esa-sceva/satcom-chunk-collection huggingface dataset:
# Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT-2 SAR Images and CNNs
###### Abstract  
With the increasing availability of SAR imagery in recent years, more research is being conducted using deep learning (DL) for the classification of ice and open water; however, ice and open water classification using conventional DL methods such as convolutional neural networks (CNNs) is not yet accurate enough to replace manual analysis for operational ice chart mapping. Understanding the uncertainties associated with CNN model predictions can help to quantify errors and, therefore, guide efforts on potential enhancements using more-advanced DL models and/or synergistic approaches. This paper evaluates an approach for estimating the aleatoric uncertainty [a measure used to identify the noise inherent in data] of CNN probabilities to map ice and open water with a custom loss function applied to RADARSAT-2 HH and HV observations. The images were acquired during the 2014 ice season of Lake Erie and Lake Ontario, two of the five Laurentian Great Lakes of North America. Operational image analysis charts from the Canadian Ice Service (CIS), which are based on visual interpretation of SAR imagery, are used to provide training and testing labels for the CNN model and to evaluate the accuracy of the model predictions. Bathymetry, as a variable that has an impact on the ice regime of lakes, was also incorporated during model training in supplementary experiments. Adding aleatoric loss and bathymetry information improved the accuracy of mapping water and ice. Results are evaluated quantitatively (accuracy metrics) and qualitatively (visual comparisons). Ice and open water scores were improved in some sections of the lakes by using aleatoric loss and including bathymetry. In Lake Erie, the ice score was improved by \(\sim\)2 on average in the shallow near-shore zone as a result of better mapping of dark ice (low backscatter) in the western basin. As for Lake Ontario, the open water score was improved by \(\sim\)6 on average in the deepest profundal off-shore zone.  
SAR; RADARSAT-2; deep learning; convolutional neural network; aleatoric uncertainty Article  
Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT-2 SAR Images and CNNs  
[PERSON]; [PERSON]; [PERSON] Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT-2 SAR Images and CNNs. _Remote Sens._**2022**, _14_, 644. [[https://doi.org/10.3390/rs14030644](https://doi.org/10.3390/rs14030644)]([https://doi.org/10.3390/rs14030644](https://doi.org/10.3390/rs14030644))
# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
## 2 Related Works
### The Allocation Goal  
QoE is defined as the degree of delight or annoyance of the user of an application or service [5]. Compared with Quality of Service (QoS), which is limited to the measurement of objective parameters, QoE covers domains beyond telecommunication. The factors of the system, human, context and content can all influence the QoE results a lot [6]. Thus, it is very important for network providers, service providers, device manufacturers and end users.  
When delivering multimedia, QoE will be affected by more specific issues, such as video capture, coding, storage, delivery, decoding, rendering, display, context of use and user factors [7]. These complex factors determine that the effects of transmission cannot be simply attributed to QoS. The true visual experience of individual users need to be represented in a precise and comprehensive way. Furthermore, the changing conditions of UAV networks with fluid topology may also introduce numerous problems in video transmission. Therefore, it is more important to study the consequences from the viewers' perspective.  
Some research in wireless transmission has better analyzed and solved the issue of resource allocation by improving QoE [8; 9; 10; 11]. Thus, resorting to the mature QoE model to allocate network resources has become popular. Meanwhile, the research on UAV is closely related to QoE, too [12; 13; 14]. In order to make the allocation mechanism serve the UAV video transmission much better, we also regard users' experience as our goal and foundation of the study.
## 3 Results
### Correlation between DAMs and DETs  
Relevance analyses of DAMs and DETs were performed. The metabolites and the corresponding genes were screened based on the correlation coefficient (CC) \(>\) 0.80 and the \(p\)-value of correlation \(<\) 0.05. As shown in Figure 7, DAMs and differentially expressed genes shared a positive relationship in the third and seventh quadrants. We connected  
Figure 6: Expression levels of _S. saughuang_ under elicitor treatment. Transcript_\(\times\times\) represents the ID of the gene. The \(y\)-axis shows the relative articulation level of the genes by the \(2^{-\Delta\Delta\text{CI}}\) method. The \(x\)-axis represents the different handles. ** above the pillars show significant differences at \(p\leq\) 0.01. Transcript_20259 and Transcript_41678: defense mechanisms (cytochrome P450 [_S. baumii_]); Transcript_20207: signaling pathways (hypothetical protein A7U60_g2886 [_S. baumii_]); Transcript_4013: sulfate anion transporter [_S. baumii_].  
diverse gene clusters with differential triterpene metabolites in the third quadrant, indicating that after excitation, signal transduction mechanisms; biosynthesis, transport, and catabolism of secondary metabolites; and defense mechanisms are closely related to the production of pentacyclic triterpene metabolites 2-hydroxyoleanolic acid and betulinic acid.
## 3 Methodology
### Method
**: _The implementation of a sustainable RPA and ML model in predictive maintenance contributes to operational efficiency, promoting environmental (such as reduced waste and energy), social (better allocation of human resources), and economic (cost reduction and increased profits) benefits._  
This study aims to provide a detailed view on how the combination of robotic process automation (RPA) and machine learning (ML) can enhance failure prediction and increase operational efficiency in predictive maintenance systems while promoting, at the same time, sustainability in its environmental, social, and economic aspects. The two hypotheses presented support the idea that the combination of RPA and ML brings operational benefits and can also be a fundamental approach for a more sustainable maintenance model. The first hypothesis (H1) suggests that the integration of RPA and ML improves failure prediction accuracy and reduces operational costs by improving maintenance processes, resulting in less downtime and greater equipment longevity. This progress is essential for reducing waste and costs, aligning with the economic objectives of sustainability. The second hypothesis (H2) is based on the premise that the adoption of a sustainable model, which combines RPA and ML in predictive maintenance, optimizes operational efficiency and also brings considerable benefits in sustainability. The expectation is that by reducing failures and optimizing maintenance interventions, it will be possible to minimize the waste of natural resources and the use of energy, favoring more sustainable business practices. Furthermore, by enabling more effective management of human resources, this model can improve social well-being in organizations by freeing professionals for tasks of greater strategic and analytical value while contributing to a more profitable and resilient economic model. To carry out the search process central to this study, the researchers utilized the online scientific library offered by the Portuguese Foundation for Science and Technology, concentrating on three specific groups (Group 1, Group 2, Group 3, and Group 4), as detailed in Table 1.  
The research tests were carried out using the \"B-on\" platform, utilizing the OR operator to link either the title, keywords (KWs), or abstract (AB) within the three defined groups. Following this, filters were applied to the sets of publications obtained during the research process, and the results, in terms of the number of publications, are summarized in Table 2.  
Following the application of the filters, the titles, keywords, and abstracts of each paper were examined to select those most closely related to the research topic. Initially, a total of 4231 articles was retrieved. After the filters were applied, 1957 remained, of which only 37 were directly relevant to the study's focus. Figure 1 shows a flowchart outlining the process of the literature search and the screening procedure followed in this study.
# Dynamic Downsampling Algorithm for 3D Point Cloud Map Based on Voxel Filtering
## 2 Related Work
### Clustering-Based Downsampling  
Clustering downsampling was not a main research direction in the early days but a technique applied to specific problems. With the popularization and application of point cloud data, clustering downsampling has become one of the common technologies in the field of point cloud processing and has also been more researched and discussed. The principle is to divide the point cloud into several clusters with a clustering algorithm and then sample those clusters. [PERSON] [23] proposed a clustering method based on K-means. This method clusters the original majority class samples into the same number of clusters as the minority class samples through K-means clustering, then finds the sample center for each cluster, and uses the sample center as the new majority class sample. The algorithm can handle irregular sampling points and is robust against changes in sampling point density. Clustering downsampling has a poor sampling effect on high-density point clouds. It is easy to lose the feature points of the point cloud image and blur the edge information. A comparison of clouds before and after clustering downsampling is shown in Figure 2.  
Figure 1: Comparison of 3D point clouds before and after grid downsampling. (**a**) Original point cloud image. (**b**) Grid downsampling.  
Figure 2: Comparison of 3D point clouds before and after cluster downsampling. (**a**) Original point cloud image. (**b**) Point cloud image after cluster downsampling.
# Dynamics of Monoterpene Formation in Spike Lavender Plants
## 4 Materials and Methods
### \({}^{13}\)CO\({}_{2}\) Labelling Experiments  
Labelling experiments were performed with controls and transgenic HMGR5 plants. For \({}^{13}\)CO\({}_{2}\) feeding, three or four potted plants were simultaneously placed in a closed gas incubation chamber (Biobox; GWS, Berlin, Germany) at 25 \({}^{\circ}\)C and illuminated with white light (Figure 10). Prior to the labelling period (pulse phase), the chamber was flushed with synthetic air containing oxygen and nitrogen until CO\({}_{2}\) was fully removed. The plants were then fed with synthetic air containing 700 ppm of \({}^{13}\)CO\({}_{2}\). During this pulse period, the concentration of \({}^{13}\)CO\({}_{2}\) and \({}^{12}\)CO\({}_{2}\) was typically detected at a ratio of 9:1. Subsequently, the plants were transferred to the laboratory.  
In preliminary experiments, performed with 5-month-old WT plants, pulse and chase periods adequate to produce detectable \({}^{13}\)C enrichments were determined. To this end, pots were feed for six different pulse periods beginning with 1.0 h up to 8.8 h. Chase periods varied from 0 to 264 h. The time settings for each experiment are listed in Table S1.  
Labelling experiments with transgenic HMGR5 plants and their respective controls were performed with 5 h pulse period and chase periods for 96, 168, 240 and 264 h.  
Subsequently, leaf essential oils were extracted and the samples were analysed to detect the labelling pattern of camphor and cineole using NMR and/or GC/MS techniques, as described below. Samples indicated with the same lower-case letter belonged to the same plant.
## 3 Results
### Transbuccal Drug Delivery Systems in PD
#### 3.2.3 Ropinirol  
Ropinirol (ROP) is a dopamine receptor agonist molecule that is widely administered orally, subject to hepatic metabolism. The buccal mucosa is also considered an effective route for ROP administration, as reported by several trials. The results obtained from the administration of ROP discs, proposed as a new dosage form, have been evaluated. The aforementioned results show that after an ex vivo evaluation on a film with an area of 0.282 cm\({}^{2}\) and a dose of 2.29 mg, the amount of the drug, through the buccal mucosa, is closely comparable to the amount of ROP reaching the steady-state bloodstream after oral administration of 4 mg of ropinirol. Thus, a lower dose can be administered to achieve the same plasma concentration, because the hepatic metabolism is bypassed with the buccal method of administration. In addition, fluctuations in plasma concentration should be limited because buccal film administration allows rapid absorption of the drug [67; 68; 69]. According to these findings, the ROP film might improve treatment outcomes in terms of efficacy, safety, and quality of life.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Delivery System** & **Drugs** & **In Vivo/In Vitro/Ex Vivo** & **Results** \\ \hline IntelliDrug & Galantamine & In vivo (in pigs) [64] & Positive, keeping plasma levels constant and reducing unwanted effects \\ \hline SynAgile’s DopaFuse & L-DOPA & In vivo (in humans) [65] & No hard data on the actual efficacy of this system \\ \hline IntelliDrug & L-DOPA methyl ester hydrochloride & In vivo (in pigs) [66] & Positive, achieving and maintaining constant therapeutic blood levels for a long time \\ \hline Discs & Ropinirole & Ex vivo [65; 66; 67] & Positive, the hepatic first-pass effect is avoided and fluctuations in plasma concentration are avoided \\ \hline Tablets with Zydis technology & Selegiline & In vivo (in humans) [70] & Positive, bypassing presystem metabolism and providing higher plasma concentrations and lower levels of undesirable metabolites \\ \hline Tablets & Apomorphine & In vivo (in pigs) [71; 72; 73] & Positive, with relevant plasma levels and no significant safety issues \\ \hline Mucoadhesive tablets & Rotigotine with hyaluronic acid and cysteine (HAC) & In vitro [74] & Positive, increasing permeation, controlling drug release and mucoadhesion \\ \hline \end{tabular}
\end{table}
Table 2: Trials investigating Transbuccal Drug Delivery Systems in Parkinson’s Disease and Alzheimer’s Disease.
## 3 Feature Extraction
### Time Domain Feature Extraction
#### 3.2.1 Root Mean Square (RMS)  
Root mean square is a commonly used time-domain feature of sEMG signals, which reflects the effective value of a sEMG signal segment. Furthermore, it indicates the contribution of various muscle tissues during the completion of gesture movement. It is defined by Equation (3):  
\[RMS=\sqrt{\frac{1}{N}\sum_{i=1}^{N}x_{i}^{2}} \tag{3}\]  
where \(x\) is amplitude in ith sample and \(N\) is total number of samples.
## 4 Discussion
### Limitations  
Fractal and multifractal analysis of HRV is conducted to validate the proposed stress simulation game and evaluate its effectiveness. The primary objective in this context is to identify statistically significant changes in HRV that reflect the physiological response to the induced stress, without requiring maximum precision in the analysis.  
However, the limitations of the present study related to the analysis of ECG signals recorded before and after the game are as follows:  
* Small sample size: Only 20 volunteers were analyzed, which limits the accuracy of the results. A larger sample size could have increased the statistical accuracy of the study.
* Limited length of ECG signals: The length of the signals is about 2000 RR intervals recorded over a period of 20 min. This duration is limited due to the nature of the experiment to simulate a temporary acute stress. Although this is appropriate for the chosen methods (fractal and multifractal analysis), longer recordings could provide additional information on the long-term dynamics of heart rate.
* High variability of some parameters: Some parameters show higher standard deviation values compared to the mean. This can be explained by the influence of various factors, such as the activity of the autonomic nervous system, metabolic processes, and external stress stimuli. Although this variability can be considered natural for biological systems such as the cardiovascular system, it limits the precision of some analyses.  
Despite the aforementioned limitations, the results of the statistical analysis performed using the \(t\)-test show that a large part of the studied parameters demonstrate statistically significant differences (\(p<0.05\)) between the two groups before and after the game.
## 3 Methodology
### uCLUST
#### Algorithm of ELM++  
_ELM++_ trains each batch incrementally to create individual models. Along with the model parameters, it also computes and stores the intensity mean of each class of images in the arrived batch. Algorithm 4 explains the working concept of _ELM++_ and also how the class intensity mean is computed and stored as an object, which are later used for the purpose of incremental learning. After creating the trained models incrementally, _ELM++_ classifies the upcoming unlabeled test images by combining the generated models. Algorithm 5 explains the procedure of choosing the right model for classifying the test images. _ELM++_ chooses a model for finding out the class labels of the test images by comparing the intensity mean of the test image with the stored intensity mean of each trained class. If the difference in the intensity means of the test image and a class is less than a defined threshold, then the test image is classified with the model with which the corresponding class was trained. Sometimes, an image may be classified using more than one model, i.e., the intensity mean of the test image may be less than the intensity mean of more than one trained class. In such cases, the test image is classified with all the models corresponding to the matched classes. The classified results of all the models are combined using Algorithm 6. ```
Input: Training sets \(X_{j}\);  Number of Class \(C\);  Images in each Class \(N_{s}\) Output: Objects storing the values-class number, and their corresponding intensity mean \(I_{c}\);  Models \(M_{j}\) with parameters (\(\beta_{j}\), \(W_{j}\), \(b_{j}\))
1. Given training sets \(X_{j}=(x_{ji},t_{ji})|x_{ji}\in\mathbb{R}^{n}\), \(t_{ji}\in\mathbb{R}^{m}\), \(i=1,2,...,(CxN_{s})\), where \(t_{ji}\) represents the target output class of \(X_{j}\)
2. For each training set \(j\), a model \(M_{j}\) is created: for\(i\in\{1,\ldots,C\}\)do for\(k\in\{1,\ldots,N_{s}\}\)do  Read each image \(k\) in class \(i\);  Compute the intensity mean \(\mathrm{I}_{s}\) for each image in class \(i\)  end for  Compute the class intensity mean \(\mathrm{I}_{c}\) for each class \(i\),  end for for for\(i\in\{1,\ldots,C\}\)do  Store class intensity mean for class \(i\) along with model number \(M_{j}\) generated for the training set \(j\) as objects. Train each training set \(j\) using the Algorithm 3 to get the model parameters. Store the model \(M_{j}\) with parameters output matrix (\(\beta_{j}\)), weight matrix (\(W_{j}\)),  bias (\(b_{j}\)) as objects. end for
```
**Algorithm 4**_ELM++_: Incremental Model Generation. ```
Input: Models \(M_{i}\) with parameters (\(\beta_{i}\), \(W_{i}\), \(b_{i}\));  Objects containing class intensity means;  Number of models available \(N_{M}\); Number of test images \(N_{Is}\) Output: Classified results and its accuracy 1. Given the models \(M_{i}\) and objects containing class intensity means generated on training the available data chunks individually, where \(i=1,2,...,N_{M}\). 2.
## 3 Implementation  
The overall architecture of F3 BFS is mainly composed of three layers (Figure 1): storage layer, access layer, and peripheral layer. Considering the high requirement of runtime performance, development efficiency and system robustness, the core components of F3 BFS are mainly implemented in Rust, which is a static low-level programming language. Since it relies on some features provided by Linux kernel, currently F3 BFS, it only supports Linux OS with kernel version \(\geq\)5.10. We plan to migrate F3 BFS to UNIX (such as FreeBSD) in future. Additionally, F3 BFS also has a simple Web GUI console and HTTP REST API binding for convenient usage.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 2 System Design  
### System Components  
The proposed system is comprised of three modules: solar supply, Wi-Fi hotspot, and a satellite. The corresponding system diagram is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & \
\begin{tabular}{c} **Monocrystalline (>18\%)** \\ **(USD)** \\ \end{tabular} &
\begin{tabular}{c} **Polycrystalline (>15\%)** \\ **(USD)** \\ \end{tabular} & **Cost per Watt (USD)** \\ \hline
10 W & \$11.74 & \$5.27 & \$0.594 \\
20 W & \$11.29 & \$11.89 & \$0.376 \\
30 W & \$16.26 & \$11.29 & \$0.391 \\
40 W & \$21.83 & \$15.65 & \$0.406 \\
50 W & \$21.06 & \$20.322 & \$0.361 \\
100 W & \$39.49 & \$36.129 & \$0.594 \\ \hline \hline \end{tabular}
\end{table}
Table 2: A table of the prices of solar photovoltaic panels gathered from the internet.  
Figure 1: Hardware components of the proposed solar-supplied satellite access point.  
* Solar module: this consists of a solar panel that provides energy to the entire access point. The solar controller is used to convert the voltage generated by the solar panel to charge the battery. The battery can be used as a buffer to provide continuous power at night or on cloudy days.
* Wi-Fi hotspot module: this consists of a single-board computer with a Wi-Fi network antenna, which acts as a Wi-Fi hotspot and provides internet connectivity to the local users (via the satellite module).
* Satellite module: this consists of a satellite phone with a SIM card inserted to connect to a satellite network.
# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 2 Analysis of Power Load Characteristics and Influencing Factors  
In short-term load forecasting, the current load is influenced by many complex factors in addition to its own load trend, including temperature, rainfall and relative humidity, as well as the current month, day of the week and current moment, which can be generalized as the meteorological and temporal factors. In this paper, the load of power at the current moment is summarized as Equation (1).  
\[L(t)=L_{T}(t)+L_{C}(t)+L_{S}(t) \tag{1}\]  
where \(L(t)\) is the load value at moment \(t\); \(L_{T}(t)\) is the trend load influencing the load at moment \(t\); \(L_{C}(t)\) is the meteorological influence on load at moment \(t\); and \(L_{S}(t)\) is the temporal influence on load at moment \(t\).  
Load trends can be divided by time span into long-term trends, medium-long-term trends, medium-short-term trends and short-term trends. The individual trends are hidden in different load components: the longer the time reflected in the trend, the smoother the load component, which is regarded as a low-frequency component; the shorter the time reflected in the trend, the more volatile the load component is, which is regarded as a high-frequency component. In this paper, the load trend is summarized in Equation (2).  
\[L_{T}(t)=LT_{1}(t)+LT_{2}(t)+\cdots+LT_{n}(t) \tag{2}\]  
where \(L_{T}(t)\) is the trend load influencing the load at moment \(t\); \(LT_{1}(t)\), \(LT_{2}(t)\),..., \(LT_{n}(t)\) are the individual trend loads; and \(n\) is the number of load components obtained by decomposition, with different components corresponding to different time spans of load trends.