esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Mathematical Formulation Part
#### 2.2.2 Organic Rankine Cycle Modeling  
The ORC is modeled by using the following set of equations. These equations are typical formulas about the energy balances in the various devices and respective efficiency definitions.  
The heat exchange in the heat recovery system (HRS) can be written as:  
\[Q_{HRS}=m_{orc}\cdot(h_{d}-h_{c}) \tag{22}\]  
The electricity output from the ORC (\(P_{d,out}\)) is calculated by using the produced electricity from the generator minus the electricity consumption from the organic fluid pump and can be written as:  
\[P_{el,out}=\eta_{mg}\cdot m_{orc}\cdot(h_{d}-h_{e})-\frac{m_{orc}\cdot(h_{b}- h_{a})}{\eta_{motor}} \tag{23}\]  
The isentropic efficiency for the organic fluid pump (\(\eta_{is,pump}\)) can be written as:  
\[\eta_{is,pump}=\frac{h_{b,is}-h_{a}}{h_{b}-h_{a}} \tag{24}\]  
The isentropic efficiency for the organic fluid turbine (\(\eta_{is,tur}\)) can be written as:  
\[\eta_{is,tur}=\frac{h_{d}-h_{e}}{h_{d}-h_{e,is}} \tag{25}\]  
The heat rate balance of the recuperator can be written as:  
\[(h_{c}-h_{b})=\left(h_{e}-h_{f}\right) \tag{26}\]  
The heat transfer modeling in the recuperator is conducted by using the minimum temperature difference concept and can be written as:  
\[T_{f}=T_{b}+\Delta T_{rec} \tag{27}\]  
The heat output of the ORC to the ambient heat sink (\(Q_{out}\)) can be written as:  
\[Q_{out}=m_{orc}\cdot\left(h_{f}-h_{a}\right) \tag{28}\]  
It is worth mentioning that the minimum temperature difference on the HRS (pinch point) is set at 5 K. Furthermore, it is significant that the outlet of the heat rejection device (state point a) is selected to be a saturated liquid.  
Lastly, the thermodynamic efficiency of the organic Rankine cycle (\(\eta_{orc}\)) is given as:  
\[\eta_{orc}=\frac{P_{el,out}}{Q_{HRS}} \tag{29}\]
## 3 Results and Discussion
### Dosimetric Characterization
#### 3.2.6 Signal Stability  
Ideally, a TL material should retain dosimetric information over an indefinite course of time under normal storage conditions. However, some unfavorable circumstances disturb the signal stability, resulting in dosimetric information loss. Signal stability, also known as fading, is a statistical phenomenon in which trapped holes and electrons are released and consequently recombine, accelerating the depopulation of trapped electrons and holes. Signal stability is significantly important in personal and environmental radiation dosimetry where the dose is accumulated over a period. However, fading is not a major disadvantage for clinical dosimetry, as it could be overcome by storing the TLD under favorable conditions. Figure 12 shows the thermal fading of the synthesized glass. Each data point represents an averaged measurement value of three samples. Significant thermally induced signal loss, 18%, can be evidenced after a week, which subsequently increases up to 40% at the end of the second week. The light-induced fading, termed optical fading, behavior is shown in Figure 13. Most magnesium borate-based TL materials suffer from optical fading, adding considerable errors in the dose estimation [9; 15]. The synthesized TL material was highly sensitive to visible light, resulting in drastic signal loss. The dosimetric information was observed to be reduced up to 60% after 1 h of storage under fluorescent light, whereas the signal was observed to be stable in sunlight for the first couple of hours, and then started to fade drastically. This behavior could be attributed to UV light, as the UV index was high during the first couple of hours of storage, whereas subsequent signal loss confirms the sensitivity to the visible light. Each data point represents an averaged measurement value of the three samples.
# Predictive Speed-Control Algorithm Based on a Novel Extended-State Observer for PMSM Drives
## 4 The Design of Extended-State Observer  
In this section, an estimated load torque \(\hat{\tau}_{L}\) is introduced to the controller instead of \(\tau_{L}\), and a novel extended-state observer is proposed. The design method of extended-state observer is as follows.  
According to the mathematical model of the PMSM in Equation (1)  
\[\dot{\omega}=\frac{n_{p}\left[\left(L_{d}-L_{q}\right)i_{d}i_{q}+\Phi i_{q} \right]}{J}-\frac{\tau_{L}}{J}-\frac{B\omega}{J} \tag{16}\]  
Define \(x_{1}=\omega\), \(x_{2}=-\frac{\tau_{l}}{J}\), according to the literature [21], then Equation (16) can be written as  
\[\left\{\begin{array}{l}\dot{x}_{1}=x_{2}-\frac{B\omega}{J}+\frac{n_{p}[ \left(L_{d}-L_{q}\right)i_{d}+\Phi]}{J}i_{q}\\ \dot{x}_{2}=0+\zeta\left(t\right)\end{array}\right. \tag{17}\]  
where \(\zeta\left(t\right)\) can be viewed as external disturbance of \(\dot{x}_{2}\).  
Let \(z_{1}\) and \(z_{2}\) represent the estimates of \(x_{1}\) and \(x_{2}\), the novel extended-state observer (ESO) is introduced as follows [21].  
\[\begin{array}{l}\dot{z}_{1}=z_{2}-\frac{B\omega}{J}+\frac{n_{p}[\left(L_{d} -L_{q}\right)i_{d}+\Phi]}{J}i_{q}+\rho|e_{1}|^{\alpha_{1}}sign\left(e_{1} \right)+\rho|e_{1}|^{\beta_{1}}sign\left(e_{1}\right)+k_{1}sign\left(e_{1}\right) \\ \dot{z}_{2}=0+\rho^{2}|e_{1}|^{\alpha_{2}}sign\left(e_{1}\right)+\rho^{2}|e_{1} |^{\beta_{2}}sign\left(e_{1}\right)+k_{2}sign\left(e_{1}\right)\end{array} \tag{18}\]  
where \(e_{1}=\omega-z_{1}\), \(1<\rho<+\infty\), \(0.5<\alpha_{1}<1\), \(\alpha_{2}=2\alpha_{1}-1\), \(\beta_{1}=1/\alpha_{1}\), \(\beta_{2}=1/\alpha_{1}+\beta_{1}-1\), \(k_{1}\) and \(k_{2}\) are the gains.  
To avoid chattering, the sign function \(sign\left(e_{1}\right)\) in Equation (18) is replaced by a sigmoid function  
\[signf\left(e_{1}\right)=\left\{\begin{array}{cc}2\left(\frac{1}{1+\exp^{-c _{1}}}\right),&|e_{1}|\leq\delta\\ sign\left(e_{1}\right),&|e_{1}|>\delta\end{array}\right. \tag{19}\]  
where \(C\) is a constant and \(\delta\) is the boundary layer thickness.  
To improve the predictive control system performance under load disturbance, a novel ESO is adopted to estimate disturbance. This ensures that the predictive speed controller based on the novel ESO has a good anti-disturbance performance while satisfying the dynamic performance requirements of the control system.
## 3 Results and Discussion
### Performance Test of SBS Modified Asphalt with Chinese Evaluation Methods
#### 3.1.3 Ductility and Elastic Recovery  
Ductility is a test index used to characterize the low temperature performance of asphalt binder. The higher the ductility, the better the low-temperature crack resistance of asphalt binder. The elastic recovery of asphalt binder is a mechanical index, which could reflect the elasticity capacity of asphalt binder from stress to recovery [13]. The elastic recovery index directly reflects the high temperature, low temperature, fatigue and durability performance. The temperature of ductility test was 5 \({}^{\circ}\)C, the stretching velocity was 5 cm/min, and the temperature of the elastic recovery test was 25 \({}^{\circ}\)C. The results of ductility test and elastic recovery test of matrix asphalt and SBS modified asphalt are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **df** & **\(F\)-Value** & **\(p\)-Value** \\ \hline SBS modifier dosage & 70\% matrix asphalt & 4 & 268.47 & 0.25 \\ SBS modifier dosage & 90\% matrix asphalt & 4 & 8641.65 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Variance analysis of softening point for SBS modified asphalt.  
Figure 3: Ductility and elastic recovery result of matrix asphalt and SBS modified asphalt.  
From Figure 3, the ductility and elastic recovery of 90# matrix asphalt was higher than that of 70# matrix asphalt. The ductility and elastic recovery of asphalt binder increases with the increase of SBS modifier dosage, which meant the addition of SBS modifier could improve the low temperature performance and elastic recovery performance of asphalt binder [14; 15]. The variance analysis for the influence of SBS modifier on the ductility and elastic recovery of asphalt binder is shown in Table 5.  
It can be seen that SBS modifier had a significant influence on the ductility and elastic recovery of asphalt binder. For the ductility and elastic recovery of asphalt binder, the F-value of 70# matrix asphalt was bigger than that of 90# matrix asphalt, which meant the effects of SBS modifier on the ductility and elastic recovery of SK70# matrix asphalt was significantly greater than that of SK90# matrix asphalt.
## 2 Physically Based Dynamic Models for Modeling Complex Landslides
### Workflow of the Integrated Dynamic Model  
To consider the transition of the landslide from progressive sliding to fast avalanche, the previously introduced multi-block model and depth-averaged model should be effectively integrated into the time sequence [32]. In both models, the blocks represented by the cells have two velocity components. In the multi-block model, each block within a group has the same velocity component, which is the average velocity component of the group. The two models share the same basic variables; therefore, they can be directly integrated. The accuracy of physical quantities within a group is not determined by the spatial dimension of the group but by the basic cells that make up the group. Since the size of the cell remains constant, grouping will not reduce the precision of the physical quantity.  
To integrate multi-block and depth-averaged models, a critical time at which model switching occurs should be determined based on the fracture mechanism or field conditions. [PERSON] and [PERSON] [32] suggest that this parameter can be determined based on an examination of the pre-sliding topography to identify topographic obstacles or sudden changes in slope that may cause the fragment. The execution process of the integrated model is shown in Figure 3, where \(t_{s}\) is the critical time corresponding to the transition time of the movement state, and \(t_{f}\) is the total time length of the simulation that is specified by the user. MBM means multi-block model, and DAM means depth-averaged model. \(N_{g\mathrm{max}}\) and \(N_{c\mathrm{max}}\) are the total number of groups and cells, respectively. At present, the determination of the critical time \(t_{s}\) for switching from MBM to DAM can mainly be based on the characteristics of the terrain. Some specific terrain or obstacles are often prone to fragmentation, such as obvious protrusions on the terrain or highways. Based on the distance between these terrains and the landslide source area, combined with the estimated landslide initiation speed, the critical time value can be estimated. In the back-analysis of landslides, this time can be adjusted after simulations to make the simulation results more consistent with the site.
## 3 Results and Discussion
### Energy Absorption  
After analyzing the bending behavior and the subsequent spring back and shape recovery, the other parameter analyzed to evaluate the behavior of structures is the absorption energy. After the bending and return tests, elastic and shape, the area under the stress-strain curves normalized to the mass of each specimen previously measured was calculated. As in the previous cases, for this calculation, reference was made to the SEA parameter, which expresses the capacity of the structure to absorb energy per unit mass [54]. In Figure 10, you can see the average values of the absorption energy calculated for the two replicates of each test piece and their respective standard deviation.  
It can be observed that the roller and hoop structures have the lowest values of absorbed energy, while the double hoop and lozenge structures show significantly higher values of absorbed energy, with values above twice those of the former. Honeycomb also shows very high values in terms of absorption energy, but due to non-optimal behavior from the point of view of recovery and deformation, they were excluded from subsequent  
Figure 9: (**a**) Before recovery. (**b**) After recovery.  
experimental tests. In Figure 11, the honeycomb structure is shown as a result of the damage due to post-cyclic testing; this structure cannot be reused for subsequent cycles due to permanent damage. Ultimately, the structures selected for the next stages were lozenge and roller. The lozenge structure was chosen for its good recovery and high energy absorption capacity, while the roller structure was preferred for its replicability of its behavior and for its good elastic and shape recovery.  
In particular, [53] studied heterogeneous lattice structures and observed that those with reinforced cell configurations showed improved energy absorption under thermal cycles. Similarly, [13] highlighted that 4D-printed metamaterials maintain a relatively high SEA across repeated loading cycles when using thermally activated SMPs, such as PLA. The present results support these trends, confirming that proper geometric design (e.g., lozenge) enhances performance even under reduced stimulus conditions.
## 5 Discussion  
With this information in mind, Table 5 shows that the MZS algorithm is able to return a positional area in most of the frames observed, more than the triangulation technique. Also, the MZS gives a more stable result, with a lower value of standard variation. However, The triangulation technique returns a smaller zone in comparison when we take the median. The triangulation technique certainly targets a more accurate position of the user when the target is stationary and unable to work properly when the subject is moving during the five seconds intervals. Also, the position of sensors within the room is not optimized for the triangulation algorithm and may play a big part in those results as situations describe in Figure 9 lead to poor performances.  
Figure 10: Properties of areas computed by both methods.  
Figure 9: Comparison between MZS and triangulation.  
In Figure 11 we show a visual representation of areas computed by both methods and held as an illustration of data from Table 5 and more precisely the tag number 8. All areas computed by those two algorithms are printed together on the same picture.  
The MZS algorithm shows his strength on this figure: The localisation of the visitor in the center of the room show comparable result between the two methods, but the difference stand in the center to bottom left of the room. We see that the triangulation method show poor result in tight spaces, where only few sensors are reachable. And thus, results returned by the MZS algorithm are more exploitable and give a more meaningful view on the visit of the subject.
# A Soft-Voting Ensemble Classifier for Detecting Patients Affected by COVID-19
## 2 Materials and Methods
### The Proposed Approach
#### 2.4.1 Data Pre-Processing  
The dataset used in this work consists of CT scans with slices of 512 \(\times\) 512 pixels and a non-uniform depth size. Therefore, it has been necessary to process the scans so that each of them has the same depth size. This was also completed with the aim to assess how the scan depth affects the classifier's performance. To this end, the CT scans in the CLEAN-CC-CCII dataset were processed to generate two sets of scans with two distinct depth sizes. These sets consisted of CT scans with a uniform depth size of 25 and 35 slices.  
This pre-processing step has required to down-sample or over-sample the CT scans according to their original depth. The adopted strategy to perform these operations was to work at the ends of the scans, where generally, the information content is lower (see Figure 5). Basically, the oversampling was implemented by replicating the slices at both ends. Similarly, the downsampling was implemented by removing the slices starting from the ends.  
In addition, the size of the slices had to be reduced to meet the memory constraints of the GPU. To this end, the central part of each slice has been cropped with a fixed size and then scaled to 256 \(\times\) 256 pixels (see Figure 6). The cropping has been aimed at removing contents non-informative for the problem at hand but that might affect the training phase.  
Figure 4: The main steps of the proposed approach. Basically, it consists of steps aimed at pre-processing the dataset, splitting it to perform a 5-fold cross-validation, buiding and assessing the individual models, and compositing them using a voting ensemble strategy.
## 4 Application to Gaussian Graphical Models  
Next, we investigate the performance of EPSOM-Hyb on marginal likelihood estimation for GGMs. Let \(G=(V,E)\) be an undirected graph with vertex set \(V=\{1,\ldots,p\}\) and edge set \(E\). Define \(\mathbb{S}^{p}\) as the set of symmetric \(p\times p\) matrices and \(\mathbb{S}^{p}_{>0}\) as the cone of positive definite \(p\times p\) matrices in \(\mathbb{S}^{p}\). Let \(X\sim\mathcal{N}(\mu,\Sigma)\), \(\Sigma^{-1}\in\mathbb{S}^{p}_{>0}(G)\), where \(\mathbb{S}^{p}_{>0}(G)=\{M=\left(M_{ij}\right)\in\mathbb{S}^{p}_{>0}\mid M_{ ij}=0,\,\forall(i,j)\
otin E\}\). Then, the likelihood can be written as follows,  
\[L(\Sigma)=(2\pi)^{-np/2}\det(\Sigma)^{-n/2}e^{-\operatorname{tr}(\Sigma^{-1}B )/2},\,B=\sum_{i=1}^{n}x_{i}x_{i}^{\prime}. \tag{9}\]  
Further, \(X\) satisfies the GGM with graph \(G\), where \(G\) dictates the conditional dependence structure and restricts the sparse concentration matrix \(\Omega=(\omega_{ij})_{p\times p}=\Sigma^{-1}\) so that \((i,j)\in E\) if and only if \(\omega_{ij}\
eq 0\), and \(x^{(i)}\) and \(x^{(j)}\) are conditionally independent if and only if \(\omega_{ij}=0\). Hence, an undirected graphical model corresponding to \(G\) restricts the inverse covariance matrix \(\Omega\) to a linear subspace of the cone of positive definite matrices.  
We first consider decomposable GGMs where the tractability of the marginal likelihood makes it easy to evaluate estimates. In Section 4.2, we broaden our focus to general, non-decomposable graphs. To address the additional computational challenges that accompany non-decomposable graphs and high-dimensional parameter spaces, we propose an extension to EPSOM-Hyb that facilitates dimension reduction and drastically reduces the time complexity of the normalizing constant calculation. In the following GGM experiments, we refer to the estimator from [PERSON] and [PERSON] [17] as GNORM, which is well-established for general graphs and can be computed easily using BDgraph [33].
# Phase Imbalance Analysis of GF-3 Along-Track InSAR Data for Ocean Current Measurement
## 3 Data Processing and Interpretation
### Analysis of Channel Phase Imbalance  
In order to analyze the source of dual channel phase imbalance in GF-3 ATI mode and verify the effectiveness of the phase imbalance calibration method mentioned in Section 2.3, the first set and the second set of GF-3 ATI data were processed and analyzed in detail, where each set of data had 15 scenes. As shown in Figure 7, the phase imbalances of the two sets of GF-3 ATI data clearly changed monotonously with the range, which changed about 4.2\({}^{\circ}\) and 4.0\({}^{\circ}\), respectively, from near range to far range. Therefore, it is necessary to analyze and remove the phase imbalance in range for the high-precision current velocity measurement.
## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Traffic Optimization
#### 4.3.1 Packet Forwarding  
In contrast to load optimization, packet forwarding (routing) includes choosing the best route for packet flow among two ends depending on a variety of variables, including the network's layout and situation, routing regulations, routing protocol, etc. Each transmitting device's routing tables must be programmed by the controller, who computes pathways using a variety of parameters. As a result, routing applications give the control plane routing strategies to employ while calculating paths.  
In the distributed controller KDN paradigm, there exists an attack known as the black hole attack, where a malicious controller exchanges malicious paths with legitimate controllers and drops packets in crossing-domain path routing for the domain controlled by the malicious controller. In order to avoid the black hole attack, blockchain has been proposed, where each controller needs to upload an abstract topology to the blockchain using smart contracts to produce a true picture of the whole network, while trust is based on a reputation score [268].  
A route accuracy technique is used to rectify a Genetic Algorithm-based Routing (GAR) computation that also optimizes the nodes' utilization of energy and inspects for malicious nodes in the route by using the blockchain to maintain a malicious list of nodes [269]. Moreover, another work employing blockchain to make routing information traceable and immutable and using reinforcement learning to select trusted links evaluates a trustworthy routing technique for Wireless Sensor Networks (WSNs) [270]. Furthermore, Intelligent Edge Network Routing (ENIR) has been studied in [271], which learns knowledge using deep reinforcement learning for closed-loop routing control and optimization where blockchain is utilized to share knowledge and routing optimizations. Similarly, work in [272] shows that blockchain and Compact Deep Reinforcement Learning (CDRL) can be utilized to learn a routing policy where the student model is implemented off-chain to improve transaction efficiency. In contrast, FLEA-RPL is a fuzzy logic-driven energy aware routing approach that utilizes fuzzy logic to compute routing metrics, which decreases data traffic, increases network lifetime, and scrutinizes blockchain to reduce the number of data packet transfers for industrial IoT networks [273].
## 2 Theory and Model
### PSF Calculation for Jitter Effect  
The system jitter is modeled as a random tilt phase screen, which can be expressed as:  
\[\psi_{jitter}(x,y)=\exp\left(ik(\theta_{x}x+\theta_{y}y)\right) \tag{7}\]where \(\theta_{x},\theta_{y}\) are the angles between the optical axis direction and \(x\), \(y\) direction, respectively, \(k\) is wave vector, and \(i\) is imaginary unit. Considering that \(\theta_{x},\theta_{y}\) is a normal distribution with a mean value of 0 and a root mean square of \(\theta\), then the \(OTF\) of the jitter effect can be expressed as:  
\[\begin{array}{cc}OTF_{jitter}&=\left\langle\exp(i\psi_{jitter}(x_{1},y_{1})) exp(-i\psi_{jitter}(x_{2},y_{2}))\right\rangle\left|{{}_{\Delta x=\lambda zf_{x} \Delta y=\lambda zf_{y}}}\right.\\ &\qquad=\exp\Bigl{(}-\frac{1}{2}(\phi(x_{1},y_{1})-\phi(x_{2},y_{2}))^{2} \Bigr{)}\Big{|}{{}_{\Delta x=\lambda zf_{x}\Delta y=\lambda zf_{y}}}\\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad\left.=\exp\Bigl{(}-\frac{1}{2}k^{2 }\theta^{2}\Delta r^{2}\Bigr{)}\right|{{}_{\Delta r=\lambda zf}}\\ &\qquad\qquad\qquad\qquad\qquad\qquad\left.=\exp\bigl{(}-2\pi^{2}\theta^{2}z ^{2}f^{2}\bigr{)}\right.\end{array} \tag{8}\]  
where \(\Delta r=\sqrt{\left(x_{2}-x_{1}\right)^{2}+\left(y_{2}-y_{1}\right)^{2}}\), \(\Delta x=x_{2}-x_{1}\), \(\Delta y=y_{2}-y_{1}\), and \(f=\sqrt{f_{x}^{2}+f_{y}^{2}}\). The corresponding PSF in the spatial domain is:  
\[\begin{array}{cc}PSF_{jitter}=\exp\biggl{(}-\frac{r^{2}}{2\theta^{2}z^{2}} \biggr{)}\end{array} \tag{9}\]