esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Research  
The principal-assistant collaboration problem with deadlines is investigated in this paper. Two scenarios are systemically discussed here, including the 1-principal-1-assistant scenario and the \(m\)-principal-\(u\)-assistant scenario. The hybrid combination algorithm is proposed to solve the 1-principal-1-assistant scenario, which combines different partial routes generated by three methods. Through comprehensive experiments, we validate the performance of the hybrid combination algorithm by comparing to several benchmark approaches. It can find a better route than the other algorithms compared except for the exact integer linear programming, but costs far less time than the integer linear programming. Therefore, this method can be feasible in 1-principal-1-assistant collaboration applications. A BP-based algorithm embedded with a density-based construction is proposed for the \(m\)-principal-\(u\)-assistant scenario. It uses the branch-and-price algorithm to find the optimal combination of the current route set and uses the density-based construction to enlarge the route set. Through comprehensive experiments, we compare the performance of the proposed algorithm with several benchmark approaches. In most cases, the proposed algorithm can serve the most vertices, although it takes more running time but is still reasonable. Therefore, the algorithm is practical in \(m\)-principal-\(u\)-assistant collaboration scenarios.  
In the future, we will mainly explore the adaptation of hybrid principal-assistant collaboration systems from two directions: (1) There are often multiple initial locations of agents in real applications, and multiple tasks may be in the same location; hence, one direction is to consider multiple initial vertices and more than one task in a vertex in the principal-assistant collaboration problem. (2) There are various collaborative relationships between principal agents and assistant agents in different environments; hence, the other direction is to improve the locomotion of the principal-assistant teams by considering appropriate collaboration pattern selection.  
Conceptualization, Y.Z. and K.D.; methodology, Y.Z., K.D. and H.X.; validation, K.D. and H.X.; formal analysis, Y.Z. and K.D.; writing--original draft preparation, Y.Z., K.D. and H.X.; writing--review and editing, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No.61807008, 61806053, 61932007, 62076060, and 61703097); and the Natural Science Foundation of Jiangsu Province of China (BK20180369, BK20180356, BK20201394, and BK20171363).  
The authors declare no conflict of interest.
# Frequency-Dependent Acoustic Reflection for Soil Classification in a Controlled Aquatic Environment
## 4 Discussion
### Comparison with Previous Studies  
Existing seabed classification techniques have primarily relied on backscatter intensity analysis, which, being an integral phenomenon, often lacks the sensitivity required to distinguish between sediments with modest differences in grain size and different grading structures. While traditional methods are effective in broad classifications, they struggle with fine-scale discrimination due to overlapping acoustic responses in materials of similar density and composition.  
A fundamental shift in approach is introduced in this study. Instead of relying on backscatter intensity, we analyze the coherent acoustic return spectrum--the outgoing to incoming energy ratio. This approach isolates the spectral response of the seabed alone, minimizing the influence of water column effects and isolating the impact of intrinsic factors on the reflection spectrum. Previous methods rely on backscatter intensity measurements, an integral quality that cannot adhere to subtle grain size, shape, and composition changes. The method presented here enables more precise classification, particularly in distinguishing between sediments that differ in grading and internal structure and more minor differences in grain size.  
The observed spectral distinction between SP and GP supports the validity of this new approach. The results indicate that grading characteristics, pore water content, and particle arrangement significantly impact energy scatter, transmission, and reflection. These findings suggest that existing classification methods could be substantially improved by integrating spectral-based approaches to enhance their accuracy and reliability.
## 2 Metal Lattice-Polyurea Parallel Composite Structure and Theoretical Modeling
### Theoretical Modeling  
A theoretical model for the metal lattice-polyurea parallel composite structure vibration isolation system, based on the system shown in Figure 1b, is established in Figure 2. In the model, the metal lattice structures and polyurea samples are represented by Kelvin models. In Figure 2, \(m\) represents the load mass of the system, while \(k_{0}\) and \(c_{0}\) are the stiffness and damping coefficient of the metal lattice structure, respectively. \(k_{1}\), \(c_{1}\), \(k_{2}\), and \(c_{2}\) denote the stiffness and damping coefficients of polyurea samples #1 and #2 under the pre-compression displacement \(x_{0}\). \(x_{1}\) and \(x_{2}\) correspond to the vibration displacements of the base and load mass.  
Figure 1: Models of polyurea damping structure and metal lattice–polyurea parallel composite structure vibration isolation system: (**a**) polyurea damping structure; (**b**) metal lattice–polyurea parallel composite structure vibration isolation system.  
Figure 2: Theoretical model of the metal lattice–polyurea parallel structure vibration isolation system.  
Since the connections between \(k_{1},c_{1},k_{2},c_{2}\), and the load mass are compression contacts, and the polyurea samples are identical with the same preload, \(k_{1}=k_{2}\) and \(c_{1}=c_{2}\). Therefore, the motion equation of the system is  
\[m\ddot{x}_{2}+2k_{0}(x_{2}-x_{1})+2c_{0}\big{(}\dot{x}_{2}-\dot{x}_{1}\big{)}+4 k_{1}(x_{2}-x_{1})+4c_{1}\big{(}\dot{x}_{2}-\dot{x}_{1}\big{)}=0 \tag{1}\]  
where \(\dot{x}_{1}\) and \(\dot{x}_{2}\) are the vibration velocities of the base and load mass, respectively, and \(\ddot{x}_{2}\) is the vibration acceleration of the load mass.  
Taking the Laplace transform of Equation (1) and simplifying yields the system's vibration transmissibility:  
\[H(s)=\frac{2(c_{0}+2c_{1})s+2(k_{0}+2k_{1})}{ms^{2}+(2c_{0}+4c_{1})s+2(k_{0}+2 k_{1})} \tag{2}\]  
The frequency-domain expression for the vibration transmissibility is given by  
\[H(\omega)=\bigg{|}\frac{X_{1}}{X_{2}}\bigg{|}=\sqrt{\frac{1+4\zeta^{2}_{0} \frac{\omega_{0}^{2}}{\omega_{0}^{2}}}{\bigg{(}1-\frac{\omega^{2}}{\omega_{0}^ {2}}\bigg{)}^{2}+4\zeta^{2}_{0}\frac{\omega_{0}^{2}}{\omega_{0}^{2}}}} \tag{3}\]  
where \(\omega_{0}\) is the system's natural frequency, \(\omega_{0}=\sqrt{k/m}\), and \(k\) is the equivalent stiffness, with \(k=2(k_{0}+2k_{1})\). \(\xi\) is the system's damping ratio, with \(\xi=c/(2\sqrt{\text{km}})\), and \(c\) is the equivalent damping coefficient, with \(c=2(c_{0}+2c_{1})\).  
When \(\omega=\omega_{0}\), the system resonates and the vibration transmissibility is the maximum. The damping ratio of the system can be calculated as  
\[\xi=\sqrt{\frac{1}{4(H_{\text{max}}^{2}-1)}} \tag{4}\]  
The shock response under half-sine wave excitation can be theoretically determined following the methodology in Ref. [30].
# UAV-Derived Himalayan Topography: Hazard Assessments and Comparison with Global DEM Products
## 2 Study Sites and Methods
### UAV Surveys  
A DJI Phantom 4 Pro+ with a 20 megapixel camera was manually flown at the AOIs during April and May 2018. The DJI Phantom 4 Pro+ does not have automated route-planning capability. The drone automatically captured near-nadir imagery every two seconds. Each image was automatically georeferenced with 3D coordinates from the UAV's global positioning system (GPS) and global navigation satellite system (GLONASS) receiver. The flight paths generally followed linear out-and-back routes at >100 m altitude. At Tal village, the flight was conducted from a police checkpoint overlooking the village from the southwest, which gave a vantage point \(\sim\)80 m vertically above the village. For the Lower Pisang AOI, flights were conducted from the road following the western bank of the Marsyangdi River and from an area of landslide runout to the south of the village. Ground control points (GCPs) were collected on prominent natural or human-made features using Emlid Reach RS global navigation satellite system (GNSS) L1 receivers. At Tal village, one receiver was mounted on a tripod to establish a temporary static base station, and one unit was used as a rover to occupy target features for \(\sim\)5 min. GNSS data were not collected for the Pisang landslides AOL GNSS data were processed using RTKLIB (RTKPOST v.2.4.3. Emlid b28) using precise GPS and GLONASS ephemeris data. First, the temporary base station data were processed against the closest operating UNAVCO permanent base station (Lamjung). Second, the rover data were processed against the corrected temporary base station location to extract GCPs for use in SfM-MVS software.  
Figure 1: Location of the two study sites in central Nepal. A hillshade generated from the Advanced Land Observing Satellite (ALOS) World 3D 30 m (AW3D30) digital elevation model (DEM) is shown on the main panel. True colour backgrounds from Planet Team [31] are shown on the insets for Tal village (PlanetScope 23 April 2018) and Pisang landslides (RapidEye 10 November 2017).
###### Abstract  
The field stress of the shale affects the proppant embedment, fracture conductivity, well production rate, and ultimately the recovery of hydrocarbons from reservoir formations. This paper presents, for the first time, an experimental study investigating the mechanical characteristics of a shale under confining pressures that simulate the in situ stress state in deep reservoirs. Bidirectional but equal confining pressures were applied to the shale sample to replicate its field stress state. Microindentation tests were conducted to assess the alterations of mechanical properties resulting from the application of confining pressures. The results demonstrate a significant increase in Young's modulus, hardness, and fracture toughness for the samples subjected to confining pressure. Considering the effect of confining pressure, the decrease in proppant embedment is proportional to Young's modulus of the shale. For larger-sized proppants (e.g., \(D=2.50\) mm), the influence of confining pressure on fracture conductivity is relatively minor. However, when smaller-sized proppants (e.g., \(D=1.00\) mm) are used, particularly in scenarios involving shale debris swelling due to prolonged interaction with fracturing fluid, there is a noticeable improvement in fracture conductivity. Importantly, previous computational models have tended to overestimate proppant embedment depth while underestimating fracture conductivity. The findings from this study contribute to advancing the understanding of shale's mechanical characteristics under in situ reservoir conditions and support the optimization of proppant embedment and fracture conductivity calculation models for the efficient extraction of shale gas.  
microindentation; confining pressure; shale gas; proppant embedment; fracture conductivity
## 4 Simulation Results
We can see that better performance can be achieved for CCT = 4500 K at low luminous flux, while smaller CCT values were preferred at a high illumination requirement to reduce power assumption. Figure 5: Error performance for the proposed system model and the conventional wavelength-division multiplexing (WDM)-VLC model under different crosstalk coefficients \(\epsilon\) at CCT = 5000 K.  
To sum up, the recent works still have some drawbacks for the practical multi-color LED-based VLC system. Based on the above-mentioned analysis, compared with the WDM-VLC technique without considering the crosstalk problem, the proposed scheme can maintain better error performance. Moreover, compared with the fixed chromaticity coordinate constraint scheme, the proposed optimal power allocation scheme with the quadrangle range chromaticity constraint can reduce the energy consumption while satisfying the illumination and communication requirements.
## 3 Results and Discussion
### Proposed Sensing Mechanism  
The basic sensing mechanism of resistance gas sensors is based on the variations of resistance in the presence of target gases. Since the present sensor exhibited an n-type nature, we will explain the sensing behavior using the electron depletion layer (EDL) concept. In fresh air, which is mainly composed of N\({}_{2}\) and O\({}_{2}\) gases, oxygen can be adsorbed on the sensor surface, and because of the high electron affinity of oxygen, electrons can be abstracted from the conduction band of SnO\({}_{2}\) as follows [42; 43]:  
\[\mathrm{O}_{2}(\mathrm{g})\rightarrow\mathrm{O}_{2}(\mathrm{ads}) \tag{1}\]  
\[\mathrm{O}_{2}(\mathrm{ads})+\mathrm{e}^{-}\rightarrow\mathrm{O}_{2}^{-} \tag{2}\]  
\[\mathrm{O}_{2}^{-}+\mathrm{e}^{-}\rightarrow\mathrm{2O}^{-} \tag{3}\]  
\[\mathrm{O}^{-}+\mathrm{e}^{-}\rightarrow\mathrm{O}^{2-} \tag{4}\]  
At low temperatures (T \(<\) 100 \({}^{\circ}\)C), molecular species are dominant, while at higher temperatures, atomic species are dominant [44]. At 300 \({}^{\circ}\)C, it can be assumed that O\({}^{-}\) is the dominant species [45]. Owing to the abstraction of electrons by oxygen ions, an EDL is created on the exposed surfaces of the SnO\({}_{2}\) particles, leading to an increase in the resistance of SnO\({}_{2}\) in air relative to that under vacuum conditions [46]. Thus, the initial high resistance of SnO\({}_{2}\) sensor in air is related to the adsorption of oxygen species and formation of EDL, where the concentration of electrons is much lower than the core parts of the sensor. Upon exposure to the H\({}_{2}\) gas, H\({}_{2}\) reacts with the adsorbed oxygen species as follows [47].  
\[\mathrm{H}_{2}+\mathrm{O}^{-}\rightarrow\mathrm{H}_{2}\mathrm{O}+\mathrm{e}^ {-} \tag{5}\]  
Figure 10: Selectivity histogram of optimized SnO\({}_{2}\) sensor at 300 \({}^{\circ}\)C to 10 ppm of different gases.  
Accordingly, water vapor is produced, and the electrons are liberated to the sensor surface. Owing to the release of electrons, the thickness of the EDL will be significantly decreased, which leads to an increase in the sensor conductivity or equivalently a decrease in the sensor resistance in the presence of H2 gas. Therefore, a sensing signal appears owing to the resistance modulation of the gas sensor in the presence of H2 gas. This mechanism is schematically illustrated in Figure 11a. In addition, at the contact points between the SnO2 particles, double Schottky barriers initially form in the air, leading to the formation of potential barriers for the flow of electrons from one grain to another grain. By subsequent exposure to H2 gas, and the release of electrons to the sensor surface, the height of these potential barriers decreases, contributing to the generation of a sensor signal (Figure 11b,c) [48]. In addition, because of the small size of the H2 molecules, they can easily diffuse into the deep parts of the sensor and occupy most of the available adsorption sites in the sensing layer. Hence, the sensor exhibits a relatively high response to small concentrations of H2 [49].
## 3 Results
### Ligand Binding Site and Protein-Protein Interface Determination in Ferredoxin I and II
There is one cluster constructed from members of each class/class union (\(D_{2}+D_{1}\), \(B_{2}\) and \(B_{2}+B_{1}\)). See description of Table 4 for information how to interpret this table. In FdI (Table 4), G23, G41 and V56 from class \(D_{2}\) yield two clusters of residues of sizes 6 (around V56) and 11 (around G23 and G41). More numerous of these clusters is located within the native P-P interface, encompassing 5 out of 7 of its members (6 if 1 residue distance in the sequence is permitted). Smaller one is closer to the ligand binding pocket, intersecting 3 (5 at 1-aa distance) out of 11 native P-L contacts. Class \(B_{2}\) is empty in FdI. Addition of neighbors of residues from class \(D_{1}\) (I12 and C14) increases size of the V56 cluster by 5 to 11, capturing all native P-P contacts. Number of true positive P-L results  
Figure 9: 3D presentation of results of FOD-based prediction of P-P and P-L interaction in FdI (IFXR) (**a,c,e**) and FdII (1 FXD) (**b,d,e**). Native interfaces are shown on (**a,b**): purple—P-P and green—P-L. Residues belonging to classes \(D_{2}\) and \(B_{2}\) (spheres) and surfaces marking their clustered spatial neighborhood are shown on (**c,d**). Each cluster is marked using different color. There are two class \(D_{2}\) clusters in FdI (orange and cyan) one class \(B_{2}\) cluster in FdII (orange). Surfaces on (**e,f**) denote the intersection of data from (**c,d**) with native contacts from (**a,b**)—the true positive results. around G23 and G41 increases to 8 (10) owing to A42 and its two neighbors. F4, Q8, E46, E48 and E49 from class \(B_{1}\) gather one large cluster, 29 residues in size. It intersects the native contact set to only a small extent: 3 (6) for P-P and 2 (5) for P-L, but at the same time produces a significantly large number of false positives. FdII displays lower result variety than FdI (Table 5). It has only a couple of class \(D_{1}\) members (M9 and A52), with one 10-residue cluster in their neighborhood that has 5 (7 at 1-aa distance) elements in common with native P-L interface out of 10 possible. Furthermore, 11 residues constitute the single cluster around K30, the class \(B_{2}\) representative. This cluster yields 3 (6) out of 7 true positive P-P contacts and 2 (3) true positive P-L contacts. V32 located close to K30 is also its member. When E3 and E12 from class \(B_{1}\) bring their neighborhood along the class \(B_{2}\) results, size of the resulting cluster grows to 25 residues. This switches E3 from gratuitous true positive to direct P-P hit and provides 8 (9) native P-L matches, but again with the cost of higher count of false positive output.
## 5 Conclusions  
For 5G C-RAN with heterogeneous traffic, the design of a dynamic network slicing system is a crucial issue. To address these problems with SSA, we have developed and analyzed an alternative adaptive slicing method (ASA). The goal of the proposed ASA system is to fairly distribute 5G CRAN network resources across three categories of slices (URLLC, eMBB, and mMTC slices). In addition, it needs to prioritize the various slices in a way that is both adaptable and dynamic, taking into account the values assigned to each slice's virtual resources and the current demand on the cell.  
Utilization, typical wait times, and throughput per patient (TBP) were used to describe and simulate the system model. The outcomes showed that the ASA method was superior to the SSA system across the board. Additionally, the DA-ASA system's delay factor improves the functionality of time-critical delay services. For instance, because the URLLC's maximum delay is smaller than the other slices', the URLLC's adaptive slicing priority (ASP) value drops as the URLLC's delay nears its maximum, leading to better performance. As a result, both ASA and DA-ASA systems maintain optimal performance for URLLC while guaranteeing sufficient quality of service (QoS) for each individual slice. Furthermore, the DA-ASA system is able to provide acceptable and good performance by striking a compromise between the maximum delay tolerance of other traffic and the high priority of URLLC users.  
The author extends his appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research. (IFKSURC-1-7116).  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author, upon reasonable request.  
Figure 29: TBP of network slices vs. total offered traffic loads in case 3 (Experiment 3).  
**Acknowledgments:** The author extend his appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research. (IFKSURC-1-7116).
# Sensors and Communication Simulation for Unmanned Traffic Management
## 5 Proposed Simulation Models for Key Elements
### Scenario Definition  
The different elements composing a simulation scenario have been identified in Section 4.2. Therefore, we are going to focus on describing some specific elements. First, we have to remember that all agents must be georeferenced. Additionally, we must be able to represent geographical shapes to define no-flight areas, flight plans, etc. To do so, the design decision is to use the GeoJSON [28] standard that allows defining points, lines, and polygons using JSON objects. Figure 5a, shows a randomly generated simulation environment with a series of communication nodes, sensors, and geofences whose position/shape is defined using this standard.  
Regarding the description of drone operations, we defined a flight plan as a succession of waypoints that allows to unequivocally define the drone's trajectory. Each point will have an associated series of additional height and time constraints. In order to compute this information taking into account both user-defined restrictions and drone dynamics, an operation planning process must be performed, as explained in [29]. This also includes a terrain avoidance step which uses information from the ground service. In addition, a flight plan will also have an associated unique identifier and a state to model the different steps and results of the authorization process. The identifying information (e-registration) of the drone to be used for the operation should also be included along with the type of operation (VLOS/BVLOS) and the priority of the flight plan. An example of a collection of automatically generated operations is provided in Figure 5b.  
Finally, the contingencies that were already identified in Section 3 (LOL, LOB, LOG, LOC, and sensor/cnode failure) can be added to a simulation scenario in the form of scheduled actions as defined in Section 4.2. Some additional pilot behaviors can also be added at this stage as action objects including:  
* changing control mode from automatic to manual and vice versa,
* stopping and resuming an operation,
* starting a return to home operation, and
* deviating from approved flight plan by flying a different operation that can be included as a secondary flight plan.