esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In the last decade, the advantages of MW heating have been explored in many fields, ranging from the extraction of natural products (MAE), the hydrolysis of biopolymers to organic synthesis [18]. In particular, the increase in extraction yields of several active phytocompounds from plant biomass by using MAE could be mainly due to the degradation of plant tissues because of the water content in the cells, which is selectively heated by MW, leading to cell-wall expansion and rupture, and finally facilitating the release of the intracellular compounds and their further transformation [19]. Enabling technologies promote the delignification, which plays a crucial role in biomass valorization. The delignification can enhance the recovery of hemicelluloses, and other bioactive compounds (BACs) [14], such as antioxidants [20; 21]. On the other hand, the accessibility of cellulose is also enhanced, thus favoring the further transformation of the remaining solid fraction into valuable products [22; 23]. Among bio-derived platform chemicals, levulinic acid (LevA) has attracted attention in recent years [24; 25] as a versatile chemical building block. This feature makes LevA a valuable compound to produce multiple high-end products with recognized application as solvents or fuels, as well as in coatings, electronics, or lubricants. These emerging technologies may assist a sustainable delignification in combination with green solvents, such as natural deep eutectic solvents (NaDESs), in contrast to the conventional alkaline conditions [26; 27]. NaDESs are fluids composed of two or three components which are capable of self-association through hydrogen-bond interactions. NaDESs are prepared by combining hydrogen bonding donors (HBDs)--such as glycerol, lactic acid or urea, and hydrogen bonding acceptors (HBAs)--especially quaternary ammonium salts such as choline chloride (ChCl) [28,29]. NaDESs are non-toxic, biodegradable, inexpensive, and simply prepared with a full atom economic reaction. By contrast, their high viscosity may hinder the mass transfer in extraction and delignification leading to time-consuming processes [30]. This problem can be overcome by using NaDESs under enabling technologies, improving heat and mass transfer thanks to high-energy microenvironments [20,30]. In this work, GS delignification was carried out by combining UAE and NaDESs. Different choline chloride (ChCl)-based NaDESs were tested, mainly focusing on the bi-component one ChLevA (ChCl + LevA). This US-assisted delignification afforded different routes for the exploitation of GS as a source of BACs. After delignification, the cellulose-rich fraction was subjected to acidic MW-assisted conversion to LevA, which can be further used to produce ChLevA, according to a circular approach.
## 3 Results
### Kinetic Equation of the Reaction of Biological Decomposition of Surfactant  
Therefore, we obtained the necessary data for deriving the kinetic equation of the reaction of biological decomposition of surfactant CAPB by bacteria of the genus _Pseudomonas_:  
\[C_{t}=C_{0}\times[\exp^{\gamma}(-0.212\times t)] \tag{2}\]  
In Equation (2), \(C_{t}\) is the concentration of the surfactant at time t; \(C_{0}\) is the initial concentration of the surfactant; and \(t\) is the duration of the biodegradation process.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Time, Day** & **C, mol/dm3** & \(k_{1}=\frac{1}{\tau}\cdot\ln\frac{c_{2}}{c}\) & \(k_{2}=\frac{1}{\tau}\cdot(\frac{1}{c}-\frac{1}{c_{2}})\) & \(k_{3}=\frac{1}{2\tau}\cdot(\frac{1}{c^{2}}-\frac{1}{c^{2}})\) \\ \hline
0 & 0.0228 & - & - & - \\ \hline
1 & 0.0191 & 0.172 & 8.2 & 395 \\ \hline
2 & 0.0139 & 0.244 & 13.8 & 799 \\ \hline
3 & 0.0114 & 0.229 & 14.4 & 949 \\ \hline
4 & 0.0095 & 0.218 & 15.2 & 1137 \\ \hline
5 & 0.0083 & 0.200 & 15.1 & 1238 \\ \hline
6 & 0.0066 & 0.205 & 17.7 & 1720 \\ \hline
7 & 0.0043 & 0.235 & 26.4 & 3599 \\ \hline
8 & 0.0038 & 0.221 & 26.8 & 4062 \\ \hline
9 & 0.0030 & 0.222 & 31.7 & 5728 \\ \hline
10 & 0.0028 & 0.208 & 30.7 & 6071 \\ \hline
11 & 0.0021 & 0.213 & 37.7 & 9489 \\ \hline
12 & 0.0019 & 0.206 & 39.6 & 11160 \\ \hline
13 & 0.0012 & 0.224 & 59.1 & 2529 \\ \hline
14 & 0.0009 & 0.224 & 69.5 & 36897 \\ \hline \multicolumn{4}{c}{k average value} & \multicolumn{1}{c}{0.216 \(\pm\) 0.017} & - & - \\ \hline \end{tabular}
\end{table}
Table 2: Experimental data and calculation of the rate constant of the biological decomposition of CAPB by the strain _Pseudomonas pseudocalcaligenes_ (B-6783) VSB-10.  
Using Equation (2), knowing the initial concentration of surfactants, it is possible to find the concentration of the substance in the reaction mixture at any time. This kinetic model of the process allows us to determine the time at which the surfactant concentration reaches a given value, which is important for determining the period of reduction in the surfactant concentration to the maximum permissible. These models are necessary for the selection of kinetic parameters when conducting research on the disposal of toxic organic substances by biological objects.  
The process of surfactant biodegradation can be influenced by such factors as the introduction of additional easily digestible nitrogen sources, which leads to an acceleration of biodegradation [4]. Considering temperature and pH as factors, it is necessary to take into account the optimum temperature and pH [2].  
In the process of studying the biodegradable characteristics of pure cultures, a number of regularities were revealed, thanks to which, it is possible to predict the future course of the process at the beginning of the experiment.
# Hyperspectral Image Super-Resolution Based on Spatial Group Sparsity Regularization Unmixing
## 2 Related Work
[26] exploited the clustering manifold structure in HS-MS image fusion based on the knowledge that the manifold structure is well-preserved in the spatial domain of the MSI. Some Matrix factorization-based methods considering the spectral basis and spatial structures have been proposed; for example, [PERSON] et al. [27] proposed a joint spatial-spectral resolution method with spectral matrix factorization and spatial sparsity constraints. The property that real-world HSI are locally low-rank is used to partition the hyperspectral image into patches and helps the optical computing of HSIs [28]. Tensor factorization-based methods are based on the prior that an HSI can be described by a 3D tensor. [PERSON] et al. [14] proposed a non-local sparse tensor factorization for HS-MS image fusion, where an HSI cube can be approximated by core tensor multiplication. It transforms the problem into the estimation of a sparse core tensor and dictionaries. [PERSON] et al. [15] proposed a coupled sparse tensor factorization-based super-resolution method which formulated the HS-MS image fusion problem as the estimation of a core tensor and dictionaries of three modes, which is solved by coupled tensor factorization of the input HSI and MSI. [PERSON] et al. [29] proposed a spatial-spectral graph-regularized low-rank tensor decomposition-based method, which infers the spectral smoothness of the HSI and spatial consistency of the MSI. The low tensor-train rank (LTTR) prior has been shown to outperform Tucker rank-based methods [30]. In the method proposed in [31], the input HSI and MSI are first clustered according to their structure, constituting a highly correlated 4D tensor. Then, a new LTTR prior is introduced to learn the correlations among the spatial, spectral, and non-local modes. Finally, the regularized optimization problem is solved using the alternating direction method of multipliers (ADMM) algorithm. Deep learning has become a popular tool in many fields, including HS-MS image fusion. [PERSON] et al. [16] made the first attempt to solve the HSI image super-resolution problem using an unsupervised sparse Dirichlet-Net, which reduces the amount of training set data. [PERSON] et al. [17] provided a new HS-MS image fusion network, which was based on observation models and unfolding the algorithm into an optimization-inspired deep network. [PERSON] et al. [32] presented an effective convolutional neural network (CNN)-based method which learns the deep prior from the external data set, as well as the internal information of input coded image with spatial-spectral constraints.
## 3 Results and Discussion
### Surface Wettability Analysis  
To confirm the surface wettability, contact angle measurements were taken. As can be seen in Figure S1, all Ti64 specimens were found to be hydrophobic, where the contact angles for untreated and heat-treated Ti64 specimens were \(126.4^{\circ}\pm 1.4^{\circ}\) and \(130.7^{\circ}\pm 2.3^{\circ}\), respectively (\(p\) = 0.05). The slightly increase in hydrophobicity for heat-treated Ti64 specimens was most likely due to the presence of air pockets created by the formation of large-scale aggregations of Ti64 metal powder particles on the heat-treated surface [36]. The hydrophobic property of SLM-fabricated Ti64 specimens was consistent with previous result by [PERSON] et al. [36], but contrary to that obtained by [PERSON] et al. [10].
# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 1 Introduction
In [9], the importance of effective noise filtering techniques to improve the quality of ECG signals was highlighted. The development of advanced preprocessing methods, which remove noise and artifacts without affecting the useful signals, is crucial for accurate VF detection. Practical implementation of VF detection algorithms in continuous monitoring and telemedicine systems is another challenge. These systems require reliable solutions that can monitor patients long-term and transmit real-time data to medical monitoring centers. The study [10] showed the potential of telemedicine systems in the continuous monitoring of cardiac patients but also highlighted the need for VF detection algorithms that are robust and reliable in the long term. Integrating these technologies into healthcare systems can significantly improve patient management and reduce VF-related mortality. This paper aims to provide a comprehensive review of modern ECG signal processing methods used for ventricular fibrillation detection. The main aim is to present and analyze recent techniques in the field, highlighting their advantages, limitations, and practical applications. This analysis aims to identify the most promising approaches and future research directions. The work is structured as follows: Section 1 is an introduction to the problem of detection and prediction of ventricular fibrillation with the presentation of the importance of detection and the main technological challenges encountered in the addressed field. Section 2 presents modern ECG signal analysis techniques, including machine learning algorithms, adaptive filtering Methods, wavelet transform techniques, and hardware implementations. In Section 3, the most relevant recent reference works on the topic of ventricular fibrillation detection and prediction are briefly presented with an analysis of the methods used and the performances obtained. Section 4 is dedicated to discussions regarding the possibility of detecting ventricular fibrillation with signal processing methods combined with neural networks with deep learning, as well as to the presentation of the main problems specific to their use in applications or methods of detecting or predicting ventricular fibrillation.
## 2 TCP Specimen
### Volumetric Ratio (VR)  
There is a relationship between this clear spacing and the fiber VR of the TCP (Figure 1), where the spacing is determined by the diameter of the fiber and the VR. In Figure 1, as an example, the maximum aggregate size is limited to 10 mm for a fiber diameter of 3 mm with the required 4% VR. A large number of small-diameter fibers makes the placing of concrete more cumbersome compared to a smaller number of large-diameter fibers for the same VR. Moreover, laying the small-diameter fibers in the formwork requires intensive labor. In the current study, only sand is used in the mortar mix; therefore, the limitations on the aggregate size are automatically satisfied.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Specification** \\ \hline Core material & Polymethyl-methacrylate (PMMA) resin \\ Cladding material & Fluorinated polymer \\ Core refractive index & 1.49 \\ Refractive index profile & Step-index \\ Numerical aperture & 0.50 \\ Core diameter (\(\upmu\)m) & 2765–3125 \\ Cladding diameter (\(\upmu\)m) & 2820–3180 \\ Approximate weight (g/m) & 8.6 \\ Tensile strength (N) & 550.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the optical fibers (OFs) [17].  
Figure 1: Effect of volumetric ratio and diameter on the required clear spacing between fibers [8].  
Six TCPs (\(300\times 300\times 30\) mm) were constructed in this study--three with NWM and three with LWM. OFs with a 3 mm (1/8 inch) diameter were inserted in the panels to transmit daylight through the TCP. Considering the arrangement of the holes in the perforated steel sheets, the VRs of the fibers were 1.5%, 3.0% and 6.0%. To understand the thermal transmission effect of the OFs, two opaque panels with the same dimensions were cast as well, both with NWM and LWM. The visual effects of the completed TCPs are shown in Figure 2. The density of the NWM and LWM panels were 21.4 and 14.1 kN/m\({}^{3}\), respectively.
# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 2 Related Work
[PERSON] and [PERSON] [26] proposed a field programmable gate array (FPGA) prototype of a CORDIC operation. The circular and linear rotation of the unified CORDIC are proposed to fulfill the derotator [26]. [PERSON] and [PERSON] presented a CORDIC-based unified Very Large Scale Integration (VLSI) architecture that uses a parallel pipeline architecture with latency equal to twice the CORDIC length plus three extra cycles [27]. It includes a linear CORDIC and circular CORDIC with first in and first out register (FIFO) [27]. [PERSON] and [PERSON] proposed a rotary encoder using the CORDIC algorithm to calculate the arctangent with an iterative technique to compute hyperbolic and trigonometric functions. The modified CORDIC algorithm reduces the iteration time. However, the changes of the error margin are similar [28]. [PERSON] and [PERSON] designed a CORDIC algorithm to compute trigonometric functions [29]. This method conserves resources and reduces the power dissipation in FPGA [29]. The design makes full use of the CORDIC algorithm in rotation mode to obtain the sin \(\theta\) and cos \(\theta\) values [29]. A 16-level pipeline structure CORDIC is realized in FPGA, which uses 16 layers of modules [29]. The inner CORDIC module is serially cascaded [29]. However, the traditional CORDIC algorithm is tedious and time consuming. The area will become larger if a look-up table is used. The schemes in the literature are not suitable for small-sized or fast circuit designs.
# Retention of Antioxidants from Dried Carrot Pomace in Wheat Bread
## 3 Results and Discussion
### The Influence of Carrot Pomace on Bread Properties  
#### 3.3.1 Chemical Composition of Bread  
The first stage of the study concerned determinations on carrot pomace (Table 3). Under the applied drying conditions, moisture content was around 6%, while carbohydrates predominated among the components of the dry matter. In the case of bread, the moisture content was approximately 13% for both samples. The content of protein in carrot pomace was above 8%, a little higher than the 6.7% reported by [PERSON] et al. [35], but significantly less than in the bread, which seemed little affected by the applied formulation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Wheat Flour** & **WF + 15\% CP** \\ \hline Moisture content [\%] & 12.7 \(\pm\) 0.1 b & 11.7 \(\pm\) 0.1 a \\ Water absorption [\%] & 54.0 \(\pm\) 0.1 a & 63.7 \(\pm\) 0.3 b \\ Development time [min] & 1.5 \(\pm\) 0.1 a & 6.0 \(\pm\) 0.3 b \\ Stability [min] & 4.3 \(\pm\) 0.1 a & 7.3 \(\pm\) 0.1 b \\ \hline \hline \end{tabular} Different letters in the row mean statistically different average values (\(\alpha\) = 0.05).  
\end{table}
Table 1: Farinographic properties of wheat flour (WF) and its mixture with 15% carrot pomace (CP).  
On the other hand, the fat content was significantly higher in carrot pomace, as compared to the bread, although it was a little lower than the 2.1% reported by the above-mentioned authors [35].  
The content of ash was almost three times higher than in the aforementioned study [35], but less than 5.5% higher than what was reported by [PERSON] et al. [23]. A significant amount of minerals found in the carrot pomace affected their levels in bread. The ash contents reported for fresh carrot varieties may change significantly. In the study [37] on twelve carrot varieties, the values of ash content changed from 5.18 to 9.91%, which could be regarded as the upper limit for its content in dried pomace, because the soluble minerals are likely to be removed with the juice. The partial replacement of wheat flour (containing 0.65 % ash) with dried carrot pomace caused an adequate increase in ash contents of the bread.
## 2 Mathematical Modeling  
Figure 1 shows a schematic representation of a PV-thermal module which is composed of several layers and a channel in which SiO\({}_{2}\)-water nanofluid is flowing throughout. Thermophysical properties of the layers in the PV-thermal module is given in Table 1.  
An energy balance between the solar irradiance and heat transfer to the heat transfer fluid with nanoparticles is considered. Within the layers of the PV-module, the heat conduction equation is used.  
Within the layers of the PV-thermal module steady state, the heat conduction equation is valid and is given by the following equation:  
\[\
abla.\left(k_{layer}\
abla T\right)=0. \tag{1}\]Navier-Stokes and energy equations for the fluid flow and heat transfer in the channel are given by the following equations:  
\[\
abla.(\mathbf{u})=\mathbf{0}, \tag{2}\]  
\[\rho\mathbf{u}.\
abla\mathbf{u}=-\
abla p+\mu\
abla^{2}\mathbf{u}, \tag{3}\]  
\[\rho c_{p}\mathbf{u}.\
abla T=\
abla.\left(k\
abla T\right). \tag{4}\]  
The PV cell electrical efficiency is given by the following equation:  
\[\eta_{pv}=\eta_{T_{ref}}\left[1-\beta_{ref}\left(T_{pv}-T_{ref}\right)\right]. \tag{5}\]  
Thermal efficiency is defined as the ratio of the energy gained by the collector divided by the total incident energy  
\[\eta_{th}=\frac{\mathit{m}c_{p}\left(T_{out}-T_{in}\right)}{G}. \tag{6}\]  
* For the upper surface, the heat flux boundary condition with incident radiation and convective heat loss due to wind (heat transfer coefficient \(h\) and wind speed are related) is considered: \(q^{\prime}=q-hA(T_{upper}-T_{\infty})\).
* Among the layers of of the PV-module, heat flux continuity is utilized, \(q_{layer,n+1}=q_{layer,n}\).
* At the inlet of the channel, temperature and velocity are uniform, \(u=u_{0},\ v=0,\ T=T_{c}\).
* At the exit of the channel, gradients in the \(x\)-direction are set to zero, \(\frac{\partial u}{\partial x}=0,\ \frac{\partial v}{\partial x}=0,\ \frac{\partial T}{\partial x}=0\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Thickness (mm)** & **Density (kg/m\({}^{3}\))** & **Thermal Conductivity (W/m K)** & **Heat Capacity (J/kg K)** \\ \hline glass & 3.2 & 2515 & 0.98 & 820 \\ eva & 0.45 & 960 & 0.31 & 2090 \\ PV & 0.2 & 2330 & 150 & 712 \\ tedlar & 0.35 & 1162 & 0.23 & 1465 \\ aluminum & 1 & 2700 & 160 & 900 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermophysical properties of layers of the PV-thermal module.  
Figure 1: Schematic representation of the layers in the PV-thermal module.
## 3 Double-Beam System Traversed by a Moving Force
### Parametric Studies
#### 3.3.3 Mass Ratio  
Figure 16 shows maximum dimensionless displacements (\(\overline{w}_{1,\max}\)) and (\(\overline{w}_{2,\max}\)) versus the mass ratio (\(s_{\rm m}\)) for different boundary conditions when \(\alpha=0.5\), \(s_{\rm e}=50\) and \(s_{\rm k}=1\). It can be seen from the figure that \(\overline{w}_{i,\max}\ (i=1,\ 2)\) increases firstly and then decreases with \(s_{\rm m}\). In addition, the difference between \(\overline{w}_{1,\max}\) and \(\overline{w}_{2,\max}\) is smaller for looser boundary conditions.
# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 2 Supporting Technologies in the IoT-Edge-Cloud Continuum
### Device to Device Communications  
In an IoT environment, a specific type of content might be requested from several user terminals or other IoT devices in close geographical proximity. In this case, user-experienced latency can be improved if the content is requested from adjacent IoT devices that share the same content, instead of centralized APs. In this case, a particular node requests content by broadcasting a short-range signal in order to set up a link connection with the node having the content. Therefore, D2D connectivity should be supported in this case [58; 59]. However, apart from content caching, D2D connectivity can also support subnetwork organization, as was mentioned in the previous subsection, as well as dynamic IoT node deployment and reconfiguration if necessary.  
In general, D2D communication offers autonomous intelligent services or mechanisms without centralized supervision. Hence, the provision of ultra-low latency services in the IEC continuum can be achieved, as D2D communication offers more reliable connectivity between devices. In addition, the concept of green network deployment can be supported as well, due to the shorter propagation paths and consequently reduced transmission power.  
Figure 5: X subnetwork deployment within wide area networks.  
In the same context, device interconnection can be established via mesh networking [60; 61]. A mesh network comprises a type of local area network (LAN) topology, where multiple devices or nodes are connected in a non-hierarchical manner so that they can cooperate and provide significant network coverage to a wider area compared to the area coverage achieved by a single router. As mesh networks consist of multiple nodes, which are responsible for signal sending and information relaying, every node of the network needs to be connected to another via a dedicated link. Since mesh networks leverage a multi-hop wireless backbone formed by stationary routers, they can serve both mobile and stationary users. Mesh networks have significant advantages such as fast and easy network extension, self-configuration, self-healing, and reliability, as a single node failure does not result in total network failure.