esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Instrument and Simulation Assumptions
### Simulation Assumptions
#### 3.2.3 Error Covariance Matrix  
As described in Section 2, we constructed the inversion framework as described in [10]. The state vector has only one parameter, which is  
\[\mathbf{x}=[H]^{T} \tag{12}\]  
At the same time, the other aerosol model surface parameters were retrieved from other satellite channels with certain retrieval errors under the known inversion framework. For the double-peak aerosol models, the vector is  
\[\mathbf{b}=\left[V_{0},V_{0}^{sm},r_{\text{eff}},r_{\text{eff}}^{sm},v_{\text{eff} },v_{\text{eff}}^{sm},m_{r},m_{i},m_{r}^{sm},m_{i}^{sm},\rho_{s},k_{1},k_{2} \right]^{T} \tag{13}\]  
where \(V_{0}\) and \(V_{0}^{sm}\), \(r_{\text{eff}}\) and \(r_{\text{eff}}^{sm}\), \(v_{\text{eff}}\) and \(v_{\text{eff}}^{sm}\), \(m_{r}\) and \(m_{r}^{sm}\), \(m_{i}\) and \(m_{i}^{sm}\) correspond to the total aerosol volume, effective radius, variance, as well as the real and imaginary parts of the refractive index in fine-mode (coarse-mode) or submicron fine mode (super-micron coarse mode); \(\rho_{s},k_{1},k_{2}\) are the improved BRDF model parameters.  
For the error covariance matrix, here, we assume that \(\sigma H=75\%\,H\); and the prior error covariance matrix is an identity matrix.  
\[\mathbf{S}_{a}=\left[\sigma_{H}^{2}\right], \tag{14}\]  
The prior error is 20% for \(V_{0}^{f}\) and \(V_{0}^{sm}\), and 10% for \(\rho_{s}\). For the fine-mode, the a priori errors \(r_{\text{eff}}\), \(v_{\text{eff}}\), \(m_{r}\), \(m_{i}\), \(k_{1}\),50%, 35%, and 35%, respectively. Here, the a priori error refers to the retrieval errors of SONET [41; 42]. The diagonal error covariance matrix of \(\mathbf{b}\) (\(S_{b}\)) is  
\[\mathbf{S}_{b}=\text{diag}\Big{(}\Big{[}\big{(}\Delta V_{0}\big{)}^{2}\,,\big{(} \Delta V_{0}^{sm}\big{)}^{2},\dots,\big{(}\Delta m_{i}^{sm}\big{)}^{2},\big{(} \Delta\rho_{s}\big{)}^{2},\dots,\big{(}\Delta k_{2}\big{)}^{2}\Big{]}\Big{)} \tag{15}\]  
The uncertainties of satellite measurements (\(\mathbf{S}_{y}\)), as part of the observation uncertainty, include systematic and random errors that occur during the instrument observation process. For the MAPMU instrument, we estimated radiance and polarized measurement errors of 5% and 2%, respectively. The correlations of observations between multiple viewing angles should be considered in multi-angle satellite observations, but polarization and intensity measurements can still be assumed to be independent. Therefore, \(\mathbf{S}_{y}\) is a 10 \(\times\) 10 off-diagonal matrix, and the details of the construction are shown in Equation (A1).
# Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing
## 2 Related Work
### The Binary-Objective Optimization Problem in SC  
According to task matching, the researchers in [34] proposed the multi-objective optimization problem to optimize only two objectives, namely, travel costs and task reliability in SC. Then, they reduced the problem to the minimum-cost maximum weight bipartite matching problem. To solve the proposed problem, they introduced the distance reliability ratio (DRR) approach, which is based on combinatorial fractional programming. Then, they extended this approach by adapting two algorithms, namely, the combinatorial multi-armed bandit model with semi-bandit learning, in order to estimate the reliability of a worker.  
Additionally, meta-heuristic algorithms were utilized to a limited extent to resolve the MOO problem [12; 35]. For instance, [PERSON] et al. [12] extended their proposed approach, which was introduced in [32], to solve the MOO problem in hyper-local spatial crowdsourcing using a genetic algorithm. The two objective optimizations were maximizing the coverage task and minimizing the highest workload across all workers to avoid worker overloading, all under budget variants. Meanwhile, MOPSO was used to solve the problem in [35]; this is a heterogeneous SC task allocation (HSC-TA) problem that optimizes only two objectives. In [35], MOPSO was used to simultaneously minimize the incentive cost and to maximize the task coverage under some constraints.  
Indeed, previous studies [12; 34; 35] addressed only two objectives for the optimization in SC. Unlike all of the abovementioned studies, we addressed TS-MOO in SC based on three objectives and produced a novel solution, i.e., the MOTSO model. Our model involves two main algorithms, namely, the MOPSO algorithm [19] and the ranking strategy algorithm, to enhance our MOPSO algorithm. This is the first model that aims to achievemulti-objective task scheduling based on three conflicting objective optimizations in an SC environment. Table 1 shows a summary of the literature for task assignment in SC.
# Synthetic Jet Actuators with the Same Cross-Sectional Area Orifices-Flow and Acoustic Aspects
## 3 Results and Discussion
### SPL Measurements  
Figure 9 presents the SPL for different SJA real powers at a characteristic frequency. The SPL was measured at \(P\) = 0.02; 0.05; 0.1; 0.2; 0.5; 1; 1.2; 1.5; 2; 2.5; 3; 3.5; 4; 5 W. However, the SPL at \(P\) < 1 W was lower than 46 dB for all orifice type. According to ISO 3746:2010 [38], the measured SPL should be 10 dB higher than the noise background level, which was 36 dB. With that in mind, only the measurements at \(P\) > 1.5 W can be considered compliant with the standard. However, the authors decided to extend the presented range of measurement data because it shows the same trend.  
The SPL was the lowest for the square orifice at all SJA powers. It is important that difference of the SPL for different orifice shapes was relatively constant. The SPL for the square orifice was about 5.7 \(\pm\) 0.3% (2.8 \(\pm\) 0.2 dB) lower than the SPL for the circular orifice and about 7.9 \(\pm\) 0.7% (4.2 \(\pm\) 0.5 dB) lower than the SPL for the slot orifice. The SPL for the slot orifice was higher about 2.7 \(\pm\) 0.8% (1.4 \(\pm\) 0.4 dB) than the SPL for the circular orifice.  
The SPL for different orifice types can also be compared for different frequencies [51]. For this reason, the SPL as a function of SJA frequency at \(P\) = 3 W is presented in Figure 10. Similarly, at the resonant frequency the lowest SPL was observed for the square orifice. This relationship was maintained in the entire studied frequency range. The slot orifice generated higher SPL than the circle orifice across the entire frequency range, except for frequencies close to 100 Hz.  
[PERSON] et al. [51] studied the SPL generated by SJAs with circular, elliptical, square, and elliptical orifices. The results presented in their work were not so clear-cut. The SPL generated by an actuator with a different orifice shape was strongly dependent on the frequency and on the basis of measurements presented by [PERSON] et al. In [51] it is impossible to clearly assess which orifice generated less noise. In the case of the research presented in this paper, however, it is unequivocally possible.  
Figure 10: The SPL as a function of SJA frequency at \(P\) = 3 W.  
Figure 9: The SPL for different SJA real power at a characteristic frequency.  
Additionally, [PERSON] et al. [51] showed that the higher the orifice, the greater the SPL. According to these studies, the shorter the orifice (in our case slot), the lower the noise level generated. This relationship is not maintained in the case of the presented measurements. It must therefore be concluded that it is the shape of the orifice that is decisive regarding the SPL and not the orifice.
# Smooth Bend Structures Using Hybrid Compensation for Common-Mode Noise Reduction
###### Abstract
Therefore, the width and spacing of the lines must be carefully controlled to avoid the situation mentioned above. The equivalent circuit model simulation differs slightly from that used in previous studies [5;7]; it includes the loss of coupled lines on symmetrical sections. The signal transmission includes the differential mode, common mode, reflection, and transmission terms. Figure 1 shows the mixed-mode S-parameters. The authors of [8] described another method involving an SMD capacitor and asymmetric coupled line to reduce the occupied compensation circuit area on the PCB. The design concept in [8] is similar to those presented in previous papers [5;7]. The asymmetric coupling line increases the inductance characteristics through narrower lines. In addition, SMD capacitors acquire more capacitance characteristics, which can achieve capacitance  
Figure 1: Mixed-mode S-parameters. and inductance compensation. However, the SMD components are more expensive and additional vias are required on the PCB, further increasing the cost. In another study [9], a stepped impedance line structure, also known as a slow-wave structure, was proposed. The design concept in this thesis was to change the velocity difference between the inner and outer traces on differential transmission lines by placing different values of capacitance and inductance on an inner trace. This is called a slow-wave structure because it changes the velocity to compensate for the timing shift in the time domain. The stepped-impedance line structure includes wide and narrow lines on the inner traces of differential transmission lines, representing capacitance and inductance. Only the width of the inner trace is adjusted to meet the value of velocity and improve the performance in the time domain. However, this must be considered and designed carefully because combinations of capacitance and inductance will lead to different continuous impedance effects. Finally, the authors of [10] proposed coupled lines with a right-angle bend, together with axis-aligned and non-axis-aligned stepped impedance lines. This paper uses the concepts described in [10,11] to propose a hybrid compensation design for common-mode noise suppression. Our paper includes several sections. In Section 2, we compare 90-degree, 135-degree, and 150-degree bend structures, analyze and discuss the 90-degree bend as well as two smooth bend structures, describe the equivalent circuit model verification, and set out the simulation results and extracted values in tables. In Section 3, based on our analysis of the three bend structures, we introduce and discuss four kinds of hybrid compensation design, including a simulation result in the time and frequency domain. Section 4 sets out the measurement validation to ensure that the proposed design works well in practice. Finally, our conclusions and the lessons learned from the results are presented in Section 5.
# Density Self-Adaptive Hybrid Clustering Routing Protocol for Wireless Sensor Networks
## 4 Performance Evaluation
### Simulation Results
#### 4.2.2 The Total Residual Energy of Nodes  
Figures 7 and 8 describe the total residual energy of the nodes. At the beginning, the energy of each node is 0.5 J, and it will gradually reduce as the rounds continue. As the energy of the node is the focus of the whole network, it plays a very important role in routing protocols. In the LEACH-C protocol, the nodes need to communicate with the BS each round, resulting in extra energy consumption. The O-LEACH protocol selects a distributed manner to choose CHs considering the remaining energy. However, it can lead to uneven distribution of the CHs. As a result, some nodes die quickly, changing the topology of the network as well. In our proposed approach, a distributed strategy is used to reduce the times of communicating with the BS. Nodes only need to communicate with the CH. As some nodes are far away from the BS, especially when it is outside, and the distance between member nodes and the CH is much shorter, the cost of the hybrid methods reduces enormously. Thus, the lifetime of this method is longer. From the two pictures, it can be seen that the remaining total energy of each round of the DAHC protocol is more than the other two regardless of the location of the BS, that is to say, the energy consumption is less, and the goal of saving energy is achieved.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Protocol** & **Round** \\ \hline DAHC & 1159 \\ LEACH-C & 707 \\ O-LEACH & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The round of the FND (first node dies) when the BS is inside.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Protocol** & **Round** \\ \hline DAHC & 703 \\ LEACH-C & 186 \\ O-LEACH & 422 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The round of the FND when the BS (base station) is outside.  
Figure 7: The residual energy of nodes versus rounds when the BS is inside.
# Editable Image Generation with Consistent Unsupervised Disentanglement Based on GAN
## 3 Theoretical Method and Simplified Calculation
### Hessian Penalty and Jacobian Penalty
#### 3.2.2 Jacobian Penalty  
Focusing on the Jacobian matrix, and to more clearly reflect the role of the input parameters and the implied variables on the model, the previous equation \(f_{G}(u,v)\) in Equation (3) is now replaced by \(f_{G}(x,z)\). It is well known that in such a deep generative model, if the correlation coefficients of changes due to different latent dimensions are zero, i.e., \(Cov(z_{i},\ldots,z_{j})=0\), then it is considered that each potential dimension controls the variation of only one variation factor. Using the Jacobian matrix mentioned in the kinetic Equation (4), combined with [14], using the Jacobian vector and constraining it to be orthogonal in different dimensions, we can obtain Equation (8).  
\[J_{ij}=\Big{[}\frac{\partial f_{G}(x,z)}{\partial z_{i}^{\prime}}\Big{]}^{T} \frac{f_{G}(x,z)}{\partial z_{j}^{\prime}}=0 \tag{8}\]  
In Equation (8), \(\partial z_{i}^{\prime}\) and \(\partial z_{j}^{\prime}\) represents the parameters of the function \(f_{G}(x,z)\) in different directions in the \(Z\) space. Then the \(G\) loss of Jacobian penalty becomes the sum of the squared term of Equation (8), and to reduce the computational effort following [43], we rewrite the formula using the Hutchinson estimate as Equation (9).  
\[\mathcal{L}_{J}(G)=Var_{v}\Big{[}\big{(}j_{d}v\big{)}^{T}j_{d}v\Big{]} \tag{9}\]  
In Equation (9),we use \(j_{d}=\frac{\partial f_{G}(x,z)}{\partial z}\) to represent a Jacobian vector. \(v\) is the Rademacher vector (each entry has an equal probability of \(-1\) or \(1\)) and \(Var\) denotes the variance. \(j_{d}v\) is the first-order directional derivative in the \(v\) times \(|v|\) direction. \(j_{d}v\) can be efficiently computed by the first-order finite difference approximation as Equation (10).  
\[j_{d}v=\frac{1}{\varepsilon}[G(z+\varepsilon v)-G(z)] \tag{10}\]  
The operation of this step (Equation (10)) is very similar to the simplification of the Hessian matrix ((Equation (7)), which allows our subsequent calculations to be simplified with similar operations, and it is easy to associate them with each other and the characteristics of their application on GAN networks. We will explain this in Section 3.3.
# Design and Experimental Verification of a PZT Pump with Streamlined Flow Tubes
## 4 Flow Field Simulation  
The 3D model of the fluid area was established by Solidworks (Solidworks2016, Dassault Systemes S.A, Waltham, MA, USA), and imported into Design Modeler (ANSYS, Canonsburg, PA, USA) to mesh. The meshing model was swept with a size of 0.1 mm, and the grids obtained were about 584,664 nodes and 569,800 elements. Finally, FLUENT (Fluent 18.1, ANSYS, Canonsburg, PA, USA) was used to conduct fluid simulation. The inlet and outlet pressure were set as 1500 Pa and 0 Pa respectively, during simulating fluid flow in forward direction. Vice versa in backward direction. The steady-state with \(k-\varepsilon\) calculation model was used; and the fluid medium was room-temperature liquid water.  
The pressure distribution and velocity streamline diagram of the streamlined flow tube are shown in Figures 4 and 5, respectively. It can be seen that no matter whether the fluid flowed in the forward or backward direction, the pressure distribution inside the flow tube began with increase, and then decrease. The trend of the simulated pressure results was basically consistent with that of the theoretical analysis. The trend of velocity change in the flow field was opposite to the trend of pressure change. In Figure 5, the flow rate decreased first and then increased, no matter whether the simulation was in forward or backward flow. The relationship between pressure and velocity satisfies the Bernoulli equation, and was consistent with the theoretical analysis. The simulation results of the backward flow show that a small number of vortices appeared near the maximum flow zone (Figure 5b), but there was no obvious vortex phenomenon in the forward flow (Figure 5a). It shows that the problem of vortex can be alleviated by changing the flow tendency of the streamlined flow tube.  
Figure 4: Pressure distribution of the streamlined flow tube. (**a**) Forward pressure distribution, (**b**) Backward pressure distribution.  
Figure 5: _Cont._
# CTprintNet: An Accurate and Stable Deep Unfolding Approach for Few-View CT Reconstruction
## 4 CTprintNet
### Forward/Backward Operators and Hyper-Parameter Setting  
The iterative methods for the reconstruction of tomographic images require two linear operators, usually referred to as the forward and the backward (or back-projection) projectors, which describe the geometry and the physics of the problem. In order to make the best use of the hardware and the memory hierarchy, it is common to use different discretization techniques for the two operators. This choice implies that the standard routines implementing discretized versions of the Radon transform and its adjoint (such as, e.g., the radon and iradon functions in Matlab's Image Processing Toolbox and Python's scikit-image one) lead to an unmatched projector/back-projector pair, in which the matrix representing the back-projection is not exactly equal to the transposed matrix of the projector operator. Although this mismatch contributes to speeding every iteration up and to reducing the global computational cost, it can lead to a less accurate reconstruction since the convergence properties of the iterative method are lost [35; 36]. In order to overcome this issue, we discretized the projection operator with a ray tracing algorithm and stored it in memory as a sparse row matrix, \(H\). We then used its exact transpose, \(H^{\mathcal{T}}\), as a back-projection. Since we could not make use of the built-in implementation of the PyTorch  
Figure 4: Diagram of the final layer \(\mathcal{L}_{f}\).  
functions acting on tensors, we implemented the functions necessary to compute the partial derivatives, which are essential during the back-propagation algorithm.  
This choice represents the crucial difference with respect to iRestNet, in which this matter was not an issue since the convolution operator and its adjoint can be easily and exactly executed through fast Fourier transforms and the conjugate operator. Moreover, it strongly affects the choice of some hyper-parameters, such as the number of epochs and the batch size, which had to be set to lower values with respect to the iRestNet values due to the lower amount of available memory, especially in the end-to-end training of the final \(\mathcal{L}_{n}\circ\cdots\circ\mathcal{L}_{K-1}\circ\mathcal{L}_{f}\) block. Their final values, together with the learning rate values, were set following several experimental tests and are reported in Table 1.  
All the training and tests of the network were performed on an Nvidia RTX A4000 GPU. We have chosen the mean square error (MSE) as a loss function in the learning process and stochastic gradient descent as an optimizer, which performed better with respect to the structural similarity index measure (SSIM) and the Adam optimizer exploited in iRestNet.
# An SSI-Based Solution to Support Lawful Interception
## 2 Background
### Self-Sovereign Identity
#### 2.2.1 Verifiable Credentials  
The Verifiable Credential (VC) is a set of information that some authority claims to be true about a specific entity that is the subject of the credential. In SSI, the authority is called issuer, while the subject of the VC is called holder. The holder can prove something about themselves by presenting their VC to a verifier. At a high level, the structure of the VC includes four sections:  
* Identifier of the VC;
* VC metadata (e.g., issue date, expiration date, state of the credential);
* VC's claims about the subject;
* Proof, which is the digital signature of the issuer of the credential. It can be verified through the blockchain.
# Ferrite-Loaded Spidron Fractal Loop VHF Antenna for UAV Applications
## 4 Conclusions  
A ferrite-loaded Spidron fractal loop VHF antenna for UAV applications is proposed. A ferrite material with high permeability was chosen to improve the gain of the antenna. To solve the gain reduction problem when applying the ferrite in the high frequency band, the configuration of ferrite is optimized by a GA. To consider the weight of the ferrite, the number of stacked layers and the amount of the ferrite are restricted. Through optimization, the gain improvement in the low frequency band is maintained and the gain reduction in the high frequency band is minimized. The realized peak gains of the fabricated proposed antenna with a 0.3 m \(\times\) 0.3 m ground plane vary from \(-31.6\) to \(-1.9\) dBi within  
Figure 11: Simulated radiation patterns of the proposed antenna mounted on a UAV at 60, 180, 300 MHz: (a) \(xz\)-plane; (b) \(yz\)-plane.  
Figure 10: Simulated realized peak gains of the proposed antenna on a UAV and on flat ground.  
the measured frequency range. Measured radiation patterns are omni-directional at all frequencies. The characteristics of the peak gain and radiation pattern of the proposed antenna mounted on a UAV were also analyzed. The proposed antenna is extremely small at 0.0242 \(\times\) 0.0242 \(\times\) 0.0051 \(\lambda_{L}^{3}\) in size at the lowest operating frequency. Therefore, the proposed antenna can be used for UAV applications as a receiving antenna.  
Writing, W.B.P.; Antenna design and simulation, W.B.P.; Laboratory tests, W.B.P.; Methodology, Y.-M.P. and K.C.H.; Writing Review and Editing, W.B.P. and K.C.H.; Conceptualization, W.B.P., Y.-M.P. and K.C.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the research fund of Signal Intelligence Research Center supervised by the Defense Acquisition Program Administration and the Agency for Defense Development of Korea.  
The authors declare no conflict of interest.