esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Among them, magnetic solid phase extraction (MSPE) can be employed to purify complex food matrices, reduce the influence of matrix effect in mass spectrometry analysis, decrease the trace concentration of extremely low concentration components and convert solvents that can also be achieved by traditional solid phase extraction technology. Additionally, MSPE also outperforms traditional sample pretreatment technologies in separation and purification, and has been considered an important component in sample pretreatment methods [22; 23]. In recent years, new efficient, green, and environmentally friendly magnetic adsorption materials have become a research hotspot, and these materials are also the key to the detection of veterinary drug residues. The magnetic polymer composite microsphere is a new functional material compounded from inorganic magnetic nanoparticles and organic polymer materials. These microspheres have both the functional properties of polymer materials and the magnetic response of inorganic nanoparticles. They can attract functional groups (such as amino, carboxyl, and aldehyde) through modifying polymer materials, so as to graft enzymes, catalysts and other guest molecules [24]. At the same time, magnetic polymer composite microspheres can also realize magnetic separation and directional movement under the action of external magnetic fields. As a new functional material, magnetic polymer composite microspheres have broad application prospects in many fields, such as cell separation, targeted drug delivery, biological separation and immunoassay [25]. At present, there are many methods for the synthesis of magnetic polystyrene microspheres, such as monomer polymerization, the blending embedding method and the activation swelling method. The sample pretreatment process is shown in Scheme 1. In this paper, magnetic polystyrene microspheres with favorable performance were prepared by the optimized process based on the suspension emulsion polymerization method. In addition, the characterization, functionalization and practical sample adsorption application were carried out on these magnetic polystyrene microspheres.
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 5 Performance Evaluation
### Simulation Environment
#### 5.1.2 Network Lifetime  
The operation of the UWSN is subjected to several constraints, among which, available energy of the nodes is one of the most critical. Furthermore, network lifetime is an essential parameter in UWSNs as it represents the number of dead nodes generated in every simulation round because of energy exhaustion. In UWSNs, both UAVs and WSNs are power-constrained devices operated by batteries. Specifically, the SNs cannot be charged and replaced repeatedly. Hence, the energy of the SNs determines the lifetime of the UWSNs. The primary goal of our study is to minimize the energy consumption of the SNs. However, we have also attempted to use the UAVs' minimum possible energy to accomplish the defined mission. To minimize the energy of the SNs, we minimized the period for which the CHs remained active by synchronizing the UAV's flight time with their wake-up cycles. Besides this, we used the clustering approach, which enabled the extension of the network lifetime. To minimize the UAV energy, we have generated the smoothest and shortest path coordinates.
## 3 Results
### Example Application of the Methodology
#### 3.1.1 Initial Conditions  
For evaluation purposes we possess a test vehicle that is equipped with OBU Commsignia ITS OB4, GPS data logger, OBD2 CAN bus logging device, and HMI. As part of the evaluation process, we also use the C-ITS SIM tool, which is described in more detail in article [21]. This SIM tool was mainly used as the primary source for capturing the communication of C-ITS units to capture data of individual driver's rides. The GPS logger (Canmore GP-102+) tracker and CAN-BUS data logger (CANedge1) were used as additional recording devices. These recording devices were used mainly as a backup in case of failure of the main data source and for other experimental purposes. More information about the C-ITS SIM recording device is described in article [21].
# Comparative Study on Physicochemical and Nutritional Qualities of Kiwifruit Varieties
## 3 Results
### Energy, Carbohydrates, Protein, Lipid and Dietary Fiber
The mean values of protein content from _A. chinensis_ and _A. chinensis_ var. _deliciosa_ were 1.23 g/100 g and 1.14 g/100 g, respectively, in a previous study [20]. In this study, the results were similar to those for carbohydrate content, which indicated that our results were reliable. In the analyses based on the flesh color of kiwifruits, there are no significant differences between the three types, as shown in Figure 2E. As seen in Figure 2F, based primarily on the different species, the protein contents of _A. chinensis_ and _A. chinensis_ var. _deliciosa_, as initial commercial varieties, were higher than those of other species. Lipids are a vital component of the human body, and the lipid content in the study ranged from 0.59 g/100 g (R2, 'Donghong') to 1.01 g/100 g (G5, 'MHYX'); the lipid content of green-fleshed kiwifruits (0.82 \(\pm\) 0.14 g/100 g) was significantly higher than that of red-fleshed (0.64 \(\pm\) 0.06 g/100 g) and yellow-fleshed kiwifruits (0.69 \(\pm\) 0.08 g/100 g), as shown in Figure 2G. Based on different species, the lipid content of _A. eriantha_ was statistical higher than that of other species (Figure 2H). However, the results for _A. chinensis_ and _A. eriantha_\(\times\)_A. chinensis_ were similar. Therefore, the hybrid of _A. eriantha_\(\times\)_A. chinensis_ was similar to paternal parents (_A. chinensis_) in this study. Dietary fiber is an ingredient with a low calorie content that supports normal intestine function. On the one hand, the dietary fiber content of green-fleshed kiwifruits was significantly higher than that of red-fleshed and yellow-fleshed kiwifruits, which was similar to lipid content (Figure 2I). On the other hand, the dietary fiber content of _A. chinensis_ was significantly lower than that of _A. chinensis_ var. _deliciosa_ and _A. eriantha_ (Figure 2). The highest content of dietary fiber was 3.19 g/100 g in 'Hayward' in this study, and this was similar to a previous study, in which the dietary fiber content was 3.00 g/100 g in 'Hayward' [20]. In addition, the dietary fiber content of 'Hort16A' in this study was 1.5 times greater than that of 'Hort16A' in this previous study. The reason for this might be due to differences in climate, temperature and geography. Moreover, the average content of dietary fiber is 2.62 g/100 g in kiwifruit. The average dietary fiber contents were 0.98 g/100 g in dragon fruits [21], 2.28 g/100 g in apples [22] and 1.46 g/100 g in tomato fruits [30]. The dietary fiber content in kiwifruit was higher than that in dragon fruit, apple and tomato. Therefore, it suggested kiwifruit has high dietary fiber content. To sum up, the 14 kiwifruit varieties in this study had the characteristics of low energy and carbohydrates and high dietary fiber and are very suitable for people with \"three highs\" and poor stomachs.
## 1 Introduction
Land cover changes have a great impact on the land surface biogeochemical and biophysical processes [28; 29], which means that it could also change the ecosystem WUE. Northern China, which is also known as the Three North Regions (TNR) of China [30], is a typical area of arid and semiarid ecosystems where the shortage of water is one of the threats to plant growth. The ability of the plants to survive under limited water conditions is indispensable for the ecosystems of this region [8]. Ecological restoration projects, such as afforestation programs, have contributed to the accelerated greening trends in the TNR [31], but they have fewer effects on the biomass change in Northeast China [32] and on the hydrological cycle over the entire TNR during 1989-2009 [33]. Although land cover changes play a critical role in certain areas, the role is not large enough to shape the WUE pattern on a regional scale [26]. Therefore, the results of these studied imply that there are still insufficient studies on the impact of afforestation on WUE, especially in the TNR of China. It is a key scientific issue to assess the suitability of afforestation in arid and semiarid areas. Therefore, WUE was used as an indicator to reflect ecologically sustainable development, and the role of climate factors and human activity on WUE were quantitatively assessed in the arid and semiarid TNR. Specifically, the main objectives in this study were (i) to verify the effectiveness of the ecosystem WUE with the GLASS products, (ii) to analyze the spatiotemporal changes in the GLASS WUE from 1982 to 2015, (iii) to explore the impact of climate change and human activities on the WUE change trends, and (iv) to evaluate the relative importance of these two types of factors. Our results can contribute to the design of governmental ecological policies.
# DDL R-CNN: Dynamic Direction Learning R-CNN for Rotated Object Detection
## 4 Experiment
### Parameter Setting and Tuning Methods  
We trained our model using an RTX 2080 Ti with a batch size of 4. The experimental results were achieved with the mmdetection [24] toolkit. Both ResNet50 [25] and ResNet101 [25] served as our backbone networks, which were pre-trained on ImageNet [26]. Data augmentation included horizontal and vertical flipping. We utilized the SGD optimizer for our network, setting the learning rate at 0.01, momentum at 0.9, and weight decay at 0.0001. The training was conducted for 36 epochs with R-CNN [27], with a gradual warm-up of the learning rate to its peak of 0.01 during the initial 1-4 epochs, followed by a gradual reduction until training completion. The Non-Maximum Suppression (NMS) threshold was set to 0.8. For the UCAS-AOD dataset, we resized the original images to \(1024\times 1024\) pixels. For the HRSC2016 dataset, we adjusted the original images to approximately maintain an aspect ratio of 1333 \(\times\) 800, with the shorter side resized to 800 pixels and the longer side restricted to no more than 1333 pixels.  
In this experiment, to optimize the performance of our proposed oriented object detection method and ensure the reproducibility of the results, we employed a variety of comprehensive tuning strategies. Initially, we utilized the mmsplict tool from the mmdetection package to perform data augmentation, which included techniques such as random horizontal flipping, vertical flipping, random rotation, scaling, color jittering, and random cropping. These methods aimed to increase the diversity of the training data, enhance the model's adaptability to different scenarios and object orientations, and reduce overfitting to the training set. In terms of hyperparameter tuning, we meticulously documented the basis and range of initial hyperparameter choices and employed methods like grid search and random search for optimization, ensuring the identification of the optimal hyperparameter configuration to boost model performance. Additionally, we analyzed the specific impact of each hyperparameter on model performance and ultimately determined the best parameter settings. To further mitigate the risk of overfitting, we introduced Dropout layers to enhance the model's robustness, implemented L2 weight regularization to limit model complexity, used early stopping to monitor validation set performance and prevent overfitting, and applied batch normalization to reduce internal covariate shift, accelerate the training process, and improve the model's generalization capabilities. These strategies ensured the model's accuracy and stability when dealing with complex backgrounds and multi-directional targets. Through these integrated tuning methods, the experiment achieved high detection accuracy while maintaining consistency across different settings, laying a solid foundation for future research and applications.
# Effect of Water Content and Cementation on the Shear Characteristics of Remolded Fault Gouge
###### Abstract  
The strength parameters of fault gouge are critical factors that influence sealing capacity and fault reactivation in underground gas storage reservoirs. This study investigates the shear characteristics of remolded fault gouge under varying hydro-mechanical conditions, focusing on the coupled influence of water content and cementation. Sixty fault gouge samples are prepared using a mineral mixture of quartz, montmorillonite, and kaolinite, with five levels of water content (10-30%) and three cementation degrees (0%, 1%, 3%). Direct shear tests are conducted under four normal stress levels (100-400 kPa), and microstructural characteristics are examined using SEM. The results show that shear strength and cohesion exhibit a non-monotonic trend with water content, increasing initially and then decreasing, while the internal friction angle decreases continuously. Higher cementation degrees not only enhance shear strength and reduce the softening effect caused by water but also shift the failure mode from ductile sliding to brittle, cliff-type rupture. Moreover, clay content is found to modulate the degree--but not the trend--of strength parameter responses to water and cementation variations. Based on the observed mechanical behavior, a semi-empirical shear strength prediction model is developed by extending the classical Mohr-Coulomb criterion with water-cement coupling terms. The model accurately predicts cohesion and internal friction angle as functions of water content and cementation degree, achieving strong agreement with experimental results (\(R^{2}\) = 0.8309 for training and \(R^{2}\) = 0.8172 for testing). These findings provide a practical and interpretable framework for predicting the mechanical response of fault gouge under complex geological conditions.  
fault gouge; water content; cementation degree; shear strength; cohesion; internal friction angle +
Footnote †: journal: Applied sciences
## 4 Results
### Contribution of Spatial Interactive Second-Order Feature and Multiorder Hybrid  
To further explore the contribution of the proposed spatial interactive second-order feature and hybrid order strategy, individual streams of MoAS-CNN as well as different structures are compared, and the results are shown in Figure 10. To validate the spatial interactive second-order features, we compared the recognition accuracies for \(f^{\text{stream1}}\) and \(f^{\text{stream1}}\) + \(f^{\text{stream1}}\)\(\otimes\)\(f^{\text{stream1}}\) with \(f^{\text{stream2}}\) and \(f^{\text{stream2}}\) + \(f^{\text{stream2}}\)\(\otimes\)\(f^{\text{stream2}}\), respectively. Due to the introduction of the spatial interactive features, the accuracy of stream1 increased by3.36%, 4.11%, and 1.96% on the three scales, and stream2 was 1.98%, 2.54%, and 1.75%. This fully demonstrates the effectiveness of the spatial interactive-based second-order feature.  
Figure 8: Gradient distribution on 16 \(\times\) 16, 20 \(\times\) 20, 32 \(\times\) 32 datasets during training.  
Figure 7: The performance of MoAS-CNN on three-scale datasets. (**a**)The losses in training and testing process, (**b**) the recognition accuracies in training and testing process.  
To verify the effectiveness of the multiorder fusion features, the multistream first-order model \(f^{\text{stream1}}+f^{\text{stream2}}\) and the multiorder hybrid \(f^{\text{stream1}}+f^{\text{stream2}}+f^{\text{stream1}}\otimes f^{\text{stream2}}\) are compared. The results showed that the recognition accuracies were promoted by 2.26%, 2.54%, and 1.69%, respectively. Furthermore, it can be seen from the results of stream1 and stream2 that the accuracy is improved by replacing the large convolutional kernels with multiple layers of small convolutional kernels. This can be attributed to the increased depth of the network, where high-level features are more fully exploited.  
Figure 10: The contributions of cross-flow spatial interactive feature and multiorder hybrid strategies.  
Figure 9: Confusion matrix of MoAS-CNN on 32 \(\times\) 32 dataset.
## 3 Results
### Main Effects of In-Vehicle Location  
The ANOVA results revealed that the main effects of in-vehicle location were statistically significant for all nine measures: accuracy \(F(2.66,\,104)=26.2\), \(p<0.001\); efficiency \(F(2.83,\,110)=4.43\), \(p<0.01\); rapidity \(F(2.88,\,112)=4.19\), \(p<0.01\); learnability \(F(3.14,\,122)=18.1\), \(p<0.001\); intuitiveness \(F(3.08,\,120)=23.5\), \(p<0.001\); safety \(F(2.62,\,102)=25.5\), \(p<0.001\); preference \(F(2.71,\,106)=22.4\), \(p<0.001\); memorability \(F(2.77,\,108)=15.9\), \(p<0.001\); and satisfaction  
Figure 2: The main effects of the operation method (mean {standard deviation} values of each operation method for each measure): (**a**) accuracy, (**b**) efficiency, (**c**) rapidity, (**d**) learnability, (**e**) intuitiveness, (**f**) safety, (**g**) preference, (**h**) memorability, and (**i**) satisfaction.  
\(F(2.78,\,108)=24.1,\,p<0.001\). The mean and standard deviation values of each in-vehicle location for each dependent measure are shown in Figure 3, with asterisks indicating the statistical significance of the post hoc comparisons.  
The statistically significant differences between the in-vehicle locations are as follows:  
* **L\({}_{\text{RWS}}\)** showed significantly higher mean scores than **L\({}_{\text{LWS}}\)** in efficiency and satisfaction.
* **L\({}_{\text{UCF}}\)** exhibited significantly higher mean scores than (1) **L\({}_{\text{LWS}}\)** in accuracy, learnability, intuitiveness, safety, and memorability; and (2) **L\({}_{\text{RWS}}\)** in accuracy.
* **L\({}_{\text{LCF}}\)** had significantly higher mean scores than (1) **L\({}_{\text{LWS}}\)** across all measures except rapidity; (2) **L\({}_{\text{RWS}}\)** across all measures except efficiency and rapidity; and (3) **L\({}_{\text{UCF}}\)** in accuracy, intuitiveness, safety, preference, memorability, and satisfaction.
* **L\({}_{\text{CC}}\)** obtained significantly higher mean scores than (1) **L\({}_{\text{LWS}}\)** across all nine measures; (2) **L\({}_{\text{RWS}}\)** across all measures except efficiency and rapidity; and (3) **L\({}_{\text{UCF}}\)** across all measures except efficiency.
## Nomenclature  
\begin{tabular}{l l} STFT & Short-Time Fourier Transform \\ CWT & Continuous Wavelet Transform \\ FFT & Fast Fourier Transform \\ CNN & Convolutional Neural network \\ BiLSTM & Bidirectional Long Short Time Memory \\ GA & Genetic Algorithm \\ ReLU & Rectified Linear Unit \\ DL & Deep Learning \\ AE & Acoustic Emission \\ EMD & Empirical Mode Decomposition \\ VMD & Variational Mode Decomposition \\ \end{tabular}
# Segmented Hybrid Impedance Control for Hyper-Redundant Space Manipulators
## 6 Simulation Analysis
### First Scenario
When only the force/position hybrid control method is employed, the robotic arm experiences multiple intense collisions with external objects, with the instantaneous contact forces exceeding 650 N. These high forces can cause significant impacts on the robot's joints, potentially leading to instability and compromising the HRSM hardware. Impedance control effectively imparts \"softer\" characteristics to the robotic arm, aligning with the original design goal of achieving active compliance control. This control strategy allows the manipulator to quickly adapt to unpredictable contact scenarios, particularly when anchoring and applying forces to external objects. As demonstrated in Figure 12a,b, the manipulator successfully applies a desired force of 10 N on the obstacle, exhibiting clear impedance behavior. In contrast, when impedance control is disabled, the manipulator fails to exhibit this level of compliance, highlighting the crucial role of impedance control in achieving the desired dynamic interaction with the environment. \[\text{K}_{\text{p\_force}}=15,\text{K}_{\text{i\_force}}=7\]1 Normal unfiltered contact force: (**a**) normal contact force link 14 without impedance control; and (**b**) normal contact force link 14 with impedance control. \[\text{K}_{\text{p\_force}}=15,\text{K}_{\text{i\_force}}=7\]2 Contact states (0—no contact, 1—contact): (**a**) contact states without impedance control; and (**b**) contact states with impedance control. According to Figures 13a,b and 14a,b the end-effector exhibit better position and velocity under impedance control influence.
# Influence of Rice Variety and Freezing on Flour Properties
## 3 Results and Discussion
### Texture Properties of Flour Gels  
The texture properties of the gels obtained by thermally treating the suspension prepared with flours from raw and frozen Basmati and Arborio rice samples are presented in Table 5.  
The gels obtained from Basmati rice flour presented significantly higher value of hardness compared to the one from Arborio (\(p<0.05\)), indicating a more advanced retrogradation starch of Basmati flour. According to [PERSON] et al. [7], this hardness can be related to the amylose crystallization in a shorter time. Moreover, the high content of amylose and the presence of long amylopectin chains lead a harder gel. Additionally, springiness, gumminess, and chewiness were also significantly higher in case of the gel obtained from Basmati rice flour compared to the one from Arborio (\(p<0.05\)), indicating that the gel network was more rigid. The results from Table 5 revealed that the gel prepared with frozen Basmati rice flour had significantly lower hardness values compared to the corresponding raw sample (\(p<0.05\)). On the other hand, in case of samples from Arborio variety, freezing the rice sample prior to grinding resulted in gels with significantly higher hardness value (\(p<0.05\)) (Table 5). This diversity regarding the mechanical behavior of the gels could be explained by the differences of starch properties, more specifically by amylose content and amylose retrogradation. Otherwise, the two rice varieties have different behavior after freezing and grinding: the flour from Arborio variety had higher amylose content compared to raw Arborio sample, while the flour from frozen Basmati rice had lower amylose content compared to the raw Basmati sample. The texture properties of the grains and behavior during grinding [2], but also the starch properties, defined mainly by amylose content, gelatinization temperature, gel texture, and pasting viscosity [3], could justify the differences in the results registered for the two rice varieties (Table 5).
## 4 Declarative Solution Approach
### The Method to Determine the Clusters of Customers  
The given variables are \(G=(N,E)\), \(F_{l}\), \(vw_{l}=\left[\overline{vw_{l}},\underline{vw_{l}}\right]\), \(\theta_{l}\)  
Question: _How do we determine clusters of customers \(CL_{m,l}=\left(N_{m,l},E_{m,l}\right)\) which should be extracted from the transportation network in each flying time window?_  
Determining the number of customers to be put in the cluster can be achieved in several ways such as hierarchical clustering and expectation-maximization clustering and \"\(k\)-means\" clustering. The \"\(k\)-means\" clustering algorithm is selected to do the clustering because it is less time consuming and it is already used in existing literature [36,41]. Customer locations are taken as the feature of the clustering algorithm.
# Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks
## 4 Theoretical Analysis and Experimental Results
### Results and Analysis  
In this section, we will first discuss the impact of system parameter variations on network performance. We will then compare APCDBRP with other protocols and analyze the experimental results.