esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Input Parameters for Estimating Bloom Indicators
#### 3.1.1 Chl-a and Turbidity
Cluster 1: Kassari-Ounaku Bay and Moonsund Sea; Cluster 2: Haapsalu Bay; Cluster 3: Hiiu Shallow, Kihelkonna Bay and NW Gulf of Riga, Soela Strait, Cluster 4: Párnu and Matsalu Bay; Cluster 5: Eru-Kásmu, Hara and Kolga, central Gulf of Riga, Muuga-Tallinna-Kakumae, Narva-Kunda, Pakri bays; Cluster 6: NE Gulf of Riga. Figure 6: Scatterplot showing matchups between in situ chl-a (mg m\({}^{-3}\)) and (**A**) C2 RCC chl-a, (**B**) POLYMER chl-a. The satellite-derived turbidity (FNU) at sampling stations is given as a color-scale for C2 RCC. The dashed line represents a 1:1 relationship, black line represents the line of best fit, and 95% confidence interval shown in grey.
## 2 Recent Advances in 3D Orthotropic Damage Models  
Since the 2000s, several 3D orthotropic damage models have been proposed, which is primarily due to the scientific community's availability of commercial finite element software used for implementing new material constitutive relations [13; 14].  
Due to the composite material's ply-by-ply behavior and the formulation's simplification, basic 2D orthotropic damage models were employed for shell elements prior to the widespread adoption of 3D orthotropic damage models [15]. These 2D orthotropic damage models were sufficient to simulate beams [4], columns [16], and composite sandwich panels [17], however, with significant constraints when modelling 3D beam-to-column connections [6].  
The difficulty in evolving from a 2D to a 3D failure model is primarily related to the first being assembled and tested using only plane analysis [18], as this leads to different results when a formulation is then extended into the third dimension [19]. In addition, the 3D failure criterion and damage evolution may be significantly influenced by the kind of orthotropic composite material being researched. The use of the 3D Hashin failure criterion is not novel and has been suggested for shell elements [20] and solid elements [21; 22], in any case, without any application for the interlaminar failure. An established 3D method based on the combination of several elliptic surfaces for the adoption of separated failure criteria [23] presented encouraging results in simulating the connection of three bolt joints for T300/5228A composite material, with discontinuous damage evolution. For the stochastic analysis of peek laminates, a few different iterations of these elliptic surfaces were used [24]. For composite fiber metals, the fiber and matrix 3D failure criteria are based on a square root sum of the total strain [25], but with a cohesive interlaminar failure criterion. A variation of this last method was applied in FRP composites [26; 27], but with restrictions on the failure criterion for material orientation. One of the first successful applications for a full 3D analysis with Larc05 was presented in the early work of [28], with variation proposed by [29]. Due to its simplicity of implementation, some 3D failure criteria with simple degradation models [30] in terms of elastic parameters have also been successful and continue to be popular among structural engineers. One of the best examples of a fully 3D Hashin failure criterion using simplified degradation models was suggested by [31] for bolted connections. Recently, the Hou criteria were successfully used to simulate drilling damage, based on an improved 3D elliptic failure surface [32]. The NU failure criterion was proposed and implemented in a three-point bending load to improve the matrix damage evolution due to increased shear [33]. Recently, using an invariant-based concept to study the bearing in composite connections showed excellent findings [34; 35].
# Deep Eutectic Solvents as Extraction Media for
## 2 Factors Affecting the Extraction of Flavonoids Using DES Separation Techniques
### Separation Techniques
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Plant Source** & **DES** & **Target Flavonoids** & **Bioxicity** & **Ref.** \\ \hline \multirow{6}{*}{_Channecyperis obhasa_} & & Ethyl Glycol Glycerol & & & \\  & & 1,2-Butaned & & & \\  & ChCl (1.2) & 1,3-Butaned & Myricetin & & \\  & ChCl (1.2) & 1,4-Butaned & Amendflavone & Anti-oxidative phenols. & [38] \\  & & 2,3-Butaned & & & \\  & & 1,6-Butaned & & & \\ \hline \multirow{6}{*}{_Ralix satellite_} & & 1,4-Butaned & & & \\  & & Glycerol & & & \\  & & Ethylene glycol & & & \\  & & Crime acid & & & \\  & & Male acid & & & \\  & ChCl (1.2) & Lattice acid (also 3,1,2,1,1,1,1,3 & & \\  & & 1,4) & Biacalin Wogonside & Antiviral, antitumor, anticonvulsant, & \\  & & Glucose & Baiclein & anti-allergic, anti-inflammatory, anxiolytic, and & \\  & & Sorbied & Wogonin & anti-oxidant properties. & \\  & & Storage & & & \\  & & Malose & & & \\ \cline{2-5}  & & Citric Acid (1.2) & Sucrose & & \\ \cline{2-5}  & & Lactic Acid (1.2) & Sucrose & & \\ \hline \multirow{6}{*}{_Channecyperis obhasa_} & & Ethylene Glycol & & & \\  & ChCl (1.2) & 1,2-Butaned & & & \\  & & 1,6-Etaned & & & \\ \cline{2-5}  & & Ethylene Glycol & & & \\  & ZnCl\({}_{2}\) (1.2) & 1,2-Butaned & & & \\  & & 1,6-Etaned & & & \\  & & Phenol & Amendflavone & Quercetin, Myricetin, & \\  & & & Unc & & & \\ \cline{2-5}  & & Oakle acid & & & & \\ \cline{2-5}  & & Mo(Pb)PbR (1-4) & 1,2-Butaned & & & \\  & Glycerie & & & & \\ \hline \multirow{6}{*}{_Channecyperis obhasa_} & & Ethylene Glycol & & & \\  & & (1,1,1,2,1,3,3,1,4,15) & & & \\ \cline{1-1} \cline{2-5}  & & & & & \\ \cline{1-1} \cline{2-5}  & & & & & \\ \cline{1-1} \cline{2-5}  & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Overview of extraction examples from the literature, presenting the plant source, DES employed, target compound, and the bioactive role the writers mention (literature presented in chronological order).
# Survey of Network Coding Based P2P File Sharing in Large Scale Networks
## 4 Network Coding Based P2P File Sharing Systems  
Practically, five network coding schemes are applied to P2P File sharing Systems: (1) full coding approach in which the encoded piece is a linear combination of all the original pieces, (2) sparse coding approach in which only subset of pieces are selected for encoding, and (3) generation-based approach in which the file is segmented into groups; then, network coding is applied per group separately, (4) combined coding scheme which uses a mix between generation-based and sparse coding, and (5) an overlapped coding scheme that is fundamentally a generation-based approach but allows overlapping between groups. In the following subsections, we extensively survey the studies that are related to each of these approaches.
## 3 Nonradiative Processes
### Capture on Dangling Bonds  
There is one more relatively fast process inhibiting efficient light emission from NCs, which is photo-carrier trapping on surface defects called P\({}_{b}\)-centres or dangling bonds. The P\({}_{b}\)-centres produce rather deep energy levels within the nanocrystal band gap, which act as electron traps. The capture on neutral dangling bonds is a multi-phonon process, and its rate strongly depends on temperature. The earlier performed calculations [140] for Si crystallites yielded \(\tau_{\mathrm{C}}^{-1}\) shown in Figure 3 as function of the nanocrystal radius in comparison with the rates of the Auger recombination and radiative transitions.  
It can be seen from Figure 3 that for the NC radius greater than \(\sim\)1 nm, the capture rate becomes larger than the radiative recombination rate. Moreover, starting from \(R\) close to 1.5 nm, the trapping on dangling bonds turns out to be even faster than the Auger recombination. Obviously, for nanocrystals whose radii are greater than 1.5 nm, the nonradiative trapping on the surface defects suppresses all the other competing relaxation processes inside the NC if the surface dangling bonds are not passivated.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **X = H** & **X = Cl** & **X = Br** & **X = H** & **X = Cl** & **X = Br** \\  & **(_eeh_)** & **(_eeh_)** & **(_eeh_)** & **(_ehh_)** & **(_ehh_)** & **(_ehh_)** \\ \hline Si\({}_{35}\)X\({}_{36}\) & 390 & 3 & 4 & 40 & 163 & 10 \\ Si\({}_{59}\)X\({}_{60}\) & 78 & 0.3 & 7 & 5 & 27 & 19 \\ Si\({}_{87}\)X\({}_{66}\) & 120 & 4 & 2 & 1780 & 59 & 11 \\ Si\({}_{123}\)X\({}_{100}\) & 8 & 3 & 280 & 108 & 25 & 24 \\ Si\({}_{147}\)X\({}_{100}\) & 59 & 84 & 2 & 474 & 20 & 65 \\ Si\({}_{163}\)X\({}_{116}\) & 120 & 21 & 100 & 47 & 9 & 19 \\ Si\({}_{175}\)X\({}_{116}\) & 270 & 2 & 2 & 161 & 90 & 0.8 \\ Si\({}_{317}\)X\({}_{172}\) & 0.3 & 0.4 & 0.02 & 5 & 3 & 78 \\ \hline \end{tabular}
\end{table}
Table 2: Calculated Auger rates (\(\times 10^{10}\).s\({}^{-1}\)) for _eeh_ and _ehh_ processes in halogenated silicon nanocrystals.
## 4 Numerical Analysis Results and Data Analysis
### Interaction Analysis
#### 4.3.3 Decision Tree  
Decision tree is an analysis method that charts decision-making rules to classify a subject group into several subgroups or to make predictions. A preprocessing step was performed prior to analysis. According to the amplitude of vibration, as shown in Table 12, the dataset was divided into three parts, high, middle, and low, and the decision tree was analyzed by converting the dataset into categorical data. Before analysis, it was divided into training data (70%) and verification data (30%).  
The results of the decision tree analysis using the C5.0 algorithm are diagrammed and shown in Figure 8. Among the four independent variables, joint orientation, aspect ratio, cover depth and vehicle speed, the root node was classified as vehicle speed, and the intermediate node was classified according to the joint orientation level. At the terminal node (Node 9), high and low vibrations were confused and classified. As a result of cross-validation using test data, the accuracy was relatively low, at 50%.  
Multiple regression analysis aims to perform prediction well by increasing the explanatory power of the model, and decision tree is an analysis method aimed at classifying data with low impurity. Therefore, it is judged that the best way to analyze the interaction is to use the interaction plot because it can be sufficiently explained with only the main effect factors.
# An Efficient Task Autonomous Planning Method for Small Satellites
###### Abstract  
Existing on-board planning systems do not apply to small satellites with limited onboard computer capacity and on-board resources. This study aims to investigate the problem of autonomous task planning for small satellites. Based on the analysis of the problem and its constraints, a model of task autonomous planning was implemented. According to the long-cycle task planning requirements, a framework of rolling planning was proposed, including a rolling window and planning unit in the solution, and we proposed an improved genetic algorithm (IGA) for rolling planning. This algorithm categorized each individual based on the compliance of individuals with a time partial order constraint and resource constraint, and designed an appropriate crossover operator and mutation operator for each type of individual. The experimental result showed that the framework and algorithm can not only respond quickly to observation tasks, but can produce effective planning programs to ensure the successful completion of observation tasks.  
small satellite; autonomous task planning; rolling-planning framework; IGA [table]capposition=top
###### Abstract  
This work is focused on the valorization of brewer's spent grains (BSG) into volatile fatty acids (VFA) through acidogenic fermentation. VFAs are building blocks for several applications, such as bioplastics' production. Using acid hydrolysis as pre-treatment, several batch assays were performed and the impact of organic load (OL) and pH on VFA production from BSG hydrolysate was assessed. Regardless of the condition, the produced acids were mainly butyric and acetic acids followed by propionic acid. The OL had a direct impact on the total organic acid concentration with higher concentrations at the highest OL (40 gCOD L\({}^{-1}\)). pH affected the concentration of individual organic acid, with the highest fermentation products (FP) diversity attained at pH 5.0 and OL of 40 gCOD L\({}^{-1}\). To assess the potential application of organic acids for biopolymers (such as polyhydroxyalkanoates) production, the content in hydroxybutyrate (HB) and hydroxyvalerate (HV) monomers was estimated from the respective precursors produced at each pH and OL. The content in HV precursors increased with pH, with a maximum at pH 6.0 (ca. 16% C-mol basis). The acidogenic fermentation of BSG hydrolysate was also assessed in continuous operation, using an expanded granular sludge bed reactor (ECSB). It was shown that the BSG hydrolysate was successfully converted to VFAs without pH control, achieving higher productivities than in the batch operation mode.  
brewer's spent grain (BSG); acid hydrolysis; acidogenic fermentation; pH; organic load +
Footnote †: journal: Applied Sci. 2021, 11, 3222. [[https://doi.org/10.3390/app1107322](https://doi.org/10.3390/app1107322)]([https://doi.org/10.3390/app1107322](https://doi.org/10.3390/app1107322))
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 2 Fast Charging Techniques for Electric Vehicles
### DC Fast Charging (DCFC)  
DC fast charging, also known as level 3 charging, is a prominent fast charging method that relies on high-power charging stations to deliver DC electricity directly to the vehicle battery, bypassing the onboard charger [18; 64]. Unlike slower AC charging methods, which involve electricity passing through the vehicle's charger to convert AC power to DC, DCFC systems employ control strategies such as CC and CV modes and temperature monitoring for safe and efficient charging. These systems often incorporate intelligent power electronics and communication capabilities to optimize the charging process and grid integration. With the charging power typically ranging from 50 kW to 350 kW, DC fast charging drastically reduces charging times to as little as 30 min or less for a full charge, depending on factors like vehicle battery capacity and charging power level [65; 66; 67]. The global expansion of DC fast charging infrastructure, strategically placed along highways and urban areas [68; 69], addresses range anxiety and promotes the feasibility of EV adoption. It is important to note that EV charging capabilities vary, with factors like vehicle model and battery technology influencing charging rates and maximum power support [70; 71]. DC Fast Charging offers enhanced convenience with its faster charging times compared to standard AC charging methods, although it does require some user interaction. While generally safe, it is imperative to prioritize thermal management to mitigate potential risks. These charging systems are moderately weatherproof, ensuring a reliable performance under various conditions. Integration with existing infrastructure is widely available at dedicated charging stations but mandated DC fast charging equipment is required. The compatibility of this system spans most electric vehicle (EV) models, though it is not guaranteed to work for all makes and models. However, the faster charging speeds associated with DC Fast Charging may lead to slightly accelerated battery degradation compared to slower charging methods. Yet, the scalability and upgradability of these stations are notable, allowing for future upgrades to deliver even higher power outputs. Despite their higher efficiency, there is still some energy loss during the conversion process that is inherent to DC fast charging.
## 3 Probability of Chip Error in Coherent O-QPSK
### Half-Sine Pulse Shaping
The amplitude in a quadrant is:  
\[i=\sqrt{I}\cos(z)\quad-\pi/2\leq z<\pi/2 \tag{14}\]  
Using Equation (14), the PDF of the amplitude is written as:  
\[f_{i}(i)=\frac{2}{\pi\sqrt{1-(i/\sqrt{I})^{2}}}\quad 0\leq i\leq\sqrt{I}. \tag{15}\]  
Then, we can also obtain the probability that the interferer amplitude lies in an interval \((a,b]\) using Equation (15):  
\[\text{Prob}(a<i\leq b)=F_{i}(b)-F_{i}(a)=\int_{\frac{a}{\sqrt{I}}}^{\frac{b}{ \sqrt{I}}}f_{i}(i)di=\frac{2}{\pi}(\sin^{-1}(\frac{b}{\sqrt{I}})-\sin^{-1}( \frac{a}{\sqrt{I}})) \tag{16}\]  
where \(F_{i}(i)\) is the Cumulative Distribution Function (CDF) of \(i\). Without loss of generality, as we did in Section 3.1, it can be assumed that the transmitter is transmitting \(m_{3}\). Therefore, to obtain \(P_{c}\) and \(P_{s}\), we should consider the following two cases: (a) \(\sqrt{I}<\sqrt{C}\), and (b) \(\sqrt{I}\geq\sqrt{C}\).
## 2 Research Methodology
### Fft-Lstm-Fcn  
The studied model presented here is an LSTM model that belongs to a regression problem of sequential property and is adapted in our research study to achieve superior results. LSTM has the potential to recall the previous time series data and has the advantage of being able to determine whether the features are important. Several reports in the literature indicated that the LSTM model cannot deal with raw data; as a result, the FFT is combined with the LSTM model to extract the represented features in the present investigation. The framework of the LSTM model is shown in Figure 2. There are three main gates to control the cell state. The input gate controlling the new information can be stored in the cell; this process can be expressed by Equations (1) and (2). The forget gate controlling the previous information can be discarded from the cell; this process can be expressed by Equation (3). The output gate determines the information extracted from the cell; this process can be expressed by Equations (4)-(6). The LSTM model, combined with a fully connected network, makes a decision. The equations of the LSTM model used in present paper are as follows:  
\[i_{t}=\sigma(W_{i}[h_{t-1},x_{t}]+b_{i}) \tag{1}\]  
\[\widetilde{C}_{t}=\tanh(W_{C}[h_{t-1},x_{t}]+b_{C}) \tag{2}\]  
\[f_{t}=\sigma(W_{f}[h_{t-1},x_{t}]+b_{f}) \tag{3}\]  
\[C_{t}=f_{t}C_{t-1}+i_{t}\widetilde{C}_{t} \tag{4}\]  
\[o_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o}) \tag{5}\]  
\[h_{t}=o_{t}\tanh(C_{t}) \tag{6}\]  
where \(i_{t}\) is the input gate, \(\sigma\) is a sigmoid function, \(W\) is the weighting factor, \(h_{t-1}\) is the cell output, \(b\) is the bias, \(f_{t}\) is the forget gate, and \(O_{t}\) is the output gate.
# A Data Secured Communication System Design Procedure with a Chaotic Carrier and Synergetic Observer
## 4 The System Hardware and Software Implementation Options
### Example of Software Implementation on a Standard PC without MATLAB Computational Engine  
The promising one is a model-oriented approach to modeling when the system created in MATLAB/Simulink is transformed into code in a high-level language (for example, C ++), and then it is executed as a separate application program on a standard PC or compatible embedded system. We used this approach when modeling a single-channel data transmission system with a chaotic carrier created in our earlier work [43].  
In order to validate the system for transmission of useful digital data, our team with the help of [PERSON] converted the MATLAB/Simulink system diagrams into C++ code and used it to create the virtual test bed providing transmission of a test phrase. The main window of the testbed is presented at Figure 20a,b. The test phrase is \"_Lorem ipsum dolor sit amet, consecetur adipiscing elit_\". As expected, the error of signal reconstruction in the proposed algorithm did not allow it to be used to transfer useful digital data without additional processing. As we needed only to validate the data transmission system, we only used multiple transmission as the simplest method of eliminating transmission errors. Figure 20a shows the result of the transmission with a number of retransmissions of 20,000.  
A smaller number of transmissions, for example, 8000, resulted in the persistence of transmission errors (Figure 20b). We also evaluated the necessary number of repetitions to provide the desired error rate in transmission. However, this issue requires future study. The same procedure may be used for two-channel communication system validation.  
The simulation software system used the simplest error correction algorithm: multiple transmission. This algorithm was chosen only to demonstrate the feasibility of the system with a synergetic observer to practical implementation for transmitting binary data. In Figure 21, we show the results of a statistical analysis of the created software system, having studied the dependence of the number of repetitions on the number of errors. The dependencies in Figure 21 make it possible to identify the best combination of the number of retries and the transmission rate that was used in the simulation results in Figure 20.
# Considerations about Parameters Estimation into a Minimum Variance Control System
## 2 Analytical Demonstration Based on Minimum Variance Control Law Design
Thus, the only solution for achieving equality described by Equation (7) remains the fulfillment of condition \(A(1)\hat{B}(1)-\hat{A}(1)B(1)=0\) (in both members). Equation (8) does not necessarily involve strictly equalities of the fractions numerators, (respectively denominators), but only the equality of these ratios. These fractions are practical gain factors of the process. By noting:  
\[\left\{ \begin{array}{l}k_{gain}=\frac{B(1)}{A(1)}\\ \hat{k}_{gain}=\frac{\hat{B}(1)}{\hat{A}(1)}\end{array} \right. \tag{9}\]  
where: \(k_{gain}\)-the open-loop process gain factor and \(\hat{k}_{gain}\)-the closed-loop process gain factor, based on equality described by Equation (8) it results:  
\[k_{gain}=\hat{k}_{gain} \tag{10}\]As a conclusion, in steady-state closed-loop regime, although parameters estimates may differ from those in the open-loop (so, \(\hat{A}(1)\
eq A(1)\) and \(\hat{B}(1)\
eq B(1)\)), the process gain estimates are similar both in open-loop and closed-loop (see Equation (10)). Therefore, for a proper operating of the control system, it's not even necessary an accurate estimation of the real parameters of the process linearized model, but only an accurate estimation of the process gain.
# Secure Image Signal Transmission Scheme Using Poly-Polarization Filtering and Orthogonal Matrix
## 3 Signal Model and Problem Description
As \(\bf H\) was always non-ideal due to the complex propagation environment and the limited \(\rm{XPD}\) of the dual-polarized antennas on the receiver side, the channel coefficient \(\bf H\) was written, as follows:  
\[\bf H=\left[ \begin{array}{cc}h_{\rm HH}&h_{\rm VH}\\ h_{\rm HV}&h_{\rm VV}\end{array} \right]=\bf U\Sigma V=U\left[ \begin{array}{ cc}\sqrt{\lambda_{1}}&0\\ 0&\sqrt{\lambda_{2}}\end{array} \right]V \tag{7}\]  
where \(\sqrt{\lambda_{i}}\) and \(i=1,2\) denote eigenvalues. \(\bf U\) and \(\bf V\) are unitary matrices. Therefore, a non-ideal channel state has a negative influence on signal demodulation, and this effect is called polarization dependent loss (PDL), which was measured by \(P_{dl}=10\log^{\lambda_{1}}\!/\lambda_{2}\), \((\lambda_{1}\geq\lambda_{2})\). Then, the \(k\)-th receiver signal was written, as follows:  
\[\bf y_{k}=\left[ \begin{array}{c}h_{\rm HH}s_{\rm Hk}+h_{\rm VH}s_{\rm Vk}\\ h_{\rm HV}s_{\rm Hk}+h_{\rm VV}s_{\rm Vk}\end{array} \right]+n_{k}\] \[=\left[ \begin{array}{c}h_{\rm HH}s_{\rm Hk}\\ h_{\rm VV}s_{\rm Vk}\end{array} \right]+\underbrace{\left[ \begin{array}{c}h_{ \rm VH}s_{\rm Vk}\\ h_{\rm HV}s_{\rm Hk}\end{array} \right]}_{\rm J}+n_{k} \tag{8}\]  
The non-ideal H led to interference \(\bf J\) in the received signals, which further affected the polarization matching, as shown in Equation (5), and led to BER performance degradation. Fortunately, based on the interoperability of the wireless channel, both the transmitter and receivers could acquire the channel transfer function H. Therefore, the pre-compensation method on the transmitter side or the zero-forcing method at the receiver side could be utilized to relieve the PDL [4]. However, the PDL could not be fully eliminated by these two methods. For resolving the PDL and enhancing the transmission security, the PPF-OM scheme was proposed, as described in the following sections.