esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Overview of the Type of Classifiers Used for MR
### Dt  
A DT is a decision support tool that uses a treelike model of decisions and their possible consequences (i.e., modulation scheme type) [7]. DT, as a classifier for AMC, follows sequential decision-making procedures in which specific thresholds are selected to separate the modulation type and orders. The decision making in any stage depends on the previous stage decision (test), which is taken on the basis of predefined threshold values that mainly influence the overall performance [31]. A DT algorithm can handle a multiclass problem. Each stage in the DT evaluates a specific feature. Any additional modulation involves a new decision by making new branches to the tree. Therefore, DT is considered to be less complex with acceptable performance than PR techniques [45]. However, DT consumes considerable time during design and optimization [87].  
In [35], instantaneous FB DT was used to classify the digital modulation schemes. However, M-PSK and the required increased number of samples were not identified to enhance classification accuracy. Previous studies [48; 49; 52; 53; 58; 60; 88] used HOS as the features for DT, whereas [59] combined instantaneous and HOS as the features for DT. In [40], DT used a hierarchal hybrid by combining FB and LB, where the first stage was to discriminate among MFSK, PSK, and QAM subclasses depending on the variance threshold of instantaneous amplitude. The second stage in each DT depended on predefined threshold to recognize the types of FSK and fourth HOCs for differentiating M-PSK and M-QAM. For probability correct classification (PCC), MLE was used to identify the order of PSK, and QAM was utilized to enhance the classification accuracy. In [66; 71; 74], and [89], the authors used wavelet domain as the features for identifying the digital modulation.
## 2 Optimization Design Process and Modeling Method
### Numerical Method
#### 2.3.3 Governing Equations and Boundary Conditions  
The three-dimensional turbulent flow inside the pump is basically controlled by the law of mass and momentum conservation. For steady and incompressible flow, the control equations can be formulated as:  
\[\
abla\vec{V}=0 \tag{10}\]  
\[\frac{\vec{V}}{dt}=\vec{F}-\frac{1}{\rho}\
abla p+\
u\
abla^{2}\vec{V}, \tag{11}\]  
Figure 4: Computational meshes.  
Figure 5: Analysis of grid independence.  
where \(\overrightarrow{V}\) represents the velocity vector, \(p\) represents pressure, \(\overrightarrow{F}\) represents mass force, \(\
u\) represents kinematic viscosity, \(\rho\) represents the fluid density.  
Due to different turbulence models influencing the calculation results of specific flow problems, selecting the suitable turbulence model can improve the calculation accuracy. In the present study, the fluid medium is set as water at 25 \({}^{\circ}\)C. The simulation is conducted by ANSYS CFX14.1 solver, which provides a number of turbulence models. Among the turbulence models, \(k\)-\(\varepsilon\) and \(k\)-\(\omega\) are known in engineering applications. The \(\varepsilon\) equation of the standard \(k\)-\(\varepsilon\) model must use a wall function to solve the terms, and the simulation results are not accurate in the case of severe flow separation. The standard \(k\)-\(\omega\) model is improved for a low Reynolds number and shear flow. Moreover, the Shear Stress Transfer (SST) \(k\)-\(\omega\) turbulence model uses a hybrid function which acts as a standard \(k\)-\(\omega\) model on the near wall and the standard \(k\)-\(\varepsilon\) model on the far wall area to correct the turbulent viscosity formula, while taking into account the turbulent shear stress. It has been proven to be more suitable for rotating machinery because it provides a better solution to the boundary layer [30]. The internal flow of the rotating pumps is three-dimensional turbulence flow, with strong flow separation, rotor-stator interaction, backflow, and so on. In addition, the electronic water pump has a high speed, and its flow losses mainly appear near the wall surface. Therefore, the SST \(k\)-\(\omega\) turbulence model is adopted in this study to simulate the internal flow of the model pump based on the finite volume method.  
The boundary conditions consist of an imposed stable total pressure with a turbulence intensity of 5% at the inlet and flow rates at outlet. All physical surfaces are set as no-slip walls. Considering the rotational characteristic, the rotating coordinate system is applied in impeller domain. The interfaces between the rotational domain and stationary ones are set as frozen rotor for steady simulation. The root mean square (RMS) residual error is used to judge whether the calculation is converged, setting values as \(10^{-5}\).
# VLC Network Design for High Mobility Users in Urban Tunnels
## 2 Related Works  
VLC technology is still in the early stages of development, in consequence, there are some problems in the superior layers that have not been deeply studied yet. One of the less-studied topics is the mobility process when a user changes from one AP to another. The main drawback of this procedure is the time spent during the association process to initialize a new session. If the vehicular node requires to move across many cells, the continuous cell exchange will limit the communication. Without a mobility scheme, a user's node has to accomplish some processes to recover service each time it enters a new network. First, it needs to detect the link failure which presents a compromise between sensibility and reliability. If the criteria are too sensitive, the user can disconnect needlessly with a ping-pong effect. On the other hand, resistant detection can hold the node without service for a long time. The second step is scanning the channel looking for a new AP with appropriate connection conditions. When the user's node has decided on the target AP, it executes an entering protocol. Unfortunately, these actions take a long time and disable the link communication for some seconds.  
Although there are several proposed mechanisms in the current IEEE 802.15.7 VLC standard [22] to help with mobility, they are considered basics. These mechanisms include cell arrangements to get an accurate cell location and time distribution to avoid conflicts for users leaving the coverage area. For the localization process, a group of \(n\) cells emits sequentially a frame to estimate a position, taking a long time. But, this is an unpractical solution for a user with high mobility speed. Moreover, such a standard does not provide a resolution protocol to make the handshake. It also does not give a procedure for estimating when a user moves away from the AP's coverage area. Thus, the lack of previous studies on this topic represents a potential research gap that needs to be fully explored.
# Demand-Response Control in Smart Grids
## 4 Comparison between Centralized and Distributed Control for Demand Response
### Comparison between Centralized and Distributed Control
As a result, we recommend using a distributed solution for DR in smart grids for the following reasons: (i) the centralized solution is not appropriate for smart grids because it is highly unstable due to having a single point of failure and requiring a high level of connectivity; (ii) the distributed solution reduces communication burden and computational complexity, ensures security (anonymity), and is more suitable for smart grids, which are distributed by nature.
# Robust Estimation of Vehicle Motion States Utilizing an Extended Set-Membership Filter
## 4 Calculation of Tire Forces
### Longitudinal Tire Force Estimator  
As shown in Figure 3, the single tire is modeled as a rigid body that rotates around its mass center.  
Figure 3: Tire rolling dynamic model.  
Ignoring the influence of rolling resistance, the rolling dynamics satisfies:  
\[I_{w}\dot{\omega_{i}}=T_{i}-F_{xi}R \tag{20}\]  
where \(I_{w}\) and \(R\) are the rolling inertia and radius, respectively, \(\omega_{i}\left(i=1,2,3,4\right)\) represents the tire revolving speed of each tire, and \(T_{i}\left(i=1,2,3,4\right)\) is the tire torque of each tire, namely, the sum of the braking and driving torques.  
The state space for the longitudinal force estimator is expressed as:  
\[\begin{split}\dot{\mathbf{x}}_{a}&=\mathbf{F}_{a}\mathbf{x}_{ a}+\mathbf{G}_{a}\mathbf{u}_{a}+\mathbf{w}_{a}\\ \mathbf{z}_{a}&=\mathbf{H}_{a}\mathbf{x}_{a}+\mathbf{v}_{a}\end{split} \tag{21}\]  
The state vector, input vector and measurement vector are chosen as follows:  
State vector: \(\mathbf{x}_{a}=[F_{xi}\ \ \omega_{i}]^{\mathrm{T}}\);  
Input vector: \(\mathbf{u}_{a}=[T_{i}]^{\mathrm{T}}\);  
Measurement vector: \(\mathbf{z}_{a}=[\omega_{i}]^{\mathrm{T}}\).  
Consequently, according to Equation (20), the state transition matrix \(F_{a}\), input transition matrix \(G_{a}\) and measurement matrix \(\mathbf{H}_{a}\) in the state equation and measurement equation are as follows:  
\[\mathbf{F}_{a}=\left[\begin{array}{cc}\mathbf{0}_{4\times 8}&\\ \hline-\dfrac{R}{I_{w}}\cdot\mathbf{I}_{4\times 4}&\mathbf{0}_{4\times 4}\end{array} \right],\mathbf{G}_{a}=\left[\begin{array}{cc}\mathbf{0}_{4\times 4}&\\ \dfrac{1}{I_{w}}\cdot\mathbf{I}_{4\times 4}\end{array}\right],\mathbf{H}_{a}=\left[ \mathbf{0}_{4\times 4}&\mathbf{I}_{4\times 4}\right]\]  
The estimated results are obtained through the iteration of the time update process and the measurement update process in the KF algorithm.
## 3 Materials and Methods  
### Computational Analysis  
#### 3.1.1 Bioinformatic Protocol  
Due to the lack of the crystal structure of SpoT protein for any \(H\). _pylori_ strain at the time of writing this paper, a homology modeling pathway had to be undertaken. In the first step, an ATCC Genome Portal entry corresponding to the J99/ATCC 700824 strain [65] used within this study was thoroughly searched for the sequence encoding the SpoT protein. Since none of the identified product was classified as such, the UniProt database [66] was explored for the known sequence responsible for the aforementioned activity. The found entry (O25466), corresponding to the HP_0775 gene in \(H\). _pylori_ (strain ATCC 700392/26695) [67], was submitted for BLAST procedure [68], using BLOSUM62 matrix [69], against the genome of J99, yielding a sequence described by the satisfying results of 98% identity, 99% positivity, and 0% gaps.  
It was trimmed out and used as an input for ThreaDomEx [70] to establish domains. With the cutoff of 0.56, five were identified, and each was submitted for I-TASSER [71; 72; 73; 74], known for its continuous success in CASP experiments [75], to predict their structure and the activity resulting from it. Based on the anticipated GO:Scores, we decided to pick the  
Figure 8: Dioscin interactions with the binding pocket of a SpoT protein of \(H\). _pylori_.  
first model of the third domain for further studies as the only one possessing a positive and satisfying C-Score (1.24), and the only one expressing kinase activity (GO:0016301).  
The predicted secondary structure of the protein was satisfying, both with a confidence score average of 7.5 and a predicted normalized B-factor (Figure 9) [76; 77] mostly below 0, which indicated that most of the residues are relatively stable in the structure. Three entries were found in the top 10 threading templates: 1 vj7A (6 times), 5 iqrA (2 times), and 5 kpvA (2 times), and their corresponding Z-scores of 4.43, 4.09, and 3.32 indicated very good alignment between sequences. Importantly, 1 vj7A represents a bifunctional catalytic fragment of RelSeq, the RelA/SpoT homolog from _Streptococcus equisimilis_. Four final tertiary structures were predicted, but only the first one represented a satisfying C-score parameter of 1.24, whereas the range may vary from \(-5\) to 2, thus pointing to its satisfyingly correct global topology; corresponding to a TM-score equaling 0.88 \(\pm\) 0.07 and a corresponding RMSD = 2.3 + 1.8 A.  
Apart from the structural features, function was also established. Although the highest C-score was only 0.40, the corresponding ligand was found to be guanosine 5'-(tetrahydrogen triphosphate) 3'-(trihydrogen diphosphate). This corresponds with the established EC numbers that represent activities of GTP diphosphokinase and guanosine-3',5'-bis(diphosphate) 3'-diphosphatase and a GO:Score of 0016301 as one expressing kinase activity (C-score of all of these was 0.450).
# Microalgal Proteins and Bioactives for Food, Feed, and Other Applications
## 1 Introduction
### Microalgae for Food and Functional Food Applications
#### 1.1.5. Vitamins  
Some species of microalgae contain high levels of different water and lipid-soluble vitamins, including vitamins A, B-complex, C, D2, D3, E and K [8]. _Nannochlorosis oceanica_ is an excellent source of vitamin D [87], _Tetraselmis suecia_ and _Dunaliella tertiolecta_ contain high amount of vitamin E [88] and some microalgae including _Chorella_ sp. and _Dunaliella salina_ accumulate vitamin C in considerable amounts. _Chlorella_ sp. was also mentioned as a source of vitamin B12 [89]. Some studies show that the active form of vitamin B12 is normally not presented in microalgae because it is synthetized from pseudocolaamin; however, they can accumulate B12 from the aquatic environments where they are cultivated [90]. Vitamins found within the biomass of selected microalgae in high, significant amounts are summarized in Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Species** & **Vitamin** & \
\begin{tabular}{c} **Vitamin Recommended** \\ **Daily Allowance (RDA)** \\ \end{tabular} &
\begin{tabular}{c} **Vitamin** \\ **[mg/100 g DW]** \\ \end{tabular} & **References** \\ \hline _Arthospira_ sp. & A & 800 \(\mu\)g & 0.34 & [91] \\ _Chlorella_ sp. & A & 30.77 & [91] \\ \hline _Arthospira_ sp. & B3 & 18 mg & 12.8 & [91] \\ _Chlorella_ sp. & B3 & 18 mg & 23.8 & [91] \\ \hline _Arthospira_ sp. & B9 & 200 \(\mu\)g & 0.094 & [91] \\ _Chlorella_ sp. & B9 & 200 \(\mu\)g & 0.094 & [91] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Reported vitamin levels in microalgae species.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Species** & **Carbohydrate** & **Application** & **References** \\ \hline \multirow{4}{*}{_Chlorella_ sp.} & \multirow{4}{*}{\(\beta\)-1,3-glucan} & Immuno-stimulator, antioxidant, reduce blood lipid levels, thicker in the food industry & [81] \\ \cline{1-1} \cline{3-4}  & & Alternative to antibiotics, prebiotic effect & [8] \\ \hline \multirow{2}{*}{_Purobridium_ sp.} & \multirow{2}{*}{Sulfated polysaccharides} & Thickening/lubrication agent & [82] \\ \cline{1-1} \cline{3-4}  & & & agent \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._
# Ensemble-Based Knowledge Distillation for Video Anomaly Detection
## 3 Proposed Approaches
### Confidence Based Weighted Combination (CBWC) Approach  
In this approach, a weighted combination mechanism based on confidence scores has been integrated within the total loss function (Figure 4):  
\[L_{Total}=(1-\alpha)*L_{GT}+L_{CBWC} \tag{6}\]  
\[L_{\text{CBWC}}=\alpha*(\beta_{T_{1}}*H(\sigma(Z_{t_{1}};T=\tau),\sigma(Z_{s}; T=\tau))+ \tag{7}\]  
\[\beta_{T_{2}}*H(\sigma(Z_{t_{2}};T=\tau),\sigma(Z_{s};T=\tau)))\]  
\[\beta_{T_{1}}=\frac{C_{T_{1}}}{C_{T_{1}}+C_{T_{2}}}\quad\beta_{T_{2}}=\frac{C _{T_{2}}}{C_{T_{1}}+C_{T_{2}}} \tag{8}\]  
Figure 3: Visual overview of Confidence Based Maximum Selection approach.  
where \(\beta_{T_{1}}\) and \(\beta_{T_{2}}\) are dynamic weight values recalculated from respective confidence scores \(C_{T_{1}}\) and \(C_{T_{2}}\).  
Different from EWC and CMBS, the CBWC approach follows a merging operation of different feedback signals from teacher models by dynamically weighting each feedback signal of a teacher model with respect to its confidence level. The confidence level of each teacher model again is calculated by the same transformation function (Equation (5)) with respect to the specific sample. Subsequently, \(\beta_{T_{1}}\) and \(\beta_{T_{2}}\) weight values are calculated for each teacher model by applying a normalization operation (Equation (8)) on the confidence scores \(C_{T_{1}}\) and \(C_{T_{2}}\). Consequently, the CBWC approach provides the student model with a mixture of information feedback comprised of different interpretations of samples by AR-Net and GCN teacher models, but additionally with a dynamic weighting factor.  
In summary, compared to the EWC and CBMS approach, this method provides a student model to combine a weighted combination of teacher models (GCN or AR-Net) with respect to their current confidence scores calculated on their end outputs.
## 2 Materials and Methods
### Network Analysis Per Group  
Network research aims to understand how a process works and identify the system components as well as the statistical relations between them, with the former being represented as the nodes of the system and the latter as links between the nodes [55]. Following this systems medicine approach [56], psychological networks have been widely used in recent years to conceptualize the interplay of different components of human behavior [57].  
In this study, a network analysis was performed to identify the network structure for each group of patients based on their baseline characteristics, their adherence during the familiarization phase, and their adherence to the whole program. The data previously used for the creation of the clusters related to the baseline characteristics and the adherence during the familiarization phase were also used to create the networks. For the network analysis of adherence to the whole exercise program, both types of data were considered. However, in all cases, only features that presented statistically significant differences between the clusters using the Kruskal-Wallis test were used for the creation of the networks. In addition, for a more accurate estimation of the networks, the number of nodes had to be less than the number of members of the group. Therefore, the features were ordered based on the \(p\)-values calculated using the Kruskal-Wallis test, and only the most significant were included in the analysis.  
The network analysis was implemented in R, using the \"glasso\" package [58] based on [59] for LASSO regularization. In more detail, the Gaussian graphical model (GGM) [60] was estimated using \"glasso\" and EBIC model selection, since it has been found that this combination works well in retrieving the correct network structure [61]. To assess the importance of the nodes in the network structure, three measures were used: _node strength_ and _closeness_ quantify how well a node is directly or indirectly connected to others, respectively, while _betweenness_ quantifies the node's importance in the average path between two other nodes [57].  
Regarding the metrics that were captured during the session (_adher_, \(HRnorm\), and \(HRtime\)), temporal networks were created using the \"graphicalVAR\" package [62]. The number of LASSO tuning parameters that were tested was set to 50.
# Transient Attitude Motion of TNS-0#2 Nanosatellite during Atmosphere Re-Entry
## 4 Measurements Processing Results and Passive Attitude Motion Regimes Analysis
### Magnetic Stabilization of TNS-0 # 2 Nanosatellite  
Telemetry for approximately two orbits was stored and downloaded on 2 October for the proper assessment of the nominal magnetic stabilization motion. Figure 9 shows the deviation of OZ axis from the local magnetic field vector. This angle does not exceed 12 degrees, and the average deviation along the orbit is about 5 degrees. This is typical for a passive magnetically stabilized satellite. The characteristic period of the forced oscillations (about 9 min) caused by the uneven rotation of the local geomagnetic field induction vector is clearly seen. The frequency is close to the natural frequency of the satellite's oscillations as a rigid body with a permanent magnet in a constant external magnetic field. The period of comparatively slow amplitude variation of these oscillations (a time equal to half the satellite's revolution in orbit around the Earth) is associated with a change in the magnitude of the induction vector of the local geomagnetic field. Figure 10 depicts the angular velocity during the motion. The satellite slowly rotates around the longitudinal axis with angular velocity of 0.4 deg./s. This rotation cannot be damped by the hysteresis rods installed perpendicular to the rotation axis.  
The described passive magnetic stabilization maintained since October 2017 till the last several months of the mission. The deviation of the permanent magnet axis from the local geomagnetic field for a number of months in 2018 is shown in Figure 11.  
Figure 8: Angular velocity damping history.  
Figure 9: Deviation from the direction of the local magnetic field.  
The deviation eventually increased due to the increasing influence of the aerodynamic torque as the satellite orbit decayed. The satellite altitude for the whole mission duration is shown in Figures 12 and 13 shows the magnet axis deviation from the local geomagnetic field starting from 1 July 2019. The deviation increased up to 40 degrees in July. The magnetic stabilization was completely lost in August; the satellite was tumbling.  
The satellite achieved aerodynamic stabilization in about two weeks. Then the satellite lost this attitude on 6 September, the last day of the mission. The transient motion and aerodynamic stabilization are discussed in the following section.
## 2 A Conceptual Model for Adaptive _IoT_ Communications
The singular predominance of Internet Protocol (IP) at this layer gives it the potentiality to be established as the \"standard language\" universally supported by virtually incalculable types of _IoT_ products [10], eliminating the dependence on numerous one-to-one translators between individual pairs of incompatible technologies and standards. This approach not only means _things_ are able to natively communicate with each other using the comprehensively studied IP, but also greatly simplifies the integration with the existing IP-based Internet [11]. The transport layer has the opportunity to exert end-to-end adaptations in the paradigm. Spectral knowledge such as interference levels can be employed by processes such as flow control for more refined operations. This information can also provide congestion control schemes with precaution against imminent disruptions. Accordingly, adaptation can be exploited at this layer to improve communication reliability. The application layer, though not typically concerned with underlying details such as the spectrum, has the potential to employ high-level adaptation techniques to improve the robustness against disruptions in the context of _IoT_. Delay- and disruption-tolerant networking (DTN) is one of the approaches dedicated to addressing reliability issues common for communication among low-powerdevices, such as transmission delay and disruption due to shadowing or fading [12, 13]. Since it is designed to work atop existing Internet infrastructure, DTN can be adopted as an overlaying adaptation facility to meet the application requirements. Adaptation at the application layer can also be realized through middleware that facilitate service-level interoperability, detection, and so forth.