esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Moment Tensor Solutions  
The second goal of this study was to calculate the source parameters for the two strong and moderate events with a local magnitude M\({}_{\rm L}\geq\) 4.0. The selected events presented a high signal-to-noise ratio, and we computed the moment tensor. Using the method of inversion for the seismic moment tensor, the Ammon code determined the seismic parameters [63]. It is noted that particular emphasis was given to stations close to the fault plane. However, due to their location, these stations contributed positively to the outcome of the inversion procedure.  
Seismological broadband data (in epicentral distances, \(\Delta<6^{\circ}\)) were selected and analyzed to determine the focal mechanism and the source parameters. In most cases, regional broadband data were selected, processed, and applied to the proposed methodology of at least six stations at an excellent azimuthal coverage. This was based on creating synthetic seismograms and directly comparing them with the observed ones for a given velocity structure. The reflectivity method of [PERSON] [64], as implemented by [PERSON] [65], was used, and a bandpass filter was applied to the data. Filter frequencies varied depending on the type of waveform: a bandpass filter of 0.1-0.2 Hz corner frequencies for traces recorded by local stations and 0.01-0.1 Hz for regional waveforms. The same bandpass filter was applied both on the observed waveforms and synthetics, having a fixed length of 70 s. The next step was the deconvolution of the instrument response from the waveforms, and the waveform conversion through integration produced pure displacement. Finally, the horizontal components with rotation were converted to radial and transverse waveforms. The three previous displacement components must be cut in the same starting time (nearly 10 s before the first arrival) with a total duration of 300 s.  
The inversion results indicate that inverting waveforms longer than 70 s resulted in higher misfits. The quality of the moment tensor solutions can be evaluated by considering the average misfit and the compensated linear vector dipole (CLVD). A quality code consists of the letters A-D [65] for each solution for the minimum misfit and between the numbers 1 and 4 for the percent of CLVD [66].  
The results of the inversion for the two main events as well as their comparison with those of other institutes/universities, are presented in Table 2. Only the DC part of the moment tensors is discussed because we did not consider the CLVD parts as having a physical meaning.
## 2 Experimental Materials and Methods
### Anisotropy in the Elastic Modulus  
The linear segment of the stress-strain curve in each triaxial incremental loading and unloading test was used to calculate the elastic modulus during each loading and unloading cycle. The formula used is as follows:  
\[E\ =\ \frac{\Delta\sigma}{\Delta\epsilon}, \tag{2}\]  
where \(E\) is the elastic modulus, \(\Delta\sigma\) is the stress difference of the linear segment, and \(\Delta\epsilon\) represents the difference in strain that corresponds to \(\Delta\sigma\).  
The elastic modulus of shale with the same \(\theta\) under the three different pressures followed a similar trend. Taking \(\sigma_{3}\) = 10 MPa as an example, the change curves describing the elastic modulus of shale under cyclic loading and unloading with different \(\theta\) are shown in Figure 7. The results indicate that during loading and unloading, the elastic modulus of shale reaches a maximum value when the \(\theta\) is \(0^{\circ}\) and a minimum value when the \(\theta\) is \(60^{\circ}\). In addition, the unloading modulus of elasticity exceeded the loading modulus of elasticity in each cycle. A gradual increase was observed in the loading elastic modulus over each successive cycle, leading to a decrease in the difference between loaded and unloaded elastic moduli; however, the rate of increase for the loading elastic modulus deceased over the same period.  
Figure 6: Variation curve showing compressive strength anisotropy under different confining pressures.  
Figure 7: Variation curve describing elastic modulus during cycling.  
The degree of anisotropy, \(R_{E}\), in the elastic modulus during loading and unloading is defined using the following:  
\[R_{E}\ =\ \frac{E_{(0)}}{E_{(\text{min})}}, \tag{3}\]  
where \(E_{(0)}\) represents the elastic modulus of shale with a \(0^{\circ}\) bedding inclination and \(E_{(\text{min})}\) denotes the minimum elastic modulus of shale with different bedding inclinations at the same stage under the same testing conditions.  
The variation curves obtained for \(R_{E}\) during cyclic loading and unloading under the three tested confining pressures are shown in Figure 8. The results show that the difference in \(R_{E}\) was more significant during the first loading stage than the other loading stages. Additionally, the \(R_{E}\) decreased as the confining pressure increased, with reductions of 38.79, 33.96, and 29.05% for confining pressures of 10, 20, and 30 MPa, respectively, observed during the fifth loading stage as compared to the first. The results thus indicate that the elastic modulus anisotropy of shale is weakened as the number of cycles of triaxial incremental cyclic loading and unloading increases; however, weakening as a result of confining pressure was observed only during the first loading and unloading stage.
## 3 Methodology
### Siamese-Based Approach: Feature-Level Change Detection
#### 3.3.1 Siamese Encoder Path  
A shared-weight encoder \(\mathcal{E}_{\theta}\), composed of \(L\) levels, processes both \(\mathbf{X}_{\text{pre}}\) and \(\mathbf{X}_{\text{post}}\). Each level \(i\) consists of convolutional blocks \(\mathcal{C}_{\theta}^{(i)}\) and a downsampling operator \(\mathcal{P}\). The feature maps \(\mathbf{f}^{(i)}\) generated at each level are defined as  
\[\mathbf{f}_{\text{pre}}^{(i)}=\begin{cases}\mathcal{C}_{\theta}^{(i)}(\mathbf{ X}_{\text{pre}})&\text{if $i=1$}\\ \mathcal{C}_{\theta}^{(i)}(\mathcal{P}(\mathbf{f}_{\text{pre}}^{(i-1)}))&\text{if $i \in\{2,\ldots,L\}$}\end{cases} \tag{6}\]  
(and identically for \(\mathbf{f}_{\text{post}}^{(i)}\) using \(\mathbf{X}_{\text{post}}\)).
# Quadrotor Stabilization and Tracking Using Nonlinear Surface Sliding Mode Control and Observer
###### Abstract  
We propose a control method wherein the estimated angles converge to the desired value for quadrotor attitude stabilization and position tracking. To improve the performance of a quadrotor system, the unmeasured states of the quadrotor are estimated using a sliding mode observer (SMO). We set up a quadrotor dynamic model and augment the quadrotor dynamics by an SMO. We also derive the control inputs by sliding mode control (SMC) and calculate the desired angle of the quadrotor to reach the target position with the control inputs. For fast convergence speed and increased robustness of tracking performance, a nonlinear sliding surface is applied to SMC. The angle of the quadrotor converges to the desired value through the operation of SMC with a nonlinear sliding surface. The target tracking performance is improved by adaptively switching the deceleration curve of the sliding mode surface with a nonlinear curve. Using a tracking system based on a nonlinear surface sliding mode control (NSMC) and SMO, the quadrotor reaches the target position with a decreased settling time. The performance and effectiveness of the proposed system are proved through simulation results.  
sliding mode control; sliding mode observer; quadrotor; stabilization; tracking control
# CTprintNet: An Accurate and Stable Deep Unfolding Approach for Few-View CT Reconstruction
## 5 Results and Discussion  
In this section, we show and analyze the performance of the proposed CTprintNet architecture for two different datasets composed of synthetic images with geometric elements and real medical images. Let us first describe the training procedure and the experimental setting.  
In all the implemented experiments, we used the same protocol to simulate the few-view geometry, i.e., a reduced scanning trajectory limited to 180 degrees with \(N_{\theta}=60\) scanning views that were equally distributed, with \(n_{p}=512\) and \(n_{p}=1024\) for the synthetic and realistic datasets, respectively. We constructed the test problem as  
\[\tilde{y}=Hx+\mathcal{N}(0,\sigma)\cdot N_{\theta}, \tag{30}\]  
where \(\mathcal{N}(0,\sigma)\) is a realization of a normally distributed random variable with a mean equal to zero and a standard deviation equal to \(\sigma\). As we will see in the results, different values of \(\sigma\) were considered in the training and testing phases. The sinogram was then used to compute the first iteration \(x_{0}=H^{T}y\).  
The results obtained with the proposed architecture were compared with both an iterative solver and a deep learning approach. For the first class of methods, we chose the scaled gradient projection method (SGP) [37], which has already been used several times to reconstruct CT images [38; 39; 40]. Regarding the choice of a competitor among the deep learning methods, it has been very hard for us to exploit existing unfolding architectures specifically designed for tomographic image reconstruction problems. Although algorithms of this type can be found in the literature (see, e.g., the references cited in the Introduction), the codes required to produce the results either are not available or require a notable amount of modifications to be usable. As a result of this, we decided to compare CTprintNet with an image-to-image approach constituted of a UNet residual network applied to a coarse FBP reconstruction. We will denote this framework as LPP in the following. In particular, the different LPP trainings were carried out on the same datasets used for CTprintNet by performing 100 epochs for the synthetic dataset and 50 for the realistic one.  
We evaluated the performances through qualitative visual comparisons and by quantitatively computing the peak signal-to-noise ratio (PSNR) and the SSIM [41].  
\begin{table}
\begin{tabular}{l l l l} \hline Parameters & \(\mathcal{L}_{0}\) & \((\mathcal{L}_{i})_{1\leq i\leq 29}\) & \(\mathcal{L}_{30}\circ\cdots\circ\mathcal{L}_{39}\circ\mathcal{L}_{f}\) \\ \hline epochs & 5 & 10 & 50 \\ \hline batchsize & 8 & 8 & 5 \\ \hline learning rate & \(10^{-1}\) & \(10^{-2}\) & \(10^{-1}\) \\ \hline \end{tabular}
\end{table}
Table 1: Training hyper-parameters.
## 3 Simulation Analysis  
According to the designed electrodes, COMSOL software was used to simulate the electric field gradient, DEP force and resultant force (DEP force and centripetal force combinedly act on abrasive). The diameter of the upper electrode was set as 60 mm, the electric field intensity was 300 V/mm and the frequency was 40 Hz. Figure 4 shows the simulation of the electric field gradient. It can be seen that the electric field gradient increases gradually from the center to the edge of the upper electrode, so the DEP force in the electric field increases gradually from the center to the edge of the upper electrode. Because the electrodes were axisymmetric, only the symmetrical parts were simulated. Figure 5 shows the magnitude and direction of DEP force on the abrasive between the upper and lower electrodes. It can be concluded that the abrasive between the electrodes will be subject to DEP force in an approximately vertical direction, and at the edge of the upper electrode, the DEP force is the largest, which is consistent with the simulation results of the electric field gradient.  
Figure 4: The electric field gradient in geometry.  
Figure 3: Schematic diagram of the DEPP apparatus.  
The movement of the SiO\({}_{2}\) abrasive under both DEP force and centrifugal force was simulated. As shown in Figure 6, 0.1 s was the initial state of the SiO\({}_{2}\) abrasive. At 1.35 s, some of the abrasive were aggregated on the lower surface of upper electrode by DEP force, and at the same time some were moved to the right by centrifugal force. At 2.85 s, more abrasive gathered on the lower surface of the upper electrode, and some abrasive were thrown out of the electric field area. At 1.35 s and 2.85 s, due to the larger DEP force at the edge of the upper electrode, much abrasive gathered on the edge and the upper surface of upper electrode. This phenomenon will not occur in actual processing, because the workpiece is located below the electrode, and the diameter of the workpiece is larger than that of the upper electrode, so the abrasive will only gather on the surface of the workpiece.  
The simulation results show that the abrasive can still move towards the surface of the workpiece when it is subjected to both DEP force and centrifugal force, which indicates that the idea of using the DEP effect of abrasive to assist polishing is feasible.
# Effect of Scents on Gazing Behavior and Choice
## 3 Results
### Product Effects on Gazing Behavior  
Analyzing the significant effects in more detail, univariate tests show that the product had a significant effect on FC in the muesli bar category and DC in the chocolate category (Table 3). Between product and choice, TTFF and FFD showed no significant effect, supporting appropriate randomization. Significant interactions were found for FD, FC and DD for all four product categories, and for DC for chocolate, yoghurt and muesli bars. There was also no significant interaction between product and odor for TTFF, but there was a significant interaction for FFD for the yoghurt, muesli bar and tea product categories. For the DC indicator, significant results were obtained only for the yoghurt product category. RMANOVA gives similar results in all four product categories, so we will only present them through the example of the yoghurt product group. The RMANOVA results of the other three product groups can be found in the supplementary materials (Figures S1-S3). Figure 3 shows whether participants chose the product that received the most visual attention. The figure shows that for all four products, participants chose the product that they spent the longest time looking at. Yoghurt, tea and muesli bars have a faint smell compared to chocolate. When we see a product photo of a strawberry yoghurt, the smell of strawberries comes to mind. Figure 4 clearly shows that the strawberry scent significantly increased (\(p<0.05\)) the FFD for the strawberry- and mango-flavored yoghurts. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Effect** & **Chocolate** & **Yoghurt** & **Muesli Bar** & **Tea** \\ \hline Product & 3.068 & 1.057 & 3.999 & **5.181 *** \\ \hline Choice & 0.854 & 0.974 & 0.923 & 1.919 \\ \hline Odor & 6 & 1.762 & 1.908 & 0.533 \\ \hline Product \(\times\) Choice & **1.673 *** & **1.527 *** & **1.723 *** & **1.134 *** \\ \hline Product \(\times\) Odor & 0.702 & **2.667 *** & 0.78 & 1.329 \\ \hline \hline \end{tabular} Bold and * indicates significant effect at a significant level of \(p<0.05\).  
\end{table} 
Table 2: Repeated measures ANOVA presenting the F-values of the three main effects and two of their interactions for the four product groups. Figure 2: Frequency of choice for all four choice sets with each four alternatives. * indicates a significant effect at a level of \(p<0.05\). Product categories had different flavors; only the strawberry flavor was common, which is colored in red. The length of the first fixation did not influence other eye-tracker parameters, so no higher values were recorded for FD, FC, DD and DC when the strawberry odor was present. However, the choice frequency of products was partially influenced by the odor in the case of chocolates (\(\chi^{2}\) = (3,6) = 13.675, \(p<0.05\)) and yoghurts (\(\chi^{2}\) = (3,6) = 110,077,703, \(p<0.001\)). For chocolate products, strawberry-, raspberry- and Oreo-flavored alternatives received higher choice frequencies when the odor was present, while the peanut-flavored alternative was chosen less frequently. For yoghurts, participants chose strawberry- and
## 2 Materials and Methods  
### Chemicals and Test Preparation of NP Solutions  
Ag NP (<150 nm, Lot # 06412 DE), ZnO NP (<100 nm, Lot # MKBJ7906V), Al\({}_{2}\)O\({}_{3}\) NP (<50 nm, Lot # BCBG4586V), dimethyl sulfoxide (DMSO), 17\(\beta\)-estradiol (E2), and amino acids needed for cell culture were purchased from Sigma Aldrich (Steinheim, Germany). Citrate-capped Ag NP (Agcit NP) was obtained from ABC Nanotech (Daejon, Korea). Yeast extract, agar, dextrose, and peptone were purchased from Bacto Laboratories Pty Ltd. (Mt Pritchard, NSW, Australia). Metallic NP stock solutions were prepared in deionized (DI) water by vigorously stirring for 24 h with a shaking machine and were stored in the dark until the beginning of experiments. Working solutions were obtained by serial dilution of stock solutions in DI water prior to the tests. To prevent aggregation of NPs, stock solutions were sonicated for at least 10 min before dilution. An aliquot of the prepared working solution was immediately transferred to quartz cuvettes, and the hydrodynamic sizes of NP dispersions were measured using a dynamic light scattering (DLS) method with an ELS-Z-2 analyzer (OTSUKA, Japan). Solution pH was unadjusted, all measurements were carried out at least in triplicate, and average values were reported.
# Pattern Recognition in Dried Milk Droplets Using Lacunarity and Deep Learning
## 2 Experimental Details
### Lacunarity Analysis of Dried Droplets  
The \"lacunarity\" \(\lambda\) quantifies the surface heterogeneity and roughness of an object at different scales [29; 33; 35]. Mathematically, lacunarity is formally defined as the nondimensional ratio of the second and first moments of mass distribution within a box of side length \(l\), defined as:  
\[\lambda(l)=1+\left(\frac{\sigma(l)}{\mu(l)}\right)^{2} \tag{1}\]  
where \(\mu(l)\) and \(\sigma(l)\) represent the mean and the standard deviation of pixel intensity in a specific region of a digital image. Lacunarity is estimated using the sliding box count method in computer vision (see Figure 1), where the number of pixels within a box of side length \(l\) is counted. We analyze 310 \(\times\) 310 pixel images using boxes of side \(l=\) 2, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, and 200 pixels, with corresponding total numbers of boxes to cover the image: 1763, 1763, 1680, 1520, 1368, 1224, 1088, 960, 840, 728, 624, 528, 440, 360, 288, 224, 168, 120, 80, 48, 24, and 8.
# Cartesian Aerial Manipulator with Compliant Arm
## 4 Estimation and Control
### Cartesian Aerial Manipulator Control Scheme  
The control scheme of the Cartesian aerial manipulator is shown in Figure 9. The Task Manager [2,7] is the high-level functional block that implements the functionalities of the robot, generating the appropriate control references for the manipulator and the aerial platform, relying on the low level controllers of the Cartesian manipulator (Sections 4.1 and 4.2) and the autopilot. Some of the tasks are:  
* For the multirotor: take-off, land, follow trajectory, go to workspace, approach reference position, interaction force control [25,27,33,34,35,37].
* For the manipulator: move manipulator to rest/operation position, object grasping [7,8] and store, sensor installation/retrieval [2,11], teleoperation.  
Since the aerial robot is intended to operate in contact with objects and the environment, it is necessary to estimate and monitor the interaction wrenches exerted by the manipulator to ensure the stability of the aerial platform, exploiting for this purpose the linear and angular deflection sensors of the compliant arm (see Figure 5). The Overload Detector, evaluated in Section 5.3, will report the Task Manager in case the arm is not able to retrieve a grabbed object, whereas the Wrench Estimator [25,27] can be used by the Attitude Controller to compensate for the external forces (see Section 5.4). In this way the attitude control is partially coupled with the manipulator control. The stability of the interaction control in the Cartesian configuration has been analyzed in terms of Lyapunov in [25,37].
## 2 UAV in Wireless Communication Networks
### Practical Applications of UAV Wireless Networks
#### 2.3.2 IoT and Wireless Sensor Networks  
A UAV can play an important role in IoT networks where small wireless devices such as sensors are connected, and these devices cannot transmit over a long distance due to battery constraints [64]. IMT-2020 and beyond for 5G network and METIS, the EU flagship 5G project, are required to provide the connectivity for \(10^{6}\) devices per 1 km\({}^{2}\)[2] and for 300,000 devices within one cell [61], respectively. In IoT networks, UAV can act as a static aerial BS for a small range of IoT communications, or as a moving aggregator to collect (deliver) a data from (to) IoT devices [48; 52; 65; 66]. Several challenges have been presented to utilize UAVs in IoT networks for ensuring a reliable connectivity to a moving IoT gateway [65], collecting data successfully from the distributed sensors [52], reducing energy assumption or shortening response time [66], and minimizing the transmit power at IoT devices by optimal clustering and trajectory of UAVs [48]. Most importantly, the deployment of a static UAV BS and the trajectory of a mobile UAV aggregator are the main issues to efficiently construct IoT networks and prolong the service time to disseminate (collect) data to (from) the distributed IoT devices or sensors.
## 2 Rock Mass Characteristics
### Intact Rock Parameters  
The planned auxiliary workings, located within a depth range of 957.00-1012.20 m, were predominantly excavated in compact rocks with uniaxial compressive strength (\(\sigma_{c}\)) ranging from 35.6 to 73.9 MPa, tensile strength (\(\sigma_{t}\)) varying from 3.8 to 8.9 MPa, and Rock Quality Designation (RQD) values ranging from 60% to 100%.  
Due to the lack of laboratory tests for deformation modulus (\(E_{i}\)) for the considered rock formations, the values of deformation modulus for intact rock were estimated using the modulus ratio (\(MR\)) using the following equation:  
\[E_{i}=MR\cdot\sigma_{c,i}. \tag{1}\]  
The modulus ratio values used in this study were based on laboratory tests conducted by [PERSON] et al. [7], who investigated the relationship between Young's modulus and uniaxial compressive strength for Carboniferous sedimentary rocks, including sandstones, mudstones, and claystones.  
The basic mechanical parameters of intact rock, along with RQD values for different lithological units in the area of Shaft II within a depth range of 957.00-1012.20 m, are summarized in Table 1.  
Possible geological uncertainties, such as spatial variability of rock mass properties and the potential presence of fault zones, were addressed by adopting conservative lower-bound values for geotechnical parameters derived from laboratory tests on intact rock samples. Additionally, safety factors in accordance with relevant regulations were applied in the later design stages to ensure the stability and reliability of the excavation and support systems under diverse geological conditions.
## 2 System Model
### SBS Service Cost  
#### 2.2.1 SBS Service Delay  
When a user sends an access request to the SBS, the user waits for the SBS to transmit content for the user according to the queuing theory, so the higher the traffic load of the SBS, the higher the resulting service delay, which is defined as [21]:  
\[d_{i,s}^{t}\big{(}\rho_{i}^{t}\big{)}=\frac{1}{1-\rho_{i}^{t}} \tag{9}\]