esa-sceva/satcom-chunk-collection huggingface dataset:
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models
#### 3.4.4 Bioventingplus  
Bioventingplus is a computer program that calculates the cost and effectiveness of remediation from the air, taking into account site-specific conditions and the type of pollutants [659]. Functions include airflow, mass recovery, and cost analysis. The airflow module aims to determine the air pressure, flow rate, pore volume turnover rate, and extraction efficiency. These parameters are calculated taking into account the injection conditions, i.e., pressure and soil properties. Mass removal is estimated based on the multicellular, multiphase, and multicomponent model. The model was successfully tested. The results can be found in Parker and Islam [660], Johnson and Parker [659], and Benner et al. [661]. The study by Benner et al. was conducted at the site of the former drum storage facility where several releases of LNAPL occurred, resulting in contamination of soils with a total mass of 4600 \(\pm\) 2300 kg, of which about 350 \(\pm\) 175 kg were identified as toluene,ethylbenzene, and total xylenes (TEX). Using Bioventingplus simulations, it was estimated that in situ air sparging during the 1113-day remediation could result in the removal of 140 kg of petroleum hydrocarbons. If biodegradation is also taken into account, the mass of contaminants removed increases to 760 kg. The numerical modelling of the total pollutant removal using volatilisation and biodegradation agreed with the results on site.
## 3 Methodology
### Communications Diagram  
Figure 1 shows the communications diagram, where the ground control station and the underwater robot communicate through the VLC. In the GCS, a computer runs the Unity-based HRI, where the operator visualizes the images and, at the same time, changes the operation mode and parameters of image transmission. The Unity-based HRI is connected to an ROS node using the ROS-TCP-Endpoint package [21], which allows for communication between Unity scripts (programmed in C Sharp) and the ROS network congestion control node (programmed in C++). From the Unity script, a topic is published, and in the ROS node, this topic is read, using this information to change the operation mode. The ROS node runs the control algorithm and consistently requests ROS services from the underwater robot's main computer. ROS communications are performed by ROS TCP, which is a transport layer for ROS messages and services that uses the standard TCP/IP sockets to transport message data. The GCS computer is connected to an Ethernet switch, and taking advantage of the transparency of the VLC link, a connection with the underwater robot can be established.  
On the side of the underwater robot, communications are performed in the same way as the GCS; the only difference is that the ROS master runs on the main computer of the underwater robot.
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 2 Related Work  
### UAV Landing on Rugged Terrain  
The task of autonomous landing on rugged terrain requires methods for autonomously choosing a safe landing site in an unknown environment. Most techniques employ image segmentation or 3D mapping to identify level terrain that is suitable for landing. Geometric information typically enables intuitive inference of the smoothness of a landing terrain, thereby aiding in the decision process regarding landing suitability. For example, [PERSON] et al. [18] proposed a method for UAV landing based on point cloud data; they used a fine-grained grid elevation map and a comprehensive risk model to minimize risks during touchdown. To improve the real-time capabilities of the system, [PERSON] et al. [19] used coarse-to-fine point clouds in different dimensional spaces within the landing framework. A principal component analysis is often used to detect suitable UAV landing sites in real time by evaluating geometric features, slopes, distances, obstacles, roughness, and ground conditions, thus demonstrating applicability in diverse scenarios [20]. However, considering the geometric factors of the environment without assessing semantic information may be dangerous, even for flat landing areas.  
Edge detection and semantic recognition technologies applied to images can help UAVs identify large areas within the same category, thereby guiding the UAVs for landing [20]. [PERSON] et al. [21] extracted image edges and employed a diameter area estimation algorithm to cluster contours to calculate the shortest distance between polygons to determine potential landing zones. [PERSON] et al. [22] introduced Markov chain codes to perform histogram operations on Gabor response images. They grouped neighboring pixels sharing the same value based on probability as a region segmentation criterion. [PERSON] et al. [23] guided UAV landing by identifying specific areas in urban scenes, such as roads, vehicles, and buildings. It is important to note that in practical applications, these technologies are susceptible to variations in lighting conditions, sensor performance, and environmental changes, which diminish the robustness and reliability of the system.  
However, relying only on individual information sources to determine the safety of a landing site may lead to UAV accidents. In recent years, novel approaches [24] have emerged that integrate two types of information to promote precise landing site selection. These methods typically involve frame-by-frame recognition of input images or point cloud data, which make them unsuitable for real-time execution on computationally constrained UAVs or for selecting landing sites over expansive geographical areas. The UAV landing framework proposed herein integrates 3D mapping to identify suitable landing sites within its range and combines these data with semantic information from images to pinpoint the optimal landing site.
## 8 Conclusions  
The renewed interest in IR thermal imaging as a promising technique for the detection of skin and breast cancers, due to the improvement of both IR camera performance and computational capability, has been outlined in the present review. Regarding the thermographic techniques, the following conclusions can be drawn:  
* Steady-state thermography is suited to detecting shallower tumors.
* Dynamic thermography can detect deeper tumors using cold/hot stress.
* Lock-in thermography and frequency-modulated thermal wave imaging represent
* New promising methods for the detection of skin lesions.
* Rotational breast thermography has the potential to overcome the limits of conventional breast thermography by allowing the whole breast surface to be analyzed.  
Accurate modeling techniques predicting the complex relationship between surface thermal patterns and the underlying pathophysiological conditions represent significant support for the success of this methodology. In particular, an example of these modeling efforts is given by real breast geometry-based models. Moreover, relevant information about thermophysical parameters and growth features of tumor lesions can be inferred from surface temperature distributions using inverse techniques. Data processing algorithmscan help clinicians reach the goal of reducing the number of unnecessary biopsies required for tumor stage characterization.  
Lastly, thermogram post-processing methods, especially those based on artificial intelligence algorithms, represent helpful tools for distinguishing between benign and malignant lesions.  
In further work, a new review paper should address other clinical applications of IR imaging, such as blood clot imaging.  
Conceptualization, G.D. and S.S.; writing--original draft preparation, G.D.; writing--review and editing, P.T. and S.S.; visualization, G.D.; supervision, P.T. and S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflicts of interest.
# A Focused Event Crawler with Temporal Intent
## 4 Temporal Intent-Based Focused Crawler
### Relevance Calculation with Start Time  
The start time can be used as an indicator to judge whether a web page is relevant to the given event, as discussed in Section 3.1. However, only topical keywords are usually used in traditional relevance calculation methods generally only use topical keywords. Therefore, the relevance between a given event and a web page is calculated from the hierarchy of the temporal and topical keywords in this study. In the hierarchical method, there are two steps to calculate and judge the topic's relevance.  
First, the temporal relevance \(sim(t_{PT},t_{ST})\) is calculated by comparing the publication time of a web page with the start time of the given event using Equation (11).  
\[sim(t_{PT},t_{ST})=\begin{cases}0&t_{PT}<t_{STs}\\ 1&t_{PT}\geq t_{STs}\end{cases} \tag{11}\]  
where variables are the same as Equations (6) and (9). If the publication time \(t_{pt}\) of the web page is earlier than the start time of the event, the value of temporal relevance is zero and the web page is abandoned. Otherwise, the value of temporal relevance is one, which indicates that the web page may be relevant to the given event.  
Secondly, the topical relevance \(sim(V_{Dk},V_{TK})\) is still calculated by using the cosine formula (as shown in Equation (12)) if the publication time \(t_{pt}\) of the web page is later than the start time of the event.  
\[sim(V_{Dk},V_{TK})=\frac{\sum_{i=1}^{s}w_{Tki}\times w_{Dki}}{\sqrt{\sum_{i=1 }^{s}w_{Tki}^{2}\times\sum_{i=1}^{s}w_{Dki}^{2}}} \tag{12}\]  
where variables are the same as Equations (7) and (10). If the relevance \(sim(V_{Dk},V_{TK})\) is greater than or equal to the specific threshold, it means that the web page is relevant to the given event and the focused crawler will store the web page in a web page repository. Otherwise, the web page will be discarded.
# An Ultra-Wideband Frequency Selective Rasorber with Low Infrared Emissivity
## 2 Analysis, Design and Experimental Verification
### Design of the FSR
#### 2.2.1 Ultra-Wide Transmission Band RS Based on Sinusoidal Microstrip Lines
For instance, to adjust the wave transmittance performance, one only needs to modify the size of parallel LC in the EC by adjusting the corresponding structural parameter \(r_{1}\) or \(n\), as shown in Figure 7. As \(r_{1}\) and \(n\) decrease, \(f_{t}\) increases, and the curve shifts toward the high-frequency side. It is evident that smaller values of \(r_{1}\) and \(n\) result in a narrower range from \(r_{p}\) to \(r_{1}\) and a narrower bandwidth of the non-frequency variable structure, which aligns with the inherent characteristics of a sinusoidal microstrip line structure, which also validates the accuracy of the second part of the conclusion on \"Transmission Conditions\" in Section 2.1. Therefore, considering the constraints of processing technology and the requirements of transmission bandwidth, \(n=5\) and \(r_{1}=1.8\) mm were chosen. In conclusion, this section presents the design of a resonator structure (RS) with an exceptionally wide transmission band based on a sinusoidal microstrip line. The RS demonstrates robust angular stability within \(\pm 60^{\circ}\) and good polarization stability, with a transmission band ranging from 8.72 to 16.80 GHz. The full-wave simulation results of this design are consistent with those of the equivalent circuit model, which verifies the accuracy of the design. Thus, the RS provides a solid foundation for future research on wideband FSR. Figure 6: S-parameters of RS calculated by CST and ADS. Figure 7: S-Parameters of the RS vary with \(r_{1}\) (**a**) and \(n_{1}\) (**b**).
## 5 Results and Discussion
### Parameter Estimation Results
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Algorithm** & **SSE** & **AE** & **MAE** & **MSE** & **RMSE** \\ \hline \multirow{4}{*}{**GSA**} & **Minimum** & \(1.02\times 10^{-5}\) & \(3.17\times 10^{-3}\) & \(1.63\times 10^{-4}\) & \(5.10\times 10^{-7}\) & \(7.81\times 10^{-4}\) \\  & **Maximum** & \(9.28\times 10^{-5}\) & \(9.06\times 10^{-3}\) & \(4.86\times 10^{-4}\) & \(4.64\times 10^{-6}\) & \(2.15\times 10^{-3}\) \\  & **Mean** & \(4.16\times 10^{-5}\) & \(6.41\times 10^{-3}\) & \(3.20\times 10^{-4}\) & \(2.08\times 10^{-6}\) & \(1.48\times 10^{-3}\) \\  & **Standard deviation** & \(2.67\times 10^{-5}\) & \(5.17\times 10^{-3}\) & \(2.55\times 10^{-4}\) & \(1.34\times 10^{-6}\) & \(1.15\times 10^{-3}\) \\ \hline \multirow{4}{*}{**MVO**} & **Minimum** & \(1.52\times 10^{-4}\) & \(1.07\times 10^{-2}\) & \(5.05\times 10^{-4}\) & \(5.72\times 10^{-6}\) & \(2.37\times 10^{-3}\) \\  & **Maximum** & \(3.97\times 10^{-3}\) & \(6.27\times 10^{-2}\) & \(3.15\times 10^{-3}\) & \(1.90\times 10^{-4}\) & \(1.40\times 10^{-2}\) \\  & **Mean** & \(5.02\times 10^{-4}\) & \(2.32\times 10^{-2}\) & \(1.16\times 10^{-3}\) & \(2.78\times 10^{-5}\) & \(5.14\times 10^{-3}\) \\  & **Standard deviation** & \(4.21\times 10^{-4}\) & \(2.00\times 10^{-2}\) & \(1.14\times 10^{-3}\) & \(2.00\times 10^{-5}\) & \(4.44\times 10^{-3}\) \\ \hline \multirow{4}{*}{**PSO**} & **Minimum** & \(1.59\times 10^{-2}\) & \(1.26\times 10^{-1}\) & \(6.31\times 10^{-3}\) & \(7.04\times 10^{-4}\) & \(2.82\times 10^{-2}\) \\  & **Maximum** & \(8.85\times 10^{-1}\) & \(9.41\times 10^{-1}\) & \(4.71\times 10^{-2}\) & \(4.42\times 10^{-2}\) & \(2.11\times 10^{-1}\) \\  & **Mean** & \(4.18\times 10^{-1}\) & \(6.47\times 10^{-1}\) & \(3.53\times 10^{-2}\) & \(2.09\times 10^{-2}\) & \(1.45\times 10^{-1}\) \\  & **Standard deviation** & \(2.57\times 10^{-1}\) & \(5.06\times 10^{-1}\) & \(2.53\times 10^{-2}\) & \(1.28\times 10^{-2}\) & \(1.13\times 10^{-1}\) \\ \hline \multirow{4}{*}{**SCA**} & **Minimum** & \(3.00\times 10^{-3}\) & \(5.50\times 10^{-2}\) & \(2.70\times 10^{-3}\) & \(1.58\times 10^{-4}\) & \(1.23\times 10^{-2}\) \\  & **Maximum** & \(1.59\times 10^{-1}\) & \(3.99\times 10^{-1}\) & \(2.00\times 10^{-2}\) & \(8.00\times 10^{-3}\) & \(8.94\times 10^{-2}\) \\  & **Mean** & \(5.38\times 10^{-2}\) & \(2.32\times 10^{-1}\) & \(1.15\times 10^{-2}\) & \(2.69\times 10^{-3}\) & \(5.18\times 10^{-2}\) \\  & **Standard deviation** & \(4.38\times 10^{-2}\) & \(2.09\times 10^{-1}\) & \(1.04\times 10^{-2}\) & \(2.19\times 10^{-3}\) & \(4.68\times 10^{-2}\) \\ \hline \multirow{4}{*}{**HBO**} & **Minimum** & \(1.00\times 10^{-3}\) & \(3.20\times 10^{-2}\) & \(1.65\times 10^{-3}\) & \(5.13\times 10^{-5}\) & \(7.11\times 10^{-3}\) \\  & **Maximum** & \(7.77\times 10^{-3}\) & \(8.77\times 10^{-2}\) & \(4.31\times 10^{-3}\) & \(3.82\times 10^{-4}\) & \(1.96\times 10^{-2}\) \\  & **Mean** & \(3.54\times 10^{-7}\) & \(5.95\times 10^{-2}\) & \(2.95\times 10^{-3}\) & \(1.74\times 10^{-4}\) & \(1.33\times 10^{-2}\) \\  & **Standard deviation** & \(2.29\times 10^{-3}\) & \(4.69\times 10^{-2}\) & \(2.34\times 10^{-3}\) & \(1.81\times 10^
# An Accurate Jacobian Matrix with Exact Zoeppritz for Elastic Moduli of Dry Rock
## 6 Discussion  
Recently, AVA (AVO) or elastic impedance inversion has been carried out using approximations of the exact Zoeppritz equations. These approximations are only applicable for the formations with weak reflection interfaces and seismic data with small incidence angles (or small offsets). To overcome the weaknesses of these approximations, it is necessary to derive the accurate Jacobian matrix, which consists of the partial derivatives of reflection coefficients with respect to unknown parameters.  
Regarding reservoir geophysics, an important step is to establish a theoretical model between the seismic attributes and the physical properties of rocks. [PERSON] proposed equations to represent the influences of rock pore and fluid properties on seismic responses and attributes [21]. Although there are many assumptions, the Biot-Gassmann equations are still the most practical and commonly used methods.  
Previous research demonstrated these dry rock moduli are mainly obtained indirectly from rock physics experiments, well logging or numerical modeling. Researchers had not built a direct inversion relationship between the theoretical rock physics model and the Zoeppritz equations. Here, we combined the Biot-Gassmann and the Zoeppritz equations to establish the relationship between the seismic wave responses of lithology and dry rock properties. The derived accurate Jacobian matrix, which consists of the partial derivatives of reflection coefficients with respect to bulk and shear moduli of dry rock, can be used in the further AVO/AVA inversion. The characteristics of the partial derivatives were analyzed and indicate that the reflectivity \(R_{ps}\) should not be used to invert the bulk moduli. Our future research will incorporate these partial derivatives to the AVA inversion and get the dry rock moduli directly from seismic data.  
Figure 11: The comparison of the relative (**a–c**) and absolute (**d,e**) errors of partial derivatives of the reflectivity \(R_{pp}\) with respect to \(\mu_{1}\) and \(\mu_{2}\) by using the Aki-Richards and Shuey approximate solutions.
# Microplastics in Water Resources: Threats and Challenges
## 5 Methods for Determining Microl plastics
### Fluorescence Microscopy  
Spectroscopic methods such as Raman and FTIR are commonly used for identifying types of polymer materials. However, these spectroscopic methods are expensive and require labor-intensive sample purification, leading to parallel interest in alternative detection methods such as cytometry [141], thermal degradation [142], the hot needle method [143], and fluorescence microscopy [144]. Among these methods, fluorescence microscopy has the potential for rapid detection because it involves a simple fluorescent labeling step with the solvatochromic dye Nile Red. Nile Red is an organic fluorescent dye that exhibits fluorescence in the presence of hydrophobic substances, particularly fats and lipids. Its fluorescent properties make it a useful tool in lipid studies, cellular research, and more recently in microplastics analysis [145]. The ability of Nile Red as an indicator to distinguish plastic samples based on their polarity has been investigated using existing protocols that employ fluorescence microscopy [146]. In these published protocols, fluorescence micrographs of Nile Red-stained primary microplastics were sufficient to successfully classify plastics based on their polarity (polar/less polar) [147].  
However, most studies that use Nile Red dye do not specify the type of plastics found in the microplastics. Previous studies only considered the emission color in fluorescence images. In the work of [PERSON] et al. (2025) [148], spectral emission was additionally examined using photoluminescence (PL) spectroscopy by studying the effects of staining parameters: dye concentration, solvent type, staining temperature, and staining time. As a result of these studies, an optimized staining protocol was developed, but it was not possible to completely eliminate the confusion of MPs with natural materials such as cellulose or protein fragments of animal origin [148]. In their study, [PERSON] et al. demonstrated that fluorescence microscopy images of microplastics and natural particles dyed with Nile Red (NR) and its derivative (NR3) reveal that selectivity depends on the emission wavelength. Using green fluorescence filters (excitation: 455-495 nm, emission: 505-555 nm), nonpolar plastics such as PE and PP show strong signals, while polar plastics such as PVC are barely detectable. Natural materials such as wood and chalk show minimal green fluorescence, but chitin exhibits strong signals, leading to false positives. This can be mitigated with treatment with hydrogen peroxide or chitinase. Orange (excitation: 533-558 nm, emission: 570-640 nm) and red fluorescence filters (excitation: 575-635 nm, emission: 660-710 nm) further complicate differentiation, as polar plastics, chitin, and wood all show strong signals, making it harder to distinguish them from non-polar plastics [149].
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 4 Uplink Transmission
### Spectral Efficiency and Optimal LSFD Weights  
The received signal for User Equipment (UE) \(i_{k}\) is described by [4]:  
\[\hat{s}_{kl}=\mathbf{v}_{kl}^{H}\mathbf{D}_{k}\mathcal{Y}_{\text{uU}}, \tag{95}\]  
where \(\mathbf{D}_{k}\) selects the signals meant for UE \(k\).  
By substituting (95) into (77) [4], the estimated signal \(\hat{s}_{k}\) for UE \(k\) in a distributed setting becomes \[\hat{s}_{k}=\sum_{l=1}^{L}a_{kl}\mathbf{v}_{kl}^{H}\mathbf{D}_{kl}\mathbf{h}_{kl}s _{k}+\sum_{i=1}^{K}\sum_{\begin{subarray}{c}l=1\\ l\
eq k\end{subarray}}^{L}a_{kl}\mathbf{v}_{kl}^{H}\mathbf{D}_{kl}\mathbf{h}_{ il}s_{i}+n_{0k}, \tag{96}\]  
where \(n_{0k}=\sum_{l=1}^{L}a_{kl}\mathbf{v}_{kl}^{H}\mathbf{D}_{kl}n_{l}\) represents the noise term. The vector \(\mathbf{g}_{ki}\in\mathbb{C}^{L}\) is defined as  
\[\mathbf{g}_{ki}=\begin{bmatrix}\mathbf{v}_{k1}^{H}\mathbf{D}_{kl}\mathbf{h}_{ il}\\ \vdots\\ \mathbf{v}_{kl}^{H}\mathbf{D}_{kl}\mathbf{h}_{il}.\end{bmatrix}.\]  
This vector captures the combined receive channels between UE \(i\) and all APs serving UE \(k\). In terms of this notation, (96) can be expressed as  
\[\hat{s}_{k}=a_{k}^{H}\mathbf{g}_{ki}s_{k}+\sum_{i=1}^{K}\sum_{ \begin{subarray}{c}l=1\\ l\
eq k\end{subarray}}^{L}a_{kl}\mathbf{g}_{ki}s_{i}.\]  
To compute the achievable Spectral Efficiency (SE) for UE \(k\) in a distributed context, we substitute (96) into the SE formula [4], leading to  
\[\mathrm{SE}(u_{lk},d)=\frac{\tau_{u}}{\tau_{c}}\log_{2}(1+\mathrm{SINR}(u_{lk},d))\ \mathrm{bit/s/Hz},\]  
where the effective Signal-to-Interference-plus-Noise Ratio (SINR) is given by  
\[\mathrm{SINR}(u_{lk},d)=\frac{p_{k}|a_{k}^{H}E\{g_{kk}\}|^{2}}{a_{k}^{H}\Big{(} \sum_{i=1}^{K}p_{i}E\{g_{ki}g_{ki}^{H}\}-p_{k}E\{g_{kk}\}E\{g_{kk}^{H}\}+F_{k} \Big{)}a_{k}}, \tag{97}\]  
with \(F_{k}=\sigma_{il}^{2}\mathrm{diag}\Big{[}E\{n_{k}D_{k1}v_{k1}^{k}\}^{2},\dots, E\{n_{k}D_{kl}v_{kL}^{k}\}^{2}\Big{]}\in\mathbb{R}^{L\times L}\).  
The combining vector \(\mathbf{v}_{kl}\) that minimizes (97) is  
\[\mathbf{v}_{kl}^{\mathrm{L-MMSE}}=\frac{p_{k}}{\sum_{l=1}^{K}p_{i}}\Big{(} \mathbf{b}_{il}\mathbf{b}_{il}^{H}+\mathbf{C}_{il}\Big{)}+\sigma_{il}^{2} \mathbf{IN}^{-1}\mathbf{D}_{kl}\mathbf{b}_{kl}.\]  
The optimal effective SINR for UE \(k\) is achieved by  
\[a_{k}^{\mathrm{opt}}=\frac{p_{k}}{\sum_{i=1}^{K}p_{i}}E\Big{\{}g_{ki}g_{ki}^{H }\Big{\}}+F_{k}+\sigma_{Dk}^{2}\mathbf{e}^{H}(E\{g_{kk}\})^{-1},\]  
where \(\mathbf{e}_{Dk}\in\mathbb{R}^{L\times L}\) is a diagonal matrix with the \((l,l)\)-th element being one if \(l\
otin\mathcal{M}_{k}\) and zero otherwise.
# Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security
### Our Contributions  
This study proposes the multi-mobile agent-based trust framework for RPL (MMTM-RPL) to mitigate internal attacks (i.e., sinkhole, Sybil, and rank attacks) and support the use of RPL in LLNs. It reduces the message overhead using mobile agents (MAs) and minimizes energy utilization using a fog layer, which performs all the computing and analysis. The major contributions of this paper are as follows:  
* A novel security framework for mitigating Sybil, rank, and sinkhole attacks in RPL-enabled LLNs is presented.
* Authenticated multiple MAs and dynamic itineraries minimize the message overhead and improve efficiency.
* A framework with a fog layer is presented in detail. It minimizes energy, computation, and memory overheads at the node level, which increases network lifetime.