esa-sceva/satcom-chunk-collection huggingface dataset:
# ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion
###### Abstract  
To enhance ship detection accuracy in the presence of complex scenes and significant variations in object scales, this study introduces three enhancements to ReDet, resulting in a more powerful ship detection model called rotation-equivariant bidirectional feature fusion detector (ReBiDet). Firstly, the feature pyramid network (FPN) structure in ReDet is substituted with a rotation-equivariant bidirectional feature fusion feature pyramid network (ReBiFPN) to effectively capture and enrich multiscale feature information. Secondly, K-means clustering is utilized to group the aspect ratios of ground truth boxes in the dataset and adjust the anchor size settings accordingly. Lastly, the difficult positive reinforcement learning (DPRL) sampler is employed instead of the random sampler to address the scale imbalance issue between objects and backgrounds in the dataset, enabling the model to prioritize challenging positive examples. Through numerous experiments conducted on the HRSC2016 and DOTA remote sensing image datasets, the effectiveness of the proposed improvements in handling complex environments and small object detection tasks is validated. The ReBiDet model demonstrates state-of-the-art performance in remote sensing object detection tasks. Compared to the ReDet model and other advanced models, our ReBiDet achieves mAP improvements of 3.20, 0.42, and 1.16 on HRSC2016, DOTA-v1.0, and DOTA-v1.5, respectively, with only a slight increase of 0.82 million computational parameters.  
artificial intelligence; deep learning; remote sensing images; ship detection; bi-directional feature fusion; feature pyramid network; anchor size; K-means; sampler
## 3 Artificial Intelligence Approach
### Unsupervised ML
#### 3.2.2 Principle Component Analysis (PCA)  
Principal component analysis (PCA) transforms a set of potentially correlated variables into a new set of uncorrelated variables, known as principal components. It diminishes the dimensionality of the original data while maintaining the most important information within the dataset [1]. PCA is of great help in reducing the dimensions, which, in turn, allows for faster processing and better visualization in a more comprehensible manner. In this work, critical dimensions from hyperspectral data were selected using PCA and merged with a deep learning model in classification tasks to improve the accuracy of the classification process of sorghum seeds. This study also showed how feature extraction using PCA increased performance to almost 99% correct classification [84]. The application described here highlighted how PCA is very efficient in handling large complex datasets, which usually happens when performing food item inspections. Chemical composition, color, and fat content provided significant compositional markers that enabled the successful classification of all types of cheese by PCA models. This application shows how feature selection through PCA can help improve estimation accuracy in food composition [144]. Indeed, PCA accelerates the processes of qualification in food, including those contributing to consumer safety and product quality by reducing data dimensions and increasing their interpretability.
# Trajectory Planning of Shape-Following Laser Cleaning Robot for the Aircraft Radar Radome Coating
## 2 Materials and Methods
### Laser Cleaning Robot Trajectory Planning  
The trajectory of the robot should be generated prior to the laser coating cleaning process. To achieve optimal cleaning results, two principles must be followed:  
(1) The trajectory of the robot for laser coating cleaning should align with the surface height of the component to be cleaned;  
(2) The central axis of the laser cleaning actuator held by the robot should always be perpendicular to the surface of the workpiece.  
3.1 Calibration of Coordinate Relationship between Radome Measurement Point Cloud and Cleaning Robot  
The aligned point cloud coordinate system differs from the robot coordinate system. In this study, a homogeneous transformation matrix was employed to perform the transformation from the point cloud coordinate system to the robot coordinate system. The transformation matrix was defined as follows:  
\[M_{4\times 4}=\begin{bmatrix}R&t\\ 0&1\end{bmatrix} \tag{12}\]  
where \(R\) is a \(3\times 3\) rotation matrix and \(t\) is the translation vector.
# Poly(lactic acid)-Based Electrospun Fibrous Structures for Biomedical Applications
## 5 Engineering of PLA-Based Electrospun Structures for Biomedical Applications  
Considering the wide range of potential applications of PLA in the biomedical field, one of the main challenges in the electrospinning approach is the design of structures with controllable features. By tuning the substances and methods of electrospinningning, the composition, architecture, and characteristics of the PLA-based nanofibrous structures can be engineered for specific applications [35]. In this context, several process and post-treatment modifications are proposed, such as the employ of multiple jets [39], manipulating the alignment and/or patterning using different collection devices or manipulating the electric field [37; 38; 40; 41], coaxial electrospinningning [9; 20; 42; 43], yarn electrospinningning [37; 44; 45], blends with other biopolymers [8; 9; 15], the use of micro or nanoparticles, and biomolecules in the polymer matrices [22; 26; 46], and surface modifications [4; 38; 40; 41]. In addition, by controlling electrospinningning process parameters, environmental conditions (e.g., temperature, humidity), and precise choice of solvent system, the electrospun PLA structures may exhibit distinguished features, such as wrinkles, porous, beaded, or hollow morphologies [35; 36; 37; 48; 47].
# Grapevine Phenology in Four Portuguese Wine Regions: Modeling and Predictions
## 3 Results
### Performance Analysis of the Phenological Model
#### 3.2.1 Budburst  
For budburst, the SM reveals higher performance metrics for Lisboa compared to the other regions, with an RMSE average value of five days and EF of 0.53. Conversely, the Vinhos Verdes region shows poorer results. In general, all varieties have an RMSE below one week (\(<\) 7 days). The efficiency of the model is equal to or greater than 0.5 for 26 varieties (\(>\) 0.75 for one variety).
## 3 Results
### ROS and RNS Scavenging Assays  
Table 3 shows the scavenging activity of baobab pulp extracts against the ROS (O2\({}^{\bullet-}\), H2O2, HOCI, and ROO\({}^{\bullet}\)) and RNS (\({}^{\bullet}\)NO and ONOO\({}^{-}\)).  
### ROS and RNS Scavenging Assays  
Table 3 shows the scavenging activity of baobab pulp extracts against the ROS (O2\({}^{\bullet-}\), H2O2, HOCI, and ROO\({}^{\bullet}\)) and RNS (\({}^{\bullet}\)NO and ONOO\({}^{-}\)).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{4}{c}{**Reactive Oxygen Species (ROS)**} \\ \hline Baobab pulp extracts IC\({}_{50}\) (mg/mL) & O\({}_{2}\)*\({}^{-}\) & H\({}_{2}\)O\({}_{2}\) & HOCI \\ \hline Aqueous & \(235\pm 18\) & \(21\pm 2\)* & \(156\pm 16\) \\ Hydroalcoholic & \(114\pm 5\) & \(55\pm 1\)* & \(46\pm 4\) \\ Alcoholic & \(338\pm 10\) & \(58\pm 0.3\)* & \(85\pm 7\) \\ Positive controls & - & \(62\pm 4\) & \(0.46\pm 0.03\) \\ Ascorbic acid & \(54\pm 3\)* & \(177\pm 9\) & \(2.33\pm 0.08\) \\ Tiron & \(83\pm 4\) & - & - \\ \hline Baobab pulp extracts & \multicolumn{4}{c}{ROO\({}^{\bullet}\) (SSample/STrolx)} \\ \hline Aqueous & & \(0.0030\pm 0.0003\) & \\ Hydroalcoholic & & \(0.0026\pm 0.0002\) & \\ Alcoholic & & \(0.0067\pm 0.0007\) & \\ Positive controls & & \(4.09\pm 0.52\) & \\ Ascorbic acid & & \(0.53\pm 0.06\) & \\ \hline \hline \multicolumn{4}{c}{**Reactive nitrogen species (RNS)**} \\ \hline Baobab pulp extracts IC\({}_{50}\) (mg/mL) & \({}^{\bullet}\)NO & \
\begin{tabular}{c} ONOO\({}^{-}\) \\ (without NaHCO\({}_{3}\)) \\ \end{tabular} &
\begin{tabular}{c} ONOO\({}^{-}\) \\ (with NaHCO)\({}_{3}\)) \\ \end{tabular} \\ \hline Aqueous & \(54\pm 6\) & \(64\pm 1\) & \(59\pm 4\) \\ Hydroalcoholic & \(27\pm 5\) & \(19\pm 1\) & \(17\pm 1\) \\ Alcoholic & \(40\pm 4\) & \(46\pm 1\) & \(42\pm 3\) \\ Positive controls & & \(0.091\pm 0.009\) & \(2.02\pm 0.08\) & \(3.67\pm 0.24\) \\ Ascorbic acid & - & \(0.41\pm 0.02\) & \(0.78\pm 0.03\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Superoxide radical (O2\({}^{\bullet-}\)), hydrogen peroxide (H2O2), hypochlorous acid (HOCI), peroxyl radical (ROO\({}^{\bullet}\)), nitric oxide (*NO), and peroxnritte (ONOO\({}^{-}\)) scavenging activities of aqueous, hydroalcoholic, and alcoholic extracts from _A. digitata_ pulp fruits and positive controls.  
Figure 2: Total phenolic content (TPC, **(A)**), total flavonoid content (TFC, **(B)**), ferric reducing antioxidant power (FRAP, **(C)**), and activity inhibition of 2,2-diphenyl-1-picrylhydraz (DPPH\({}^{\bullet}\), **(D)**). Values represent mean \(\pm\) SD (\(u=6\)). Results are expressed as mg of gallic acid equivalents (GAE), mg of catechin equivalents (CE), \(\mu\)mol of ferrous sulfate equivalents (FSE), and mg of Trolox equivalents (TE) per 100 g of baobab pulp. Different letters denote significant differences (\(p\leq 0.05\)).
# An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs
## 5 Conclusions  
This paper proposes an AGAN-based segmentation method for the task of X-ray chest image segmentation. The method combines the GAN framework with an adaptive learning algorithm. The whole framework includes three parts: a feature extractor, a discriminator and a selector. Taking a pair of GAN models as an example, the input image is segmented using an adaptive mechanism. First, a feature extractor based on two neural network models is used in the AGAN to generate feature vectors of specific dimensions from the input image. Second, a discriminator based on the same neural network structure is used to score the extracted dimensional features. Finally, an adaptive-control-based selector is implemented in the AGAN to dynamically adjust the training process and select optimal features. The discriminator is used to score the generator output and adaptively train the generators for different dimensions through an automatic adjustment algorithm so that the feature extractor will extract more representative feature vectors from the image input and achieve better generalizability. The experimental results obtained on a chest X-ray database show that the proposed AGAN is effective for X-ray chest radiograph segmentation and that its evaluation indexes are better than those of several other algorithms in general. The whole proposed system shows improved robustness and stability for image segmentation.  
However, training the AGAN requires a large amount of calculation. In this study, only the coordination of two different-dimensional networks with a small amount of data was implemented. Our future work will first focus on larger data volumes and a wider range of applications. In addition, we will also improve the adaptive learning framework and algorithm, through measures such as introducing neural network layers that share parameters to enable the model to further learn to extract common features between different dimensions, in order to reduce the computational burden and achieve stable results with multiple coordinated networks.  
Figure 10: ROC curve of four algorithms.  
**Author Contributions:** Conceptualization, Methodology and Validation, X.W. and X.T.; Formal analysis, Writing--original draft preparation and Writing--review and editing, X.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by THE SCIENCE AND TECHNOLOGY DEVELOPMENT FUND, MACAU SAR, grant number 0009/2018/A.  
**Conflicts of Interest:** The authors declare that there is no conflict of interests.
## 3 Fundamentals of RF Energy Harvesting
### Components of an RF Energy-Harvesting System
#### 3.2.3 Rectifier  
The rectifier is a crucial component in RFEH, responsible for converting RF signals into DC power. Its efficiency directly impacts the overall energy conversion performance of the system. The rectenna, an integrated structure combining a receiving antenna and a rectifier, as shown in Figure 5, is a key advancement in RFEH technology. Various rectifier configurations, including single-stage, voltage doubler, and multi-stage rectifiers, as shown in Figure 6, have been explored to optimize conversion efficiency. Schottky diodes are widely used in rectifier circuits due to their low forward voltage drop and high switching speed, making them suitable for low-power applications [33; 34]. Figure 6a shows a single-diode half-wave rectifier, also known as an envelope detector. This circuit consists of a series diode followed by a shunt capacitor and operates by allowing only one half-cycle of  
Figure 4: L–C impedance matching networks. (**A**) Series L component. (**B**) Shunt L component. (**C**) Pi-network. (**D**) T-network.  
the alternating-current (AC) signal to pass while blocking the other half. However, half-wave rectifiers introduce significant voltage ripples in the rectified output, necessitating additional filtering to reduce harmonic distortions and stabilize the DC voltage. Figure 6b presents a voltage doubler, also referred to as a single-stage voltage multiplier [35]. This configuration functions as a full-wave rectifier, ensuring more stable DC output compared to the half-wave rectifier. The circuit consists of a one-stage clamper with a pumping capacitor at the input, a shunt diode, a series-rectifying diode, and a shunt capacitor at the output. The output capacitor acts as a low-pass filter to smooth out variations in the rectified DC voltage. This setup results in a higher average DC voltage, making it more efficient for energy-harvesting applications. Figure 6c illustrates a multi-stage voltage multiplier, which is essentially an extended version of the voltage doubler. It further increases the output voltage using multiple capacitor-diode stages. Each stage builds upon the voltage of the previous stage, and the maximum output voltage is determined by the total number of stages [36; 37]. However, the primary drawback of multi-stage rectifiers is their decreasing overall efficiency. As more stages are added, cumulative power losses occur due to the inherent inefficiencies of each stage, making the design a trade-off between output voltage and energy conversion efficiency.  
The choice of rectifier topology significantly impacts the efficiency of RF-to-DC conversion. In scenarios where ample AC power is available, multi-stage rectifiers can substantially enhance DC voltage output. However, the reduction in overall power efficiency must be carefully considered when designing RF energy-harvesting systems for practical applications [39; 40].
# Improved Honey Badger Algorithm and Its Application to K-Means Clustering
## 5 Simulation Experiment and Analysis
### Experimental Comparative Analysis of Improved Honey Badger Algorithm
When the dimension was set to 30, the average value of IHBA reached 0 in F12, which showed a lower value than other algorithms, indicating that it was more efficient in finding the optimal solution. In F14, the average value, standard deviation, and optimal value of IHBA were all 0, which far exceeded the performance of other algorithms, reflecting its powerful ability to deal with complex functions. In F16, IHBA also obtained the optimal value and revealed a gap with the results of other algorithms. This suggests that it possesses a greater capacity to circumvent the local optimal solution. For F10, IHBA was slightly less stable, and SWO was better. When \(D\) = 200, the efficacy of the comparative algorithms across all three metrics diminished; however, IHBA continued to maintain a superior position over its counterparts throughout the solution process. In F12, the accuracy and stability of other comparison algorithms decreased to varying degrees. ARO decreased by six orders of magnitude compared with the average value of \(D\) = 30, and the optimal value in 30 runs decreased by nine orders of magnitude. Compared with \(D\) = 30, the performance of HBA in this test function also decreased, and the average value decreased by 20 orders of magnitude, while IHBA still reached the theoretical optimal value of 0 in the three indexes. In F10 and F15, the IHBA results decreased relative to the mean and standard deviation at \(D\) = 30. This shows that the change in dimension had a certain influence on IHBA, which affected the accuracy and stability of the algorithm. In general, although IHBA showed a certain optimization ability and excellent stability on multi-modal test functions, demonstrating its wide applicability in complex optimization problems, it still needs to be further improved. In the evaluation of fixed-dimension functions, IHBA demonstrated strong performance for functions F17 and F19-F22, achieving both optimal mean values and optimal standard deviations. In function F18, all six algorithms found the optimal value, but IHBA
## 5 Post-Processing
### Redesign Heat Treatment for a Sustainable PBF-LB/M Manufacturing
ASTM F3318-18 specifies the properties of finished PBF-LB/M AlSi10 Mg parts and includes guidelines for thermal processing techniques such as stress relieving, solution heat treatment, T6 tempering, and HIP [64]. HIP applies high temperatures, typically above 70% of the material melting point, combined with isostatic pressure, up to approximately 200 MPa, in a gas medium such as argon or nitrogen. HIP is primarily used to close internal pores and correct internal defects, thereby enhancing overall material performance, though it has limited effectiveness on surface-connected defects, such as surface pores [193]. Recent advancements in AM have led to the development of novel aluminum alloys specifically optimized for PBF-LB/M processes, which eliminate the need for post processing heat treatments like T5, T6, or HIP. These new alloys are engineered to deliver high strength, toughness, and ductility directly from the build process, reducing production time and costs while streamlining the manufacturing workflow. This breakthrough not only enhances efficiency and sustainability but also mitigates the risks of part distortion and microstructural degradation associated with traditional thermal processing. As a result, industries such as aerospace, automotive, and tooling benefit from faster production cycles, reduced energy consumption, and improved part quality, marking a significant advancement in AM technology. As mentioned in Section 4.1.1, Elementum 3D has developed enhanced versions of traditional alloys using its RAM technology, which incorporates ceramic nanoparticles to improve printability. The company launched aluminum alloys A5083-RAM2(tm) and A5083-RAM5(tm), featuring 2 and 5% volume of RAM additives, respectively, to the A5083 alloy. These high-strength aluminum alloys offer excellent printability and good corrosion resistance and do not require post-build heat treatment. For instance, A5083-RAM5 consistently exhibits uniform tensile properties in both horizontal and vertical orientations, whether in the as-printed or stress-relieved states. The inclusion of RAM additives substantially increases its strength relative to conventional wrought 5083 alloys. Specifically, in the as-printed condition, A5083-RAM5 achieves 1.8 times the yield strength of strain-hardened wrought 5083-H116, and in the stress-relieved state, it attains 2.9 times the yield strength of wrought 5083-O [194]. Figure 12 compares various heat treatment methods for PBFed aluminum alloys, highlighting their effectiveness in enhancing material properties and energy efficiency.
## 2 Monosaccharide Composition, Combinations, and Bioactivities of Mushroom Polysaccharides  
Glucan is a well-studied PS and can be produced by different fungi with various terms, such as lentinan, gonadotropin, grifolan, and schizophrenia [2; 17]. Many studies have addressed the structure-bioactivity relationships of glucans, particularly lentinan [2]. Aside from glucans, no other homo-PSs have been reported. Many mushroom hetero-PSs have been reported in the past two decades, and attempts have been made to elucidate their structure-bioactivity relationships. Few of the PSs have been commercialized, apart from high production or purification costs and low or erratic PS yields, partly because of their unstable chemical characteristics, e.g., monosaccharide composition/combinations that give rise to great variability [17]. The most extensively studied monosaccharide compositions involve glucose (Glc), mannose (Man), galactose (Gal), xylose (Xyl), arabinose (Ara), rhamne (Rha), and fucose (Fuc). Less frequently encountered components of mushroom PSs include fructose (Fru), ribose (Rib), glucuronic acid (GlcA), galacturonic acid (GalA), \(N\)-acetyl-glucosamine (GlcNac, GlcN), and \(N\)-acetyl-galactosamine (GalNac, GalN).
## 2 Materials and Methods
### Spectroscopic Ex Situ Analysis  
X-ray diffraction (XRD) analysis was performed using Bruker D2 Phaser X-ray powder diffractometer (30 kV, 10 mA) equipped with an LYNXEYE detector using Cu K\(\alpha\) radiation (0.15418 nm at room temperature). The patterns were collected for the same set of samples as for the FTIR measurements, with a step size of 0.02 and scan rate of 1 s in the 2\(\uptheta\) range of 5-40; repeated once due to large sampling spot size (about 15 mm). The diffractograms were resolved by fitting the six Gaussian curves and calculating the crystallinity estimator as a quotient of integrals of peaks attributed to the \(\upbeta\)-sheet region to the rest of the peaks [23].  
Infrared spectra were collected employing the Fourier Transformed Infrared Thermo Nicolet 8700 spectrometer with an MCT-B detector equipped with a Pike GladiATR attenuated total reflection accessory with a diamond crystal and heated table. The spectra were collected in the 4000-650 cm\({}^{-1}\) range with an optical resolution of 4 cm\({}^{-1}\), averaging 200 scans. Predefined and constant pressure between the sample and the ATR window was ensured. For each sample, three spectra were acquired. Each spectrum was corrected using automated ATR correction [9].  
Before ATR-FTIR measurements, samples aged 1 to 14 days were dried at 110 \({}^{\circ}\)C for 10 min to remove the water bonded to the silk structure that would shield the vibrations of Amide I and other carbonyl groups evolving upon oxidation and hydrolysis. For this purpose, a heating ATR stage was used.