esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Visual SLAM Method with Adaptive Feature Extraction
## 5 Conclusions  
Aiming to extract high-quality, uniformly distributed feature points and reduce corner clusters, this work suggests an adaptive feature extraction approach and applies it to SLAM. The AGAST algorithm is employed in this technique to detect feature points. Before feature detection, we perform adaptive image pyramid building and adaptive image meshing, design adaptive thresholds based on image gray information to better adapt to environmental information, and then detect keypoints in each region of the image using different thresholds. After detecting the features, the algorithm utilizes NMS to filter out the local dense feature points and the quad-tree algorithm to homogenize the features, which can also reduce corner clusters. Finally, we use the intensity centroid method to calculate the direction of the feature points, aiming to provide rotation invariance.  
After that, we applied it to SLAM and tested it on three public datasets. Our method performs well on three datasets. The RMSE of the absolute trajectory error achieved by our approach in the factory scene of the EuRoc dataset is 13.8% lower than that of ORB-SLAM3. The experimental results show that our approach enhances the performance of SLAM without increasing the time consumption. It is clear that the method described in this research can enhance the quality and speed of feature extraction while better adjusting to the environment.  
Our algorithm solves some issues and enhances its performance when applied to SLAM, but not much, because its subsequent feature matching will also significantly affect the performance of SLAM. Most methods are based on the assumption of a static environment, omitting the influence of the mismatching of dynamic feature points on SLAM. Due to this issue, we plan to combine deep learning to distinguish dynamic points from static points in the image and use different methods to track them in future work so as to improve the ability of SLAM to cope with complex environments and improve positioning accuracy.  
Conceptualization, X.G. and L.Z.; methodology, X.G. and K.Z.; software, X.G. and K.Z.; validation, X.G. and M.L.; formal analysis, B.X.; investigation, M.L. and K.Z.; resources, B.X. and M.L.; data curation, B.X.; writing-original draft preparation, X.G.; writing-review and editing, X.G., M.L. and L.Z.; visualization, K.Z. and X.G.; supervision, B.X.; project administration, X.G. and L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 62001272.  
Not applicable.  
Acknowledgments are made to the experts and scholars who provided the public datasets and open source code.  
The authors declare no conflict of interest.
## 3 Experimental Results and Analyses
### Hysteretic Curve and Skeleton Curve
#### 3.2.2 Skeleton Curve  
Figure 17 shows the skeleton curve for the test specimen. Whether the specimen moves to the positive or to the negative directions, the force-displacement curve almost shows a linear behaviour, which means that the specimen shows a linear elastic behavior during the test. The structural stiffness of the specimen is 3.21 kN/mm in the positive direction, and 0.41 kN/mm in the negative direction. The former is as much as 7.76 times of the latter, which is mainly due to the soil resistance behind the abutment.
## 8 Conclusions  
Thanks to their potential application, WSN with sensors deployed at ground level will play a bigger role in the future. we studied the properties of the radio link in a WSN with nodes deployed in such conditions. Our goal was not only to study the impact of the ground surface proximity on the radio link, but also to understand the impact of parameters such as the message size, the communication channel frequency or the topography of the environment on the quality of links between nodes deployed in such conditions.  
For this, we conducted an extensive experimental campaign, comparing radio links at ground level with radio links in a network deployed above the ground. Our results indicate the feasibility of a WSN with nodes deployed at the ground level using off-the-shelf hardware. However, the link quality in this network is relatively poor compared with a deployment at height when considering the absolute values of the measured parameters (RSSI and PRR), the temporal behavior of the links, and the spatial or asymmetrical properties of links.  
We evaluated the impact of several parameters, such as the message size, the communication channel frequency, and the topography of the deployment area in the case of the ground-level deployment. Our results show a deteriorated link quality when the message size is too small, when the communication channels shared by other technologies, e.g., WiFi, are used, or when sensors are deployed on a hill.  
Finally, we summarized our results as recommendations for WSN designers who might be interested in deploying ground-level networks, for example in the case of large infrastructure monitoring. Some intriguing results obtained in our experiments, such as the poor PRR for packets with a very small application payload, or the poor link quality in the case of a hill deployment, open interesting research perspectives, which we plan to explore in the future. Source codes and experimental data are available online through the link [[https://github.com/rdomga/WirelessLinkCharacterization](https://github.com/rdomga/WirelessLinkCharacterization)]([https://github.com/rdomga/WirelessLinkCharacterization](https://github.com/rdomga/WirelessLinkCharacterization)).  
**Author Contributions:** [PERSON] wrote the software, conducted the experimental campaigns, collected and analyzed data, and visualized the results. [PERSON], [PERSON] and [PERSON] designed the methodology of the experimental campaign. [PERSON] and [PERSON] obtained the analytical results. All the authors contributed to the writing of the paper and did their best to interpret the obtained results.  
**Funding:** This research was partly funded by Agence Universitaire de la Francophonie.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussions
### Statistical Significance  
Despite the noted variations in Figure 5, it is apparent that averaging equations for visibility estimations exhibited noteworthy improvements, mainly if considered together with the resulting percentage of error. These conclusions are supported by the simple statistical analysis presented in Table 4, which describes the equation recurrences (average to maximum) with estimation percentage of errors (minimum to average) probabilities under two cases: in combination and singly.  
Figure 6: Observed dominance of E3, E4, and E5 equations, single or in-combination, compared to a complete absence of E1 and negligible contribution of E2. The numbers mediating the pie-charts are the number of data points retrieved from the source.  
The probabilities (\(P\))--extracted based on the Z-score (Z) and normal distribution curve--of combination equations indicate that targeted range of recurrences (10.11 to 26) scored a probability of 46.09% with a chance of a relatively low estimation error (0.28% to 11.66%) rated at 37.27%. Conversely, single equations were found to have a probability of 28.93% for a range of 13 to 18 recurrences with a lesser chance (29.58%) of low estimation error.  
For conformation, the combination equations in Figure 5 were evaluated using the sources in Table 2 and analyzed based on the technique used for Table 3, as shown in Table 5. In addition to substantiating E3, E4, and E5, the tabulated values indicate a considerable reduction in estimation error (reduced average RMSE) when in combination with each other.  
Furthermore, despite the relatively poor performance of the other models, i.e., E1 and E2 in Table 3, pairing them with other equations may introduce a minor enhancement to their performance. Nonetheless, compared to E3, E4 and E5, they are deemed an unreliable estimator in our study.  
In order to validate the importance of our findings, we can consider a case--provided in the next section--where a reported work of signal attenuation measurements in a dust storm lacked visibility information. Consequently, visibility estimation is needed with reasonable accuracy, thereby making the reported measurements benchmarkable for further investigations using any of the provided attenuation equations, i.e., Equations (12)-(17).
## 2 DFIG and Battery Control System
### DFIG Control System
A pitch angle controller, as shown in Figure 1, ensures that the rotor speed (\(\omega_{r}\)) does not surpass the maximum operating limit (\(\omega_{\text{max}}\)). Furthermore, the pitch angle controller rate is set as \(\pm 10^{\circ}/\)s and the angle limiters are set as \(30^{\circ}\). Figures 3 and 4 show the power-rotor speed and the power-wind speed curves of a DFIG, respectively. During the low-wind-speed condition, the control purpose is to ensure  
Figure 2: The two-mass shaft is designed as the drivetrain system [26]. that the operating point follows the maximum power point tracking limit and attains the maximum wind power. The rotor speed attains the limit and maintains the maximum as the wind speed increases [27; 29]. During the high-speed condition, the wind power tracking efficiency is reduced by regulating the pitch angle so that the DFIG power output is maintained at the rated active power. Furthermore, as seen in Figure 4, in the low-speed region (\(v_{w}<5\) m/s), the speed of the rotor is maintained at \(\omega_{\min}\); thus, during any frequency drop, the DFIG cannot respond by supplying the rotational kinetic energy. At the center speed region (\(5\) m/s \(\leq v_{w}\leq 11\) m/s), the DFIG operates in the maximum power tracking condition to exploit the efficiency of the wind power being captured. The fast-frequency response is attained by delivering the rotational kinetic energy. In the high-speed mode (\(11\) m/s \(<v_{w}\)), the DFIG power output is limited by the converter capacity, and no extra power can be generated; thus, the fast-frequency response action cannot be performed by the DFIG. Figure 4: DFIG power–wind speed curve. Figure 3: Power–rotor speed curve of a DFIG.
## 3 Experimental Scenarios
### Scenario II: Cloud Case Evaluation  
Following the framework experiment on the cloud cases and the better performance results achieved by the GRU models compared to slideNN, we conducted experiments centered on significantly larger parameter sets, deeper internal layers, and a more complex hybrid GRU-NN architecture. These configurations were more effectively implemented using cloud computing resources, which provide the necessary resources in terms of memory and processing power to support training, loading, and short inference intervals. In this scenario, only the GRU architecture was employed, as it has been proven to be more suited for handling long temporal sequences and complex sequential dependencies. Since GRU outperformed the NN models, maintaining a better forecasting profile of the minimal loss in variable timesteps, only the variable GRU architecture was evaluated across all four input-output window configurations (64-2, 128-4, 256-8, and 512-16) to provide a comprehensive comparison and to examine how the architecture performs under varying temporal resolutions and forecasting horizons when deployed in a cloud-based environment.  
Transitioning to a cloud GRU architecture that broadens model instantiation memory requirements and allows for real-time inference resulted in a substantial increase in the number of trainable parameters compared to the edge computing scenarios. This increase was attributed to the higher number of GRU cells and the deeper, more complex network structure employed in these experiments. In cloud-based model deployment, a widely accepted threshold for qualifying a model as appropriate for cloud inference is a parameter memory size exceeding 100 MB, as mentioned in [69]. To illustrate this difference, Table 8 below summarizes the number of parameters and their corresponding memory size for the two configurations used in this scenario. All of the models maintained almost similar parameter sizes while increasing the timestep depth and forecasting lengths similarly to the slideNN and edge-GRU outputs of the edge computing scenarios.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configurations** & **Input/Output Size** & **Parameters (p)** & **Memory (MB)** \\ \hline
1 & 64/2 & 37,196,882 & 141.89 \\
2 & 128/4 & 37,197,044 & 141.9 \\
3 & 256/8 & 37,197,368 & 141.9 \\
4 & 512/16 & 37,198,016 & 141.9 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Model configurations with the parameter count and corresponding memory size.
# A Comparative Study of Local Descriptors and Classifiers for Facial Expression Recognition
## 2 Related Works
### Local Descriptors
#### 2.1.2 Compound Local Binary Patterns  
Compound Local Binary Patterns (CLBP) extend the LBP descriptor by encoding both the magnitude and the sign of the differences between a center (or threshold) pixel and its \(P\) neighbors [22]. Unlike LBP, which replaces each gray value which a \(P\)-bit code, CLBP assigns a \(2P\)-bit code. Considering \(i_{c}\) is the gray value of the center pixel, \(i_{p}\) is the gray value of the \(p\)-th neighbor, and \(M_{agg}\) is the average magnitude of the difference between \(i_{p}\) and \(i_{c}\) in a local neighborhood, the CLBP code is defined as  
\[f_{c}(x,y)=\sum_{p=0}^{p-1}s(i_{p},i_{c})2^{2p} \tag{3}\]  
where \(s(i_{p},i_{c})\) is defined as  
\[s(i_{p},i_{c})=\begin{cases}00&\text{if }\;\;i_{p}-i_{c}<0\;\;\;\text{and}\;\; \;|i_{p}-i_{c}|\leq M_{agg}\\ 01&\text{if }\;\;i_{p}-i_{c}<0\;\;\;\text{and}\;\;\;|i_{p}-i_{c}|>M_{agg}\\ 10&\text{if }\;\;i_{p}-i_{c}\geq 0\;\;\;\text{and}\;\;\;|i_{p}-i_{c}|\leq M_{agg}\\ 11&\text{otherwise}\end{cases}\]  
The code is then split into two sub-CLBP patterns by concatenating the bit values corresponding to the following sequence \((1,2,5,6,\ldots,2P-3,2P-2)\) and \((3,4,7,8,\ldots,2P-1,2P)\)respectively, of the original CLBP code. The two sub-CLBP codes are treated as separate \(P\)-bit codes, and a histogram is computed for each. Finally, the two histograms are combined to form a feature vector.
## 4 Hardware Demonstration
### Tests
The mean values of total data transmission time are presented in Table 2. The standard deviation calculations based on Formula (1) are calculated for the time it takes to deliver data from the device to the satellite, based on monthly data, as follows:  
\[\sigma=\sqrt{\frac{\sum(x_{i}-\bar{x})^{2}}{n-1}} \tag{1}\]  
where \(x_{i}\) is each value in the distribution, \(\bar{x}\) is the sample mean value, n is the number of all the cases in the sample, and \(\sigma\) is the standard deviation. The following results of standard deviation were found and performed in Excel, based on each month's average of five experiments, as follows:  
1. **June:**\(\sum(x_{i}-\bar{x})^{2}\) = 182.20, the number of experiments is \(n=5\), and the final standard deviation \(\sigma=6.75\). The following result indicated that the data are more spread out. The longest time it took to transmit one message was 62 min, while the lowest was \(-46\) min. 2. **July:**\(\sum(x_{i}-\bar{x})^{2}\) = 49.04, the number of experiments is \(n=5\), and the final standard deviation \(\sigma=3.50\). The following result indicated that the data are less spread out; however, it is not yet perfect. The longest time it took to transmit the message from the device to the satellite was 11 min. The time has improved significantly due to changes to the original code in AWS, the weather was improved, and the time when the average RSSI was high was properly chosen. The highest and lowest times to transfer messages were 11 and 2 min, respectively. 3. **August:**\(\sum(x_{i}-\bar{x})^{2}\) = 2.01, the number of experiments is \(n=5\), and the final standard deviation \(\sigma=0.71\). The following result indicated that the data are ideal and are clustered tightly around the mean value. The highest time to transfer the message was 4 min, while the lowest was around 3 min. Based on the experimental data, the best conditions for rapid message transfer are found in open, unobstructed fields located far from major cities. Such locations provide the clearest satellite signal paths and minimize potential disruptions, ensuring more efficient and reliable communication. Under optimal conditions, such as those in Greenland, where there is minimal obstruction and a higher frequency of satellites, we estimated that the signal could reach the satellite within a maximum of 2 to 3 min. The improved satellite coverage and unobstructed line of sight in such remote areas contribute to this significantly faster transmission rate.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 5 Enhancing Safety Through Large Language Models
### Compliance and Regulatory Support
#### 5.2.1 Navigating International Regulations  
International maritime operations are governed by a multitude of regulations issued by bodies such as the International Maritime Organization (IMO), national authorities, and classification societies. These regulations are often intricate and subject to frequent updates. LLMs can assist in interpreting complex regulatory documents by summarizing key requirements and highlighting relevant sections applicable to specific operations or vessel types [152].  
By integrating LLMs into compliance management systems, organizations can ensure that they remain informed about the latest regulatory changes. The models can monitor updates from regulatory bodies, extract pertinent information, and notify stakeholders of new obligations or modifications to existing standards. This continuous monitoring facilitates timely adjustments to operational procedures, reducing the risk of non-compliance and associated penalties.
## 4 Rice Grain Morphology and Nutritional Composition  
### Rice Grain Morphology  
Most cultivated rice are considered as a semiaquatic annual grass, but can grow in the tropical areas as perennial plant. Mature rice plants have a main stem and fillers with each filler producing a panicle. Plant height varies from 0.4 to 5 m depending on variety and environmental conditions. Rice morphology includes vegetative phase (germination, seedling, and tillering) and reproductive phase (panicle initiation and heading) [16]. Rice grains consist of an edible rice caryopsis or fruit (also known as brown, cargo, dehulled, or dehusked rice), which is surrounded by the hull or husk [17]. The hull consists of multiple layers of distinct tissues that surround the kernel, as seen in Figure 1. The hull of _India_ rice is composed of palea, lemmas, and rachilla while Japonica rice has a hull that includes rudimentary glumes and portion of pedicel. Grain length, width, and thickness differ among varieties. A single grain weighs 10-15 mg (dry weight basis). Hull weight is about 20% of total grain weight [17]. Brown rice consists of 1-2% pericarp, 4-6% aleurone layer, 1% embryo, 2% scutellum, and 90-91% endosperm [17]. The composition and properties of brown rice and its fractions greatly depend on varietal, environmental, and processing conditions. Brown rice, when milled to remove the bran fractions, results to polished white rice or milled rice.  
Bran contains the aleurone layer, which is the primary tissue responsible for accumulating various phenolic compounds. These phenolic compounds play a crucial role in crosslinking non-starch polysaccharides with other cell wall components, such as lignin, thereby enhancing the structural integrity of the cell walls while providing defence against invading pathogens. The rice phenolic content is linked to variation in thickness of the bran. Still, the specific mechanisms of phenolic accumulation in different fractions of rice are yet to be understood [6].
## 2 Materials and Methods
### RNA Extraction and Expression Analysis  
Total seedlings at LT or NT were sampled from inbred Jing72464 and JingX201. Whole seedlings with the first expanded true leaf were scissored from the upper ground, wrapped in the foil paper, then were directly frozen in the portable liquid nitrogen container in the field. Total RNA was isolated using the Plant RNA Purification Kit (Thermo Fisher Scientific, K0801, Waltham, MA, USA). Full-length cDNA was generated from 2 ug total RNA per sample using oligo(dT) primer (cDNA synthesis kit, Thermo Fisher Scientific, K1622). The resulting cDNA were made a 1:10 dilution for the following quantitative RT-PCR (qRT-PCR). qRT-PCR was conducted using TB Green\({}^{\text{\text{\textregistered}}}\) Premix Ex TaqMan\({}^{\text{\text{\textregistered}}}\) II Real-Time PCR System (Takara, Japan, RR820A) in a typical 20 uL PCR mixture that included 10 uL of premix, 3 uL of template cDNA, and 0.4 uM of each PCR primer. Cycling conditions were 95 \({}^{\circ}\)C for 2 min, followed by 40 cycles at 95 \({}^{\circ}\)C for 20 s, 56 \({}^{\circ}\)C for 1 min, and 72 \({}^{\circ}\)C for 30 s, and samples were run on the LightCycler 480 Real-Time PCR System (Roche, Basel, Switzerland). The melt curve data were utilized in every experiment to verify that the appropriate PCR product was being amplified. ZmUBI2 was used as the internal control to monitor sample uniformity of initial RNA input and reverse transcription efficiency. The expression of the genes of interest under each experimental condition was calculated by using the standard curve method [16], while the 2\({}^{-\Delta\Delta\text{CI}}\) method was used to calculate the expression. The relevant primer sequences are in Table S2.
###### Abstract  
As China's third national-level new area, Xiong'an New Area plays a pivotal strategic role in relocating non-capital functions from Beijing while serving as a model for sustainable urban development. This study investigates the spatiotemporal evolution of ecosystem service value (ESV) and landscape patterns in Xiong'an before (2014-2016) and after (2017-2022) its establishment, assessing the policy-driven impacts of green development initiatives. Using remote sensing data, random forest classification, and landscape pattern analysis, we quantified land use dynamics, landscape index, and ESV variations. Key findings reveal significant land use transformations, with cultivated land declining by 7.51% and coniferous forest expanding by 189.84%, driven by urbanization and afforestation efforts. The comprehensive land use dynamic degree reached 4.96% (2014-2022), while the land use intensity index decreased by 20.95%. Concurrently, the fragmentation index increased significantly (Diversity Index (SHDI) +45%; Edge Density (ED) +66.23%). Despite these changes, ESV surged by 57.51% (CNY 334.63 billion), primarily due to wetland and forest expansion. Statistical analysis revealed positive correlations between ESV and the fragmentation index (ED, NP, and SHDI), whereas the aggregated index (CONTAG and AI) exhibited negative correlations. The findings substantiate the policy effectiveness of Xiong'an's ecological initiatives, revealing how strategic landscape planning can balance urban development with ecosystem protection, offering valuable guidance for sustainable urbanization in Xiong'an and comparable regions.  
Xiong'an New Area; remote sensing; land use classification; landscape pattern; ecosystem service value +
Footnote †: journal: Applied sciences