esa-sceva/satcom-chunk-collection huggingface dataset:
# Borehole Trajectory Optimization Design Based on the SAC Algorithm with a Self-Attention Mechanism
## 2 Materials and Methods
### Data Collection and Generation Method  
The 3D geological data used for wellbore trajectory optimization in this study were generated using Wasserstein generative adversarial network (WGAN) technology, with public logging data from Daqing Oilfield as the foundational training samples. The WGAN model consists of a generator and a discriminator, which operate through an adversarial mechanism: the generator is responsible for producing simulated geological data, while the discriminator evaluates the similarity between the generated data and real samples (derived from Daqing Oilfield logging data). By reconstructing the loss function using Wasserstein distance, this approach effectively addresses the training instability of traditional GANs in scenarios with sparse geological data, avoids mode collapse (where generated data becomes overly homogeneous), and stabilizes gradient updates during training. The generated data accurately reproduce complex spatial distributions and statistical characteristics of geological features in the target area (based on Daqing Oilfield's geological properties), including stratigraphic interfaces, fault strikes, lithology distributions (e.g., sandstone, shale), and pore pressure fields. These data are output in 3D point cloud format (.ply) and mapped to the reinforcement learning environment, providing real-time geological constraints for trajectory optimization.
## 2 Materials and Methods
### Description of the Selected DL Architectures
#### 2.2.3 Hybrid Systems  
Hybrid systems combine ViTs and CNNs with the aim of further improving image processing efficiency. In the hybrid system, local features are captured with CNNs, while more global relations are obtained with the attention mechanisms of ViTs. These systems have been included in the comparative study carried out in this work because they are increasingly used in the medical field.  
Hybrid systems are very recent, but there are already several architectures trained with Imagenet and available to the scientific community. In the present work, DeiT [27], CaiT [26], CeiT [28], and ConViT [29] have been used. We describe them briefly below:  
* DeiT is a ViT trained with a transfer learning technique called \"knowledge distillation\" [27]. A larger convolutional model is used as a \"teacher\" to guide the training of the smaller model, which is the ViT part. For this purpose, the \"distillation token\" is introduced, and the goal of the ViT part is to reproduce this label instead of the class label (Figure 10);
* CaiT [26] is based on DeiT and also uses distillation training. In CaiT, a LayerScale normalisation is added to the output of each residual block, and new layers called \"attention class\" layers are incorporated. These layers allow the separate computation of inter-patch self-attention and classification attention to be finally processed by a linear classifier (Figure 11). In our work, we have tested two versions of this model: the \"CaiT_XXS36\" version with 17.3 million parameters and the \"CaiT_S24\" version with 46.9 million parameters;
* CeiT [28] incorporates several modifications over the original ViT. Firstly, it uses a low-level feature extraction module (image-to-token) that is applied to the input image. In the encoder blocks, the Feed-Forward Network (FFN) is replaced with a Locally Enhanced Feed-Forward Network (LeFF), which promotes correlation between tokens with convolution operations (Figure 12). Finally, a new type of block called \"Layer-wise Class Token Attention\" (LCA) is added, which contains a Multi Self-Attention MSA layer and an FFN. Its mission is to compute attention only on the class token, thus reducing the computational cost;
* ConViT [29] introduces a new attention network called \"gated positional self-attention\" (GPSA). This network consists of a subnetwork that mimics convolutional behaviour and an attention head. There is a parameter that regulates the influence of both parts (Figure 13).  
Figure 9: Architecture of Twins-PCPVT.  
Figure 11: Architecture of CaiT.  
Figure 12: Architecture of CeiT.  
Figure 10: Architecture of DeiT.
# Naphthoquinone-Based Meroterpenoids from Marine-Derived _Streptomyces_ sp. B9173
## 2 Materials and Methods
### Extraction and Isolation of Flaviogenanin Congeners  
For small-scale analyses, the Streptomyces cultures were clarified by centrifugation after fermentation. The metabolites were extracted three times with ethyl acetate (EtOAc) from supernatant, concentrated by rotary evaporation, and the resulting residue was dissolved in methanol prior to HPLC-MS analysis, with 0.1% formic acid as solvent A and acetonitrile (ACN) as solvent B. A 46-min gradient elution condition for HPLC analysis started with a linear gradient from 10% B to 60% B for 13 min, followed by a gradient from 60-95% B for 22 min, and an additional isocratic step for 5 min. The column was washed by increasing the gradient to 100% B and was equilibrated for 5 min, followed by equilibration for 5 min under the starting conditions (10% B).  
Large-scale fermentation (150 L) was performed using 2-L baffled flasks containing 0.5 L liquid MS medium with 8% seed culture. The cell pellet was separated from fermentation broth by centrifugation. The supernatant was extracted three times with half volume of EtOAc, combined together and evaporated under reduced pressure to yield a 27.1 g dark brown crude extract. The obtained EtOAc crude extract was loaded onto a silica gel column and eluted using an increasing gradient of 0% to 100% petroleum ether-CH2 Cl2 and CH2 Cl2-MeOH as mobile phase. Every 200 mL was collected as a fraction at the 10 mL/min flow rate. In total, 12 fractions (Fr.1-Fr.12) were collected and analyzed by HPLC. Fr.3 (2.1 g) was further purified using flash RP-C18 column chromatography with an increasing gradient of 20% to 50% MeOH to give 15 fractions (Fr.3.1-Fr.3.15). Fr3.9 (118.2 mg) was purified by a Sephadex LH-20 column packed with Sephadex LH-20 resin (40 g) and eluted with MeOH and acetone, respectively, to yield **6** (13.7 mg) and **7** (18.3 mg). Fr.3.10 (72.6 mg) was separated by a Sephadex LH-20 column and eluted with MeOH to yield **3** (15.2 mg). Fr.3.8 (853.1 mg) was subjected to an RP-C18 column, eluted with 50%MeOH in H2O and followed by Sephadex LH-20 separation to yield  
**1** (11.1 mg), **2** (10.8 mg), **4** (17.7 mg), and **5** (15.7 mg). The NMR data for new compounds (**1-3**) are presented in Figures S3-S5 and summarized in Tables 1-3. The \({}^{1}\)H-NMR spectra and HR-ESI MS data of known compounds (**4-7**) are listed in Figures S6-S9 and Table S1, which are matched by the literature [10; 23; 24; 25; 26].
###### Abstract  
'Seolhyang' strawberry is harvested before it is fully ripened and treated with CO\({}_{2}\) to extend the shelf-life. However, the volatile changes in the 'Seolhyang' strawberry after short-term CO\({}_{2}\) treatment have not been investigated, although the volatile profile is an important quality attribute. Herein, we investigated the effect of short-term high CO\({}_{2}\) treatment on the changes in the composition of volatile compounds in 'Seolhyang' strawberries at two ripening stages (i.e., half-red and bright-red) during cold storage using headspace solid-phase microextraction and gas chromatography-mass spectrometry. Furthermore, the effect of CO\({}_{2}\) treatment on fruit quality with respect to the aroma was investigated. A total of 30 volatile compounds were identified. Storage increased the volatile compound concentrations, and the total concentration of volatiles in the CO\({}_{2}\)-treated strawberries was lower than that of the untreated strawberries during storage. The production of some characteristic strawberry volatiles (e.g., 4-methoxy-2,5-dimethyl-3(2_H_)-furanone) was inhibited in CO\({}_{2}\)-treated strawberries. However, CO\({}_{2}\) treatment helped maintain the concentrations of hexanal and 2-hexenal, which are responsible for the fresh odor in strawberries. Interestingly, CO\({}_{2}\) treatment suppressed the production of off-odor volatiles, acetaldehyde, and hexanoic acid during strawberry storage. Thus, short-term CO\({}_{2}\) treatment may help maintain the fresh aroma of strawberries during cold storage.  
Keywords:strawberry; volatile; gas chromatography-mass spectrometry; cold storage; maturity; modified atmosphere +
Footnote †: journal: _m molecules
# Nonlinear Metasurface for Structured Light with Tunable Orbital Angular Momentum
###### Abstract  
Orbital angular momentum (OAM) beams may create a new paradigm for the future classical and quantum communication systems. A majority of existing OAM beam converters are bulky, slow, and cannot withstand high powers. Here, we design and experimentally demonstrate an ultra-fast, compact chalcogenide-based all-dielectric metasurface beam converter which has the ability to transform a Hermite-Gaussian (HG) beam into a beam carrying an OAM at near infrared wavelength. Depending on the input beam intensity, the topological charge carried by the output OAM beam can be switched between positive and negative. The device provides high transmission efficiency and is fabricated by a standard electron beam lithography. Arsenic trisulfide (As\({}_{2}\)S\({}_{3}\)) chalcogenide glass (ChG) offers ultra-fast and large third-order nonlinearity as well as a low two-photon absorption coefficient in the near infrared spectral range.  
nonlinear optics; metasurfaces; structured light +
Footnote †: journal: Applied Physics Letters  
1
# Image Deraining Algorithm Based on Multi-Scale Features
## 4 Experiments and Results Analysis
### Drain Results  
Table 2 shows that our method achieves better PSNR/SSIM scores on all 5 datasets, significantly improving the technical level. Compared to MSPFN, we obtained a performance gain of 0.986 dB.  
Figure 6 shows a visual comparison of challenging images. Our model can effectively remove rain streaks of different directions and sizes and generate images that tend to be realistic. It can be seen that the SEMI method still leaves some rain patterns, the PreNet method damages the structural content and fails to restore the region obscured by rain patterns, our model can effectively remove the rain patterns and produce an image that tends to be realistic.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Test100**} & \multicolumn{2}{c}{**Rain100H**} & \multicolumn{2}{c}{**Rain100L**} & \multicolumn{2}{c}{**Test400**} & \multicolumn{2}{c}{**Test500**} \\ \cline{2-10}  & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline DrainNets & 22.77 & 0.810 & 14.92 & 0.592 & 27.03 & 0.884 & 20.35 & 0.757 & 22.35 & 0.831 \\ SEMI & 22.35 & 0.788 & 16.56 & 0.486 & 25.03 & 0.842 & 20.52 & 0.735 & 25.13 & 0.816 \\ DIDMDN & 22.56 & 0.818 & 17.35 & 0.524 & 25.23 & 0.741 & 24.21 & 0.765 & 28.73 & 0.895 \\ UMRL & 24.41 & 0.829 & 26.01 & 0.832 & 29.18 & 0.923 & 25.78 & 0.807 & 29.62 & 0.904 \\ RESCAN & 25.00 & 0.835 & 26.36 & 0.786 & 29.80 & 0.881 & 27.44 & 0.810 & 29.51 & 0.877 \\ PreNet & 24.81 & 0.851 & 26.77 & 0.858 & 32.44 & 0.950 & 27.67 & 0.822 & 30.46 & 0.906 \\ MSPFN & 27.50 & 0.876 & 28.66 & 0.860 & 32,40 & 0.933 & 28.26 & 0.827 & 31.17 & 0.915 \\ \hline MFD (Ours) & 29.45 & 0.874 & 28.67 & 0.870 & 35.01 & 0.959 & 28.12 & 0.839 & 31.67 & 0.919 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Image deraining results.
## 5 Numerical Studies
### Test Instances and Experimental Setting  
To the best of our knowledge, there are currently no publicly available benchmark instances specifically designed for the problem under investigation. Therefore, we randomly generate test instances to conduct our numerical studies. Specifically, all locations of customers, the depot, and the terminal are generated on an imaginary time plane spanning 3 h in width and length. To calculate the travel time between two sites, we utilize their Euclidean distance as the metric. The time required for packing/unpacking a full container follows a uniform distribution ranging from 0.2 to 1 h. Each request is associated with two distinct time windows, and each of these time windows is assumed to have a duration of about 4 h. Eight working hours are included in the one-day planning horizon. Dispatching a truck incurs a fixed cost of 10, along with a travel cost of 1 per unit of time. Furthermore, parameters \(e\) and \(Q\) are randomly generated. We conduct numerical tests on different sets of random instances, with request sizes ranging from 15 to 75. We create five random instances for each problem size. Each instance is labeled \"Ins-X-Y\", where X represents the number of inland container transportation requests, and Y indicates the instance index within the set.  
To evaluate the efficacy of the proposed solution approach, we compare its results with those derived from directly solving the MILP formulation using CPLEX, as well as with the results achieved by the genetic algorithm (GA) [43], the tabu search algorithm (TS) [17], the large neighborhood search (LNS) [2], and the simulated annealing algorithm (SA) [44]. In our experiments, the proposed branch-and-price-and-cut algorithm and CPLEX have a maximum runtime of 3600 s to solve each instance. The GA algorithm is configured with a population size of 30, a crossover probability of 0.4, and a mutation probability of 0.1. For the TS algorithm, the settings specify a neighborhood size of 30 and a tabu tenure of 20. Similarly, for the LNS algorithm, the neighborhood size is also set to 30 in each iteration. These three metaheuristic algorithms are executed for a maximum of 150 times the total number of requests for small-scale instances and 200 times the total number of requests for large-scale instances. For the SA algorithm, the initial temperature is set to 1000, and it decreases geometrically with a cooling rate of 0.95 until reaching a stopping threshold of 0. The neighborhood structure is generated using swap, insert, and reverse operations, and the acceptance probability of worse solutions follows the standard Metropolis criterion. To ensure a fair comparison, SA is executed for the same number of search as the other metaheuristic algorithms. Additionally, each algorithm is executed 10 times for each test case. We code the proposed algorithm in MATLAB, and use CPLEX 12.10 to solve the RLMP and MILP formulations. All experiments are conducted using a personal computer equipped with an Intel Core i5 1.6 GHz CPU, 8 GB RAM, and running the Windows 10 operating system.
## 4 Discussion  
### Evolution and Function of the _RcTPT Genes_  
The present study documented that the _R. chinensis_ genome comprised seven complete _RcTPTs_, indicating that the number of TPTs in _R. chinensis_ is less than those reported in _Arabidopsis thaliana_ (16), tomato (10), _Cinnamomum camplora_ (10), and _Oryza sativa_ (12), and more than or equal to that in _Chlamydomonas reinhardtii_ (4) and _Physcomitella patens_ (7) (Table S5) [25; 28; 58]. Unlike the TPT gene family in _Cinnamomum camplora_ (Lauraceae) that has segmentally duplicated TPT genes [28], no such TPT genes were observed in the genome of _R. chinensis_, which may be due to that _R. chinensis_ exhibited only the core eudicot-specific gamma whole-genome triplication with no recent polyploidization [59].  
The RcFPPS is predicted to produce FPP, the precursor of sesquiterpene, so the expression patterns of _RcFPPSs_ combined the expression patterns of different putative sesquiter  
Figure 8: Functional analysis of _RcTPS18_. (**a**) Protein sequence alignment of _RcTPS18_ and other (_E,E_)-\(\alpha\)-farmesene synthases. The arrows indicated the crucial residues for AFS activity. (**b**) Micro-synteny analyses among _Rosa chinensis_, _Prunus persica_ and _Fragaria vesca_. The collinear gene pairs are linked in the same color. The characterized genes are in bold. 1: Characterized _RcLIN-NERS3_ with incomplete sequence. 2: The location of _FalNES2_ in the strawberry genome was revised based on its amino acid sequence. (**c**) Emission abundance of (_E,E_)-\(\alpha\)-farmesene and _RcTPS18_ expression levels in three rose samples. _RtLIB12_ was used as an internal control. QL: _Rosa_ 'Qinglian Xueshi’. Data are presented as the mean \(\pm\) standard error (\(n=3\)). Different lowercase letters indicate statistically significant differences (ANOVA test, \(p<0.05\)).  
pene synthase genes. Both homodimeric and heterodimeric geranyl(geranyl)diphosphate synthase are involved in monoterpene biosynthesis [60; 61; 62]. Among the five homodimeric GPPS clustered with RcGPPS (Figure 2), three enzymes (SIDPPS, CrGPPS, and MiGPS1) were predicted to be located in mitochondria like RcGPPS. Both CrGPPS and MiGPS1 produced GPP as the sole or main product with IPP and DMAPP as substrates [60; 63]. However, the SIDPPS produced C45 and C50 prenyl diphosphates [64]. The VvGPPS and AtPPPS were predicted cytosolic localization. The expression of _VvGPPS_ were upregulated preceding and during the increase in precursor volatile organic compounds of monoterpenol [65]. The AtPPPS that originally proposed as a homomeric C\({}_{10}\)-geranyl pyrophosphate is identified as a _trans_-type polyprenyl pyrophosphate synthase, so it is suggested that the precursor C\({}_{10}\)-GPP for monoterpene biosynthesis in Arabidopsis may be provided only by heteromeric G(G)PPS [66]. Although the _RcGPPS_ expression and geraniol emission in MU samples showed similar trend, the products of RcGPPS in rose need further research.
# Hybrid-Attention Network for RGB-D Salient Object Detection
## 2 Proposed Method
### Hybrid Attention Predictor  
The depth principle error in non-salient objects described above makes the closest objects to the depth sensor to have either the lowest or highest intensities in a disparity map. When a neural network searches for salient objects in depth maps, it can be misled by such objects. Therefore, a single-modal attention map containing only depth information is biased. By leveraging the complementarity between RGB and depth information, we can eliminate the depth principle error by constructing a hybrid attention map. This map combines the RGB and depth modes to obtain a weighted attention map in which each pixels has information on its likelihood to belong to a specific object.  
Figure 1: Framework of our HANet. The RGB-D image is selected form Ref. [31].  
To obtain the hybrid attention map, we devise a decoder network (orange blocks in Figure 1) that consist of a \(3\times 3\) convolutions and binary interpolation upsampling. After each upsampling, we concatenate the lower-level and current features. The decoder blocks can be represented by the following formula:  
\[R^{n}=\sum_{k=1}^{C}U(F(R_{k}{}^{n-1}\oplus r_{k}^{n}.W)), \tag{1}\]  
where \(F\) represents convolution, U represents upsampling, \(k\) is the feature channel, \(R_{k}{}^{n\ -1}\) is the \(k\)-th channel of the \((n-1)\)-th RGB attention features extracted by the corresponding block in the decoder network, \(r_{k}{}^{n\ -1}\) is the \(k\)-th channel of the \((n-1)\)-th RGB features extracted by Res2 Net, whose number of channels is reduced by the convolutions, \(\bigoplus\) denotes concatenation, and \(W\) is the parameter for convolution.  
When the RGB attention map is obtained after decoding, we aggregate the depth attention map to generate the hybrid attention map. This cross-modal attention map provides accurate localization of objects in the image. Then, we multiply the map with low-level RGB features, and several convolutions and upsampling operations lead to the prediction map for salient object detection.
## 3 Results and Discussion

Table 11: Performance of the traditional machine learning algorithms and CNN model on the dataset (MSE). In the end, the importance of the loss function to train the LSTM models was investigated for Loc. 3. The model was trained using MSE, RMSE, MAE, and Huber loss function. The Huber loss function (\(H_{\delta}\)) is the composition of the MAE and MSE and can be calculated by Equation (15):  
\[H_{\delta}=\left\{ \begin{array}{ll}\frac{1}{2}(y-\hat{y})^{2}&\text{if }|y-\hat{y}|\leq\delta\\ \frac{1}{2}\delta^{2}+\delta|y-\hat{y}|&\textit{otherwise}\end{array} \right. \tag{15}\]  
where \(\delta\) is a hyperparameter that should be set. The Huber function is basically MAE, which becomes MSE when the error is small. Unlike the MAE, the Huber function is differentiable at 0. Table 12 shows the performance of the model on the validation set using Huber, MAE, RMSE, and MSE as loss function for Loc. 3. The model with MSE as a loss function outperformed the others.
## 3 Results
### A Rainstorm Forecast Result Analysis
#### 3.2.2 Radar Reflectivity Simulation  
The radar reflectivity can be used to reveal the development of convection and hydrometeor, which is important for rainfall development. Figure 11 shows the observed radar reflectivity from 0100 UTC to 0300 UTC 30 June, and simulated reflectivity at a corresponding time from CON and POD experiment. The observed radar reflectivity comes from the radar map of China meteorological data sharing service network ([[http://data.cma.cn/data/online/t/4](http://data.cma.cn/data/online/t/4)]([http://data.cma.cn/data/online/t/4](http://data.cma.cn/data/online/t/4))). According to the observed radar reflectivity (Figure 11a-c), clear radar echo is found from southwest to northeast in the precipitation area The reflectivity of 40 dBZ (with a maximum of 45 dBZ) appears in the border among Hunan, Jiangxi and Hubei Provinces, indicating strong convection.  
At 0100 UTC, the reflectivity (\(>\)10 dBZ) from CON (Figure 11d) is mainly located in the border among Hunan, Jiangxi and Hubei province, with the maximum reflectivity of about 35 dBZ. Obviously, the reflectivity in CON is weaker than the observation. In comparison, stronger convections are found in POD (Figure 11g). The coverage of reflectivity in POD is better consistent with observation, with a maximum of 40 dBZ. However, the area of 40 dBZ is slightly larger than the observations. It is inferred that at the beginning of forecast, the convection is slightly overpredicted, which is consistent with the overestimated rainfall in Figure 9. At 0200 UTC, the reflectivity in CON (Figure 11e) is strengthened a little, with a maximum up to 40 dBZ. However, it remains a weaker intensity and smaller area than the observations. For POD (Figure 11h), the area of 40 dBZ decreases compared to the first hour of the forecast. Thus, since POD overestimates the intensity of convention at the beginning of the forecast time, it adjusts itself to agree with observations. However, a relative stronger reflectivity can be found compared to CON. In the border among Hunan, Jiangxi and Hubei, where the main precipitation took place, POD yields about 40 dBZ, while 30-35 dBZ in CON. Similar results appear at 0300 UTC, in which reflectivity in POD shows better agreement with observation.  
Overall, the comparison of simulated reflectivity with observation provides more insight into the differences with and without assimilations. POD better capture the convection in the heavy rainfall area. Thus, assimilating satellite and radar data is able to strengthen the development of convection, improving the forecast of heavy rainfall.