esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper proposed effective solutions for the problems encountered in the UAV delivery process, such as complex route planning, low detection accuracy, and hardware limitations, which have essential theoretical value and broad application prospects. Integrating bionic optimization algorithms and image processing technology improves the efficiency and accuracy of material delivery and plays a vital role in enhancing emergency response capabilities. In the simulation conducted in Texas, USA, the Whale-Swarm Hybrid Algorithm, when optimizing paths, reduced the average optimal path cost by about 9.08%, the worst path cost by about 10.58%, the average path cost by about 9.69%, and the difference between the best and worst path costs by about 34.39%, compared to the single IWOA algorithm. In target detection for emergency airdrop stations, the APCR-YOLOv8 model, by integrating SimAM, a lightweight backbone, and a lightweight LSCDHead, reduced Params and FLOPs by 44.33% and 34.57%, respectively, increased [EMAIL_ADDRESS]/% from 88.5 to 92.4, and achieved an FPS of 151.3, effectively addressing the original model's issues of missed detection, false detection, and duplicate detection while meeting real-time requirements. Despite the apparent success of the current research work, some limitations still exist. The adaptability of the currently proposed algorithms in highly complex environments or specific situations still needs to be strengthened. Future research will focus on further exploring the multiple GTSP issues of multiple UAV cooperative material delivery, aiming to improve the efficiency of material delivery and enhance the model's adaptability. In addition, we plan to deploy the algorithm to different UAV platforms to broaden its application scope further.  
Conceptualization, H.L. and Y.W.; Data curation, Z.W. and J.Y.; Formal analysis, Y.W. and J.Q.; Methodology, H.L., Y.W. and J.Y.; Resources, H.L.; Software, Q.W. and X.S.; Validation, Z.W. and Q.W.; Writing--original draft, Y.W.; Writing--review and editing, H.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Anhui Provincial Natural Science Foundation (No. 2308085 MF218) and the open Foundation of Anhui Engineering Research Center of Intelligent Perception and Elderly Care (No. 2022 OPB01).  
Not applicable.  
The data supporting the conclusions of this paper are accessible from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
The authors would like to thank each team member sincerely for their efforts.
## 5 Joint Optimization of Resource Allocation and Computation-Offloading Decisions  
Here, we describe the algorithm used for joint optimization of resource allocation and computation-offloading decisions using PSO to address the problem in Equation (16). In particular, Equation (16) is an MINLP problem of NP-hard complexity [10; 34] based on the presence of both continuous (resource allocation profile \(F\)) and discrete (offloading decision profiles \(Y\) and \(\mathbf{Z}\)) variables. To address this problem, we divided it into two subproblems: (1) computational resource allocation, which is a convex problem for a given offloading decision and solved by KKT optimality conditions, and (2) computation-offloading decision, which is solved by binary PSO. PSO has the advantages of easy implementation and fast convergence, as well as the ability to escape from local optima and to converge to an approximate global optimum [27; 28]. Therefore, to solve Equation (16), we developed a PSO-based algorithm to jointly optimize resource allocation and computation-offloading decisions (JROPSO) in order to minimize total computing overhead of MDs.
## 2 Channel Estimation in OFDM
### Data Interface  
This article aims at modelling realistic UWA channels through BELLHOP model, which means the simulation platform should implement the constructing of input files with real data. Although modelling in measurement-based approaches is a complex process with numerous parameters that need to be determined, there are lots of open databases available on the Internet that can be interfaced through script functions. The data flow diagram of simulation platform is illustrated in Figure 4.  
In the channel modelling, users enter basic channel parameters such as position, frequency, and number of beams to the GUI. After getting the data of position, the simulation platform will be able to read real data about sea surface boundary, SSP, and sea bottom boundary from the databases. Sea surface boundary is a stochastic parameter that can be generated from the Gauss-Lagrange model. The function of Gauss-Lagrange is as follows,  
\[x(t,u)=\sum_{j=0}^{N}\sqrt{S_{j}\Delta\omega}\rho_{j}R_{j}\cos(k_{j}u-\omega_{ j}t+\Theta_{j}+\theta_{j}) \tag{12}\]  
where \(\rho_{j}\) is the amplitude response and \(\theta_{j}\) is the phase response. WafoL [33], a toolbox of MATLAB, is used to solve the function and generate Gauss-Lagrange waves. In order to make the waves closer to reality, amplitudes of Gauss-Lagrange waves should be adjusted according to real SWH, which can be obtained from altimetry satellite missions in Aviso+ [34]. The speed of sound in sea water is the basic variable of acoustic channel, which is affected by many factors such as water temperature, salinity, and depth. World Ocean Atlas 2013 (WOA2013) [35] is a data product of National Oceanic and Atmospheric Administration (NOAA) where the ocean properties can be accessed with the index of time and position. With the real data, SSP in any geographical location can be calculated by the UNESCO equation [36]. Sea bottom boundary can reflect and scatter the sound ray. According to Google Map API [37], the simulation platform gets a set of discrete depth samples in order to interpolate the sea bottom terrain.  
After getting the values of sea surface boundary, SSP, and sea bottom boundary, the simulation platform will be able to construct BELLHOP files (environment file, altimetry file, bathymetry file, etc.) and calculate the eigenray coordinates, travel time and amplitudes of UWA channel. Therefore, the eigenrays tracing is plotted on the simulation platform with the coordinates. Furthermore, the CIR is calculated by accumulating travel time and amplitudes for each multipath. For the sake of convenient simulation in OFDM communication system, the CIR is normalized and saved in channel list. In OFDM  
Figure 4: Data flow diagram.  
channel estimation, the modelled channels are simulated with Monte Carlo method based on specific input parameters of channel estimation and the BER and MSE curves are plotted for analyses.
# A Low-Profile Dielectric Resonator Antenna Array for OAM Waves Generation at 5G NR Bands
## 3 Results and Discussion
### Reflection Coefficient (\(S_{11}\))  
The measurement setup to measure the reflection coefficient (\(S_{11}\)) and bandwidth of the proposed antenna is shown in Figure 5a. A Keysight Technologies E5071C E-Series vector network analyzer was employed to carry out the measurement. This type of vector network  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameters** & **Values (mm)** & **Parameters** & **Values (mm)** & **Parameters** & **Values (mm)** \\ \hline \(L_{s}\) & 160 & \(W_{dr}\) & 22 & \(L_{slot}\) & 36 \\ \(W_{s}\) & 185 & \(h_{dr}\) & 32 & \(W_{slot}\) & 1.5 \\ \(L_{dr}\) & 22 & \(h\) & 1.6 & \(R\) & 77 \\ \hline \end{tabular}
\end{table}
Table 1: The dimensions of the proposed DRA.  
Figure 4: Fabricated prototype. (**a**) Front view; (**b**) back view.  
Figure 3: Simulated results of the feeding network. (**a**) Phase shifts between the elements; (**b**) amplitude.  
analyzer is an integrated network analyzer with two or four ports and an S-parameter test set and covers a wide frequency range, from 300 KHz to 20 GHz. The simulated and measured reflection coefficient for the proposed antenna is shown in Figure 5b. It was observed that the measured reflection coefficient values range from 3.2 to 3.66 GHz, which are slightly greater than the simulated reflection coefficient values. Nevertheless, the proposed design has an acceptable reflection coefficient value and at least 80 to 100 MHz bandwidth as these are the conditions of 5G NR band deployment.
## 2 Time Series Modeling  
### Auto-Regressive Series  
In classical TS modeling, the relationships between different variables are established through regression analyses to link different target observations. The AR series is a family of regression models used for general TS problems [16], which includes AR, moving-average (MA), ARMA, ARX, ARIMA, etc. There are several SHM applications using these models but with fewer cases of ARIMA, which is the focus of this study. We denote the basic AR model with order \(p\) as AR(\(p\)), which aims to determine the dependent relationship of an observation with the order \(p\) of lagged observations. Similarly, we denote the MA model with order \(q\) as MA(\(q\)), which aims to determine the dependent relationship of the observation with order \(q\) of observed white noise error terms.  
\[\text{AR}(p):\ \ x(t)=\sum_{i=1}^{p}\alpha_{i}x(t-i)+\epsilon(t)+c, \tag{1}\]  
\[\text{MA}(q):\ \ x(t)=\epsilon(t)+\sum_{i=1}^{q}\beta_{i}\epsilon(t-i), \tag{2}\]  
where the TS \(x\) at the current discrete time \(t\) is \(x(t)\), which depends on the values at the previous time steps, i.e., \(x(t-i)\), \(\alpha_{i}\) and \(\beta_{i}\) are the coefficients corresponding to the \(i\)-th order term of AR(\(p\)) and MA(\(q\)), respectively; the white noise \(\epsilon\) at time \(t\) is \(\epsilon(t)\); and \(c\) is a constant.
# Decomposition of Multicomponent Micro-Doppler Signals Based on HHT-AMD
## 3 Decomposition Method Based on HHT-AMD
### Signal Decomposition Algorithm  
The signal decomposition algorithm combined HHT with AMD method is proposed to decompose multicomponent micro-Doppler signals. Firstly, EMD is applied to decompose the signal into several intrinsic mode functions. The frequency spectrum of the signal and the frequency values are obtained by Hilbert transform. Then, AMD method separates the signal component with frequency aliasing to single-frequency components. In detail, the algorithm process is described as the following steps.  
Step 1: To decompose the original signals by EMD and get the time spectrum and marginal spectrum by Hilbert transform.  
Step 2: The frequency components are obtained by searching the peak values of the spectrum. Thus, the signal components are extracted.  
Step 3: To distinguish whether there is frequency aliasing phenomenon in each extracted signal.  
Step 4: If there is frequency aliasing in a signal component, we can firstly use the frequency value as the dimidiate frequency to decompose it by AMD. By the analysis of the correlation coefficient, the frequency range turns narrow and the search step is reduced, until the best dimidiate frequency value is found, and the signal is successfully decomposed into two signals.  
Step 5: To repeat the steps 2, 3, and 4 for the two signals, until there is no frequency aliasing for all the extracted signals.  
The process of this algorithm is shown in Figure 3.  
Figure 2: AMD (analytical mode decomposition) signal extraction principle diagram.
## 5 Performance Analysis  
### Message Complexity  
The message complexity of the PEAFC-CL protocol is \(O(N)\). In the network initialization stage, there is one Beacon broadcast message, \(N_{active}\) probe request explorer packets and one \(START\_MSG\) start data packet. In the clustering stage, the CH formation rate is \(c\%\); thus, there are \(c\%N_{active}\) CHs broadcasting \(c\%N_{active}\) messages to the surrounding nodes, and \((1-c\%)N_{active}\) member nodes broadcasting the message of applying to participate in the cluster set. Because the inter-cluster single-hop communication mode is adopted, each CH directly transmits data directly, and the total number of messages sent in the network is:  
\[2+N_{active}+c\%N_{active}+(1-c\%)N_{active}+1=3+2N_{active} \tag{19}\]  
Therefore, it can be proven that the message complexity of the PEAFC-CL algorithm is \(O(N)\).  
A comparison of the simulation results and analytical values is presented in Figure 6. The number of messages controlled in the clustering protocol is largely consistent with that of the theoretical analysis, and the existing minor errors are mainly from the differences in CH number.
## 4 Surface Wave Motion Generated by a Time-Harmonic Point Load  
This is a three-dimensional problem. The surface wave motion generated by a time-harmonic point load can be calculated in a simple manner using the reciprocity theorem, with input being the actual surface wave with an unknown amplitude and a virtual free surface wave [30; 31]. The method requires expressions for the displacements and the stresses of free surface waves, preferably in an analytical form, but numerically obtained forms can also be used. Suppose that the half-space is subjected to a time-harmonic point load at the surface pointing in an arbitrary direction. Without loss of generality, the coordinate system can be chosen such that the load acts in the \(xz\)-plane at the origin of the system. The surface wave response is then sought as the superposition of the responses due to the normal component of magnitude \(P\) in the \(z\)-direction and the horizontal component of magnitude \(Q\) in the \(x\)-direction.  
For surface wave motions radiated from a point load, it is convenient to also use a cylindrical coordinate \((r,\theta,z)\) defined by \(x=r\cos\theta,\ y=r\sin\theta\), and \(z=z\) along with the Cartesian coordinates \((x,y,z)\). Solutions of surface wave motion by the reciprocity approach are obtained in [PERSON] et al. [30]. Here, we summarize the results, which are later used for solving the three-dimensional scattering problem. The vertical displacement of surface waves generated by a vertical loading \(P\) in the \(z\)-direction may be written as:  
\[u_{z}^{P}=\frac{k}{4i}\frac{PW(0)}{\mu E}W(z)H_{0}^{(1)}(kr). \tag{2}\]For a horizontal load \(Q\) in the \(x\)-direction, the generated displacement is of the form:  
\[u_{z}^{Q}=\frac{k}{4i}\frac{QU(0)}{\mu E}W(z)H_{1}^{(1)}(kr)\cos\theta. \tag{3}\]  
In Equations (2) and (3), \(H_{j}^{(i)}(\xi)\) represents the \(j^{\text{th}}\) order Hankel function of the \(i^{\text{th}}\) kind and \(E\) is a dimensionless quantity determined using:  
\[E=\Bigg{(}\frac{d_{4}d_{1}-d_{6}d_{3}}{2p}+\frac{d_{4}d_{2}+d_{5}d_{1}-d_{7}d_ {3}+d_{6}}{p+q}+\frac{d_{2}d_{5}+d_{7}}{2q}\Bigg{)}. \tag{4}\]
## 4 Conclusions  
This study highlights that tortuosity estimation must be lithology-dependent. The empirical model based on Equation (17) provides the best overall agreement with streamline-based tortuosity and is most suitable for sandstones, offering a balance between simplicity and flow-relevance. [PERSON]'s algorithm performs better in carbonates due to its sensitivity to geometric complexity, making it a valuable tool when flow path geometry dominates. In contrast, Equation (13), while computationally simple, is only reliable for high-porosity materials (typically above 50%) and is not recommended for general use. For fast, large-scale screening where moderate accuracy is acceptable, Equation (17) is preferred. Overall, these distinctions help guide the practical application of tortuosity models in different rock types, supporting more informed engineering decisions. The observed weak correlation between porosity and tortuosity is not a limitation but confirms the complex physics of porous media and demonstrates that porosity alone is insufficient to predict fluid flow behavior. Therefore, incorporating additional metrics such as coordination number, throat length, and pore network structure is essential. The analysis of 1963 samples revealed that carbonates exhibit greater heterogeneity compared to sandstones. Future research should focus on hybridizing geometric and streamline approaches, potentially leveraging machine learning to enhance tortuosity prediction accuracy across lithologies.  
[PERSON] and [PERSON] conducted the experiments, including sample collection and data acquisition. Sample analysis and data processing were performed by [PERSON] under the guidance of I.A.D. The research concept was developed collaboratively by [PERSON] and I.A.D. [PERSON] was primarily responsible for writing and revising the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All authors affirm their agreement with this submission. The data included in this manuscript are original and have not been published or submitted for publication in any other venue.  
The data that support the study findings are openly available: the raw, filtered, and segmented data of the eleven sandstones at [[https://doi.org/10.17612/4](https://doi.org/10.17612/4) fh1-w124]([https://doi.org/10.17612/4](https://doi.org/10.17612/4) fh1-w124) and the multi-resolution micro-CT images of sixteen Brazilian pre-salt carbonates at [[https://www.doi.org/10.17612/xx50-s717](https://www.doi.org/10.17612/xx50-s717)]([https://www.doi.org/10.17612/xx50-s717](https://www.doi.org/10.17612/xx50-s717)) (both accessed on 30 October 2024). The dataset used in this study is available from the corresponding author upon reasonable request.  
The authors acknowledge the Department of Geophysics, Universitas Padjadjaran, for the supercomputing resources 'RockExplorer' made available for conducting the research reported in this paper.  
The authors have no conflicts to disclose.
## 1 Introduction
In addition to PEG's biocompatibility, it carries tunable properties, such as variable hydrophilicity with changes in its molecular weight (MW) [26; 27]. This study aimed to tune PEG's MW to maximize the adsorption capacity of polyphenols extracted from grape pomace onto PEG-grafted silica particles. To our knowledge, this is the first study using PEG as an affinity ligand to capture polyphenols from grape pomace extract. The release of adsorbed polyphenols from PEG-grafted silica particles is needed for complete recovery. A green solvent swing process was also developed in this study for the recovery of polyphenols from grape pomace. The release of polyphenols was achieved by switching from aqueous ethanol (loading) to aqueous- or ethanolic-PEG cosolvent (unloading). The recovery of polyphenols from PEG-grafted silica particles was further optimized by altering the concentration of PEG in the solution. In addition, MW of PEG used in the desorption medium was tuned to selectively recover certain polyphenols from polyphenol loaded silica particles. PEG has been commonly used in the final formulation of pharmaceutical products [28; 29] and it demonstrates outstanding solubility of polyphenols compared to ethanol or water [30]. It also improved the cytotoxicity of insoluble polyphenols against target cancerous cell lines [31; 32]. Therefore, PEG-assisted solvent medium can simplify downstream processing of polyphenols upon isolation from grape pomace. In summary, combining solvent extraction, purification by the use of a tunable adsorbent with affinity ligands, and PEG-assisted cosolvent recovery can be an effective and environmentally benign process for obtaining highly potent polyphenols from grape pomace.
## 4 Test Equipment and Protocols
### 3D Scanning to Determine Seal Surface Characteristics  
The surface characteristics of the different seals before and after APT were determined and compared using 3D scanning with handheld EinScan equipment [16; 35]. After scanning each of the samples using the handheld EinScan Pro, the digitised mesh was stored in stereolithography (STL) file format. CloudCompare (an open-source point-cloud processing tool) was used for point-cloud generation and statistical analysis. This approach provides high-accuracy assessment of the characteristics of the surfacing subjected to APT loading, allowing for an accurate statistical comparison to the surfacing without APT loading.  
Due to optical limitations, the EinScan Pro is best suited for scanning lightly coloured, opaque materials. Hence, a thin layer of evenly distributed white spray paint was applied to the area to be scanned. The spray paint was applied at an angle of 45\({}^{\circ}\) relative to the horizon, from all cardinal directions, to ensure that the crevices and cracks were coated adequately. The minimum suitable sample size for accurate scanning was determined as 100 \(\times\) 100 mm\({}^{2}\). This sample size mitigates the effects of small, hollow voids caused by the reflective tracking markers, etc. Calibration prior to scanning was conducted, with the reported calibration accuracy shown to be within a range of 10-15 \(\upmu\)m.  
For larger aggregate sizes (>10 mm), handheld scanning provides superior angles to cover a larger percentage of the exposed surface area compared to that of a fixed scanning method. A wider range of rotation of the scanner is required to cover all areas and angles. For the Handheld HD Scan setting, an accuracy of 0.1 mm and a point distance of 0.2 to 2.0 mm is obtainable. For adequate coverage, approximately 16 scans were required around the circumference of the sample, with 2 to 3 different pitch angles for 32 to 48 scans. Typically, a point cloud composed of more than 5 million points was found to be indicative  
Figure 6: Graphical illustration of the setup and working of the 3 rd Model Mobile Load Simulator (MMLS3) on-site.  
of a complete scan. The scanning process takes approximately 20 min per sample. The scans are scaled in the appropriate unit of measurement (millimetres). For samples selected at random locations, the surface area varied by less than 2%.  
Analysis of the digitised samples was performed using CloudCompare. Distributions of roughness, curvature and point densities were performed and compared for different samples and specimens with the built-in functionality. Considering the resolution of the scanner and the sample size, a point-cloud density of 100 points/mm\({}^{2}\) was found to have sufficient resolution and repeatability. This equates to 1,000,000 points for a 100 mm \(\times\) 100 mm sample. A typical example of an imported mesh of the 20 mm stone prior to APT loading is shown in Figure 7.
## 2 Materials and Methods
### Analysis Methods  
Scanning electron microscopy (SEM; VEGA3, Tescan, Kohoutovice, Czech) was used to observe the stabilization process of the pitch, chemical activation, and surface changes after pellet production. After the stabilization process, the thermal stability of the pitch was measured using thermogravimetric analysis (TGA; TA Q-400, TA Instrument, New Castle, DE, USA) under N\({}_{2}\) gas flow at a heating rate of 10 \({}^{\circ}\)C/min. After microwave treatment, an elemental analyzer (EA; Vario MICRO Cube, Elementar, Langenselbold, Germany) was used to confirm the introduction of oxygen and other elements. After stabilization, pitch was analyzed using Fourier transform infrared spectroscopy (FT-IR; ALPHA II, Bruker, Billerica, MA, USA) and X-ray photoelectron spectroscopy (XPS; Nexsa, Thermo Fisher Scientific Brno, Waltham, MA, USA) to determine the chemical composition, distribution, and density of certain elements, as well as the hydrophilic oxygen operators on the pitch surface. For FT-IR analyses, pitch was mixed with KBr powder and the mixtures were pressed into pellets. After activation, the specific surface area, work volume, and work size of the activated carbon and pellets were analyzed and compared using the Brunauer-Emmett-Teller method (BET; Tristar 3020, Micrometrics, Norcross, GA, USA). To evaluate the adsorption performance of the pellets, the adsorption characteristics were analyzed by calculating the time and concentration values using an HCl gas detector (FIX800, Wandi Korea, Gunpo-si, Republic of Korea) with 50 ppm HCl gas. Additionally, the sample for each experiment was 0.8 g.