esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Study Area and Data Resources
#### 2.1.2 Remote Sensing Products  
1. GLC_FC30 product  
The latest global 30 m land-cover product, released by the Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing, China, covers the period from 2010 to 2020 and has an update frequency of 5 years. This was used to calculate FCI with 500 m spatial resolution in the study area. The GLC_FCS30 product was generated using all Landsat satellite data (Landsat TM, ETM+, and OLI) spanning a period from 1984 to 2020 with a classification system following the benchmark data of 2020, and incorporated a total of 30 surface cover types [27; 28; 29]. This classification product can be freely downloaded from the data sharing and service portal website ([[https://data.casearth.cn/en/](https://data.casearth.cn/en/)]([https://data.casearth.cn/en/](https://data.casearth.cn/en/)) (accessed on 2 November 2023)).  
2. Global MuSyQ products  
The global MuSyQ LAI product, with a spatial resolution of 500 m and a temporal resolution of 4 days from 2010 to 2020, was extracted from Aqua-Terra Moderate Resolution Imaging Spectroradiometer satellite (MODIS) data based on the three-dimensional radiative transfer (3 DRT) model for a heterogeneous surface mixed with water and vegetation [30; 31]. The global 500 m/4-day MuSyQ FVC product from 2010 to 2020 was estimated using the gap probability theory based on the clumping index and the MuSyQ LAI product [32]. The global 500 m/4-day MuSyQ GPP product from 2010 to 2020 was generated using an improved satellite data-driven light-use efficiency (LUE) model by identifying the potential maximal GPP (GPP_POT) and the dominant climate control factor for various plant functional types (PFT) [33]. All of these global 500 m/4-day MuSyQ products, including MuSyQ LAI, FVC, and GPP, which were released by AIR, CAS, Beijing, China, can be obtained from the National Earth System Science Data Center at [[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/)) (accessed on 2 November 2023).  
3. Climate classification map  
The Koppen-Geiger system classifies the climate into five main classes (i.e., tropical (A), arid (B), temperate (C), cold (D), and polar (E) climates) and 30 sub-types on a global scale based on the threshold values and seasonality of monthly air temperature and precipitation [34]. The new global map of Koppen-Geiger climate classification with 1 km spatial resolution from Princeton University, Princeton, United States of America (USA) is freely available for download at www.glob2o.org/koppen (accessed on 2 November 2023).  
Figure 1: The location and elevation (**a**); and land cover types (**b**) of the study area.
# Seismic and Geodetic Crustal Moment-Rates Comparison: New Insights on the Seismic Hazard of Egypt
## 5 Geodetic Data  
A recent update of geodetic velocities and horizontal strain-rates for Egypt has been published by [PERSON] et al. [61], therefore in the following, we refer to these results. The geodetic velocity field has been derived from a set of episodic and continuous GNSS observations collected during the 1995-2005 and 2000-2018 time spans, respectively. GNSS stations prevailing cover the eastern sector of Egypt, allowing measurement of the crustal deformation related to the most relevant tectonic elements of the region. In particular, [PERSON] et al. In [61], it is evidenced that (i) the north-western coastal area and central Egypt show a pattern in agreement with the rigid motion of the Nubian Plate, (ii) the stations located along the western side of the Red Sea show a northward motion of \(\sim\)1 mm/yr, (iii) the stations located on the north-eastern Sinai Peninsula show an NNW motion with rates larger than 1.6 mm/yr, and (iv) the stations on the Nile Delta show a complex pattern, both on the horizontal and vertical components, reflecting the general subsidence of the area.  
Figure 3: Historical earthquakes (2200 B.C.–1899 A.D.) of Egypt and its surroundings (after [48,61]). Black lines refer to EGSA [62]. Polygons labeled from EG-01 to EG-10 refer to the delimited seismic sources in the current work.  
[PERSON] et al. [61] also estimated the horizontal strain-rate field over a regular 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) grid. Considering such a horizontal strain-rate field (Figure 6), values up to \(\sim\)25 nstrain/yr can be observed along the Gulf of Aqaba-Dead Sea Transform Fault with extension along with the WSW-ENE orientation, passing to SW-NE attitudes along the Red Sea basin. Along the region between the Nile Valley and the Red Sea, the strain field shows a NNE-SSW attitude with rates progressively decreasing westward. The Gulf of Suez shows values ranging from \(\sim\)20 nstrain/yr (southward) to \(\sim\)8 nstrain/yr (northward), while on the northern sector of the Nile, the shortening strain axes show an E-W orientation with values up to \(\sim\)8 nstrain/yr.
# Enhanced Retrieval-Augmented Generation Using Low-Rank Adaptation
## 4 Experiments
### Model Comparison
However, the improvement from \(\lambda=0.7\) to \(\lambda=0.9\) is marginal, while the higher value of \(\lambda\) may overly favor relevance and reduce output diversity. Thus, \(\lambda=0.7\) offers the best trade-off between relevance, diversity, and overall generation quality. These findings demonstrate that the careful tuning of the MMR coefficient enhances retrieval quality and significantly improves the factual and semantic fidelity of generated road traffic legal responses. Table 3 presents a comparison of model efficiency between the baseline BERT-based uncased retriever and our proposed LoRA-enhanced retriever. The comparison includes the number of trainable parameters, floating-point operations per second (FLOPs), memory usage, and training time required for 100 epochs. The results indicate that applying LoRA to the BERT-based retriever leads to a significant improvement in computational efficiency while maintaining high retrieval performance. Specifically, during the training phase, the number of trainable parameters is \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\lambda\) & **BLEU-4** & **CIDEr** & **SPICE** \\ \hline
0.1 & 0.1543 & 0.1516 & 0.2127 \\
0.3 & 0.9142 & 0.5696 & 0.6106 \\
0.5 & 0.9321 & 0.7100 & 0.7408 \\
0.7 & 0.9456 & 0.7807 & 0.8057 \\
0.9 & 0.9385 & 0.8235 & 0.8447 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effect of MMR coefficient \(\lambda\) on generation quality. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Parameters (M)** & **FLOPs (GFLOP/s)** & **Memory (MB)** &
\begin{tabular}{c} **100 Epoch Time** \\ **(min)** \\ \end{tabular} \\ \hline BERT-based uncased (baseline) & 110 & 24.0 & 850 & 75.52 \\ BERT + LoRA (our method) & 95 & 19.5 & 700 & 66.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of model efficiency with and without LoRA. reduced by approximately 13.6% (from 110 M to 95 M), which directly contributes to faster convergence. FLOPs are decreased by 18.8%, and memory usage is reduced by 150 MB. Additionally, a training time over 100 epochs is shortened by 9.2 min compared to the baseline model. For the training phase, we observe an 18.7% reduction in computational overhead, calculated as a weighted average of FLOPs (18.8%) and memory usage (17.6%) reductions, with respective weights of 0.6 and 0.4, reflecting their impact on system performance in our deployment environment. This composite metric provides a comprehensive assessment of the model's efficiency across the training stage. These findings confirm that the proposed LoRA-enhanced retriever is well suited for accurate road traffic-related laws and regulations decision-making response in autonomous driving systems, where high accuracy and computational efficiency is important. It should be noted that our performance metrics were obtained using a specific dataset of traffic laws from a specific jurisdiction.
## 4 Simulation Test of the Grouser Effect  
The analysis in the previous section preliminarily clarified that the traction force of track shoes is not only related to the soil parameters but also the track shoe's width \(b\), the track shoe's pitch \(l\), the grouser's height \(h\), the grouser's thickness \(\lambda l\), and the grouser's angle \(\alpha\). To further verify the implications of the previous section, an experimental design was carried out, and the parameters of the grouser for the optimal performance of the reconfigurable wheel-crawler walking mechanism were determined through simulation tests.
# A Novel Method Based on Topological Perception Theory for 3D Landmark Building Model Retrieval
## 6 Experiment and Analysis
### Model Retrieval Results and Analysis  
Model retrieval experiments were conducted in experiment 2 to verify the performance of this paper's algorithm in model retrieval. Our dataset had six categories, as mentioned in Section 3. We collected 215 models of landmark building models, with 35 in each category.  
In order to verify the retrieval performance of the algorithm, the proposed algorithm was compared with the spherical harmonics descriptor (SPH) [42], LightField descriptor (LFD) [17], multi-view CNN retrieval algorithm (MVCNN) [22], group-view CNN retrieval algorithm (GVCNN) [43], and projective approach of LFD combined with the QDAH feature descriptor (LFD_QDAH). The P-R curve refers to the relationship curve between the precision rate and recall rate, which is a common method for evaluating the performance of information retrieval systems, text categorization systems, and so on. In the P-R curve, the horizontal axis is generally the recall rate and the vertical axis is the precision rate. To objectively assess the algorithm's retrieval performance, this paper plotted the P-R curves of each algorithm, as shown in Figure 11.  
From Figure 11, the following results were obtained: The performance of the topological feature-based algorithm proposed in this paper was better than that of the other four algorithms for the classification of the topological structure of the landmark building models. The SPH method was the worst of the five algorithms in terms of classifying  
Figure 10: Precision rate and recall rate: (**a**) the precision rate of each algorithm and (**b**) the recall rate of each algorithm.  
topological structures. Moreover, in contrast to the LFD_QHDA methods, the curves of the two methods almost overlapped, indicating no significant difference between them. Notably, our method employed fewer views, contributing to increased speed in both the projected view and the view feature extraction phase.
# Changes in Quality Features of Pork Burgers Prepared with Chokeberry Pomace During Storage
## 3 Results and Discussion
### Microbiological Quality of Pork Burgers During Storage
[17] examined the effect of two types of red grape pomace extracts on the microbiological quality of pork burgers packed under aerobic conditions and stored at +4 \({}^{\circ}\)C for 6 days, simulating retail conditions. The authors found no significant effect of grape pomace extracts on total viable count, psychrophilic bacteria, or coliforms. Instead, the primary factor influencing microbial growth was storage duration. By day 6, regardless of raw material modifications, bacterial counts in all burgers exceeded the spoilage threshold (10\({}^{6}\)-10\({}^{7}\) cfu/g). The authors attributed this to the relatively low dose of added extracts (0.06 g per 100 g of final product), which may have been insufficient to exert a strong antimicrobial effect. [PERSON] and [PERSON] [15] investigated the effect of freeze-dried tomato pomace (0.5%, 1%, 1.5%) on the quality of dry fermented sausages with reduced nitrite content. Their findings demonstrated a significant reduction in _Enterobacteriaceae_ counts with increasing levels of tomato pomace powder. [PERSON] et al. [55] compared the antimicrobial properties of plant-based powder preparations, including chokeberry pomace, in both raw and cooked minced pork. The study concluded that plant preparations have a strong potential for developing meat products with satisfactory microbiological quality. However, the highest antimicrobial effectiveness was observed in combinations of various plant powders rather than individual components.
## 3 System Model  
In satellite communication, the communication between ground users and satellites is an important part. With the continuous movement of satellites, the connections between users and different satellites need to be switched frequently. Before the satellite serves the users, the user identity must be authenticated. The speed and correctness of user identity authentication will affect the user experience and satellite security. In this paper, we aim at finding how to efficiently and accurately authenticate the user identity by satellites. In this section, we introduce the background and the system model of our blockchain-based authentication scheme in LEO satellite networks.
## 9 Future Directions and Research Opportunities
### Advancements in AI Technologies  
Recent developments in artificial intelligence offer the potential to significantly improve the capabilities of autonomous vessels, particularly in terms of adaptability, precision, and decision-making. Among these, deep reinforcement learning stands out as a powerful approach that allows AI agents to learn optimal strategies by interacting with simulated maritime environments. This technique is especially valuable in navigation and collision avoidance tasks, where dynamic learning is needed to account for changing conditions at sea [157; 158; 159] (Figure 7).  
Another promising area is the application of generative adversarial networks (GANs), which enable the generation of synthetic data to augment training sets. Given the challenges of collecting diverse and high-quality maritime datasets due to the variability of sea conditions and the limited availability of labeled data, GANs can create realistic operational scenarios to improve the robustness of AI models [160; 161].  
Federated learning represents a further step forward by allowing decentralized model training across multiple vessels without the need to share sensitive operational data. This approach enhances fleet-wide intelligence while preserving data privacy, creating a distributed learning network that improves with every voyage.  
Digital twin-based simulations and hybrid AI-physics models represent a promising avenue for enhancing the reliability and explainability of autonomous maritime systems. These approaches allow AI agents to be trained and validated within physics-consistent virtual environments, where real-time simulations mirror vessel behavior under a range of maritime conditions. By incorporating physical constraints directly into learning or inference processes, hybrid models improve robustness, facilitate anomaly detection, and support predictive maintenance. Additionally, digital twins enable continuous system monitoring and verification, providing an extra safety layer through real-world mirroring and feedback mechanisms.  
Equally important are developments in explainable AI (XAI), which are essential for ensuring that AI decisions in safety-critical maritime contexts are interpretable and auditable. As the complexity of autonomous systems increases, the ability of AI agents to provide transparent and understandable justifications for their actions will be crucial for building trust among stakeholders and regulators.
## 5 Results  
### Evaluation of EmoSDS  
#### 5.1.1 Emotion Recognition Performance  
Table 1 presents the input emotion recognition performance of EmoSDS and the emotion predictor across different datasets. The emotion predictor (Section 4.1) was fine-tuned on each dataset. The results indicate that EmoSDS achieved comparable or superior performance to the emotion predictor, demonstrating its ability to effectively capture emotional cues from speech. Specifically, EmoSDS outperformed the emotion predictor on both StyleTalk (91.1% vs. 90.0%) and DailyTalk (55.4% vs. 45.4%), suggesting its robustness in recognizing emotions even in datasets with varying levels of emotional expressiveness. On EmoSC, our model achieved comparable accuracy to the emotion predictor, attaining 98.0% and 98.7%, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **EmoSDS** & **Emotion Predictor** \\ \hline StyleTalk & 91.1\% & 90.0\% \\ DailyTalk & 55.4\% & 45.4\% \\ EmoSC & 98.0\% & 98.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Emotion F1 scores of EmoSDS and emotion predictor.  
Figure 5: In Stage 3, each sample was presented with a predefined prompt. The emotion examples in the prompt were replaced with the corresponding emotion categories from DailyTalk, StyleTalk, and EmoSC.
###### Abstract
However, the operational complexity of H-WSN requires defining effective architectures, coping with implementation details, and testing in realistic deployments. Integration schemes for integrating sensor networks with robots have been proposed based on the state-of-the-art technologies in each field [29; 30], such as robot operating system (ROS) or FIWARE. In [31], the long-range wide area network (LoRaWAN) communication protocol was tested with a terrestrial WSN, a gateway onboard an unmanned aerial vehicle (UAV) and a simulated satellite. In [32], a cloud robotics testbed was used to implement the autonomous navigation of a very remote unmanned ground vehicle (UGV). In [33], a lab-scale implementation was used to test a cloud computing architecture for the distributed remote control of heterogeneous unmanned vehicles. In this article, we present a cloud robotics architecture to integrate heterogeneous unmanned vehicles (including UGVs and UAVs) within an H-WSN based on 5G communications and MEC. In particular, we propose the Internet of cooperative agents architecture (X-IoCA), consisting of a sensory and communication system. The architecture is based on two fundamental entities: an Internet of robotic things (IoRT) and a feedback information system (FIS). Our IoRT includes a hybrid and heterogeneous network of wireless transceivers (H2 WTN), based on long-range (LoRa) and Bluetooth low energy (BLE) technologies, and an ROS network. The IoRT is connected to the FIS through MEC centers in the fog or the cloud. Furthermore, we present SAR-IoCA, an implementation of the architecture for SAR missions, integrated into a 5G network. The FIS for this application consists of an SAR-FIS (including a path planner for UGVs considering the risks detected by a LoRa H-WSN) and an ROS-FIS (for real-time monitoring and processing of information published throughout the ROS network). Moreover, we discuss lessons learned from using SAR-IoCA in a realistic exercise where three UGVs, a UAV, and responders collaborated to rescue victims from a tunnel accessible through rough terrain. The article is structured as follows: After this introduction, Section 2 describes the elements of the proposed sensory and communication architecture (X-IoCA). Next, Section 3 explains a particular implementation of X-IoCA for SAR missions: SAR-IoCA. Section 4 presents the experimental case study of this architecture in a realistic disaster response exercise. Finally, Sections 5 and 6 present the discussion and the conclusions.
# Near-Real-Time Detection of Insect Outbreaks in Urban Trees Using a PlanetScope Time Series
###### Abstract  
A critical challenge for urban forests is the arrival of _Toumeyella parricornis_ (or pine tortoise scale) in Italy, as this species damages stone pine (_Pinus pinea_ L.), an emblematic Mediterranean species. The aim of this study is to evaluate the effectiveness of remote-sensing data for monitoring pest invasions in the urban area of Rome, using PlanetScope images with a 1-day revisit time and 3 m spatial resolution, making them ideal for detecting outbreaks in complex urban areas. First, we constructed a reference dataset, georeferencing 238 healthy trees in Tenuta San Rossore (Tuscany) and more than 2000 damaged trees in Rome's green areas. In any case, this dataset of healthy trees--obtained from forest areas--was expected to exhibit higher photosynthetic activity compared to urban green areas. Second, more than 30,000 PlanetScope images were analyzed to test the effectiveness of the Renormalized Difference Vegetation Index in detecting this specific forest disturbance. Finally, different thresholds were examined, allowing for the identification of an optimal threshold to discriminate healthy trees from damaged trees. The index results showed a marked drop during the summer in the infested areas, compared to the healthy areas. The identified threshold provided 99% accuracy in detecting infested trees. The approach applied in this study demonstrated that PlanetScope imagery proved effective in detecting _T. parricornis_, leading to promising results.  
urban forest monitoring; remote sensing; _Toumeyella parricornis_; invasive pests; vegetation indices +
Footnote †: journal: Journal of Forest Geomatics, Department of Agriculture and Agriculture  
Article
_Article_  
Forward Transformation from Reactive Near-Field to Near and Far-Field at Millimeter-Wave Frequencies  
**[PERSON] 1**, **[PERSON] 1,2\({}^{\copyright}\)**, **[PERSON] 1**, **[PERSON] 1\({}^{\copyright}\)** and **[PERSON] 1,2\({}^{\copyright,\copyright}\)**  
Footnote 1: footnotemark:  
Footnote 2: footnotemark:  
Received: 18 May 2020; Accepted: 7 July 2020; Published: 11 July 2020  
**Abstract:** With the advent of 5G mobile communications at millimeter-wave frequencies, the assessment of the maximum averaged power density on numerous surfaces close to the transmitter will become a requirement. This makes phasor knowledge about the electric and magnetic fields an inevitable requirement. To avoid the burdensome measurement of these field quantities in the entire volume of interest, phase reconstruction algorithms from measurements over a plane in the far-field region are being extensively developed. In this paper, we extended the previously developed method of phase reconstruction to evaluate the near and far-field of sources with bounded uncertainty, which is robust with respect to noisy data and optimized for a minimal number of measurement points at a distance as close as \(\lambda/5\) from the source. The proposed procedure takes advantage of field integral equations and electric field measurements with the EUmmWVx probe to evaluate the field phasors close to the radiation source and subsequently obtain the field values in the whole region of interest with minimal computation and measurement costs. The main constraints are the maximal noise level regarding the peak electric field and measurement plane size with respect to the percentage of transmitted power content. The measurement of a third plane overcomes some of the noise issues. The method was evaluated by simulations of a wide range of antennas at different noise levels and at different distances and by measurements of four different antennas. A successful reconstruction in the near and far-field was achieved both qualitatively and quantitatively for distances between 2.5-150 mm from the antenna and noise levels of \(-24\) dB from the peak. The deviation of reconstruction from the simulation reference for the peak spatial-average power density with an averaging area of 1 cm\({}^{2}\) was, in all cases, well within the uncertainty budget of 0.6 dB, if the reconstruction planes captured >95% of the total radiated power. The proposed new method is very promising for compliance assessment and can reduce test time considerably.  
**Keywords:** exposure assessment; millimeter-wave antennas; electromagnetic simulation; propagation; radiation integrals; field integral equations; equivalent currents; near-to-far field transformation; power density