esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Solutions for Minimizing Environmental Impacts  
To minimize not only environmental impacts but also the number of raw materials, water, and energy needed during the manufacturing process of new products, one possible solution is the transition from a linear economy model to a circular economy in order to use the products and reintroduce them into various supply chains to recover value [10,11]. Various measures could, therefore, be implemented regarding the use and consumption of textiles to achieve a circular economy that is sustainable and free of toxic substances [5].  
Recycling and reuse are, therefore, solutions whereby products that cannot be used for their original purpose can be transformed into new raw materials for manufacturing. However, after consumption, the majority is deposited in landfill sites without any kind of treatment. In Europe, every year, the population consumes around 26 kg of textiles and wastes around 11 kg. Worldwide, less than 1% of clothing is recycled as clothing, mainly because of a lack of suitable technologies for this purpose [5].  
Before being recycled, waste goes through a sorting process in which the materials are identified and divided according to various characteristics such as color, type of fabric, or quality [10,11]. This stage is the most complicated given the complexity of these products, as they often contain other components, including buttons, zips, or other decorative elements [11]. Once this stage is completed, the waste goes on to recycling, which can be characterized based on the nature of the process--mechanical or chemical [11].  
The mechanical recycling method is commonly applied to fabric scraps from cutting operations. In this process, the waste is organized by the type of material and defragmented to form new fibers, which become more fragile as they are defragmented and are then sold to other textile spinning companies (shoddy spinning) or reused in the manufacture of various products, such as cleaning materials, filling products, or insulation [11,12]. In turn, chemical recycling is a method of transforming waste previously destroyed by mechanical processes using chemical substances to break it down and obtain smaller polymers while maintaining a similar quality to the initial product [13].
# Strengthening Polymer Concrete with Carbon and Basalt Fibres
## 3 Results and Discussion
### Diagrams \"\(\sigma\)-\(\varepsilon\)\" of Material Deformation  
In order to analyse in detail the stress-strain state of bending element cross-sections, functional relationships between stresses and relative strains in polymer concrete samples with reinforcing additives were constructed. The methodology of the tests, as well as their geometrical parameters and schemes of measuring devices arrangement, is described in Section 2. As a result of conducting compression tests for the samples, graphs of the relationship between stresses and relative strains were plotted for polymer concrete without the reinforcement, as well as for concrete reinforced with carbon fibres (1%) and basalt fibres (2%). These graphs are presented in Figure 8. This graph shows that fibre reinforcement allows for a significant increase in the sample deformation value before failure. Reinforcing fibres continue to work and prevent the material from failing for much longer. At the same time, the use of basalt fibres provides the maximum increase in relative elongation before failure of 55%. When reinforcing with carbon fibres, this value is 27%.  
Figure 8: Stress–strain diagram for testing specimens for bending strength: 1—specimen without reinforcing fibres; 2—specimen with 1% carbon fibres; 3—specimen with 2% basalt fibres.  
Figure 7: The graph of the dependence of the compressive strength on the reinforcement percentage.
## 3 Results
### Patients Follow-Up  
A total of 34 patients were studied, 14 with GBM, 1 with LLG (low-grade glioma), 1 with HGG (high-grade glioma) III and 18 with meningioma (Table 1A,B). Follow-up via blood sampling was completed in 31 patients. Three GBM and 1 meningioma patients died within the period of this investigation.
# Deep Reinforcement Learning Based Resource Management in UAV-Assisted IoT Networks
## 6 Experimental Results and Discussions
Therefore, in terms of managing resources for IoT users in each cluster, the proposed framework contributes to better performance. Figure 10 shows the accuracy of the average values for user throughput in the target area based on the DRL method of iterations. As presented in the figure, the real and forecasted throughput values converge rapidly at a value of 0.015 after 20 iterations. Subsequently, the actual values are closely aligned with the expected values. It is feasible that the network cannot be over adapted to the training data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**User Density**} & \multicolumn{5}{c}{**Performance Outcomes**} \\ \cline{2-7}  & **1** & **3** & **6** & **11** & **21** & **26** \\ \hline Average throughput rate [Mbps] (\%) & 0.96 & 0.967 & 0.967 & 0.97 & 0.97 & 0.97 \\ Average energy consumption rate [dBm] (\%) & 0.96 & 0.965 & 0.967 & 0.967 & 0.96 & 0.96 \\ Average SINR level [dBm] (\%) & 0.96 & 0.97 & 0.75 & 0.966 & 0.966 & 0.966 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The average accuracy of the target clusters and the user density in different learning processes. Figure 9: Performance analysis of average throughput and power rates: (**a**) average throughput delay from the UAV and (**b**) data access rate per ground user equipment as compared to various iterations. Table 7 contains the sample performance comparison of our system with previous related works. Previous studies [22; 29] applied DRL and Q-learning+D3 QN algorithms, respectively. At the same time, our work used DRL+K-means and integrated the scheduling algorithm of round robin. The accuracy performance is 87%, 81.3%, and 94% for the specified related works and our proposed method estimation, respectively. The RMSE estimation evaluation indicates 5.2%, 7.33%, and 2.40% for the specified related works and our proposed method estimation, respectively. The testing time is 2.5%, 2.01%, and 1.05% for the specified related works and our proposed method estimation, respectively. Hence, our proposed system outperforms other related works. Compared to the performance in similar scenarios, all the previous methods (i.e., DRL, Q-learning+D3 QN) achieved lower system accuracy and RMSE evaluation. Therefore, our proposed method shows better performance in terms of accuracy and RMSE testing time evaluations. This implies that our system has good generalization ability, low computational time, and adaptability to real and dynamic IoT environments. Furthermore, this indicates that the proposed DRL+K-means+round-robin method has capability in terms of solving large-scale learning problems. Therefore, the testing time of the proposed model is smaller than the other models and this indicates that the proposed model can be trained faster.
# Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory
## 2 Proposed Method
### Color Balance Correction  
Images captured by digital image equipment may exhibit color imbalances due to defects in the equipment or adverse weather conditions [13]. In particular, in challenging weather like sandy weather, the acquired images often display significant color disparities [14]. This not only hampers image enhancement but also impacts its suitability for  
Figure 1: The algorithmic framework of this paper.  
computer vision tasks such as image stitching [15]. To solve these problems, it is necessary to perform color balancing on the acquired image to eliminate or reduce the change in an object's color due to the acquisition process, so as to enhance the original characteristics of the image. Firstly, the dynamic range values of each channel are normalized, then the green channel is used to compensate for the loss of the blue channel of the acquired image, and the red channel values are attenuated to some extent. The blue channel \(I_{B}\) is expressed as [16]:  
\[I_{B}(x)=\overline{I}_{R}(x)+\lambda(1-\overline{I}_{R}(x))\cdot(\overline{I}_ {G}(x)-\overline{I}_{R}(x))\cdot\overline{I}_{G}(x) \tag{1}\]  
\[\lambda=\frac{\sigma(I_{R}(x))}{\sigma(I_{G}(x))} \tag{2}\]  
where \(\overline{I}_{R}\) and \(\overline{I}_{G}\) represent the average of the red and green channels of the original image \(I\), respectively. \(\lambda\) represents the ratio of the red channel to the green channel, and is used to adjust the color distribution of each channel. Finally, the traditional gray world algorithm is utilized to compensate the color cast of the light source.
# Enhancement of Conventional Beat Tracking System Using Teager-Kaiser Energy Operator
## 2 Dataset and Methods
### Dataset  
First of all, we tested whether the TKEO improves the estimation of the GT in general. The GT is the median of differences between the time positions of beats throughout the whole analysed track. For this purpose, we used the _SMC_MIREX_ database [26], which consists of different recordings, from classical pieces to guitar solos. The recordings are sampled by 44.1 kHz. Their annotations contain manually corrected beat time positions, which will be used as a reference.  
Music by string quartets is very specific because the tempo can be more or less stable but the musical ornaments, intended gaps, fermatas, or other expressive musical attributes can be present. Every musician has her/his own style of agogic performance. If we define meaningful musical parts by choosing important musical motifs, we can create segments that could be processed separately.  
The second dataset consists of 33 different interpretations of _String Quartet No. 1 e minor \"From My Life\"_, composed by the Czech composer [PERSON]. We also included two interpretations played by orchestra. We divided the first movement into six segments of musical motifs in the view of the musical meaning. The first movement consists of an introduction (Beg), exposition (A), coda (B), development (C), recapitulation (D) and the last coda (E). For every segment, we calculated the estimated average tempo (EAT), but without any expressive elements and information about beat positions, using a physical length of the tracks and information of rhythmic patterns in sheet music. The EAT will be used as a reference tempo for setting up the default tempo parameter in the beat tracking system. The first page of the sheet music is provided as an example in Appendix A.
## 5 Design Goals and Evaluation Criteria of Middleware of WSN Virtualization
### QoS  
Many applications demand QoS, therefore the requirements of the application must be satisfied. In virtualization specifically, processing latency, choice of service, virtual links, nodes, transmission power and radio selection are very critical all these decisions must be made in such a way that it meets the QoS requirements along with the minimum use of power. The applications like personal health monitoring,fire detection and disaster monitoring require high QoS. Virtualization of sensor networks enhances the QoS by virtual links and QoS manager a vital module in all middleware frameworks.
## 5 Discussion
However, the difference in the performance was marginal. It is worth noting that both configurations failed to segment lesions whose sizes are in the range \((0,278)\). Considering the violin plots in Figure 2, it can be observed that this range corresponds to the Small and Medium Down categories of the mask sizes in the axial view of ISLES 2015. Since the proposed model presented N. S. images in these categories, it can be pointed out that its behavior is consistent when segmenting the images from ISLES 2022. The non-segmentation of small lesions significantly dropped the mean F1-Scores. Nonetheless, the performance of the proposed model is competitive against other state-of-the-art models evaluated using the ISLES 2022 dataset as shown in Table 8, considering that these methods employed more weighty architectures, such as in the works of [PERSON] et al. [25] and [PERSON] et al. [26], or performed transfer learning techniques, as in [PERSON] et al. [27]. [PERSON] et al. [27] provided the results of their method trained only with the DWI images from ISLES 2022, which can be directly compared with our proposed model. They achieved a mean F1-Score of 0.7641, while the model proposed reached 0.7517. Since the difference is minimal, it can be inferred that the proposed model could accomplish higher segmentation results by employing transfer learning techniques. Finally, the proposed approach presents efficient training since its 7,939,778 parameters can be trained overall in less than an hour and a half using around 4500 images in the training set, and the training can be performed in general-purpose mid-range GPUs. Furthermore, the learning curve in Figure 4a shows a fast convergence flattening the learning curves at epoch 125. The version of the proposed model using SGD as the optimizer also converges quickly. Nonetheless, their performance is poor compared with the one obtained using AdamW.
## 2 Operational Principle and Mathematical Model
### Mathematical Model
The \(\otimes\) symbol is the convolution operator. The variable \((\cdot)_{f}\) refers to the used filter. The error \(e_{f}(t)\) can be written as follows:  
\[e_{f}(t)=y^{*}_{f}(t)-\psi^{*}_{f}(t)^{T}\varphi \tag{20}\]  
where  
\[\psi^{*}_{f}(t)^{T}=\left[-H^{\alpha_{1}}y^{*}_{f}(t),\ldots,-H^{\alpha_{m_{A} }}y^{*}_{f}(t),H^{\beta_{0}}u_{f}(t),\ldots,H^{\beta_{m_{B}}}u_{f}(t)\ \ \right] \tag{21}\]where the variable \(\left(\cdot\right)^{T}\) denotes the transposition. The minimum sum of squared errors \(e_{f}(t)\) leads to the estimation of parameters using least squares state variable filter algorithm, which can be approximated as follows:  
\[\underset{LSSVF}{\overset{\wedge}{\underset{g}{\varphi}}}=\left({\phi_{f}^{*}}^{T }{\phi_{f}^{*}}\right)^{-1}{\phi_{f}^{*}}^{T}Y_{f}^{*} \tag{22}\]  
where  
\[\phi_{f}^{*}=(\psi_{f}^{*}(T_{s}),\ \ \psi_{f}^{*}(2T_{s}),\ldots,\psi_{f}^{*}( KT_{s}))^{T} \tag{23}\]  
Based on an instrumental regression, the following can be written:  
\[\psi_{f}^{IV}(t)^{T}=\left[-H^{a_{1}}y_{f}^{IV}(t),\ldots,-H^{a_{m_{A}}}y_{f}^ {IV}(t),-H^{\beta_{0}}u_{f}^{IV}(t),\ldots,-H^{\beta_{m_{B}}}u_{f}^{IV}(t)\right] \tag{24}\]  
where the variable \(\left(\cdot\right)^{IV}\) defines the used instrumental variable. The estimated parameters using \(IV\) state variable filter algorithm are approximated as follows:  
\[\underset{IVSVF}{\overset{\wedge}{\underset{g}{\varphi}}}=\left({\phi_{f}^{IV }}^{T}{\phi_{f}^{*}}\right)^{-1}{\phi_{f}^{IV}}^{T}Y_{f}^{*} \tag{25}\]  
where  
\[\phi_{f}^{IV}=\left(\phi_{f}^{IV}(T_{s}),\ \phi_{f}^{IV}(2T_{s}),\ldots, \phi_{f}^{IV}(KT_{s})\right)^{T} \tag{26}\]  
When [PERSON]'s filters are changed by the optimal filter introduced in [30], the optimal regression vector can be written:  
\[\psi_{f}^{opt}(t)^{T}=\left[-H^{a_{1}}y_{f}(t),\ldots,-H^{a_{m_{A}}}y_{f}(t), H^{\beta_{0}}u_{f}(t),\ldots,H^{\beta_{m_{B}}}u_{f}(t)\right] \tag{27}\]  
where the variable \(\left(\cdot\right)^{opt}\) defines the optimum. The filter will be updated with the new estimated parameters. Then, the pre-filter derivatives of \(u(t)\), \(y(t)\) and \(y^{IV}(t)\) are written as in [29]. Hence, the regression vectors \(\psi_{f}^{*}(t)^{T}\) and \(\psi_{f}^{IV}(t)^{T}\) are generated as in Equations (21) and (24). Finally, the estimated parameters are calculated at each iteration as follows:  
\[\phi_{SRIV}^{iter}=\left({\phi_{f}^{IV}}^{T}{\phi_{f}^{*}}\right)^{-1}{\phi_{f }^{IV}}^{T}Y_{f}^{*} \tag{28}\]  
where the variable \(\left(\cdot\right)^{iter}\) defines the iteration. The iteration will stop when the estimated parameters reach optimal values.
## 2 Methodology
### Handcrafted Fractal Features
#### 2.1.1 Probabilistic Fractal Dimension  
The fractal dimension based on a probabilistic approach (\(DF_{p}\)) was determined following the method outlined in [24; 41], which involves the gliding box process. In this technique, for a given color image provided as input, utilizing the RGB color model, each  
Figure 1: Illustrative overview of the proposed approach.  
pixel was represented by a 5D vector \((x,y,r,g,b)\) with spatial coordinates \((x,y)\) corresponding to the color components \((r,g,b)\). Using a hypercube with side \(L=3\), the image was scanned in steps of one pixel from the top left to the bottom right. After this, the size of the hypercube was incremented by two units until it reached \(L_{max}\). An illustration of this process is presented in Figure 2.  
Each pixel \(i\) of a hypercube, \(F_{i}=f(x_{i},y_{i},r_{i},g_{i},b_{i})\), was compared to the corresponding central pixel \(F_{c}=f(x_{c},y_{c},r_{c},g_{c},b_{c})\) using a distance measure. Here, the chessboard (\(\Delta_{cles}\)), Euclidean (\(\Delta_{eucl}\)) and Manhattan (\(\Delta_{manh}\)) distances were considered, according to Equations (1)-(3), respectively. Each _pixel_\(F_{i}\) with a distance \(\Delta\) less than or equal to \(L\) was labeled as \(1\) to indicate that it belongs to the hypercube under analysis. Otherwise, the assigned label was \(0\). By counting the pixels labeled as \(1\), it was possible to define a matrix \(P(m,L)\), which characterized the probability \(P\) that \(m\) points belong to a hypercube with side \(L\). The result was a structure according to the illustration available in Table 1.  
\[\Delta_{cles}=max(|F_{i}(o_{i})-F_{c}(o_{c})|),\ o\in r,g,b, \tag{1}\]  
\[\Delta_{eucl}=\sqrt{\sum_{o}(F_{i}(o_{i})-F_{c}(o_{c}))^{2}},\ o\in r,g,b, \tag{2}\]  
\[\Delta_{manh}=\sum_{o}|F_{i}(o_{i})-F_{c}(o_{c})|,\ o\in r,g,b. \tag{3}\]  
Thus, the matrix \(P(m,L)\) was normalized according to Equation (4), ensuring that the sum of the elements of a column is equal to \(1\).  
\[\sum_{m=1}^{L^{2}}P(m,L)=1,\forall L. \tag{4}\]  
From the matrix \(P(m,L)\), it was possible to obtain the local fractal dimension \(N(L)\) from each size \(L\) through Equation (5). This quantification allowed obtaining the angular coefficient of the linear regression, defined by \(log\ L\times log\ N(L)\), as the probabilistic fractal dimension \(DF_{p}\) of the image under analysis.  
\[N(L)=\sum_{m=1}^{L^{2}}\frac{P(m,L)}{m}. \tag{5}\]  
Figure 2: A grid map with overlapping boxes (red), \(L=3\) in (**a**) and \(L=5\) in (**b**), in order to illustrate the gliding box process.
## 2 Materials and Methods
### Spatio-Temporal Characteristics Analysis
#### 2.3.1 Spatial Kernel Density Estimation  
Compared with the traditional kernel density estimation, spatial kernel density estimation provides a precise depiction through three-dimensional map and contour line density. The spatial kernel density is calculated using the Gaussian kernel function (Equation (12)):  
\[K(x)=\frac{1}{\sqrt{2\pi}}\exp\Bigl{(}-x^{2}/2\Bigr{)}, \tag{12}\]  
\[g(y|x)=\frac{f(x,y)}{f(x)}, \tag{13}\]  
\[f(x,y)=\frac{1}{Nh_{x}h_{y}}\sum\limits_{i=1}^{N}\frac{K_{x}(X_{i}-x)}{h_{x}} \times\frac{K_{y}(Y_{i}-x)}{h_{y}}, \tag{14}\]  
where \(g(y|x)\) denotes the distribution of \(y\) under the condition of \(x\), and \(f(x,y)\) denotes the joint kernel density function of \(x\) and \(y\). N represents the number of observations, \(x\) is the mean, and \(h\) is the bandwidth.
## 6 Proposed System Model and Machine Learning Prediction Models  
The system model is shown in Figure 7. We extract data from the aforementioned SIP interfaces (shown in Figure 4). Then, we apply data to the machine learning model, which is composed of three stages: (i) Data pre-processing, (ii) Feature selection and (iii) Classification. Finally, we analyze the failures to conclude the failure root causes as shown later in Section 7.3. Figure 8 shows the different eight prediction models. We use two different feature selection methods and four classifiers, as discussed in the following.
## 3 In Situ Monitoring Program
### On-Site Implementation  
Six monitoring lines of MPBXs and two symmetrical DOFS cables were arranged inside the LC1 borehole. The schematic diagram of monitoring points and monitoring lines is shown in Figure 3. Based on the logging results of the borehole, the key strata discrimination was carried out, and each monitoring point in the borehole was set and adjusted. The depth of MPBXs (from No. 1 to 6) corresponding to the 6 displacement monitoring lines was 534 m, 520 m, 492 m, 453 m, 388 m, and 324 m successively (according to 127 m, 141 m, 169 m, 208 m, 273 m, and 337 m, respectively, above the roof of the coal seam). The depth of the double-circuit DOFS cables is the same as that of 1# MPBX, which is 534 m. The installation scheme is shown in Figure 3, and the on-site installation of the monitoring instruments is shown in Figure 4.  
Figure 4: The installation process of monitoring instruments: (**a**) installation process; (**b**) data acquisition system.  
Figure 3: The installation scheme of MPBXs and DOFS inside LC1 borehole.  
The monitoring instruments have been installed and debugged since the completion of borehole sealing. The official monitoring began on 24 July 2022, when the LC1 borehole was 18 m in front of the working face. By 11 November 2022, the mining of the working face has been completed. The working face has pushed 274 m past the borehole, and the accumulative monitoring time is 110 d.
## 2 Materials and Methods
### Methods
#### 2.2.5 ANN Burned Area Prediction Metrics  
The models obtained for prediction of burned areas in big wildfires (larger than 30,000 m\({}^{2}\)) for each zone were analyzed using four error metrics based on the deviation from target for the actual burned area: mean absolute deviation (MAE) (Equation (5)), root mean square error (RMSE) (Equation (6)), mean absolute percent error (MAPE) (Equation (7)), and the bias (Equation (8)) defined by [PERSON] [94] and [PERSON] [95] in order to measure the accuracy of the simulation:  
\[MAE=\frac{\sum_{i=1}^{N}\left|y_{i}-t_{i}\right|}{N} \tag{5}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{N}\left(y_{i}-t_{i}\right)^{2}}{N}} \tag{6}\]  
\[MAPE=\frac{\sum_{i=1}^{N}\frac{\left|y_{i}-t_{i}\right|}{y_{i}}}{N}x100 \tag{7}\]  
\[\text{Bias}=\frac{\sum_{i=1}^{N}\left(y_{i}-t_{i}\right)}{N} \tag{8}\]  
where \(y_{i}\) is the predicted value, \(t_{i}\) is the target value, and \(N\) is the number of analyzed values.  
MAE, RMSE, and bias are expressed in the same units as the studied variable (m\({}^{2}\) of burned area) whereas MAPE shows the goodness-of-fit as a percentage of the error. Outliers have more effect in RMSE than MAE, therefore it will give more weight to higher difference values. Bias provides information related to the tendency to overestimate or underestimate the target value, quantifying the model systematic error.