esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Propulsion System's Configuration  
The novel propulsion system from the studied BAUV employs a 3 DOF rotational platform with 3 UCU-1S limb configuration. Compared to other traditional configurations, the platform was designed for oscillatory motion only. With the oscillation from the platform a caudal fin is moved, producing the propulsion and the thunniform locomotion. The total flapping amplitude for this design ranges from \(\pm 30^{\circ}\). Larger flapping amplitudes and higher flapping frequencies result in higher speed and thrust. Figure 1 shows the mechanical design of the propeller and the parallel mechanism that compounds it.  
One main feature from this design is its capability to switch between swimming fashions. The moving platform orients the caudal fin according to the desired motion. This allows the vehicle to achieve a vectored thrust and replicate tuna-fish-like or dolphin-like swimming locomotion. From this design, several features should be pointed out:  
* The caudal fin is attached to the moving platform. Thrust to the vehicle will be generated by the water displacement produced by the caudal fin. Hence, proper motion control in the platform's oscillation and frequency is of great interest.
* Three limbs are responsible for moving the platform. The parallel mechanism contains three robotic arms with the same configuration. Each limb contains four passive (non-actuated) universal joints at the beginning and at the end where platforms are attached. One linear (prismatic) actuator is placed between such passive joints and is the responsible of the motion generation for each limb.
* The first limb always acts as a pivot. This configuration is helpful when controlling the flapping motion from the platform. This means that the oscillation from the moving platform will be caused by moving only limbs 2 and 3.
* No translational motion is desired. The incorporation of a fourth restrictive limb with spherical joint at its end links the origins of both platforms. This causes distance from both origins to always stay constant.
* The rotational motion of the platform is analyzed based on the Euler angles disposition and measured respect to an inertial frame fixed on the origin of the fixed platform. Considering that only limbs 2 and 3 cause the moving platform's oscillation, only two rotational motions of roll (\(\beta\)) and yaw (\(\gamma\)) are produced. The roll motion is produced over the Y axis and presents a total range from \(\pm 30\lx@math@degree\). Moreover, the yaw motion is produced over the Z axis and it takes values of 0 for vertical flapping and \(90\lx@math@degree\) for horizontal flapping. Finally, it is desired that rotational motion from the platform could reach flapping frequencies ranging from 0.5 to 5 Hz.
## 3 Key Technologies of UAV-Assisted SAGINs
### Resource Management
#### 3.4.1 Task Scheduling
Through the joint optimization of task sizes transmitted from the control center to UAVs, local computation tasks, and tasks offloaded by UAVs and mobile devices, they minimized energy consumption in distributed computing systems while ensuring computational queue stability. Additionally, some studies address QoS by proposing task scheduling methods based on fairness and communication security. For example, [PERSON] et al. [107] designed a task scheduling algorithm considering both fairness and security. [PERSON] et al. [108] proposed a satisfaction model based on user task delay requirements and residual energy states to jointly optimize task offloading decisions and UAV scheduling strategies. Similarly, [PERSON] et al. [109] constructed a general task scheduling framework considering diverse task types and requirements in SAGINs, integrating intelligent learning algorithms to jointly optimize task scheduling and resource allocation strategies.
# Dynamic Response of Rectangular Tunnels Embedded at Various Depths in Spatially Variable Soils
## 4 Results and Discussion
### Effect of Tunnel Embedment Depth on Excess Pore Water Pressure (EPWP) Ratio  
The excess pore water pressure (EPWP) ratio is defined as follows:  
\[P(t)=\frac{1}{n}{\sum{m_{i}(t)}} \tag{3}\]  
where \(m_{i}(t)\) is the excess static pore pressure ratio in each element, the value of \(i\) ranges from 1 to \(n\); \(n\) is the element number in a soil layer, \(n=50\) in this study.  
The deterministic case in which the soil was set as homogeneous material, and the 200 random cases considering the spatial variability of soil shear modulus were calculated in the conditions of four embedment depths of the tunnel. The excess pore water pressure ratio of the soil below the tunnel varying with time history under different embedment depths of the tunnel is given in Figure 5. As can be seen from Figure 5, the time history curves under different embedment depths of the tunnel exhibit a similar varying tendency. Negative excess pore water pressure continuing for about 1 s appears when the earthquake excitation is applied for approximately 3 s. Then, the excess pore water pressure ratio sharply increases and the rising speed slows down after about 6 s. The rising trend of the excess pore water pressure ratio lasts until the earthquake excitation is finished.  
Compared with the results of the deterministic analysis, the calculated excess pore water pressure of soil, which considers the spatial variability of soil shear modulus, is smaller than the mean value. Figure 6 demonstrates the comparison of time history curves of the mean excess pore pressure ratio at monitoring point A with random analyses under different tunnel embedment depths. It can be seen that the mean value of soil excess static pore pressure ratio shows a trend of gradual decline with the  
Figure 5: Comparison of the EPWP ratio under different embedment depths at Point A: (**a**) \(H=6\) m; (**b**) \(H=8\) m; (**c**) \(H=10\) m; (**d**) \(H=12\) m.  
tunnel when it reaches the peak value in the later stage of the earthquake. It indicates that the liquefaction degree of soil under the tunnel decreases with the embedment depth of the tunnel, which is consistent with the results of [PERSON] et al. (2019). It can be attributed to the variation in the positions of point A in the four cases. The initial vertical effective stress of point A increases with the increase of tunnel embedment depth, resulting in the decrease of liquefaction degree.
## 2 Data and Methods
### Methodology  
This article proposes a petrophysical properties prediction by enhancing the traditional EEI inversion with the stochastic process and neural network techniques. The workflow is figured in Figure 5.  
Figure 4: Petrophysical (porosity PHIE, water saturation SWE and volume of shale VSH) logs data from Well 1, Well 2 and Well 3 are used for inversion. Two yellow dashed-line rectangles locate two interested gas reservoirs.  
Figure 5: Workflow of inversion.  
EEI inversion is implemented for the first estimate of petrophysical properties. The result is then enhanced in terms of resolution and dimensionality using a stochastic process. The generalized regression neural network is trained to learn the relationship between the enhanced petrophysical EEI with the petrophysical log data. The trained GRNN is then used to predict the volumetric petrophysical properties, as the inversion final result, from where the Geobody of the reservoir is extracted and delineated.
# BRNN-LSTM for Initial Access in Millimeter Wave Communications
## 2 System Model
### Digital Beamforming Model at the BS  
Digital beamforming architectures are used at the BS due to the abundant input power. This is also necessary to support multi-user connectivity. Hence, consider a BS equipped with a ULA composed of \(N_{\text{BS}}\) antennas. In contrast to the MS design, each \(n_{\text{BS}}\) antenna here is connected to one RF chain, \(r_{\text{BS}}\). Note that the total number of antennas is equal to the number of RF chains and the number of transmit data streams \(D_{\text{tran}}\), i.e., \(N_{\text{BS}}=R_{\text{BS}}\)\(=D_{\text{tran}}\). Additionally, the overall radiated pattern from \(r_{\text{BS}}\) is represented by a beamforming vector, \(p_{\text{BS}}\), in the beamforming matrix, \(p_{\text{BS}}=p_{\text{bb}}\), where \(p_{\text{bb}}\) represents the baseband beamforming stage, i.e., \(p_{\text{bb}}\in\,C^{N_{\text{BS}}\,D_{\text{tran}}}\).
# Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
## 4 Volumetrics
### Algorithmic Approaches
#### 4.5.10 Heuristic Binary Tree Search (HBTS) Algorithm  
The application of the Heuristic Binary Tree Search (HBTS) Algorithm, as proposed by [PERSON] et al. [9] to address the 3D-BPP. The algorithm employs a Branch-and-Bound approach to address the problem of efficiently loading a set of three-dimensional boxes into a container with limited space.  
The HBTS algorithm commences by grouping the rectangular boxes into strips, which are subsequently organised into layers. These layers form the foundation for constructing a binary tree. Each node within the binary tree corresponds to a container-loading plan,defined as a set of layers. Notably, these layers can be either perpendicular or parallel to the left side of the container. The traversal of the binary tree aims to identify the solution with the highest total volume of packed boxes. The algorithm meticulously addresses constraints such as full support, orientation, and guillotine cutting. Guillotine cutting, a method employed where cutting is consistently made perpendicular to one of the sides of the items, ensures a systematic approach to accommodating boxes within the container. The termination criterion involves exceeding a predefined computational limit, upon which the best-found solution is returned.  
The efficacy of the HBTS algorithm is substantiated by [PERSON] et al. [9] through computational experiments, comparing its performance with existing methods, and the results manifest that HBTS excels, particularly in strongly heterogeneous instances. The evaluation includes filling rates and computational times, providing a comprehensive assessment of the algorithm's prowess in diverse scenarios.
# On the Use of Focused Incident Near-Field Beams in Microwave Imaging
## NF Beams and Results
### Interrogation of Objects by the Bessel Beam Launcher
Case III, see Figure A3c, includes a new OI that is a circular dielectric cylinder with the relative permittivity of four, height of \(\lambda/3\), and the radius of \(\lambda/4\) located about \(\lambda\) away from the center of the Bessel beam launcher. Finally, Case IV adds this dark green dielectric cylinder as an undesired scatterer to Case III. Figure 12 shows Case IV where the light and dark green cylinders represent the OI and undesired scatterer, respectively. In Case IV, the OI and undesired scatterer have the same dielectric property, and the undesired scatterer is offset by \((\lambda,\lambda/2,\lambda)\) with respect to the center of the OI. (The center of the OI is the origin of the coordinates.) Figure 11: The simulated Bessel beam launcher setup. The Bessel beam launcher (orange circular structure), introduced in [48], is parallel to the \(xz\) plane. A more detailed view of the capacitive sheet is shown in the inset. The brown plane (\(xy\) plane) contains the cross section of three dielectric boxes, all of which have the same relative permittivity of two. This figure represents our Case II study where the light green dielectric box, with the size of \(\lambda/2\times\lambda/4\times\lambda/3\), represents the OI, and the two dark green dielectric boxes, with the size of \(3\lambda/4\times\lambda/4\times\lambda/3\), serve as the undesired scatterers. Finally, it should be noted that all the simulations concerning Configuration II (i.e., in the absence of the Bessel beam launcher) have been preformed assuming that the dipole is horizontally oriented along the \(x\)-axis. Since neither a horizontal dipole nor a vertical one can generate a dominant \(E_{y}\) component (similar to the Bessel beam launcher), we have oriented the dipole along the \(x\)-axis so that a dominant \(E_{x}\) can be achieved in Configuration II. This enables us to compare two different incident fields (focus and non-focused ones) with their dominant electric field components lying in the imaging plane (\(xy\) plane), similar to the transverse electric microwave tomography.
## 2 Power Loss Analysis of Integrated Si LDMOSFET
\begin{split} C_{RSS}&=\text{C}_{\text{GD}}=\frac{W_{M} L_{M}\text{C}_{ox}}{L_{M}}+W_{M}\text{C}_{ov}\\ C_{ISS}&=\text{C}_{GD}+C_{GS}=W_{M}L_{M}\text{C}_{ox} +2W_{M}\text{C}_{ov}\\ C_{OSS}&=\text{C}_{GD}+C_{DS}=\frac{W_{M}L_{M}\text{C} _{ox}}{2}+W_{M}\text{C}_{ov}+\frac{W_{M}E_{M}\text{C}_{j}+(W_{M}+E_{M})C_{j_{ min}}}{2}\end{split} . \tag{8}\]  
Based on I-V characteristic of the integrated Si LDMOSFET in the deep linear area, the on-resistance value is given by  
\[R_{on}=\frac{1}{m_{n}\text{C}_{ox}\frac{W_{M}}{L_{M}}(V_{DD}-V_{th})}. \tag{9}\]  
Based on (8) and (9), (7) can be derived as  
\[ \begin{split}& P_{switch}=Mf_{s}(Y_{1}I_{0}W_{M}+Y_{2}W_{M}+X_{4}I_{0}+Y_{3 })+Y_{4}I_{0}^{2}\frac{1}{\text{MW}_{M}}\\ & Y_{1}=X_{1}(L_{M}\text{C}_{ox}+2\text{C}_{ov})+X_{2}(L_{M} \text{C}_{ox}+2\text{C}_{ov})\\ & Y_{2}=X_{3}\Big{(}\frac{L_{M}\text{C}_{ox}}{2}+\text{C}_{ov}+ \frac{E_{M}\text{C}_{j}+E_{j}\mu}{2}\Big{)}\\ & Y_{3}=X_{5}+X_{3}\frac{E_{M}\text{C}_{j_{min}}}{2},Y_{4}=X_{6} \frac{L_{M}}{m_{n}\text{C}_{ox}(V_{DD}-V_{th})}\end{split} , \tag{10}\]  
where coefficients \(Y_{1}\), \(Y_{2}\), \(Y_{3}\) and \(Y_{4}\) are obtained from \(X_{1}\), \(X_{2}\), \(X_{3}\), \(X_{5}\) and \(X_{6}\) by excluding the channel width, \(W_{M}\). In (10), the power loss of the switch is determined by switching frequency, \(f_{s}\), output current, \(I_{o}\), switch size, \(W_{M}\), and parallel quantity, \(M\). As usual, \(f_{s}\) and \(I_{o}\) are determined by the power supply requirements. In order to reduce power loss, \(W_{M}\) and \(M\) are optimized as follows. * \(M=1\): According to (10), taking \(V_{in}=5\) V and the 350 nm process as an example, the power loss is plotted with the integrated Si LDMOSFET size in Figure 5. It is obvious that there are different optimal sizes to minimize power loss at different switching frequencies and output currents [38]. This optimal size can be obtained by the simulation scan. * \(M\
eq 1\): In fact, switch size is limited by the process. Therefore, the power switch is composed of multiple units in parallel. According to (10), the power loss is plotted with the output current in Figure 6. The size of a single unit is optimized at \(f_{s}=10\) MHz and \(I_{o}=0.25\) A, according to Figure 5. In Figure 6, comparing \(M=4\) to all load current  
Figure 5: Power loss of the integrated Si LDMOSFET with size \(W_{M}\) when \(M=1\). ranges, the parallel quantity of power switch changing with drain-source current is more beneficial to improving overall efficiency [39].
# Review of Works Combining GNSS and InSAR in Europe
## 6 Conclusions  
A review of the published scientific contributions that focused on the European continent, excluding Turkey and Russia, combining GNSS (Global Navigation Satellite System) and InSAR (Interferometric Synthetic Aperture Radar) data, is reported here. A total of 191 scientific contributions were collected and analyzed in detail. The high number of contributions includes peer-reviewed articles and book chapters published in international journals, as well as the abstracts, conference proceedings, and extended abstracts of national and international congresses and symposiums.  
An analysis of the spatial and temporal distribution of the scientific contributions was completed for Europe, while a specific focus was dedicated to Italy, since it is the most represented country, with 47 scientific contributions. Italy is also the first nation that reported a study combining GNSS and InSAR data. In fact, the first research work was recorded in 1999 and investigated the geometry and slip distribution of the fault plane originated Colforito earthquakes (central Italy), occurred on 26 September 1997.  
Most of the joint applications in Europe, as also recognizable for Italy, focus on landslide, subsidence, and seismic/tectonic case studies. The spatial distribution mostly corresponds to the distribution of geohazards in Europe, e.g., subsidence phenomena investigated in Netherlands and volcano case studies in southern Italy, Iceland, and Spanish islands.  
Considering the free access of InSAR data by Ground Motion Services (GMS), a good correspondence can be recognized. This research is interesting since it is a good demonstration that the scientific community and administrative managing offices are interested in the remotely sensed analysis of ground deformation. This can be a good starting point for the continued development of improvements in this branch of remote sensing research and in the free accessibility of data policy. Scientific advancement by the scientific community, and consequently the local administrator and the entire population, could receive a great benefit from open access data both from the perspective of research and in confidence in the products.  
Conceptualization and writing, M.D.S.; bibliographic review and creation of the database, M.D.S., P.S., and P.C.; methodology and figures preparation, M.D.S. and P.C.; review and homogenization, S.B. and N.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms
## 1 Introduction
### Why Is Research of Diatoms Relevant?  
Beyond their contribution to the planet's ecology and even their intrinsic appeal, there are some aspects of diatoms' biology that make them important to researchers:  
* The shape and decoration of the frustule--especially the valves--are very particular for each species, and most diatoms can be identified based on their shape and ornaments.
* To survive, each diatom species is adapted to a relatively specific ecosystem. Thus, the quality of aquatic ecosystems can be inferred by obtaining indexes from the diatom species present [19; 20; 21; 22].
* They are livings beings with a siliceous inorganic cellular skeleton that is very resistant to decay (i.e., puttefraction), heat, and acids. Hence, they can be collected from seabed and lake sediments even millions of years after the cells have died.  
Therefore, the study of diatoms can be applied to ecological monitoring (e.g., freshwater quality) and recreating past environments (e.g., in archaeological, geological, and forensic research) [23]. All the applied analytical research depends on the correct identification of diatom species based on a purely systematic classification and evolutionary studies carried out by a few specialized diatomists in the world.  
In physiology studies, identification answers the question of which species correspond to an exemplar, and classification provides the features that allow differentiating species [3; 6]. This distinction is important since in image pattern recognition jargon, identification determines in the image if a diatom \"is present\" and \"where\", whereas classification answers the question of \"what diatom species\" is in the image.
# Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method
## 4 Control Strategy
### Design of AFSMFTC
: The Lyapunov function is defined as  
\[s_{1}(t)=e_{1}(t)+c_{1}\int_{0}^{t}e_{1}(t)dt \tag{18}\]1  
and the derivative of \(V_{2}\) is taken as  
\[s_{1}(t)=e_{1}(t)+c_{1}\int_{0}^{t}e_{1}(t)dt \tag{18}\]2  
Equations (34) and (35) are substituted into Equation (38) to obtain  
\[s_{1}(t)=e_{1}(t)+c_{1}\int_{0}^{t}e_{1}(t)dt \tag{18}\]3 \[s_{1}(t)=e_{1}(t)+c_{1}\int_{0}^{t}e_{1}(t)dt \tag{18}\]4 \[s_{1}(t)=e_{1}(t)+c_{1}\int_{0}^{t}e_{1}(t)dt \tag{18}\]5  
For \(\mathrm{sgn}(G)=\mathrm{sgn}(G\Lambda)\), the adaptive law \(\dot{\mathrm{P}}=\kappa s_{2}^{T}\lambda\mathrm{sgn}(G)\) is substituted into Equation (39) to obtain  
\[s_{1}(t)=e_{1}(t)+c_{1}\int_{0}^{t}e_{1}(t)dt \tag{18}\]6 \[s_{1}(t)=e_{1}(t)+c_{1}\int_{0}^{t}e_{1}(t)dt \tag{18}\]7 \[s_{1}(t)=e_{1}(t)+c_{1}\int_{0}^{t}e_{1}(t)dt \tag{18}\]8 \[s_{1}(t)=e_{1}(t)+c_{1}\int_{0}^{t}e_{1}(t)dt \tag{18}\]9  
According to the LaSalle invariant set theorem, if \(t\rightarrow\infty\), then \(e_{2}\to 0\). Hence, Theorem 2 is proved to be true. In summary, the AFSMFTC method is designed as follows:  
\[\left\{ \begin{aligned} x_{2}=B^{-1}\Big{(}-A+\dot{x}_{1d}-c_{1}e_{1 }-\hat{h}(s_{1})\Big{)}\\ u=-\hat{\mathrm{P}}\lambda=-\hat{\mathrm{P}}\big{(}F+ks_{2}+c_{2}e_{2}- \dot{x}_{2d}+\epsilon\mathrm{sgn}(s_{2})\big{)}\end{aligned} \right. \tag{41}\]  
To effectively weaken buffeting, the saturation function \(sat(s)\) substitutes the sign function \(\mathrm{sgn}(s)\), i.e.,\[sat(s)=\left\{ \begin{array}{ll}1&s>\Delta\\ ks&|s|\leq\Delta\\ -1&s<-\Delta\end{array} \right. \tag{42}\]  
where \(k=1/\Delta\) and \(\Delta\) is the boundary layer. \(\square\)  
The essence of using sliding-mode control of the saturation function is that switching control is applied outside the boundary layer so that the system state quickly approaches the sliding mode, while feedback control is applied inside the boundary layer to reduce the buffeting resulting from fast switching of the sliding mode and to ensure that the function \(s\) is long kept inside the boundary layer.