esa-sceva/satcom-chunk-collection huggingface dataset:
# Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees
## 4 Discussion
### Result Transfer to Real World  
The present study is a theoretical analysis performed on signals captured in a laboratory environment under favourable circumstances. When transferring the current results to the real life scenario, one must take into account that:  
* muscles act under the effects of the gravity due to the weight of the prosthetic device and that interference should be quantified,
* the final user should place the prosthetic on his/her arm without specialized help and this could lead to problems in the EMG readings that do not occur in controlled laboratory conditions,
* the final trained model and the extracted features per window should be light enough to be loaded and extracted on real time in a micro-controllers embedded in a prosthetic hand, and
* the choice of N should be done according to desired latency the capabilities of the EMG capturing system.  
Our current investigation points the direction to follow, even if additional work is needed before achieving a robust product.
# Advanced Assistive Maintenance Based on Augmented Reality and 5G Networking
## 4 Scene Processing
### Ground-Plane Extraction  
Estimating the coordinates of the ground-plane represents a critical step for obstacle detection, as it defines the elevation level from which to start detecting isolated mesh components. The algorithm for this task was designed around the specific features of the employed HMD.  
HoloLens' world coordinates are initialized to \((x,y,z)=\mathbf{0}\) at the point in space where its operating system is booted. Also, the embedded IMU allows the device to orientate its \(y\)-axis along the direction of the gravity vector. Therefore, the ground-plane is always perpendicular to the \(y\)-axis (under the assumption of a non-skewed floor) and fully described by the one-parameter equation \(y=y_{G}\).  
Once the MEC receives the first (simplified) mesh from the AR device, \(y_{G}\) is estimated in real-time by analyzing the histogram of vertices' \(y\)-coordinates (Figure 2). Given that the depth sensor cannot detect any point below the ground-plane (except for overestimation errors), and with the floor being the largest flat surface in the bottom part of the scene, we expect to observe a considerably higher bin among those related to the lowest elevations.  
The number of histogram bins is set to  
\[k=\left\lceil\frac{y_{\max}-y_{\min}}{\delta}\right\rceil,\]  
where \(y_{\max}\) and \(y_{\min}\) are the \(y\)-coordinates of the extreme vertices of the mesh along the vertical dimension.  
Let \(m_{y}\) be the count of the histogram bin associated to the elevation interval  
\[\left[y-\frac{\delta}{2},\,y+\frac{\delta}{2}\right]\]  
and let  
\[\mathcal{Y}_{L}=\left[y_{\min},\,y_{\min}+p(y_{\max}-y_{\min})\right]\]  
be a predefined range of elevations, from the lowest vertex \(y_{\min}\) up to a given percentage \(p\) of the full span. The ground-plane parameter \(y_{G}\) is estimated as  
\[y_{G}=\underset{y\in\mathcal{Y}_{L}}{\arg\max}\,m_{y} \tag{2}\]  
Once the elevation of the ground-plane is calculated, the algorithm removes any vertex \(v\) from the environmental mesh such that \(y_{v}\leq y_{G}+\delta\). Every object lying on the floor, therefore, becomes an independent submesh.  
Figure 2: Histogram of vertex elevations in the acquired environmental mesh. The ground–plane is estimated as the middle–point of the most numerous bin (10 cm wide) among the first 10% of the mesh’s elevation range.
# Revalorization of _Posidonia oceanica_ Waste for the Thermochemical Production of Biochar
## 3 Results and Discussion
### Reduction of the Salt Content of Washed Posidonia oceanica  
The salt content reduction was determined by X-ray fluorescence. The concentration of Na and Cl was reduced after this treatment by 92% and 95%, respectively (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Materials**} & **Unwashed Oceanic Posidonia** & **Washed Oceanic Posidonia** & **Reduction** \\  & **(wt\%)** & **(wt\%)** & **(\%)** \\ \hline Na & 5.61 & 0.43 & 92.34 \\ Cl & 14.88 & 0.72 & 95.16 \\ Mg & 2.01 & 1.11 & 44.78 \\ K & 1.41 & 0.15 & 89.36 \\ P & 0.07 & 0.05 & 28.57 \\ \hline \hline \end{tabular}
\end{table}
Table 2: X-ray fluorescence results before and after _Posidonia oceanica_ washing.  
Figure 1: (**a**) TG from biomass combustion; (**b**) DTG from biomass combustion; (**c**) TG from biomass pyrolysis; (**d**) DTG from biomass pyrolysis.  
Different origins of biomass are currently being considered as a substitute for fossil fuels. This is mainly due to the fact that their CO2 emission balance is zero, and they represent renewable fuel. However, combustion produces the volatilization of certain elements that can pose a risk to the environment and people. The most volatile of these elements is chlorine.  
The aim of the wash was mainly to reduce the quantity of salts contained in the _Posidonia oceanica_. The main form of chlorine release is hydrochloric acid, which entails an additional risk due to dioxins. Highly toxic chemical compounds generated during combustion processes in the presence of aromatic chemical compounds and chlorine could be produced. Therefore, the amount of chlorine should be reduced as much as possible. The concentration (\(C_{o}\)) values for each halogen emitted during the samples' combustion before and after the washing were determined (Table 3).  
For their part, sulphate concentrations in the sample were also reduced after washing. It is worth noting that sulphur forms a wide range of both organic and inorganic compounds in biomass and in different oxidation states. When it appears as sulphate, it is taken by plant roots up to the leaves, where a reduction process takes place [52].  
Nitrates and nitrites showed an increase with respect to the unwashed plant. This may be due to the fact that _Posidonia oceanica_ absorbed the nitrates and nitrites present in the washed water used (tap water). Indeed, previous analyses have shown the existing content of nitrates and nitrites in Spanish public drinking water [53]. In nature, plants use nitrates as an essential nutrient. From a commercial perspective, most nitrates are used in inorganic fertilisers. Nitrates and nitrites are also used in food preservation, in some medicines, and in the manufacture of ammunition and explosives [52].  
Fluoride, bromide, and phosphate showed little variation, although they can be considered almost nil due to low concentrations in all samples.
## 3 Methods
### Support Vector Regression (SVR)  
SVR is one of the training tools which was developed from the support vector machine (SVM). The principle of SVR is depicted in Figure 2. In this work, \(\varepsilon\)-SVR is employed for the training of data. \(\varepsilon\)-SVR is a classic model of SVR with the aim of finding a flat function, which has a small (\(\varepsilon\)) error from the obtained target [38]. In the case of SVR, the following function is trained using given training data such as \(\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{l},y_{l}),\ldots(x_{N},y_{N})\} \subset\chi\times\mathbb{R},l=\{1,2,\ldots,N\}\), where \(\chi\) illustrates the space of the input patterns:  
\[f(x)=\langle w,x\rangle+b;w\in\chi,b\in\mathbb{R} \tag{2}\]  
Figure 1: Basic structure of multilayer perceptron (MLP).  
where b is bias, and \(\langle w,x\rangle\) represents the linear function of fitting input space to the feature space. Equation (3) is using to minimizing the risk function as follows:  
\[R=\frac{1}{2}\|w\|^{2}+c\sum_{l=1}^{N}\left(y_{1},\langle w,x\rangle\right) \tag{3}\]  
In Equation (3), the selected loss function and \(c>0\) specifies the tradeoff between the smoothness of f and allowed deviation larger than \(\varepsilon\). In order to deal with the problem, the minimization problem of (4) must be solved.  
\[\min\frac{1}{2}\|w\|^{2}+c\sum_{l=1}^{N}\left(\xi_{l}+\xi_{l}^{*}\right) \tag{4}\]  
\[s.t.\left\{\begin{array}{l}y_{l}-\langle w,x_{l}\rangle-b\leq\varepsilon+\xi _{l}\\ \langle w,x_{l}\rangle+b-y_{l}\leq\varepsilon+\xi_{l}^{*}\\ \xi_{l}+\xi_{l}^{*}\geq 0\end{array}\right.\]  
where, \(\xi_{l},\xi_{l}^{*}\) are the slack variables which tackle the infeasible constraints. In order to solve the optimization problem, the dual problem of the (4) can be derived using the Lagrange function. In addition, \(w\) can be defined as an integration of training patterns of \(x\) linearly. Therefore, Equation (2) can be reformulated as [39]:  
\[f(x)=b+\sum_{l=1}^{N}\left(\alpha_{l}-\alpha_{l}^{*}\right)\langle x_{l},x\rangle \tag{5}\]  
where, \(\alpha_{l},\alpha_{l}^{*}\) are the Lagrangian multipliers. Then, in order to put the nonlinearity in the algorithm, the training patterns \(x_{l}\) can be modified by a map \(\Phi:\chi\to F\). In addition, Kernel function can be defined as:  
\[k(x,x^{\prime}):=\langle\Phi(x),\Phi(x^{\prime})\rangle \tag{6}\]  
According to the above-mentioned equations, the optimization problem of (4) can be modified, and finally, the function \(f\) derived as follows:  
\[f(x)=b+\sum_{l=1}^{N}\left(\alpha_{l}-\alpha_{l}^{*}\right)k(x_{l},x) \tag{7}\]  
It is notable that in the nonlinear optimization problem, the flatness function is searched among the feature space, not input space [40; 41].  
Figure 2: The principle of support vector regression (SVR).
## 4 Discussion
### Coverage of Data Quality Aspects  
Using an existing DQ framework [6] as a point of reference for our comparison enabled us to structure the packages' features and to identify existing gaps. Overall, the functionalities of the packages could be fitted into the framework, and there was no need to expand in dimensions or domains. However, not all functionalities could be accommodated well at the indicator level. This concerns the capability to check for unexpected variable names, loners (i.e., values that occur only once for a data element), and the occurrence of specific and maybe unwanted values (zeros, negatives, and infinity). For example, zero values may be regarded as inadmissible for a given data element. In this case, the proportion of zero values is an indicator for inadmissible numerical values (ID 3001). If an excess number of zero values is of concern, the indicator \"Unexpected Shape\" (ID 4004) applies. Furthermore, there may be multiple ways to calculate metrics for virtually all the indicators of the DQ framework. A need to expand the framework resulted from the capabilities of some packages to check for correct value formats, such as email addresses, postal codes, or telephone numbers. While fitting within the integrity domain \"value format error\", none of the three existing indicators of the framework provides a good match. Thus, we recommend introducing a novel indicator, \"value format mismatch\", which refers to a mismatch between an observed and an expected value format that is not captured by a \"data type mismatch\". We discuss further issues in Section 4.4.
# Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory
## 2 Prototypical Networks and Extreme Value Theory
### Extreme Value Theory  
EVT is a theory dealing with the maximum and minimum values of a probability distribution and is mainly used to predict the probability of extreme events. In 1928, [PERSON] and [PERSON] published their famous paper on EVT [35], which showed that low-probability events obeyed another probability distribution. By analyzing the occurrence of past extreme events and finding their distribution patterns, it is possible to calculate the probability that an extreme event may occur in the future, including the possibility of new events. For example, using historical data of the lowest temperature in a particular location, it is possible to predict the future lowest temperature, including the probability of the record lowest temperatures occurring.  
EVT is related to many widely used distributions such as the Weibull distribution. The authors of [36] indicated that within multiple independent distributions, the set of extreme values necessarily converges to an extreme value distribution. Usually, the extreme values in a set of data can be represented by the Weibull distribution. The probability density of the Weibull distribution is expressed as:  
\[f_{1}(x;\alpha,\beta)=\begin{cases}\frac{\beta}{a}\big{(}\frac{x}{a}\big{)}^{ \beta-1}e^{-(\frac{x}{2})^{\beta}}&x\geq 0\\ 0&x<0\end{cases} \tag{4}\]  
where \(x\) is the random variable, \(\alpha>0\) is the scale parameter, and \(\beta>0\) is the shape parameter. The cumulative distribution function of the Weibull distribution is expressed as:  
\[f_{2}(x;\alpha,\beta)=\begin{cases}1-e^{-(\frac{x}{2})^{\beta}}&x\geq 0\\ 0&x<0\end{cases} \tag{5}\]  
According to the properties of the Weibull cumulative distribution function, the Weibull model can be fitted using several maximum values of each class. When the input is far from the distribution of a class, it is probably judged as an extreme value or an outlier by the Weibull model. Thus, the network output of the test samples can be weighted according to the probability, and the score of the samples belonging to the unknown classes can be obtained, which effectively enhances the robustness of open-set recognition.
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 4 Methodology
### Resource Mapping and Allocation Strategy Based on Game Theory and Reinforcement Learning
This iterative process enables the resource allocation strategy to continuously improve, converging towards a near-optimal solution in a dynamic environment. ```
Input: Satellite set \(S\), resource states \(s_{t}\), task demands \(d_{i}\) Output: Optimal resource allocation \(\mathbf{r}_{i}^{*}\) Initialize: \(\mathcal{Q}\leftarrow\) Q-network with random weights; \(\mathcal{B}\leftarrow\) replay buffer; whilenot convergeddo
1 foreach\(s_{i}\in S\)do
2\(s_{t}\leftarrow\) observe current state;
3\(a_{t}\leftarrow\) select action using \(\epsilon\)-greedy policy from \(\mathcal{Q}\);
4 Execute \(a_{t}\), observe \((r_{t},s_{t+1})\);
5 Store \((s_{t},a_{t},r_{t},s_{t+1})\) in \(\mathcal{B}\);
6
7 Update Q-network:
8 Sample minibatch \(\mathcal{M}\subset\mathcal{B}\);
9 foreach\((s,a,r,s^{\prime})\in\mathcal{M}\)do
10\(\hat{Q}\leftarrow\) target Q-value using Bellman update;
11 Update \(\mathcal{Q}\) by minimizing loss function;
12 foreach\(s_{i}\in S\)do
13\(\mathbf{r}_{i}^{*}\leftarrow\arg\max_{a}\mathcal{Q}(s,a)\);
14
15 Return:\(\mathbf{r}_{i}^{*}\);
```  
**Algorithm 2**Resource Allocation Using Game Theory and DRL (GDRL)
## 1 Introduction
Therefore, biogas must undergo pretreatment to remove CO\({}_{2}\) and other trace gases in a process known as biogas upgrading in order to produce biomethane. The extensive infrastructure available for the transport and use of NG in Europe suggests that the purification of biogas, also known as upgrading, can achieve a composition similar to that of NG [19]. High CH\({}_{4}\) purity is required for NG grid injection and vehicle fuel, meeting criteria such as high energy content, gas transportation, storage, and technical restrictions [20]. Biomethane is a biofuel that can serve as a renewable alternative to NG. It can be used as a transport fuel, to produce electricity, as feedstock in chemical processes, and for household consumption [21; 22]. Additionally, it is fully compatible with the existing gas infrastructure [21]. It is a local renewable alternative that can contribute to reducing the EU's energy dependence and GHG emissions [23]. When focusing on sustainable production, biomethane should be produced from organic waste, forest residues, and agricultural residues to avoid implications for land use and food security [9], thus promoting the goals outlined in the Circular Economy Action Plan [24]. Biomethane can be produced from different feedstocks and conversion technologies, namely (Figure 1) through the following approaches [25]:  
* By upgrading biogas from the AD of biodegradable organic matter;
* Via the thermochemical conversion of biomass (gasification), followed by syngas methanation;
* Via the methanation of hydrogen, produced through water electrolysis, and a biogenic CO\({}_{2}\) source stream, also called \"Power-to-Gas\" or, more specifically, \"Power-to-Methane\" (PtM). Biogas production is increasing in Europe. As a result, the only established way of producing biomethane is via biogas upgrading (the two others are under development). Europe reached a total of 1322 biomethane-producing facilities by April 2023 [39] (Figure 2). Figure 1: Biomethane production pathways [21; 22; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]. Figure 2: Development of number of biomethane plants in Europe. Adapted from EBA [39]. Combined biogas and biomethane production in 2021 amounted to 196 TWh of energy [8], and 2020 saw biomethane production in Europe of 31 TWh. This figure grew to 37 TWh in 2021, representing an increase of 20% [8]. Biomethane production doubled from 2018 to 2022 (Figure 3), with France, Italy, Denmark, and the UK being the fastest-growing countries [39]. By 2050, the annual consumption of biogas (biogas and biomethane) in the union is projected to increase to approximately between 628 TWh and 836 TWh, up from approximately 198 TWh in 2017 [17]. However, as shown in Table 1, the potential of biomethane production via biogas upgrading in Europe is higher than the current production. Furthermore, the International Energy Agency estimates that, if the full potential of biogas and biomethane were utilized, it could cover around 20% of the current global gas demand [40].
## 5 Conclusions
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Methods** & **DB** & **No. of ROIs** & **Features** & **Classifier** & **Acc (\%)** & **AUC (\%)** \\ \hline [PERSON] et al. (2012) [19] & DDSM & 150 & \begin{tabular}{c} statistical, shape \\ and structural \\ \end{tabular} & SVM & - & 94 \\ \hline [PERSON] et al. (2013) [3] & DDSM & 380 & \begin{tabular}{c} statistical, shape, \\ textural \\ \end{tabular} & LS-SVM & 89 & - \\ \hline [PERSON] et al. (2015) [17] & BCDR & 76 & shape and textural & NB & - & 79 \\ \hline [PERSON] et al. (2017) [16] & DDSM & 150 & textural & \begin{tabular}{c} Extreme Learning \\ Machine \\ \end{tabular} & - & 92 \\ \hline [PERSON] et al. (2017) [33] & BCDR & 134 & shape and textural & CNN & 84 & - \\ \hline [PERSON] et al. (2018) [18] & DDSM & 428 & shape and textural & SVM & 94 & 93 \\ \hline [PERSON] et al. (2019) [32] & DDSM & 280 & \
\begin{tabular}{c} shape, textural, \\ topological \\ \end{tabular} &
\begin{tabular}{c} Ensemble \\ learning \\ \end{tabular} & 85 & 82 \\ \hline [PERSON] et al. (2019) [34] & Private database & 749 & shape and textural & CNN & 87 & 94 \\ \hline Proposed approach & BCDR & 96 & textural & RF & 92 & 97 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Benign vs. malignant microcalcification cluster classification: accuracy (Acc) and Area Under the Curve (AUC) performances.
## 1 Introduction
Machine learning algorithms have been widely applied to various fields of petroleum science and engineering to improve efficiency and reliability over conventional methods [15]. Numerous previous studies have combined machine learning with mineral analysis. The use of A.I. in mineralogy prediction has received scant attention in academic literature. To better develop shale gas and oil and to provide a precise solution for various production operations without essential information about the shale mineralogical composition, it is helpful to find a suitable approach to predict these shale essential minerals in such unconventional resources [16]. The prediction of clay and quartz content in organic-rich shales has been previously investigated through the analysis of well-log responses. These responses encompass several measurements such as gamma-ray, acoustic, density, neutron porosity, resistivity, and mineralogy logs [16]. Another study used the NPOR, DEN, D.T., and G.R. logs to predict the weight fractions of quartz, k-feldspar, calcite, dolomite, pyrite, and total clay [17]. A model was proposed to predict the contents of 12 minerals in gas hydrate sediment based on XRD patterns with the integration of well-log data, using four machine learning algorithms: LSTM, MLP, R.F., and CNN algorithms [18]. We can see that nearly all of them concentrated on mineralogical contents. Combining the dimension reduction of principal component analysis (PCA) with the A.I. technique of artificial neural networks (ANNs) has proven to be a crucial combination for estimating reservoir properties in recent years. Many applications, including data mining, prediction, risk assessment, uncertainty quantification, and data integration, have emerged due to these methods' inherent capacities to capture the nonlinearity and complex heterogeneity in the reservoir [19; 20; 21; 22]This study focused entirely on illite, which causes the most extraction problems, especially for gas recovery. By developing a model based on the ANN, which is quick and shown to provide highly reliable results, this study focuses on predicting the illite wt.% while taking advantage of the conventional well log data.
# Can LiDARs Replace Meteorological Masts in Wind Energy?
###### Abstract  
This paper discusses whether profiling LiDARs can replace meteorological tower-based wind speed measurement for wind energy applications without severely compromising accuracy. To this end, the accuracy of LiDAR is evaluated in a moderately complex terrain by comparing long-term wind data measured by a profiling LiDAR against those obtained from tower-mounted cup and sonic anemometers. The LiDAR-measured wind speeds show good agreement with those measured using the sonic anemometer, with the slope of regression line being 1.0 and \(R^{2}>\) 0.99. Furthermore, the turbulence intensity obtained from the LiDAR has better agreement with that from the sonic anemometer compared to the cup anemometer which showed the lowest turbulence intensities among the three devices. A comparison of the turbulence intensity obtained from the 90 th percentile of the standard deviation distribution shows that the LiDAR-measured turbulence intensities are mostly larger (by 2% or less) than those measured by the sonic anemometer. The gust factors obtained from both devices roughly converged to 1.9, showing that LiDAR is able to measure peak wind speed with acceptable accuracy. The accuracy of the wind speed and power distributions measured using the profiling LiDAR are then evaluated by comparing them against the corresponding distributions obtained from the sonic anemometer. Furthermore, the annual capacity factor--for the NREL 5-MW wind turbine--from the LiDAR-measured wind speed is 2% higher than that obtained from the sonic anemometer-measured wind speed. Numerical simulations are performed using OpenFAST in order to compute fatigue loads for the wind speed and turbulence distributions for the LiDAR and the sonic anemometer measurements. It is found that the 20 years lifetime Damage Equivalent Loads (DELs) computed for the LiDAR wind speed were higher than those for the sonic anemometer wind speeds, by 2%-6% for the blade root bending moments and by 11%-13% for the tower base bending moments. This study shows that even with some shortcomings, profiling LiDARs can measure wind speeds and turbulence intensities with acceptable accuracy. Therefore, they can be used to analyze wind resource and wind power potential of prospective sites, and to evaluate whether those sites are suitable for wind energy development.  
Keywords:LiDAR; wind energy; mean wind speed; turbulence intensity; peak wind speed; power distribution +
Footnote †: journal: _articles Journal of Physics A: Mathematical and Physical Sciences_