36 \(\pm\) 2.93 ug/mL, respectively. Preliminary clinical trial findings indicated that  
Figure 8: Zeta potential of the USeNPs.  
Figure 7: EDX analysis of USeNPs.  
for its anticancer efficacy, dosage and selenium forms are essential factors [14]. All examined concentrations exhibited cytotoxic activity, except for 3.9 and 7.8 \(\upmu\)g/mL, which exhibited 100% cell viability and 0% inhibition in both cell lines. At concentrations of 15.6 and 31.25 \(\upmu\)g/mL, an inhibitory effect on cancer cells was observed, with inhibition rates of 1.85% and 11.96% seen, respectively. Yet they do not have any toxic effects on normal cells, which confirms the safety of such photosynthesized NPs. This is because along with the results from the remainder of the tested concentrations, which confirm the safety of these NPs, the viability percentages were 97.18 \(\pm\) 0.94, 80.72 \(\pm\) 2.06, 59.41 \(\pm\) 3.67, and 30.64 \(\pm\) 2.82% for the Vero cell line, and 71.49 \(\pm\) 2.75, 38.62 \(\pm\) 3.14, 19.43 \(\pm\) 1.79, and 8.75 \(\pm\) 1.03% for the HepG2 cell line, at concentrations of 62.5, 125, 250, and 500 \(\upmu\)g/mL, respectively, showing improved cytotoxicity of USeNPs against HepG2 cells. These findings agreed with [PERSON] et al. [58] who found that selenium NPs induced significant cytotoxicity in HepG2 cells at concentrations ranging from 10 to 50 \(\upmu\)g/mL.  
Figure 10: The cytotoxicity of USeNPS against mammalian cells from African green monkey kidney (Vero) with 50% cell cytotoxic concentration (CC\({}_{50}\)) = 331.76 \(\pm\) 4.79 \(\upmu\)g/mL.  
Figure 9: The cytotoxicity of USeNPS against HepG2 with 50% cell cytotoxic concentration IC\({}_{50}\) = 103.36 \(\pm\) 2.93 \(\upmu\)g/mL.
# Parametric Selection of Optimized Epicyclic Gearbox Layouts for Wind Power Plant Applications
###### Abstract  
The mechanical multiplier gearbox is one of the most important parts for wind power generation efficiency. Modern epicyclic gearboxes are compact, lightweight builds capable of high power ratings with coaxial input and output shafts. To achieve this, it is very important to select the proper internal gearbox layout and other relevant parameters in the early design stages as the wrong choices will result in a suboptimal solution. Parametric optimization was applied to select the optimal gearbox solution for a wind turbine application, while taking into account both two-carrier and three-carrier solutions. The large number of possible solutions has resulted in the development of the 2-SPEED software to conduct systematic analysis and comparison. The best five two-carrier solutions and the one best three-carrier solution have been selected from the solution pool, with the selection being based on the criteria of maximum efficiency, minimum weight, and minimal greater-ring diameter size. One optimal two-carrier solution was then selected from the five and compared to the three-carrier solution. Recommendations for the selection of either two-carrier and three-carrier gear train solutions according to the application demands have been deducted and provided. This will result in lighter, more efficient designs with smaller radial dimensions.  
epicyclic gear train; complex planet carrier gear train; multiplier gearbox; comparative analysis; wind power generation; renewable energy; sustainable development
###### Abstract  
_Uncaria rhynchophylla_, a medicinal plant extensively used in traditional Chinese medicine, is an important plant source of terpenoid indole alkaloids (TIAs), but the mechanism of TIA biosynthesis at molecular level remains unclear. Geraniol synthase (GES) serves as a crucial enzyme in catalyzing the formation of geraniol from geranyl pyrophosphate (GPP) in various plants, but the functional characterization of the _GES_ gene in _U. rhynchophylla_ has not been investigated. In this study, a _GES_ was identified and characterized through genome mining and bioinformatic analysis. Functional validation was performed via a protein catalysis experiment, transient expression in _Nicotiana benthamiana_, and methyl jasmonate (MeJA) induction experiments. The full-length _UrGES_ gene was 1761 bp, encoding a protein product of 586 amino acids with an estimated 67.5 kDa molecular weight. Multiple sequence alignments and phylogenetic analysis placed UrGES within the terpene synthase g (TPS-g) subfamily, showing high similarity to known GESs from other plants. Enzymatic assays confirmed that recombinant UrGES catalyzed GPP conversion to a single product of geraniol. The transient expression of _UrGES_ resulted in geraniol accumulation in _N. benthamiana_, further confirming its function in vivo. _UrGES_ expression was observed in leaves, stems, and roots, where leaves had the highest transcript levels. Moreover, MeJA treatment significantly upregulated _UrGES_ expression, which positively correlated with an increase in alkaloid content. This study functionally characterizes UrGES as a geraniol synthase in _U. rhynchophylla_, contributing to the current knowledge of the TIA biosynthetic pathway. These findings may offer insights for
## 7 Simulations and Results
### Scenario 2: HEMS with an ESS
#### 7.2.2 PAR Using RTP and CPP  
When the ESS was incorporated into the proposed architecture, a greater reduction in the PAR was observed since the ESS supplied the stored energy to meet the load demand. In the unscheduled case, the PAR was 4.4 which became 3.580, 3.413, and 3.052 using optimal scheduling with the GA, GWO, and HGWGA, respectively. The percentage reduction in the PAR after the optimal scheduling using the GA, GWO, and HGWGA was 19.38%, 23.13%, and 31.25%, respectively, relative to the unscheduled case. The same PAR values were observed in both the RTP and CPP tariff schemes. The values of the PAR for the unscheduled and scheduled cases are shown in Figure 15.  
Figure 14: Total cost with an ESS using the CPP.  
Figure 13: Total cost with an ESS using the RTP.
## 3 Results and Discussion
### Apparent Reaction Activation Energy
These findings suggested that the diffusion step was more likely to control the leaching of Ye from the activated sample. Figure 8: Kinetic model fitting curves of inactivated samples at different reaction temperatures. Figure 9: Arrhenius diagram of the sample. (**a**) Mechanically inactivated sample; (**b**) Mechanically activated samples. Additionally, the apparent activation energy (E\({}_{\text{ap}}\)) of the sample that was mechanically activated for 60 min at 1800 r-min\({}^{-1}\) ball milling speed was calculated, and the apparent activation energy of the sample was reduced to 37.4 kJ\(\cdot\)mol\({}^{-1}\) after the mechanical activation pretreatment. This result further indicates that the leaching process is controlled by the diffusion through a liquid phase boundary layer. The above study suggested that the apparent activation energy of the sample can be significantly reduced by the method of the mechanical activation pretreatment, the dependence of the sample on the temperature during the reaction process can be reduced, and the leaching process was changed from controlled by chemical reaction to controlled by diffusion in the solid product layer.