esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography
## 3 Methods
### Thermal Image Processing and Sample Collection  
Following the acquisition of thermal imagery during each UAV flight, a detailed photogrammetric workflow was implemented to produce high-resolution thermal orthophotos using Structure from Motion (SfM) photogrammetry. SfM is a computational technique that converts two-dimensional thermal images into geometrically corrected orthomosaics, effectively reconstructing the thermal landscape in three dimensions [44]. This process involved capturing overlapping photographs from multiple vantage points and employing specialized algorithms to detect and match keypoints across images, thereby generating a sparse point cloud. This sparse point cloud was subsequently refined into a dense point cloud through advanced image correlation methods. A Digital Elevation Model (DEM) was then constructed from the dense point cloud and integrated into the generation of the final thermal orthophotos, which serve as the foundational dataset for subsequent LST analysis [44; 45].  
For subsequent analysis, a stratified random sampling approach was employed within each of the identified LULC classes. To ensure spatial representativeness during stratified random sampling, we implemented distance-based controls with minimum threshold distances varying between 0.5 and 1 m based on the specific characteristics of each LULC class. This adaptive distance approach allowed for appropriate sampling density while minimizing spatial autocorrelation effects. Prior to sampling, we systematically reviewed and modified the delineation of LULC areas to exclude patches with complex geometries or insufficient size that could introduce sampling bias. This pre-processing step ensured that the final LULC classification included only well-defined, representative areas amenable to robust statistical analysis. First, regions of interest (ROIs) were delineated for each LULC category to define the spatial extents representative of their dominant surface types. Within each ROI, random sample points were generated and their locations recorded in projected coordinates for further spatial analyses. One hundred sample points were collected for each LULC class (totaling 500) to ensure statistical robustness and to accommodate potential outliers or measurement errors. During the sampling process, specific care was taken to exclude points from atypical surfaces. For instance, sample points were intentionally avoided on building materials such as air conditioners, chimneys, and metal fixtures on buildings; trees located within grassy areas; rocks present in bare soil areas; and parked vehicles on road surfaces. The spatial distribution of the sample points across the study area and within each LULC class is shown in Figure 2.
## 4 The Dual-Radio Interface Routing Protocol for LLNs
### Inferred Metrics and Preferred Interface Selection  
Consider the concept of virtual links. A virtual link towards a neighbor abstracts a nominal amount of physical links. A physical link to a neighbor is \"available\" when the neighbor can be reached over an interface of the given type. From a node's perspective, there is no difference between a physical link and the interfaces between which it exists, since each node may possess at most one interface of a given type. Hence, the concepts of interfaces and physical links are interchangeable. Assuming the use of link metrics [16], a physical link metric is associated with every interface of a neighbor. This physical link metric is inferred from link-layer operations and is also called an \"inferred metric\". Through a normalization process (see Section 4.3), the inferred metrics of all interfaces of a virtual link are combined into a virtual link metric or \"normalized metric\". Note that the use of an additive routing metric [16] (p. 8), such as the Expected number of Transmissions (ETX) [16] (Section 4.3.2), is mandatory.  
Some routing metrics, including ETX, account for unsuccessful transmission attempts. As such, a node cannot assume a physical link to be down simply because it did not receive a link-layer acknowledgment. Hence, we introduce the concept of a metric threshold. When the inferred metric of a physical link is worse than this threshold (or no metric is available), said link is considered down or \"unavailable\".  
Every time the inferred metric of one of a neighbor's interfaces changes, the preferred interface for unicast communication towards said neighbor is (re-)selected. To be clear, preferred interface selection is performed for every neighbor. Moreover, it is transparent to RPL. Anyone could select the preferred interface based on criteria of his/her own choosing, as long as they are enforced consistently throughout the network. We went with the simplest approach, i.e., the interface with the best inferred metric becomes preferred. With ETX, this means that the interface with the lowest ETX is preferred for a given neighbor.
# Focused Ultrasound in the Rehabilitation Setting:
## 3 Focused Ultrasound Applications in Disabling Conditions
### Musculoskeletal Applications  
FUS has shown promising results in various MSK applications. [PERSON] et al. [25] evaluated the efficacy and safety of MRgFUS for refractory chronic pain in medial knee osteoarthritis. MRgFUS was applied to areas of severe tenderness around the medial femorotibial joint, targeting a bone surface temperature of 55 \({}^{\circ}\)C. The procedure was conducted under real-time MRI guidance to ensure precision. After identifying the most sensitive site using a handheld algometer, a detectable marker was attached to the area, and MR imaging was used to plan the treatment within a 2 cm radius centered on the bone surface. A sonication transducer, secured to the medial side of the knee, delivered MRgFUS. The US beam was carefully angled to avoid critical structures like the joint cavity and neurovascular bundle. Authors reported significant pain relief (73.7% of patients: \(\geq\)50% reduction in pain scores at 12 months). [PERSON] et al. [26] reported sustained pain relief (VAS: 0-2/10) and improved functional capacity at 12 months in a patient with lumbar facet joint pain treated with MRgFUS. Authors applied MRgFUS to ablate the posterior facet joint capsules of the patient's lumbar spine, targeting the joint capsule with a software-generated ultrasound beam manually adjusted to avoid non-target structures, such as nerve roots or spinous processes. MRgFUS was delivered in four sonications lasting 20-25 s each, with energy levels between 1000 and 1400 J per sonication, monitoring real-time temperature maps via MRI thermometry to ensure precise ablation and sparing of surrounding tissues. The treatment was performed under moderate sedation, and post-procedural imaging confirmed successful ablation requiring no incisions or needles.
# A Flexible Infrastructure-Sharing 5G Network Architecture Based on Network Slicing and Roaming
## 4 Results and Discussion
### Throughput
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Throughput (Mbps)**} \\  & **VM2** & **VM3** & **VM4** & **VM5** & **TOTAL** \\ \hline One client/slice & 49.8 \(\pm\) 3.2 & 50.7 \(\pm\) 4.1 & 51.1 \(\pm\) 3.3 & 49.8 \(\pm\) 3.2 & 201.4 \\ \hline Four clients/slices & 89.0 \(\pm\) 13.0 & 66.6 \(\pm\) 11.6 & 89.7 \(\pm\) 11.9 & 80.3 \(\pm\) 12.8 & 325.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Maximum throughput with four users, one per VM, for both configurations. increased as the number of users increased but remained limited to a single process, resulting in a maximum CPU usage that did not exceed 99%. In contrast, in the configurations with four slices, four distinct processes were generated, one for each slice. This approach allowed for a more efficient distribution of traffic among the different slices, resulting in individualized CPU usage. As a result, the CPU usage was not restricted to a single core but was distributed among the available cores in VM1. This led to an increase in maximum throughput per user.
# Elicitation of the Allelopathic Potential of Rice by Methyl Salicylate Treatment
## 3 Results and Discussion
### Morphological Traits
Among other factors, the leaf area that can confer competitive ability against weeds. Plants with a higher leaf area capture more sunlight, thus shading neighboring growing weeds and lowering their photosynthetic rate [33; 34; 35]. Therefore, greater development of the leaf area in weedy conditions was a significant trait which was recorded at the end of the experiment (Table 1). At flowering, in contrast to the increase in leaf number, leaf area decreased under weedy conditions. The detected values ranged from 2.1% to 40% less than those measured under weed-free conditions. The genotypes UPR-2916-211 (2.5%), UPR-2962-6-2-1 (5.5%), V3R11 (2.1%), and Govind (3.5%) showed the lowest decrease, while the highest reductions were found in UPR-2992-17-3-1 (36%) and UPR 2805-14-12 (40%). At maturity, this indicator did not show a definite trend, with half of the genotypes characterized by a lower leaf area. The other half was characterized by a greater leaf area compared to plants grown in the absence of weeds (Table 1). The dry matter was also detected at the flowering and maturity stages to obtain a clear picture of accumulated biomass under weedy and weed-free conditions. At both stages, dry matter decreased in weedy conditions. The lowest losses were noted in genotypes UPR-2962-6-2-1 (6.6% and 7.4%, respectively) and Govind (4.3% and 5.9%, respectively), thus delineating their competitive nature. In agreement with previous reports, these results corroborated that the rice genotypes possess different competitiveness against weed populations [8; 9; 13; 36]. For example, [PERSON] et al. [8] screened approximately 5000 rice genotypes for competitiveness against _Heteranthera limosa_ (Sw.) Wild. Among them, about 4% demonstrated some alleopathic activity. [PERSON] et al. [37] reported the alleopathic potential of 114 rice residues on weed emergence (\(-\)51.45%), height (\(-\)39.75%), and dry weight (\(-\)5.13%) of _E. crus-galli_. Finally, our findings confirmed that Govind and UPR 2962-6-2-1 genotypes are the most competitive on emergent weeds in the early stages of their growth [32].
# Carbon Footprint Assessment on the Viability of Utilizing Brewer's Spent Grain to Produce Biochar
## 2 Materials and Methods
### Carbon Footprint Assessment (CFA) Model Assumptions and Equations
#### 2.3.3 Global Warming Potential (Impact Assessment) Output Phases  
GWP values were determined from the impact categories named in Table 1 and presented in Table 2. Wastewater treatment emissions were calculated using a predictive model from the EPA [28]. In this model, it was assumed that wastewater was sent to an activated sludge system for treatment with a methane correction factor for wastewater treatment value of 0, a conversion factor for maximum CO\({}_{2}\) generation per unit of oxygen demand of 1.38, and a conversion factor for maximum CH\({}_{4}\) generation per unit of oxygen demand of 0.5. The annual CO\({}_{2}\) emissions from processing wastewater in this system were 316 Mg CO\({}_{2}\)/year, or 3.16 \(\times\) 10\({}^{5}\) kg CO\({}_{2}\)e/year. The system processed 158 m\({}^{3}\)/h.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phase** & **Item** & **Amount per FU** & **Unit** \\ \hline \multicolumn{4}{c}{Inputs} \\ \hline Soaking & Electricity (scale) & 0.018 & MJ \\  & Electricity (ventilation) & 9.75 & MJ \\ Drying & Electricity (oven) & 0.915 & MJ \\ Grinding & Electricity (coffee grinder) & 0.018 & MJ \\ Weighing & Electricity (scale) & 0.009 & MJ \\ Pyrolysis & Nitrogen Gas & 137 & kg \\  & Electricity & 71.9 & MJ \\ \hline \multicolumn{4}{c}{Outputs} \\ \hline Soaking & Wastewater (pH 5.17) & 1.02 & kg \\  & Vented Fumes & 0.024 & kg \\ Drying & Water Vapor & 0.7 & kg \\ Pyrolysis & Biochar & 0.083 & kg \\  & Syngas & 0.018 & kg \\  & Bio-oil & 0.192 & kg \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inventory of BSG-to-biochar processing inputs.  
In this CFA, biochar was utilized in soil application to sequester carbon. The direct carbon sequestration value of biochar was estimated in terms of a carbon stability factor by utilizing Equation (2):  
\[\text{CO}_{2,\text{sequest}}=3.66\left(1-\text{e}^{\left(\text{t}_{1/2}\cdot \text{ln}(0.5)\right)/\text{TH}}\right) \tag{2}\]  
where 3.66 is the ratio of molecular weight of CO\({}_{2}\) to that of C, t\({}_{1/2}\) (the half-life of biochar in soil), and TH is the analytical time horizon [12]. The half-life of biochar in soil was calculated using Equation (4) from [12]. Equation (4) first required Equation (3) to determine O : C:  
\[\text{O}:\text{C}=\text{max}\left\{\begin{matrix}0.6-0.00079T\\ 0\end{matrix}\right. \tag{3}\]  
\[\text{t}_{1/2}=1000\text{e}^{-7\left(\text{O}\text{C}\right)} \tag{4}\]  
where T is the pyrolysis peak temperature.
## 4 Systematic Review of the Research Pillars
### Digital Twins in the Built Environment
#### 4.1.3 Integration of IoT and AI with Digital Twins  
By facilitating real-time monitoring, comprehensive analytics, and proactive decision-making, the combination of IoT and AI with digital twins greatly enhances maintenance forecasting in industrial settings. Improved efficiency and dependability are made possible by this synergy, which eventually lowers maintenance expenses and downtime. Digital twins enable the virtual representation of physical assets, facilitating data collection and real-time monitoring. This allows for continuous equipment performance tracking and optimization. Large volumes of data are gathered by IoT devices, and these data can be examined using AI algorithms to find trends and anticipate malfunctions before they happen [50; 51].  
Advanced analytics, powered by artificial intelligence (AI) techniques such as machine learning and deep learning, enhance the predictive capabilities of digital twins by analyzing complex datasets to forecast equipment failures [52; 53]. In manufacturing settings, deep learning models, such as Autoencoders, have been effectively applied to enhancepredictive maintenance outcomes [50]. This integration further supports decision-making by providing data-driven insights and actionable predictions. Real-time decision-making in response to operational difficulties is made easier by the integration of generative AI with digital twins, which enables scenario simulation and identification of anomalies [54; 55]. By using this capability, industries can optimize system performance and dependability by implementing timely maintenance interventions [53; 56].  
The integration of deep learning and machine learning algorithms with digital twins leverages their predictive and analytical power to revolutionize maintenance forecasting and optimization in industrial applications. For instance, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks are highly effective for analyzing time-series data generated by IoT sensors, enabling accurate predictions of equipment failures and performance trends [18; 47; 54]. Autoencoders, particularly denoising autoencoders, are employed for anomaly detection by reconstructing input data and identifying deviations indicative of faults. Frameworks like TensorFlow, PyTorch, and Apache MXNet facilitate the deployment of these models within digital twin systems by providing tools for training and integrating predictive algorithms. Additionally, algorithms such as Support Vector Machines (SVMs) and Gradient Boosting Machines (GBMs) are utilized for classification and regression tasks in predictive maintenance scenarios. By embedding these algorithms into digital twin platforms, industries can achieve robust, scalable solutions for real-time analytics, improving operational resilience and reducing costs [14; 19; 21].
# KD-PatchMatch: A Self-Supervised Training Learning-Based PatchMatch
## 4 Experiments
### Ablation Study  
To evaluate the efficacy of the suggested probabilistic depth sampling and feature-metric loss, we undertake ablation studies in this section. The DTU dataset [9] is used as the basis for all of the research.  
**Effectiveness of the Proposed Probabilistic Depth Sampling.** We compare the effects of multi-view reconstruction with and without the proposed probability sampling. Since the inverse depth sampling used by PatchMatchNet [15] is essentially a uniform sampling on the image space, which samples 48 depth candidates in the depth initialization phase, we use probabilistic depth sampling to sample 8 candidates in the depth initialization phase. As shown in Figure 7, our method reconstructs better while sampling fewer candidates.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Test**} \\ \cline{2-5}  & **F1 Score** & **Time (s)** & **F1 Score** & **Time (s)** \\ \hline MVE [44] & 20.47 & 13278.69 & 30.37 & 10550.67 \\ Gipuma [23] & 36.38 & 587.77 & 45.18 & 689.75 \\ PMVS [21] & 46.06 & 836.66 & 44.16 & 957.08 \\ COLMAP [24] & 67.66 & 2690.62 & 73.01 & 1658.33 \\ PVSVNet [43] & 67.48 & - & 72.08 & 829.56 \\ PatchMatchNet [15] & 64.21 & **452.63** & 73.12 & **492.52** \\ Ours & **67.92** & 637.49 & **74.86** & 639.30 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative results of different methods on ETH3D [11] (F score, higher is better). Due to the high variability of viewpoints, the only competitive purely learning-based approach currently submitted on ETH3D is PVSVNet [43].  
Figure 6: Reconstruction results on the ETH3D benchmark [11].  
**Effectiveness of the Feature-Metric Loss.** It is important to note that using feature-metric loss alone is not feasible. The reason for this is that the feature network in the MVS network is trained online. As a result, using feature metric loss alone will result in training failure when the features are constant. In Table 4, we compare the effects of adding and not adding feature-metric loss in the model.
## 4 Discussion
### Optimized Procedure for Monitoring Localized High-Sears Fishing Vessels  
EOG's VBD vessel extraction algorithm offers a comprehensive workflow for extracting and distributing fishing vessel data. This process begins with downloading extensive raw VIIRS-DNB data, followed by vessel extraction using the threshold method, and ends with public distribution (Figure 9). By providing ready-to-use CSV files with vessel location information, researchers are spared the challenges of handling massive raw imagery and can focus directly on monitoring and analyzing the dynamics of lit fishing vessels. However, this approach has limitations. Currently, the VBD data are produced in near-real time, with nightly records dating back to April 2012 for Asia and 2017 for other regions [42]. Reproducing the VBD algorithm to extract long-term time series data for specific fishing grounds outside Asia remains challenging for most researchers. Additionally, our study shows that the high adaptive threshold used in the VBD data product reduces the efficiency of detecting industry lit fishing vessels under cloudy conditions. To address these issues, we propose an improved extraction method tailored for specific \"hot spots\", using the north-central western Pacific fishing grounds as a case study. This method leverages GEE for near-real-time selection of VNP46A1 data, which outputs DNB imagery reflecting faint light (Figure 9). An enhanced YOLOv8 model then accurately extracts vessels in the targeted areas. Compared with the massive raw nighttime imagery, VNP46A1 has been preprocessed, reducing the data volume to 40 MB per file [26], and GEE allows for precise regional selection, enabling focused analysis.  
Our procedure could also include daily meteorological parameters for specific study area, such as cloud cover extent and intensity, which are crucial for accurate vessel count comparisons. Weather conditions significantly affect the detection of industry lit vessels on the high seas (Figure 5). Since not all daily data are usable, determining the number of analyzable images is vital for comparing different months and years. Our approach first filters data based on weather conditions, then enhances the quantitative accuracy by considering interannual and monthly weather variations. In summary, we propose a more reproducible procedure for extracting and monitoring lighted fishing vessels using nighttime remote sensing. This method simplifies the process for personalized monitoring and research of localized industry lit fishing vessels in the high seas.
## 6 Experimental Validation
### Transmission Error Experimental Platform Construction and Experimental Results Comparison  
According to the above test principle, the transmission error test platform is built. The test platform adopts a modular design, which is composed of a drive module, input measurement module, reducer module, output measurement module, supporting tooling module, and controller module, respectively. The grating support base can be replaced according to the model and size of the tested reducer. At the same time, the platform ensures the accuracy of the center hole of the reducer by the machining accuracy of the support base. The data of the input and output measurement modules are transmitted to the computer-based controller module by using the data acquisition card, and the test results of the transmission error are output in real time after processing by the computer. The test bench is shown in Figure 18. Figure 19 is the software measurement interface of the PC end of the transmission error.  
In the transmission of the helical planetary reducer, most of the transmission error comes from the transmission of the internal helical gear pair. From Figure 20, it can be  
Figure 19: Transmission error2.0 software measurement interface.  
Figure 17: Transmission error experimental schematic diagram.  
Figure 18: Transmission error experimental platform.  
seen that compared with the transmission error curve before and after modification, when the working time is 30 min, the maximum transmission error after modification is 16.32\({}^{\prime\prime}\), and the transmission error of the unmodified gear system is 53.25\({}^{\prime\prime}\). The modification reduces the transmission error by about 68.35%. When the working time is 60 min, the modification reduces the transmission error by 58.38%. When the working time is 90 min, the modification reduces the transmission error by about 59.32%. The average value of the overall reduction in the transmission error after modification is 62.35%. Therefore, in the design of the transmission system, if the influence of the temperature rise on the transmission error is to be ensured, it is necessary to modify the gear to ensure the stability and reliability of the system.