esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Minimal Connected Dominating Set Enumeration
Since \(\mathcal{S}\) contains all minimal transversals \(\mathcal{S}^{\prime}\) of \(\mathcal{H}\) and \(|\mathcal{S}\setminus\mathcal{S}^{\prime}|=O(n^{2})\), the algorithm enumerates all minimal transversals of \(H\) in output-polynomial time as well. The above theorem reveals that the minimal connected dominating set enumeration is \"hard\" in some sense even for claw-free graphs, in contrast to the case of the minimal connected vertex cover enumeration Theorem 3. Furthermore, the following theorem asserts that the minimal connected dominating set enumeration is also \"hard\" even for 2-degenerate bipartite graphs. We would like to emphasize that the problem of enumerating minimal dominating sets in a bounded-degeneracy graph admits a polynomial-delay algorithm [40]. This indicates that the connectivity of solutions would make the problem more nontrivial, as seen in the case of Minimal Connected Vertex Cover Enumeration. **Theorem 8**. : _If there is an output-polynomial time (or a polynomial-delay) algorithm for Minimal Connected Dominating Set Enumeration on \(2\)-degenerate bipartite graphs, then there is an output-polynomial time (or a polynomial-delay) algorithm for enumerating minimal transversals in hypergraphs._  
Proof. : Our reduction is identical to Theorem 4. Let \(G^{\prime}\) be the \(2\)-degenerate bipartite graph obtained from \(\mathcal{H}\), which is constructed in the same way as in Theorem 4. We show that every minimal connected vertex cover of \(G^{\prime}\) is a minimal connected dominating sets of \(G^{\prime}\) and vice versa. In the following discussion, we denote by \(L\) the set of pendant vertices in \(G^{\prime}\) and by \(U\coloneqq V(G^{\prime})\setminus(V\cup L)\) the set of vertices in \(G^{\prime}\) having a pendant neighbor. Let \(C\) be a minimal connected vertex cover of \(G^{\prime}\). As observed in Theorem 4, we have \(U\cap L=\emptyset\) and \(U\subseteq C\). Since \(U\) dominates all the vertices of \(G\), \(C\) is a connected dominating set of \(G^{\prime}\). To see the minimality, suppose that there is a vertex \(u\in C\) such that \(C\setminus\{u\}\) is still a connected dominating set of \(G^{\prime}\). Since each vertex in \(U\) has a private pendant vertex (i.e., a pendant vertex that is only adjacent to it), \(u\
otin U\), which implies that \(u\in V\). As \(U\) covers all the edges of \(G^{\prime}\), \(C\setminus\{u\}\) is also a minimal connected vertex cover of \(G^{\prime}\), deriving a contradiction. Let \(D\) be a minimal connected dominating set of \(G^{\prime}\). Analogously, \(D\) is a connected vertex cover of \(G^{\prime}\) containing \(U\). Suppose that there is a vertex \(u\in D\) such that \(D\setminus\{u\}\) is a connected vertex cover of \(G^{\prime}\). As \(U\) dominates all the vertices of \(G^{\prime}\) and \(u\in D\setminus U\), \(D\setminus\{u\}\) is a connected dominating set of \(G^{\prime}\), contradicting the minimality of \(D\).
# Application of Blockchain Technology in Emergency Management Systems: A Bibliometric Analysis
## 5 Discussion
### Theoretical Insights and Practical Applications
#### 5.1.1 Healthcare and Emergency Medicine  
Clusters #0 and #2 demonstrate the growing role of blockchains in health system transformation. Theoretically, this body of research explores patient-centric models, architectural frameworks for data sharing, and system interoperability, particularly in the COVID-19 pandemic context [61; 63; 64; 66; 71; 72; 84; 85; 86; 87; 89; 91].  
From a practical standpoint, blockchain technologies have demonstrated significant value in the healthcare sector by supporting vaccine distribution, contact tracing, and real-time sharing of medical records across institutions. Blockchains can enable secure medical data exchange, patient-controlled access management, and remote monitoring solutions, with prototype electronic health record systems already being tested in pilot projects [61; 62; 63; 64; 66; 71; 72]. Frameworks based on Hyperledger Fabric and Ethereum provide decentralized infrastructures for secure, scalable, and privacy-respecting health data management [85; 89]. These innovations are particularly valuable in emergency medicine, where rapid and secure access to data can be life-saving.
# Effect of Chemical Composition of Bio- and Petroleum-Based Modifiers on Asphalt Binder Rheology
## 2 Materials and Methods
### Modifier Chemistry Experimental Program
#### 2.3.3 Molecular Weight Analysis  
The molecular weight analysis was conducted using GPC. The system consists of a Waters 2695 separation module connected to two Styragel HR1 SEC columns (7.8 mm \(\times\) 300 mm) in series followed by a Waters 2414 RI detector and a computer with Empower Pro and data acquisition software. Samples of 3% _w/w_ were prepared in tetrahydrofuran (THF), a carrier solvent with a flow rate of 1.0 mL/min and an injection volume of 20 \(\upmu\)L; they were filtered using a 0.45 \(\upmu\)m millipore polytetrafluoroethylene (PTFE) syringe filter to remove suspended particulates. To detect analytes, a constant flow of fresh eluent was supplied to the column via a pump.  
The resulting chromatographic data was processed for number-average molecular weight (\(M_{n}\)), weight-average molecular weight (\(M_{w}\)), and polydispersity index (PDI) using Equations (1)-(3), respectively. The molecular weights were calculated based on the component molecular weights (\(M_{i}\)) determined from the retention time calibration curve and signal intensities (\(N_{i}\)).  
\[M_{n}=\frac{\sum M_{i}N_{i}}{\sum N_{i}} \tag{1}\]  
\[M_{w}=\frac{\sum M_{i}^{2}N_{i}}{\sum M_{i}N_{i}} \tag{2}\]  
\[PDI=\frac{M_{w}}{M_{n}} \tag{3}\]  
The retention time calibration curve was developed by fitting log-scale molecular weights to their retention time for standard material with known molecular weights using a 3-degree polynomial. The fitted curve was then used to measure the molecular weights of unknown modifiers using the chromatographic data. The distributions with shorter retention times correspond to larger molecular size whereas longer retention times represent smaller sizes. The molecular weights are reported in Daltons.
# Biochar for Wastewater Treatment--Conversion Technologies and Applications
## 7 Conclusions  
The low cost of feedstock and the simple preparation process combined with the enhanced physico-chemical properties of biochar make its application more feasible for wastewater treatment. This review systematically presented an overview of different biochar production techniques, adsorption mechanisms toward organic and inorganic contaminates, and prospective applications in the field of wastewater decontamination. The capacity of biochar to remove pollutants in aqueous solutions is directly linked with their physico-chemical properties, which depends on the nature of the starting feedstock, the thermal conversion technique, and the preparation conditions. The modification of biochar through physical and chemical activation methods was reported to adjust the functional groups on the surface of biochar, enhance its surface area and porous structure, and increase its surface oxygen-containing groups. Due to its unique and highly versatile characteristics, biochar has been effectively used in a variety of applications aiming the remediation of contaminated wastewater, including the adsorption of toxic heavy metals and dyes from the aqueous solutions, as a support for catalysts, as an immobilization support media for microorganisms and adsorbent of inhibitive compounds during anaerobic digestion. Overall, it is undoubted that the application of biochar offers several benefits and potential economic and environmental advantages, and its efficiency to remove different contaminants in the lab-scale has been widely reported. However, more in situ experiments should be performed to test the biochar efficiency using real effluents and to examine the real effect of biochar on the environment prior to its large-scale application. The stability of biochar after several cycles of use and its regeneration needs to be further investigated.  
Conceptualization, G.E. and M.L.; Section 1, G.E.; Section 2, G.E., A.B., M.L. and A.Y.; Section 3, A.Y. and A.B.; Section 4, G.E., M.L., A.B. and A.Y.; Section 5, G.E., M.L. and A.Y.; Section 6, G.E. and M.L.; Original draft preparation, G.E.; Revision of the draft manuscript, M.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
# Real-Time Face Mask Detection to Ensure COVID-19 Precautionary Measures in the Developing Countries
## 3 Methodology
### Lightweight CNN Model for Face Mask Detection  
After the face detection, an efficient CNN-based architecture is used for the training and testing of face mask detection. There are several already available architectures for the training purpose, which were already discussed in the literature review section. In this work, we presented a custom architecture to detect whether the person wears a face mask or not. In the proposed work, face mask detection is performed by facial feature analysis. The proposed lightweight CNN model consists of four convolutions, one fully connected and the output layer, and after each convolution layer, a nonlinear ReLU activation function is used to perform thresholding operation. The ReLU activation function drops neurons from the network whose values are less than zero, and the neuron with positive values are unchanged [42]. In the proposed model, we employ two maximum pooling layers for dimensionality reduction, which affects the training duration of the network. During training the model, the problem of overfitting can be occurred due to the model and data simplicity. To reduce the overfitting problem, we used the dropout and batch normalization mechanism. The output neurons in the last fully connected layer are equal to the number of classes recognized by the network, and lastly, the softmax classifier is used to classify the given input into the corresponding class such as face mask detected and no face mask detected.  
The input image size of the first convolution layer in the proposed architecture is 128 \(\times\) 128, having RGB channels with 32 different filters (kernels). The size of these filters is \(3\times 3\times 3\) with a 1-pixel stride. The output of the first convolution layer is the input of the second convolutional layer after normalization and pooling layers. There is a total of 64 kernels with a size of \(3\times 3\) in the second convolutional layer. The rest of the convolutional layers are connected without any intervening subsampling or batch normalization. In the third convolutional layer, 128 kernels are used, where each kernel has a size of \(3\times 3\times 3\). The final convolutional layer has 256 kernels having of size \(3\times 3\times 3\). The fully connected layers have a total of 128 neurons. The output of the fully connected layer is fed into a softmax layer, which produces a distribution over the 2-class labels, namely, mask detected and mask not detected. The complete detail of the proposed system is shown in Figure 3 along with subsequent layers descriptions.  
Figure 2: Represented face detection based on MTCNN.
## 1 Introduction
### Materials and Methods  
#### Modeling Extreme Droughts and Their Consequences on Yield Losses  
##### 2.1.1 DOWKI Computation on the SAFRAN Reanalysis  
We used the DOWKI, which characterizes extreme events of drought and excess water. DOWKI is (1) simple to compute, (2) purely meteorological, and (3) independent of crop categories. It can be compared to yield losses for several types of crops and on large areas. DOWKI is a cumulative efficient rain anomaly, computed on a 10-day time step, between the current year value and the historical average. It is computed for the growing period of a given crop, and starts at 0 on 1 January. It is expressed in mm. Its equation for drought event characterization is as follows:  
\[ERNC_{i,n}=\left[(P_{i-1,n}-PET_{i-1,n})-\left(\overline{P_{i-1,P}-PET_{i-1,P}} \right)\right]+\left[(P_{i,n}-PET_{i,n})-\left(\overline{P_{i,P}-PET_{i,P}} \right)\right]\]  
\[DOWKI_{drought\ c,\ n}=\min\ ERNC_{i0\to ij,c,n}\]  
where \(ERNC_{i,n}\) is the cumulative rain anomaly computed in decade \(i\) for year \(n\). \(P\) is the precipitation and \(PET\) is the potential evapotranspiration. \(\left(\overline{P_{i-1,P}-PET_{i-1,P}}\right)\) represents the average of the difference between \(P\) and \(PET\) computed for all \(i\)-1 10-day periods in period \(P\) (historical period). DOWKI is an annual value and is computed by taking the minimum of the values of \(ERNC\) for any 10-day period between \(i_{0}\) (first 10-day period) and \(i_{j}\) (final 10-day period).  
In [42], DOWKI was computed for representative meteorological stations at the departmental scale to match with available yield loss data, and we showed model uncertainties as we simulated all the crops losses. One notable limitation is the climate measure at a single point over the department. On the one hand, this measure is not necessarily representative of the climate over the whole territory of the department. On the other hand, the crop parcels were not necessarily located at the climate measuring point (meteorological station point). In this second case precisely, this would mean that we measured the hazard but not the agricultural risk. In this paper, we computed DOWKI on the SAFRAN-Grid--8 km \(\times\) 8 km over the French metropolitan area for two reasons:  
1. To reduce the uncertainties of the model;2. To match with the output scale of the ARPEGE-Climat model using a quantile-quantile downscaling method on the SAFRAN daily reanalysis data (1981-2010 for rainfall and 1989-2018 for potential evapotranspiration).  
After computing DOWKI on the SAFRAN grid, we crossed the SAFRAN reanalysis grid with the Graphic Plot Register (GPR) shown in Figure 1. To compute an index value by department, we calculated the DOWKI average values in each cell of the department where the crop was present. This methodology allowed us to measure climate risk specifically on crop production since we integrated the hazard parameter (DOWKI value) and crop vulnerability (the DOWKI computation corresponds to on the crop vulnerability period and the agricultural parcel location).
# Laser Spot Centering Algorithm of Double-Area Shrinking Iteration Based on Baseline Method
## 3 Realization Principle of Proposed Approach
### Statistics of Background Noise Baseline  
The laser spot is not disturbed by noise when collected under ideal conditions. However, it is inevitably affected by various noises in actual laser spot collection. The traditional method is setting a fixed threshold to remove the influence of background noise. Thus, the purpose of eliminating noise effectively is not achieved. In this paper, the baseline method based on the statistical characteristics of noise can be used to calculate the noise distribution from multiple background image frames to obtain the background noise baseline adaptively. The specific process is shown in Figure 3.  
After collecting multiple background image frames using a camera, the image gray average and the image gray variance can be calculated using Equations (10) and (11), respectively; subsequently, the background noise baseline can be computed according to Equation (8).  
\[B=\overline{A}+\frac{2\times\sigma}{\sqrt{mask\_x\times mask\_y}}, \tag{8}\]  
where \(B\) is the calculation result of the background noise baseline, \(\overline{A}\) is the overall average pixel value of multiple image frames, and \(mask\_x\) and \(mask\_y\) are the sizes of the filter template, set to 0.05 times of the image. If the size of the image is \(M\times N\) and the pixel value of the \(k\)-th background image at the pixel \((i,j)\) is \(f_{k}\) (\(i,j)\), then  
\[Q=\frac{1}{n}\sum\limits_{k=1}^{n}f_{k}, \tag{9}\]  
\[\overline{A}=\frac{1}{n}\sum\limits_{k=1}^{n}\left(\frac{\sum\limits_{i=0}^{M -1}\sum\limits_{j=0}^{N-1}f_{k}(i,j)}{M\times N}\right), \tag{10}\]  
Figure 1: Image acquisition.  
Figure 3: Calculation of the background noise baseline.  
Figure 2: Realization of spot center detection.  
\[\sigma=\sqrt{\frac{\sum\limits_{i=0}^{M-1}\sum\limits_{j=0}^{N-1}\left(Q^{\prime}(i, j)-\overline{Q^{\prime}}\right)^{2}}{M\times N-1}}, \tag{11}\]  
where \(n\) is the background image frame, \(Q^{\prime}\) is the mean filtering result on the average image \(Q\), in which multi-frame background images are superimposed, and \(\overline{Q^{\prime}}\) is the average pixel value of \(Q^{\prime}\).
## 5 Results and Discussion
### Full Set versus LOFO Subsets
The modal voice classification accuracies were comparable between the full set and the other subsets. This result indicated that the other features were less important than SE and the shimmer in the modal voice classification task. NN and the KNN classifier had a better performance (approximately 5% greater) than the other three classifiers on all datasets. For the breathy voice classification, the accuracies of all datasets using different classifiers were approximately 12% greater than those of the modal and the pressed voice classification. All datasets, except the \(-S_{r}\) set, had very high accuracies (approximately 88%). The \(-S_{r}\) set had a lower average accuracy (approximately 5% lower) than the other datasets, which indicated that the shimmer was important for the breathy voice classification. Other features were less important than shimmer, as no obvious accuracy drops for other LOFO subsets were found. The DT performance was approximately 5% lower than that of the other classifiers. Other classifiers had a comparable performance for the breathy voice classification. For the pressed voice classification, the SE feature was salient. The -SE average accuracy was 8.1% lower than the full set, and at least 4.8% lower than the other LOFO subsets. All classifiers showed a decreased accuracy on the -SE subset. The \(-J_{r}\) and the \(-S_{r}\) subsets also had decreased accuracies for pressed voice classification, but less notably than the -SE subset. NN had the best accuracy performance between 74% and 81.1% on all datasets. DT had an unsatisfactory accuracy performance between 58% and 71% on the LOFO subsets. Compared with the full set, DT accuracies decreased between \(-4\%\) and \(-17\%\), more than that for the other classifiers on the LOFO subsets. The LOFO strategy showed that the shimmer and the SE were the most important features for the voice type discrimination task. In particular, shimmer contributed most to the modal voice classification, as observed in Figure 5b. The modal voice samples had a much better aggregation and a lower average value than the other two types for the shimmer feature. The SE contributed most to the pressed voice classification, which could be explained by Figure 5b. The pressed samples featured less overlap with other voice type samples in terms of SE. The breathy voice was much easier to classify using the algorithms than the other two voice types, as shown in Figure 5b.
## 1 Introduction
The low value of this coefficient provides high energy efficiency of optimal FTN signals when coherent symbol-by-symbol detection algorithms are used. Therefore, the possibility of obtaining optimal FTN pulse shapes with the constraint on the PAPR of a random signal sequence should be considered. It should be mentioned that it is not reasonable to expect revolutionary results in reducing PAPR because of the physical and mathematical reasons for the optimization problem-solving. However, a PAPR reduction of 2-3 dB can be expected. When optimizing the FTN pulse shape with the constraint on the PAPR, it is necessary to establish quantitative relations between the clipping level and the extension of the occupied frequency band, as well as the change in the cross-correlation coefficient value. The main aim of the work is to determine the possibilities of reducing PAPR of oscillations by using finite-time optimal quadrature FTN signals with pulse duration exceeding the transmission time \(T\) of one symbol and with the constraint on PAPR. The paper considers the time and spectral characteristics of random sequences of optimal FTN signals with offset quadrature modulation. The results of reducing the PAPR of oscillations are also analyzed. The possibilities of increasing the average power of radiated oscillations while maintaining the total operating energy cost unchanged are introduced. The rest of the work is organized as follows. In the theoretical part of the work (Section 2), the shape of FTN signals with offset quadrature modulation and the method of obtaining the pulse shapes by the optimization problem-solving are presented. The optimization criterion ensuring the fixed reduction rate of the level of out-of-band emissions outside the occupied frequency band is used. This section is divided into two subsections. Section 2.1 describes the features of the optimization problem-solving and the iterative procedure of minimizing the optimization functional. The forms of intermediate three-dimensional surfaces, providing local minima of the optimization functional, are shown. The examples of solutions to the optimization problem for different pulse duration are given in Section 2.2. In the practical part of the work, a simulation model of information transmission with the use of optimal FTN signals is presented. Section 3 contains the algorithm of the simulation model and the possibilities of simulation modeling. This section also includes the model of information transmission with the use of adjustable preamplifier and amplitude limiter in the radio transmission path. The results of simulation modeling for a channel with additive white Gaussian noise and for a frequency-flat Rayleigh fading channel are given in Section 4. Finally, Section 5 concludes this work, summarizing the results of applying offset quadrature optimal FTN signals with reduced PAPR of radiated oscillations.
## 2 Heliostat Orientation  
Once the sun's local position is known, the second step in modeling a working heliostat field is to properly orient each individual heliostat. This orientation depends on the heliostat's location relative to the target, as well as the mechanical characteristics of the rotating device supporting the heliostat mirror. There are essentially three main designs used to rotate heliostats, as shown in Figure 1, all of which include a primary rotating axis (stronger, closer to the soil, colored purple) and a secondary rotating axis (lighter, closer to the mirror, colored green).  
1. The azimuth-elevation layout features a primary axis that points to the sky (along z) and a secondary axis that is horizontal (along x, initially pointing East). The secondary axis rotates by \(\beta\) first (to fix altitude), and then the primary axis rotates by \(\alpha\) (to fix azimuth).
2. The tilt-roll layout features a primary axis that is horizontal (along x, typically pointing east) and a secondary axis (initially horizontal, along y, pointing north) that is attached to the mirror frame. The primary axis rotates by \(\alpha\) first (to fix altitude, the tilt) and then the secondary axis, which rotates by \(\beta\) (around the tilted y' axis, the roll). As shown in Figure 1, this mechanical two-rotation sequence is mathematically equivalent (including offsets) to first rotating the secondary axis (around y) and then the primary axis (around x). The so-called polar-oriented model [26] is obtained when the tilt-roll secondary axis points north and the primary axis points east.
3. The target-aligned layout is where the main axis and the heliostat mirror point directly to the target in the starting, resting position. This layout can be mathematically handled by converting the Earth referential to a target referential and then determining the orientation by applying either an azimuth-elevation or a tilt-roll approach to the mechanical rotations, as described above, before converting back to the Earth referential in the end.