esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
## 5 Principal Results
### Findings on Machine Learning  
The aim of this section is to answer the first research question, What is the general approach used when selecting and evaluating ML algorithms? As can be seen in Table 2, the selection process found a total of 47 articles that had related studies or presented a relevant study on ML. Of the 32 articles that were found on ML, 9 discussed selection methods for ML algorithms and \(11\) focused on ML performance. A further five articles touched on both subjects.  
The articles listed in this section provide general information and definitions of selection criteria. Most often, algorithms are selected since they are known to perform well under certain general conditions. More specific methods for selecting ML algorithms are found in the publications selected to answer the final question. These will be discussed in Section 5.4.  
Since there are some minor differences in the way ML algorithms are categorized, this section presents the definitions and concepts that will be used throughout the rest of this study. Since these are general definitions, readers with advanced knowledge of the subject may want to skip to the next Section.  
The basic concept of AI is to use a set of rules and available information to make a decision (reasoning). For example, in game playing, the result of possible next moves can be scored using a given heuristic, resulting in the best move based on the highest score. The concept of AI can be enhanced by integrating learning with reasoning. The term \"Machine Learning\" was introduced in 1959 by [PERSON] [39] in precisely this context, when he discussed machine learning procedures using the game of checkers. Learning is achieved when decisions are not made based on previously coded rules, but rather based on experience. A widely used definition of machine learning, expressing this idea, was given by [PERSON] [40] in 1997.  
A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.
# Video Captioning Based on Channel Soft Attention and Semantic Reconstruct
###### Abstract  
Video captioning is a popular task which automatically generates a natural-language sentence to describe video content. Previous video captioning works mainly use the encoder-decoder framework and exploit special techniques such as attention mechanisms to improve the quality of generated sentences. In addition, most attention mechanisms focus on global features and spatial features. However, global features are usually fully connected features. Recurrent convolution networks (RCNs) receive 3-dimensional features as input at each time step, but the temporal structure of each channel at each time step has been ignored, which provide temporal relation information of each channel. In this paper, a video captioning model based on channel soft attention and semantic reconstructor is proposed, which considers the global information for each channel. In a video feature map sequence, the same channel of every time step is generated by the same convolutional kernel. We selectively collect the features generated by each convolutional kernel and then input the weighted sum of each channel to RCN at each time step to encode video representation. Furthermore, a semantic reconstructor is proposed to rebuild semantic vectors to ensure the integrity of semantic information in the training process, which takes advantage of both forward (semantic to sentence) and backward (sentence to semantic) flows. Experimental results on popular datasets MSVD and MSR-VTT demonstrate the effectiveness and feasibility of our model.  
video captioning; channel soft attention; semantic reconstructor; recurrent convolution networks +
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15 January 2021  
19 February 2021  
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**Copyright:** © 2021 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 System Implementation and Verification
### Analysis of Results  
The benefits of the digital twin system were tested by running it on an actual production line, which showed that the average machining time was reduced by 8.4%, but the quality of the processed products was not significantly improved. We followed up the experimental results and reviewed the relevant generation records and proposed that the efficiency improvement was mainly brought about by two aspects. On the one hand, the system can effectively monitor the status of the equipment, reducing unnecessary downtime such as stopping and failing to change tools in time, while the visualization of the interaction can assist workers to operate more skifully. However, the consistency of the processed products is not significantly enhanced due to the short testing time and the fact that the machining accuracy of the equipment used in the case is well maintained.
# The Power of Machine Learning Methods and PSO in Air Quality Prediction
## 6 Conclusions  
Predicting air quality and better understanding the effects of pollutants is critical for human health and environmental sustainability. Poor air quality leads to respiratory diseases, cardiovascular diseases, and other health problems. Furthermore, understanding the impacts of human activities such as industry and transport on air pollution enables the development of environmental policies and preventive measures. More accurate forecasts provide societal and ecological benefits by shaping urban planning, early warning systems, and clean energy policies. This study evaluates the effectiveness of machine learning algorithms for air quality prediction and shows the potential to improve the performance of models with the PSO algorithm. The findings of this study show that the support vector regression (SVR) model, along with XGBoost, offers a powerful alternative and exhibits superior performance in air quality prediction. These findings have important implications for a better understanding of the adverse health effects of air pollution and for the formulation of environmental policies. The dataset used in this study consists of data collected from a highly polluted region of Italy. The predictive nature of air quality may be due to the absence of specific pollution events during this study's period and the periodic nature of the daily cycles that can be easily captured. In the future, how this method works during intermittent or exceptional pollution events (e.g., fires) will be realized with a more comprehensive dataset generated by real-world data. Sensors have a degradation behavior that is common in such measurements. Testing will be performed to improve the methodology to offset the impact of this well-known phenomenon. Tests with the resulting data will reveal how the air quality prediction works based on more dynamic and variable conditions. In conclusion, this study presents the use of the PSO algorithm and machine learning regression techniques in air quality prediction as an important contribution.  
This research received no external funding.  
The data presented in this study are openly available in [10.1016/j.snb. 2007.09.060].  
The author declares no conflicts of interest.
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 8 Experimental Evaluation
### Lossy Data Reduction Algorithms
#### 8.1.3 Linear Regression
Analyzing Table 8, we can see that the Linear Regression algorithm was more efficient with the IoT Temperatures dataset, reducing \(-80.63\%\) of the original dataset data size in \(0.008\) ms. The algorithm took \(0.0012\) ms to process \(1\) MB.
# Nitrogen Oxide Removal by Coal-Based Activated Carbon for a Marine Diesel Engine
O\({}_{2}\) is much more prevalent than NO\({}_{2}\) in exhaust gas. So Equation (3) is dominant in the SCR reaction, also known as the standard SCR reaction. [10]. When NO/NO\({}_{2}\) is equal to 1, the reaction rate is the fastest. Increasing the NO\({}_{2}\)/NOx ratio appropriately helps to increase the SCR reaction rate [15]. \[4\text{NH}_{3}+6\text{NO}\to 5\text{N}_{2}+6\text{H}_{2}\text{O} \tag{1}\]  
\[2\text{NH}_{3}+\text{NO}+\text{NO}_{2}\to 2\text{N}_{2}+3\text{H}_{2} \text{O} \tag{2}\]  
\[4\text{NH}_{3}+4\text{NO}+\text{O}_{2}\to 4\text{N}_{2}+6\text{H}_{2} \text{O} \tag{3}\]  
In summary, it is important to look for a denitration catalyst that is inexpensive, has high denitration efficiency in a wide temperature range and causes no secondary pollution. Activated carbon is not only cheap and has a variety of sources, but also has good adsorption capacity and catalytic ability due to the high specific surface area and chemical activity. Some metal oxides can be impregnated onto the AC surface to further increase the denitration efficiency at high temperatures [16]; therefore, it has great application prospects in the future. There are many factors affecting the denitration efficiency of AC, including the AC type, specific surface area (mainly related to physical adsorption) and surface functional groups (mainly related to chemical adsorption and catalysis), NOx concentration, O\({}_{2}\) concentration, exhaust gas temperature, adding NH\({}_{3}\) or not, and so on. At present, there are three main types of commercial ACs, i.e., coal-based, coconut shell, and wood-based. The wood-based AC is mostly used for water treatment, and is not considered environmentally friendly because it needs to consume wood. The price of coconut shell AC is roughly twice that of coal-based AC [17]. Therefore, this paper chooses coal-based AC as the research object. Since the marine diesel engines could use different sulfur content fuels, the desulfurization means are also different, which leads to different temperatures of the exhaust gas during denitration. This paper mainly studies the performance of AC to catalyze the oxidation of NO to NO\({}_{2}\) at low temperatures and as the SCR catalyst at high temperatures, and explores the application prospects of AC in the field of ship denitration.
# A Physicist's View on Partial 3D Shape Matching
## 4 Experimental Results
### Full Shape Similarity
#### 4.1.2 Anatomical Data  
Our second test considers three anatomical datasets, representing three different parts of the skeletons of primates. The first dataset includes 61 shapes corresponding to the proximal first metataralsals (MT1) of prosimian primates, the second dataset includes 45 shapes of the radii of apes and humans, and the third dataset includes 116 molars of prosimian primates and nonprimate close relatives (see Figure 4 for examples in each dataset) [14].  
For each dataset, we performed an all-against-all comparison experiment. We used either the LD-SIFT or the WKS signatures in conjunction with unbalanced OT to find the correspondences between vertices. To evaluate the performance of our approaches, we compared the outcomes to those determined by [PERSON] et al. [14]. We ran two classification analyses on each dataset with a \"leave one out\" procedure: each surface (treated as unknown) is assigned to the taxonomic group of its nearest neighbor among the remainder of the surface in the dataset (treated as known). Tables 2-4 list success rates (in percentage) for our approaches compared to those of [PERSON] el al. for the three datasets.  
By construction, the conformal Wasserstein neighborhood distance, cWn, is the closest distance to the ones we have introduced in this study, with two significant differences. First, cWn uses a different dissimilarity measure between vertices of the two surfaces under consideration, computed as follows. The procedure starts by projecting the surfaces onto the planar disk using conformal maps. The distance between two vertices, \(v\) and \(v^{\prime}\), on the two different surfaces is then computed by comparing their conformal factors (namely, parameters associated with the conformal flattening of the surface) in their neighborhood on the plane, modulo a Mobius transform (see Ref. [14] for details). All of those distances form a cost matrix between the two surfaces. Second, cWn is obtained by solving the balanced OT problem based on this cost matrix, in opposition to the unbalanced OT formalism we introduced. We found that the distance \(SU\) with the WKS signature introduced in this study outperforms cWn on all three datasets, at all phylogenic classification levels.  
Interestingly, we found that the WKS signatures outperform the LD-SIFT signatures for the full shape comparison on those anatomical datasets. We assign the difference between LD-SIFT and WKS to the fact that, by design, the WKS signatures capture the actual geometry of the mesh representing a shape by analyzing its Laplace-Beltrami operator, while the LD-SIFT signatures are more focused on local point distributions and, as such, miss the overall geometry. We will see below, however, that this may not always be a limitation.
## 4 Results
### Ablation Experiments  
As shown in Table 4, the three-stage framework demonstrates distinct performance characteristics. Step a achieves strong baseline performance through SAM-driven pseudo-labels. The integration of sparse facial annotations and area-thresholding purification ensures high-quality masks with minimal background noise, validating the efficacy. Step b introduces architectural innovations via customized UNet++, which can improve robustness to facial appearance variations (e.g., pose changes, partial occlusions). This stage reveals a trade-off between model capacity and overfitting risks under limited training data. Step c, the twin model refinement paradigm, recovers and exceeds Step a's performance by leveraging EMA-based knowledge distillation. Despite using VIT-B (smaller backbone), the collaborative learning mechanism enhances prediction stability and boundary precision, achieving Dice = 0.85 \(\pm\) 0.01 (+1% vs. Step b) and IoU = 0.73 \(\pm\) 0.02 (+2% vs. Step b). Notably, the reduced standard deviations highlight its superior generalization stability compared to earlier stages.
## 4 Discussion
### Validation with Observational Data  
To evaluate the reliability of the numerical results, we performed a preliminary comparison between the simulated values and actual monitoring data collected by Kazhydromet for the city of Almaty over 2015-2024.  
The simulated surface-level PM\({}_{2.5}\) concentration (mean ~45 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\); max ~100 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)) is consistent with recorded daily peaks in the central districts of Almaty (e.g., Medeu, Almalinskiy) during winter smog episodes, which ranged from 35 to 95 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) on polluted days according to Kazhydromet bulletins.  
Simulated temperature fields at surface level (mean: 16.8 \({}^{\circ}\)C) and upper levels (down to 10.6 \({}^{\circ}\)C) match with vertical radiosonde profiles, confirming realistic inversion patterns typical for Almaty's basin topography.  
Wind velocities from simulation (range: 0.3 to 5.8 m/s) agree with wind rose reports from local stations (e.g., Airport, Kok-Tobe), where prevailing surface winds are below 2.0 m/s during inversion-prone periods.  
While a complete pointwise validation using continuous station data was not possible within this study's scope, the observed consistency confirms that the model captures the dominant physical patterns and magnitudes.  
Future work will focus on incorporating point-based calibration using high-resolution sensor networks and satellite aerosol data to refine vertical pollution profiles and improve predictive capabilities.
## 3 Higher-Layer Protocols: MAC & Routing
### Static Routing  
This kind of routing protocol identifies a simple approach at routing layer, where the path between two network nodes is always the same in those networks without node mobility (every node is fixed at a specific location). Although there are several routing protocols that fit into this category, the geographical-based routing protocols are very popular in Wireless Sensor Networks deployments, where all network nodes are equipped with a localization system that determine their global position. So, when a source node has to deliver a packet to a destination node located several hops away, it would forward the packet to the neighbor node that is closer to the destination. Assuming that network topology is static (no mobility pattern), the path provided by a static routing protocol to reach a specific network node will be always the same.
# Seismic Response Analyses of a Large-Span Powerhouse Cavern Considering Rock-Structure Interaction
## 2 Project Background  
The hydroelectric plant of concern has been built to replace the old one to enhance the hydraulic production capacity. Compared with the plants previously built, the new plant, with the flowing water controlled daily, will double the production of electricity and quintuple the capacity, and can provide a total power of 27 MW and an average annual electricity production of 200 GWh. This will help to reduce the huge electricity deficit of the region (now about 60% of its needs) and cut the import of a significant amount of fuel.  
The overall perspective view of the scheme is shown in Figure 1, and it mainly consists of the following components: the intake, which is located on the river at 1046 m asl; the dam, from which a minimum flow of 1000 1/s can be derived; the 14 km long diversion tunnel, with a gradient of about 1/1000 to supply daily the basin (560,000 m\({}^{3}\)); the 4 km long pressure tunnel, which leads the water to the upper surge tank and finally to the 1.3 km long penstock with an average diameter of 3.2 m; the powerhouse cavern, where two turbines (design flow 17 m\({}^{3}\)/s, speed 750 rpm) are coupled to an 85 MVA generator; and the 1.6 km long outlet tunnel, which returns water into the reservoir (420,000 m\({}^{3}\) capacity) after the downstream surge tank. One of the two above-mentioned turbines is also equipped with a pump, which has a design flow of 13.5 m\({}^{3}\)/s so that the water could be raised from the lower reservoir to the upper lake during the night time.  
The central powerhouse cavern is one of the most imposing components of the scheme. It has significantly large dimensions, with a maximum height of 49 m, a length of 50 m, and a span of about 23 m. It is also noteworthy that the design concepts adopted in this large cavern are not traditional; that is, the large cavern has been excavated and stabilized without the use of a cast-in-place concrete arch for roof support. Great attention has been paid to the design of the cavern.
# Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil
## 3 Results  
### Land Cover Composition Prior to Flooding  
The area of each land cover class flooded by the HPPS is shown in Table 2 for both high water (i.e., end of rainy season, Jan-Feb) and low water (i.e., dry season, July) periods. The class with the greatest area impacted is forest (163.04 km\({}^{2}\) during low water and 177.07 km\({}^{2}\) during high water), followed by the soil and agriculture classes (25.62 km\({}^{2}\) and 38.10 km\({}^{2}\) respectively). The spatial distribution of the flooded classes in the landscape can be seen in Figure 9. Overall, for the year 2020, we found the flooded area in the low water season was 208.6 km\({}^{2}\) and 235.08 km\({}^{2}\) in the high water season. This represents 106.9-133.4 km\({}^{2}\) less than the 342 km\({}^{2}\) stated in the HPPS planning documents [87]. In the planning documents, the extent of the flooding was expected to extend south of the MT-220 highway, but in our analysis of the high spatial resolution imagery, we found the flooding did not extend past the bridge on the MT-220 crossing the Teles Pires (Figure 9).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Land Cover** & **Area (km\({}^{2}\)) Low Water** & **Area (km\({}^{2}\)) High Water** \\ \hline Water & 19.79 & 19.91 \\ Soil/Agriculture & 25.62 & 38.10 \\ Forest & 163.04 & 177.07 \\ Total & 208.45 & 235.08 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Land cover class areas within the lentic zone of the HPPS determined for low water (July) and high water (Jan-Feb) seasons from Dove PS imagery acquired in 2020 (Table 1).  
As shown in Table 3, the accuracy of the land cover classes pre-flooding within the l  
Figure 9: Minimum extent of the HPPS lentitic zone during the low water (yellow) season and land cover classes within the flooded area as determined from the 2014 RapidEye image classification. Both are shown overlain on the 2014 satellite image mosaic.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Forest** & **Water** & **Soil/Agriculture** & **User’s Accuracy (\%)** \\  & **Reference** & **Reference** & **Reference** & **User’s Accuracy (\%)** \\ \hline Forest classification & 211 & 0 & 9 & 95.9 \\ Water classification & 1 & 54 & 0 & 98.2 \\ Soil/Agriculture classification & 6 & 0 & 88 & 93.6 \\ Producer’s Accuracy (\%) & 96.8 & 100 & 90.7 & OA = 95.7\% \\ \hline \end{tabular}
\end{table}
Table 3: Confusion matrix for the 2014 low water RapidEye classification.  
Figure 11: Estimated aboveground biomass C (circa 2010) [77,78] pre-inundation for the lentitic zone of the HPPS during high water season. The range of biomass C for the forest class is indicated in the legend. The Teles Pires river pre-inundation is shown overlain.  
Figure 10: Estimate of forest canopy height (circa 2005) from GLAS spaceborne LiDAR [76] within the lentitic zone of the HPPS during high water season. The Teles Pires river pre-inundation is shown overlain.
# Precipitation Products' Inter-Comparison over East and Southern Africa 1983-2017
## 2 Materials and Methods
### Precipitation Products: TAMSAT3, CHIRPS, and MSWEP
#### 2.1.3 MSWEP  
MSWEP is a relatively new precipitation product, with version 2.2 released in 2018. It is a three-hourly gridded product with global coverage at a 0.1\({}^{\circ}\) spatial resolution, available from 1979 to 2017 [30; 31]. Since its first release, MSWEP has been exploited in a vast range of applications, among which are modeling of evaporation and soil moisture, evaluation of climatic controls on vegetation, water resource reanalysis, and regional studies of the diurnal variability of precipitation dynamics [31]. Moreover, it provides a consistent precipitation record from 1979 until the near present, enabling trend and drought assessments. MSWEP is the first fully global high-resolution precipitation dataset derived by optimally merging gauge, satellite, and reanalysis estimates through a multi-source weighting procedure. Actually, in addition to TIR-based estimates and precipitation from ground stations, MSWEP integrates PMW estimates from the Climate Prediction Center morphing technique (CMORPH) [32], Global Satellite Mapping of Precipitation_Moving Vector with Kalman (GSMaP_MVK) [33], and TMPA 3B42 RT [29] with reanalysis data such as the European Center for Medium-Range Weather Forecast--Interim reanalysis (ERA-Interim) [34] and Japanese 55-year Reanalysis (JRA-55) [35].