esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Related Work  
Machine learning systems to support the selection of assistive devices, especially in patients with neurological deficits (such as post-stroke patients), are rare. They currently fulfil the role of second opinion systems. We intend to develop them, including further types of input to increase accuracy, but the final decision is always made by a human; hence, a functional test is usually carried out. Nevertheless, systems such as ours, by prompting the specialist decisionmaker, promote the codification of knowledge and experience and speed up decision-making and the provision of the patient with the necessary assistive devices. Among other things, this allows less experienced therapists to make fewer mistakes, reducing their burden.  
A review of the databases showed that there is no comparable software; hence, it is difficult to compare our results with those of other authors. A review of four key databases, PubMed, WoS, Scopus, and Google Scholar, returned only four scientific articles in English with the keywords 'assistive device','stroke', and'machine learning' [13; 14; 15; 16]. The introduction of a second opinion or expert system into the selection process of assistive technologies, not only in post-stroke patients, seems to be a necessity, as this has been a research gap thusfar. The novelty of the proposed solution lies in the combination of a comprehensive, population-typical dataset and the selection of a classification method in terms of classification accuracy in the selection of assistive technologies. An important novelty is the use of three different artificial intelligence solutions for this purpose and the direct comparison of their results for the same dataset. This will allow us to better plan both the replication of our research and the further development of similar solutions. This is important because the number of people in need of assistive technologies, including the elderly, will grow, and some of the assistive devices, along with changes in people'shealth status, will need to be periodically checked and readjusted.  
The number of possible assistive technologies solutions is also growing, and soon it will take effort just to know all the appropriate solutions for different levels and types of deficits. Furthermore, with the availability of 3D printing, it may be worth considering bespoke printing of personalised assistive technology devices based on AI-based eHealth system proposals and human-diagnostic decisions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Quality** & **Quality** & **Quality** & **Algorithm** \\
**Structure** & **(Training)** & **(Testing)** & **(Validation)** & **Algorithm** \\ \hline
**MLP 4-70-48** & **91.12413** & **89.231722** & **91.675412** & **BFGS** \\ \hline MLP 4-71-48 & 85.095137 & 83.6633666 & 87.128713 & BFGS \\ \hline MLP 4-75-48 & 82.241015 & 76.732673 & 77.722772 & BFGS \\ \hline MLP 4-41-48 & 80.338266 & 77.227723 & 81.188119 & BFGS \\ \hline MLP 4-37-48 & 76.321353 & 67.821782 & 74.752475 & BFGS \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results for the best MLP neural networks (best result in bold).
## 1 Introduction  
Hydrothermal carbonization (HTC) is considered an emerging technology for effective waste conversion and/or treatment. In the HTC, also known as \"wet pyrolysis\", process, feed stock (organic stock immersed in water) is heated in a pressure-resistant reactor. Autogenous pressure inside the reactor allows subcritical water temperatures (180-350 \({}^{\circ}\)C), and the feed stock is converted into a mixture of process water containing water-soluble organics and a carbonaceous solid called hydrochar [1]. Several distinctive features of HTC, such as minimal environmental impact, simplicity, cost effectiveness, low greenhouse gas emission, and energy efficiency, make the technology attractive [2].  
In 1913, [PERSON] performed the first HTC experiment to facilitate the natural coalification process of organic feedstock under laboratory conditions [3]. Since then, extensive research has investigatedHTC for the conversion of a wide range of feedstock, from pure substances (e.g., cellulose and glucose) to more complex materials, such as fruit shells and paper [4; 5; 6; 7]. The advantage of HTC over other pyrolysis techniques is that a feedstock with high moisture content can be directly converted into a solid carbonaceous material with high yield. The additional cost for feedstock drying in other conventional dry pyrolysis methods can be saved, and this enables various continuously generated waste materials, such as animal and human faeces, municipal sewage sludge, and activated sludge from wastewater treatment plants, to be used as potential feedstock [1].  
The traditional disposal methods of sewage sludge have limitations, due to potential environmental risks that could result from various pollutants, such as pathogenic micro-organisms, organic pollutants, and heavy metals [8]. Recently, HTC has been suggested as a cost-effective and eco-friendly solution for this sewage sludge management challenge [9; 10; 11]. Also, the carbonaceous solid output (hydrochar) from the HTC process, with a competitive pollutant adsorption capacity, could replace commercial activated carbon in water treatment [12].  
The research on the use of hydrodynamic derived from sewage as a low-cost adsorbent in water treatment is still in its infancy. It was reported that the hydrocarbons derived from industrial sludge and anaerobically digested sludge removed Pb(II) (q\({}_{\text{m}}\) = 11 mg/g) in laboratory conditions [13]. Also, sewage sludge-derived hydrocarbon showed a comparable Pb(II) removal efficiency (q\({}_{\text{m}}\) = 15 mg/g) [14]. For the removal of pathogenic contaminants, hydrochar derived from sewage sludge was able to achieve a 2 to >3 log removal of human pathogenic rotavirus and adenovirus [15]. However, the removal of faecal bacteria has not yet been reported. Therefore, the main objective of this research is to evaluate the hydrochar derived from sewage sludge as an additive adsorbent in sand filtration setups for _Escherichia coli_ removal during water treatment.
# Enhancement of PV Panel Power Production by Passive Cooling Using Heat Sinks with Perforated Fins
## Nomenclature  
\begin{tabular}{l l} STC & Standard test conditions \\ NOCT & Nominal operating cell temperature \\ AM & Air mass \\ \(t_{PV}\) & Cell temperature [\({}^{\circ}\)C] \\ \(G\) & Solar irradiance [W/m\({}^{2}\)] \\ \(t_{air}\) & Ambient temperature [\({}^{\circ}\)C] \\ \(v_{air}\) & Wind speed towards backside of PV panel [m/s] \\ \(t_{NOCT}\) & Cell temperature in NOCT conditions [\({}^{\circ}\)C] \\ \(t_{STC}\) & Cell temperature in STC conditions [\({}^{\circ}\)C] \\ \(P_{mp}\) & Maximum power produced [WP] \\ \(V_{mp}\) & Voltage at \(P_{mp}\) [V] \\ \(I_{mp}\) & Current at \(P_{mp}\) [A] \\ \(V_{ac}\) & Open circuit voltage [V] \\ \(I_{sc}\) & Short-circuit current [A] \\ \(\eta\) & PV panel efficiency [\%] \\ \(\beta\) & \(P_{mp}\) temperature coefficient [\(\%/^{\circ}\)C] \\ \(L\) & Length of PV panel [m] \\ \(l\) & Width of PV panel [m] \\ \(h\) & Thickness of PV panel [m] \\ \(g\) & Thickness of the layers composing the PV panel [m] \\ \(\lambda\) & Thermal conductivity of the layers composing the PV panel [W/m\(\cdot\)K] \\ \(\rho\) & Density of the layers composing the PV panel [kg/m\({}^{3}\)] \\ \(c_{p}\) & Specific heat of the layers composing the PV panel [J/kg\(\cdot\)K] \\ \(s\) & Distance between the fins of PV panel [m] \\ \(L_{\text{fins}}\) & Length of the fins [m] \\ \(h_{\text{fins}}\) & Height of the fins [m] \\ \(\Phi\) & Diameter of the perforations of the fins [m] \\ \(d\) & Distance between the centers of the perforations [m] \\ \(h_{cfront}\) & Convective heat transfer coefficient on the front of PV panel [W/m\({}^{2}\)\(\cdot\)K] \\ \(h_{c,hack}\) & Convective heat transfer coefficient on the back of PV panel [W/m\({}^{2}\)\(\cdot\)K] \\ \end{tabular}
## 4 Discussion  
The dynamic smoothing method (32) for primitive trajectories is the main result of this research. An explicit comparison of its computational complexity with analytical smoothing methods, which were described in the introduction, is rather difficult because these methods are fundamentally different from each other. The classical methods [11; 12; 13; 14; 15; 16] result in a complete analytical description of the reference trajectory (it is an approximation of a specified set of waypoints). Computational complexity is directly proportional to the number of reference points and fragments with different boundary conditions. For each fragment of the trajectory, it is necessary to calculate the coefficients of the polynomial and control to fulfill the constraints. In addition, each new sequence of points requires an individual organization of calculations.  
The method of dynamic smoothing of reference trajectories as a vector signal generates the tracking differentiator (32). The gains of the differentiator are set immediately (31) and do not change while processing different sequences of points for the same robot. The smoothing process is a numerical solution of the differential Equation (32). Complicating the shape of the reference curve does not lead to a more complex algorithm and increased computational operations necessary to smooth. If a set of waypoints considering time (38) is specified, they are automatically connected by segments (39) before being input to the differentiator. If a non-smooth reference signal is applied to the input of the tracking differentiator, it will be automatically smoothed without additional calculations.  
In this paper, we present only the results of numerical experiments. However, the simulation was organized in such a way that the reference signal was applied to the system in real time using the \"clock\" block, which is named on the Figure A1 as the \"time\" block (see Appendix A).  
It can be concluded that the complex use of machine learning methods (in the first stage) and the developed dynamic smoothing method (in the second stage) will allow full automation of the process of planning achievable trajectories. This is a prerequisite for the design of autonomous unmanned vehicles.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 2 The Development Status and Key Technology Of ISAC
### Prototypes Studies
#### 2.3.3 Prototype Based on the Terahertz Frequency Band  
Terahertz (THz) refers to the electromagnetic waves with frequency ranging from 300 GHz to 3 THz, which is one of the key enabling technologies for 6G mobile communication [36]. The communication system in the THz frequency band has received widespread attention in the research of ISAC due to its ultra-large communication bandwidth. Based on the ultra-large bandwidth, it can achieve extremely high precision in positioning and tracking technology solutions.  
In [37], the prototype adopts THz compound semiconductor devices, digital-to-analog and analog-to-digital converter boards, and low-complexity signal processing technology. The experimental bandwidth is 13.6 GHz, and the radio frequency range is 220 GHz, which can simultaneously provide long-distance coverage of up to 3.5 km, a high data rate of up to 240 Gbps, and a high spectral efficiency of 17 bit/s/Hz for ISAC services.  
In [38], a THz sensing prototype with millimeter-level imaging resolution is built to demonstrate the feasibility of THz-ISAC, considering the physical aperture constraints of typical mobile devices. A solution of virtual aperture with a MIMO structure and sparse scanning approach is proposed. The prototype operates at a carrier frequency of 140 GHz with an 8 GHz bandwidth. The feasibility of achieving millimeter-level sensing resolution using THz communication signals is verified.
## 4 Comparison of Evaluation Results
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Inspection Levels**} & \multirow{2}{*}{**Evaluation Items**} & \multicolumn{3}{c}{**Inspection Data**} & \multicolumn{1}{c}{} \\ \cline{3-7}  & & **Case 1** & **Case 2** & **Case 3** & **Case 4** & **Case 5** \\  & & **(Column)** & **(Wall 1)** & **(Wall 2)** & **(Beam 1)** & **(Beam 2)** \\ \hline \multirow{3}{*}{\(\text{CPD I}\)} & Concrete discoloration & Pink & Soot & Pink & Pink & Pink \\  & Crack width (mm) & 0.21 & 0.23 & 0.37 & 0.29 & 0.32 \\  & Spalling (mm) & 7 & 19 & 0 & 27.50 & 0 \\ \hline \multirow{3}{*}{\(\text{CPD II}\)} & Heating temperature (\({}^{\circ}\)C) & 250 & 290 & 290 & 410 & 280 \\  & Crack width (mm) & 0.21 & 0.23 & 0.37 & 0.29 & 0.32 \\ \cline{1-1}  & Spalling (mm) & 7 & 19 & 0 & 27.50 & 0 \\ \cline{1-1}  & Ratio of design compressive strength & 90.10 & 92.39 & 91.83 & 74.10 & 87.88 \\ \cline{1-1}  & Concrete carbonation depth (mm) & 13.50 & 26.50 & 26.00 & 10.90 & 16.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Inspection data of fire-damaged RC members [2; 19]. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{3}{c}{**Inspection Results by**} & \multicolumn{3}{c}{**Fire Damage Diagnosis Results by FDDS**} \\ \cline{3-6}  & & **Professionals [2; 19]** & **CPD I** & **Possibility** & **CPD II** & **Possibility** \\ \hline \multirow{2}{*}{
\begin{tabular}{l} Case 1 \\ (Column) \\ \end{tabular} } & B & B grade & 0.787 & B grade & 0.757 \\  & B & B grade & 0.827 & B grade & 0.793 \\  & B & B grade & 0.852 & B grade & 0.773 \\  & B & C grade & 0.872 & C grade & 0.771 \\  & B & B grade & 0.872 & B grade & 0.824 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Fire damage diagnosis results by the fire damage diagnosis system (FDDS) and inspection professionals.
Variables often derived quantitatively using remote sensing methods include phytoplankton pigments such as chlorophyll-a [Chl-a] [8], cyanobacterial pigment phycocyanin (PC) [9], concentration of total suspended matter (TSM) [10], absorption by colored dissolved organic matter (CDOM) [11], Secchi disk depth (SDD) or water transparency [12], turbidity [13] and water temperature [7]. It is also viable to establish the contour and surface of water bodies [14]. This work is centered on the TSM, the name provided to the total mass of suspended particles measured per volume of water [15] held in the water of a stream, river, lake or reservoir by turbulence [16]. TSM is, in general, a mixture of live and detritic (non-living) particulate organic matter, such as phytoplankton or detritus, and inorganic matter such as clay, silt and other suspended minerals [17; 18]. High TSM values (usually >1000 mg/L) affects the depth at which the photosynthetically active radiation can arrive at The physical alterations they produce can, simultaneously, lead to undesirable aesthetic effects [19], increase water treatment costs [20], reduce navigability of canals and decrease the longevity of dams and reservoirs [21]. In addition, in the abundant presence of suspended solids with high organic content, decomposition can occur in situ and reduce the dissolved oxygen levels in water, producing a critical oxygen shortage [20]. If sediments are smaller than <63 \(\upmu\)m in diameter, it is possible for toxic or harmful substances to adhere to them, which in turn, facilitates their transport [22]. Hence its importance as a variable, which is one of the most commonly used for the characterization of water and participates in the water quality indices that currently exist [23]. The ecological consequences that TSM can produce depend on whether they are composed mostly of organic compounds (phytoplankton or its detritus, decomposition of plant matter, etc.) or inorganic compounds (sand, silt, clay, etc.). Estimation of its concentration is possible using multispectral remote sensing tools. Most water quality studies by remote sensing focus solely on the determination of TSM concentration, and only a few authors separate the estimation of organic and inorganic solids, as for example the recent studies carried out by [PERSON] et al. [24], [PERSON] et al. [25] and [PERSON] et al. [26]. The main objective of this study is to develop algorithms that can estimate the total concentration of suspended solids, differentiating the organic from the inorganic fraction,through the combined use of Sentinel-2 images and an extensive database of different types of water bodies in Spain.
## 3 Description of QLM-ChPs Matrix Approach
### The Main Algorithm
We are able to rewrite the former equations (21) compactly by the aid of the following notations  
\[\mathbf{K}_{j}^{e-1}= \begin{bmatrix}\kappa_{e-1,j}(x_{0})&0&\ldots&0\\ 0&\kappa_{e-1,j}(x_{1})&\ldots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\ldots&\kappa_{e-1,j}(x_{H})\end{bmatrix} ,\quad\mathbf{P}^{e-1}= \begin{bmatrix} \pi_{e-1}(x_{0})\\ \pi_{e-1}(x_{1})\\ \vdots\\ \pi_{e-1}(x_{H})\end{bmatrix} .\]  
The resultant system of equations is shown by  
\[\mathbf{K}_{1}^{e-1}\,\dot{\mathbf{N}}_{e}+\mathbf{K}_{2}^{e-1}\,\dot{\mathbf{N}}_{e}+\mathbf{K}_{ 3}^{e-1}\,\mathbf{N}_{e}=\mathbf{P}^{e-1},\quad e=1,2,\ldots. \tag{22}\]The next fundamental matrix of the equation is derived by employing the foregoing relations (17)-(20) for the vectors \(\mathbf{N}_{e},\mathbf{N}_{e}\), and \(\tilde{\mathbf{N}}_{e}\) in (22). It follows that  
\[\mathbf{W}^{(e-1)}\,\mathbf{C}_{H}^{(e)}=\mathbf{P}^{e-1},\quad\text{or}\quad[\mathbf{W}^{(e-1) };\mathbf{P}^{e-1}], \tag{23}\]  
where \(\mathbf{W}^{(e-1)}:=\left\{\mathbf{K}_{1}^{e-1}\,\mathbf{X}_{H}\,\mathbf{Q}_{H}^{2}+\mathbf{K}_{2} ^{e-1}\,\mathbf{X}_{H}\,\mathbf{Q}_{H}+\mathbf{K}_{3}^{e-1}\,\mathbf{X}_{H}\right\}\mathbf{R}_{H}\) for \(e\geq 1\). By solving the above fundamental matrix Equation (23), we obtain a solution for the linearized Equation (13). However, this solution needs to be satisfied in the boundary conditions (14). So, in the final stage, we derive the matrix formats for the boundary conditions. For the first Neumann boundary condition, we utilize the relation (18). It is sufficient to tend \(x\to 0\). This gives us  
\[\mathbf{W}_{0}^{(e-1)}\,\mathbf{C}_{H}^{(e)}=0,\qquad\mathbf{W}_{0}^{(e-1)}:=\mathbf{Y}_{H}(0) \,\mathbf{Q}_{H}\,\mathbf{R}_{H}. \tag{24}\]  
For the second mixed boundary condition (14), we use a combination of two relations, (15) and (18). Here, we need to tend \(x\to 1\). We can now reformulate the second boundary condition in the form of a matrix given by  
\[\mathbf{W}_{1}^{(e-1)}\,\mathbf{C}_{H}^{(e)}=s_{h},\qquad\mathbf{W}_{1}^{(e-1)}:=\{\mathbf{Y}_ {H}(1)\,\mathbf{Q}_{H}+s_{h}\,\mathbf{Y}_{H}(1)\}\mathbf{R}_{H}. \tag{25}\]  
By returning to the augmented matrix \([\mathbf{W}^{(e-1)};\mathbf{P}^{e-1}]\), we now replace the first and last row of it with the new rows \([\mathbf{W}_{0}^{(e-1)};0]\) and \([\mathbf{W}_{1}^{(e-1)};s_{h}]\) obtained in (24) and (25), respectively. By updating the former fundamental matrix Equation (23), we arrive at the new modified version denoted by  
\[\widetilde{\mathbf{W}}^{(e-1)}\,\mathbf{C}_{H}^{(e)}=\widetilde{\mathbf{P}}^{e-1},\quad \text{or}\quad\left[\widetilde{\mathbf{W}}^{(e-1)};\widetilde{\mathbf{P}}^{e-1} \right]. \tag{26}\]  
Any linear solver can be utilized to solve the system (26). Therefore, after knowing the coefficients \(\mathbf{C}_{H}^{(e)}\) at iteration \(e\), \(e\geq 1\), we obtain the approximate solution of the strongly nonlinear model (1) on \([0,1]\). The utility of the proposed QLM-ChPs algorithm will be considered in the next section.
## 6 Technical Challenges in IoT Integration for Smart Buildings
### Regulatory and Policy Obstacles  
The fast development of IoT technology sometimes surpasses the establishment of suitable legal frameworks, resulting in possible security risks and privacy issues. The lack of explicit norms and standards may lead to discrepancies in device security, data management, and user privacy safeguards, thus hindering innovation. Formulating complete regulations that reconcile innovation with security and privacy requirements requires coordination among stakeholders, including governmental entities, industry representatives, and civil society. Furthermore, diverse nations may possess distinct legislative frameworks, hindering the implementation of IoT technologies on a worldwide scale. Insufficient regulation may hinder innovation, result in uneven user experiences, and expose consumers to considerable security threats.
# An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments
## 5 Dynamic Environment Modeling and Path Re-Planning  
In the dynamic environment, there are dynamic obstacles to the vessel itself, which can affect the optimal path generated. Hence, a method named the dynamic domain tunable fast marching square (DTFMS) method is proposed to establish the domain model for the own vessel and target vessel. The own vessel can makes collision avoidance actions and the target vessel does not take any collision avoidance actions.
# Optimal Design Parameters for Hybrid DC Circuit Breakers Using a Multi-Objective Genetic Algorithm
## 2 HCB Topology and Problem Statement
### HCB Topology
#### 2.1.2 Working Principle of the HCB  
The working principle of the HCB is shown in Figure 2. Under normal operation (before t0), the switches S1 and S2 and thyristors T1 and T2 are closed, switch S3 and thyristor T3 are opened, and the capacitor C is precharged to the DC system voltage by an external circuit. The polarity of the capacitor voltage is opposite to the DC system voltage. The DC system current Idc flows only through the nominal current branch (branch 1) and brings a nominal current Inom = 1 kA.  
Figure 1: Configuration of the HCB.  
It is assumed that a fault occurs at t\({}_{1}\), and the system current starts to increase rapidly. When the magnitude of the fault current is larger than a predefined threshold I\({}_{\rm th}\) at t\({}_{2}\), the fault is detected. A trip signal is sent to the HCB, and the mechanical switch S\({}_{1}\) begins to open at t\({}_{2}\), resulting in an arc current between its contacts. After a delay time of the mechanical switch S\({}_{1}\) at t\({}_{3}\), the L\({}_{\rm p}\)C\({}_{\rm p}\)-resonant circuit in the commutation current branch (branch 2) injects an inverse current by turning on the thyristors T\({}_{1\rm a-1b}\) or T\({}_{2\rm a-2b}\) (depending on the current direction). Consequently, the increase in the current through branch 2 equals the decrease in the current through the mechanical switch S\({}_{1}\) (t\({}_{3}-\)t\({}_{4}\)). After the current through the mechanical switch S\({}_{1}\) reaches the current zero at t\({}_{4}\), the arc is extinguished. The fault current immediately commutates into branch 2 and re-charges the capacitor from t\({}_{4}-\)t\({}_{5}\). Thus, when the voltage across the breaker appears, the transient interruption voltage (TIV) between the terminals of the HCB starts to build up (from t\({}_{4}-\)t\({}_{5}\)) to a protection voltage of the MOV at t\({}_{5}\). This makes the fault current commutate to the energy absorption branch (branch 3). During the period t\({}_{5}-\)t\({}_{6}\), the MOV absorbs the energy stored in the system. When the current flow of the circuit breaker I\({}_{\rm cb}\) passes through zero at t\({}_{6}\), the residual switch S\({}_{2}\) becomes an open circuit to clear the residual current and isolation of the HCB from the rest of the system. The fault current interruption is finished.  
To enable multiple reclosing operations in rapid succession, a secondary inverse current injection circuit should be implemented. Therefore, the capacitor voltage needs to return to the nominal condition. This results in a discharging capacitor circuit to reduce the overvoltage down to the DC voltage. This mode is carried out at t\({}_{7}\). Then, a circuit of reversing capacitor voltage is applied to change the polarity of the capacitor voltage at t\({}_{8}\). This mode is completed at t\({}_{9}\), and the HCB is ready for the next operation. Table 1 summarizes the sequence of operation of the HCB.  
Figure 2: Waveform of current and voltage in the branches of the HCB.