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
#### 3.4.14. Seam3D  
The concept behind the SEAM3D (Sequential Electron Acceptor Model 3-Dimensional) model is biodegradation by sequential electron acceptors and solute transport in a three-dimensional heterogeneous system [722]. A key feature of the code is its compatibility with the MODFLOW model. The reactively transported solutes can be biodegradable substrates, nutrients, and electron acceptors for microbial growth, products of biodegradation, and the transport of pesticides. The code is a dation, daughter products of the substrates, or non-reactive tracers. In general, SEAM3D is equipped with four chemical packages: biodegradation, NAPL dissolution, reductive de-chlorination, and co-metabolism. SEAM3D has been used to predict the dispersion of chlorinated solvents at Little Creek Naval Amphibious Base in Virginia Beach for the US Navy, to simulate a controlled release of jet fuel at an air force base in Mississippi, and to model simulations of a gasoline-contaminated site near Beaufort, South Carolina, to explain why natural attenuation was ineffective [723]. More recently, SEAM3D has been used to study benzene transport in shallow coastal groundwater [724] and the dissolution of DNAPL [725].
# Towards Mirror-Less Graphene-Based Perfect Absorbers
## 2 Graphene Perfect Absorbers with External Mirror
### Performance Improvement of Absorbers
#### 2.2.2 Ultra-Broadband Absorption
**(b)** Schematic of a GPA consisting of sinusoidal-patterned graphene [70]. **(c)** Left: schematic of a GPA consisting of alternating graphene and dielectric layers with 1D air grooves, forming a grating. Right: absorption spectra at normal incidence of proposed graphene metamaterial absorber (red), an absorber with angle-averaged absorption from 0 to 60\({}^{\circ}\) (green) [73]. **(d)** Schematic of a GPA consisting of three-layer structure with square-, cross-, and circular-shaped graphene metasurfaces [75]. **(e)** Left: schematic of a GPA designed for ultra-wide bandwidth absorption based on wavelength-insensitive phase matching. Right: absorption spectra as a function of incident angle [44]. Reproduced with permission from [44; 63; 70; 73; 75]. © 2023 Optical Society of America; © 2023 Optical Society of America; © 2023 Springer Nature. structure consists of a periodical graphene pattern and a double-layer graphene sheet (the two layers of graphene sheets are unpatterned graphene planes) sandwiched with a silicon dioxide layer and the gold ground plane, tightly stacked to form the unit cell. In 2018, [PERSON] et al. [75] demonstrated that the proposed absorber exhibits an absorption of >90% in an ultra-broad range of 0.55-3.12 THz. The proposed absorber is a compact, three-layer structure, comprising square-, cross-, and circular-shaped graphene metasurfaces embedded between three separator dielectrics, and all of them are backed by a metal reflector (Figure 6d). [PERSON] et al. [76] introduced a broadband THz absorber with an array of graphene-dielectric multilayered pyramids on a metal reflector. High absorption with an ultra-broad bandwidth from 8 THz to over 100 THz is achieved due to squeezing graphene plasmons at different levels of the gradually tapered pyramid stack, similarly to sawtooth anisotropic hyperbolic metamaterial absorbers in ref. [77]. Thirdly, the prism-coupling absorber scheme can be also utilized for ultra-broadband absorption. [PERSON] et al. [44] proposed GPAs of ultra-wide bandwidths based on prism coupling with wavelength-insensitive phase matching, which consists of three dielectric layers (prism-cavity-air) with monolayer graphene embedded in the cavity layer (Figure 6e). Due to inherent material dispersion of the dielectric layers in near-IR regime, with the proper choice of the incidence angle and the cavity thickness, the proposed perfect absorbers can satisfy the phase matching condition over a wide wavelength range, inducing enormous enhancement of the absorption bandwidth. According to theoretical investigation, 99% absorption bandwidth of \(\sim\)300 nm with perfect absorption at \(\lambda=1.51\) um can be achieved, which is \(\sim\)7 times wider than the conceptual design based on the non-dispersive materials.
## 2 Data  
### Sentinel-1  
SAR data from the Sentinel-1 A mission (2016-2023) were sourced and pre-processed through Google Earth Engine (GEE) [23], offering dual-polarization backscatter data at a temporal resolution of 6 days and 10-m spatial resolution (Table 1). Optical data from Landsat and Sentinel-2 were deemed unsuitable for rigorous analysis due to persistent cloud cover, underscoring the value of SAR for this study.
# Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics
## 3 Mapping and Hotspot Identification
### Hotspot Identification Training  
The main objective of the robot is to find the hotspot using an object detection model. Detection models like Faster R-CNN, ResNet, RetinaNetV2, and YOLO series detection models exist. YOLO has been used for research, specifically YOLOv8, because the YOLO series of object detection models perform well for videos, and YOLOv8 being computationally light makes it the first choice for our purpose. A comparative analysis of YOLOv8 was carried out with YOLOv2, YOLOv3, YOLOv4, and YOLOv5 versions in [44]. According to [45], YOLOv8 is the same as YOLOv5, with some minor differences between them:  
* The C3 module is replaced with the C2f module,
* YOLOv5 does not have convolution layers 10 and 14,
* The bottleneck layer was tampered, where \(1\times 1\) layer was replaced with 3 \(\times\) 3 convolution layers and the decoupled head was used instead of the objectness step [44].  
YOLOv8 introduces several improvements over YOLOv5, enhancing its effectiveness in object-detection tasks. Replacing the C3 module with the C2f module enhances feature-processing capabilities, which increases accuracy. Removing specific convolution layers leads to a more streamlined architecture, reducing computational demands. Changes in the bottleneck layer, such as replacing 1 \(\times\) 1 with 3 \(\times\) 3 convolution layers, allow for better feature capture. Additionally, using a decoupled head instead of the objectness step improves the precision of both localization and classification tasks, making YOLOv8 a robust option for real-time applications. Table 3 illustrates the details of the IPC used for training. YOLOv8 performed comparatively better with a lesser number of datasets and computational requirements. Compared to its previous counterparts, this model is different. YOLOv8 is an anchor-free model, accounting for lower bonding box predictions that result in faster Non-Maximum Suppression. YOLOv8 was trained on different hotspots, namely dustbins, coolers, drains, plants, pots, buckets, and toilets [46]. In total, 5000 images were annotated to create a diverse dataset. Image-level data augmentation, such as sheer, rotation, cut-out, and noise, was carried out before training, with the training and validation images split in an 80:20 ratio.  
Figure 4: Comparative maps of the test environment using 2D and 3D LiDAR scans. The 2D scan provides a flat, top–down view, while the 3D scan offers a detailed, multi-dimensional representation, capturing height and depth for enhanced spatial awareness and navigation.
## 3 Results and Discussion
### The Extraction Yield
#### 3.2.2 Fatty Acids  
Nineteen fatty acids (**33-51**) were identified in the _P. pinaster_ and _P. pinea_ bark lipophilic extracts, accounting for 16% and 39% \(w/w\) of the total content of identified components, respectively (Table 1). Among the fatty acids, fourteen were saturated and four unsaturated, and their chain length varied between 6 and 26 carbon atoms. In both samples, the saturated fatty acids content was much higher than the unsaturated fatty acids content, ca. 8.9-fold and 13.2-fold for the _P. pinaster_ and _P. pinea_ bark lipophilic extracts, respectively. Indeed, docosanoic acid (**43**) was the major saturated fatty acid, whereas (9\(Z\))-octadec-9-enoic acid (**48**) was the major unsaturated fatty acid. Considering the fatty acid composition of _P. pinaster_ bark, all of the presented constituents of this family were herein identified for the first time, with the exception of tetradecanoic acid (**37**), hexadecanoic acid (**39**), and (9\(Z\))-octadec-9-enoic acid (**48**) [50]. On the other hand, hexanoic acid (**33**), octanoic acid (**34**), nonanoic acid (**35**), decanoic acid (**36**), tricosanoic acid (**44**), hexacosanoic acid (**46**), and (11\(Z\))-eicos-11-enoic acid (**50**) were identified for the first time in _P. pinea_ bark in the present work.
## 1 Introduction
The proposed robot uses the water thrust as the propulsion force, similar to the abovementioned system. The robot is aimed to be applied in firefighting tasks in water areas that have an unlimited amount of water but are difficult to access and are suppressed by conventional fire-extinguishing methods. The weight-shifting mechanism adjusts the weight distribution on the system, in a similar way to human movement on the fly board, in order to fly around. The target scenarios for the system application are visualized in Figure 1. In particular, a pump and a flexible hose convey water to generate water to the robot head. The head part generates thrust to take off and adjusts the actuator mechanism to approach the flame. The fire-extinguishing sprinkler sprays the water to suppress the fire remotely or automatically. The zoomed figure visualizes the robot head with its thrust nozzles, fire-extinguishing sprinkler, and weight-shifting mechanism. In this paper, the system conceptual design is introduced. Mathematical models reveal the dynamical characteristics of the system. To demonstrate the maneuverability and  
Figure 1: The applications of the proposed firefighting robot. feasibility of the proposed system, a linear-quadratic integrator (LQI) is designed such that the water flowrate and the weight-shifting mechanism are controlled to fly the system to the fire area. Moreover, an optimal approach based on the genetic algorithm (GA) tunes the controller's gains. Thus, both motion performance and system robustness are preserved. Accordingly, the contributions of this study in comparison to the relevant studies are as follows:  
* A novel conceptual design of the flying firefighting robot using waterpower and a weight-shifting mechanism is proposed. * The mathematical models of the system are formulated, and its characteristics are analyzed. * An LQI design with a novel GA approach is introduced. * Comparative simulation studies reveal the feasibility and efficiency of the proposed system. The remainder of the paper is organized as follows. Section 2 introduces the system configuration and mathematical models. The LQI control system design with the GA approach is explained in Section 3. The simulation results are discussed in Section 4. Finally, the conclusions are drawn in Section 5.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 6 Approaches and Algorithms
### Service Area Planning
To maintain an essential balance of resource usage among SBSs, it is important to allocate resources in a way that ensures each SBS has a certain margin. When available resources are insufficient, user priority scheduling can be implemented to meet user requirements in batches. This scheduling method allows SBSs to prioritize users based on certain criteria, such as their importance or urgency, and allocate resources accordingly. * To ensure the relative stability of user connections and reduce handover overheads, it is important for users to select an SBS with the longest possible residual time. This helps reduce the frequency of user handovers among different SBSs and beams, which can be costly and disruptive. By selecting an SBS with a longer residual time, users can maintain a stable connection and avoid unnecessary handovers. * To improve the reliability and efficiency of data transmission, it is important to minimize the propagation distance between an SBS and the grid. This helps reduce propagation loss, which is the reduction in signal strength that occurs as the signal travels through space. By selecting an SBS closer to the grid, users can experience better data transmission performance and reduced latency. Additionally, minimizing propagation distance can help improve the overall capacity of the network by allowing more users to connect to the SBS without experiencing significant signal degradation. * The RRSC should not only focus on multi-satellite cooperative services in hotspot areas, but also plan for service satellites in sparsely populated and low-traffic areas to ensure that SBS resources can be allocated to these areas. Therefore, the service area scheduling algorithm in the MLSCS must consider various factors, such as coverage, interference avoidance with GEO satellites, residual time, propagation distance, and so on. That is to say, the service area scheduling algorithm should contain the following sub-algorithms: the SBS coverage area calculation algorithm, the interference avoidance calculation algorithm, the residual time calculation algorithm, the propagation distance/loss algorithm, and the rain attenuation calculation algorithm, etc.
## 3 Detailed Implementation of Layer-1 BoD Services
### User Interface of Layer-1 BoD Services  
Users submit a project application for layer-1 BoD services to NII, and network operators at NII input the project data such as the corresponding VPN name, user names, intended node IDs, physical interface types and IDs into the layer-1 BoD server. After that, registered users can request a layer-1 path on a reservation basis simply by filling out a set of Web screens (Figure 4). Each user can specify the source and destination (nodes and interfaces), the duration in fifteen-minute intervals, the bandwidth with a 150-Mbps granularity, and route preferences. After selecting the VPN name [Figure 4(a)], such as the high-quality backup project, the user chooses the source and destination nodes [Figure 4(b)], such as from among Hokkaido Univ., Kyushu Univ, NII, and Osaka Univ., and the duration [Figure 4(c)] from a pull-down menu. On the next page [Figure 4(d)], the BoD server calculates and shows the available bandwidth and the estimated delay between specified nodes during the specified duration. The user then chooses the source and destination interfaces, the bandwidth (a lambda that provides the full bandwidth of the physical interface or in multiples of 150 Mbps), and the route preferences (none or minimum delay route; or the same route constraint for multiple paths). Figure 4(e) shows the page for confirming the reservation.  
Figure 4: Sample Web screens for layer-1 BoD services.
## 5 Results and Discussion  
### Single-Method Ensemble vs. Our Multi-Method Ensemble Framework  
In order to compare the results of SME and our MME Framework, Figure 4 shows the average MAE of all SME and the multiple fusion implementations of our MME Framework across all datasets. Comparison of the average results between SME and our MME Framework in the RMSE, and the \(R^{2}\) metrics are shown in Sections S1.1 and S1.2 of the Supplementary Materials. The ensemble method with the least feature importance error is our MME Framework using majority vote for fusion, followed by the MME Framework with mean and RATE. SME such as SHAP, PI, and IG produce the worst results. The circles and bars in the figure represent the feature importance errors on the training and test datasets, respectively. The feature importance errors on the training dataset are slightly lower than the errors on the test dataset.
## 4 Methodology
### Video Transmission and Visualization
#### 4.5.2 Video Decoding  
On the HoloLens side, the decoding process starts when the connection has been established. For the purposes of the decoding task, we have implemented a dedicated library using the FFmpeg tool, namely, Decoder, to handle video frames. The library has been implemented as a dynamic-link library for the Microsoft Windows operating system and built for UWP architecture compatible with HoloLens 2.  
The Android app feeds the AR app with h.264 encoded frames. In the AR application decoding and visualization of the received video frames are handled in two background processing threads. The first thread runs the Decoder module while the second thread is responsible for the frame visualization. Decoding is performed in a 30 Hz (native) rate while rendering in 15 Hz for performance reasons and efficient management of computational resources. Rendered frames are displayed in a virtual projection panel. In contextualized video mode, this panel is placed in front of the drone, in the direction its camera is facing, while in FPV mode it is centered on the user's field of view.  
During development, different methods of video streaming were implemented and tried (as shown in Figure 9). These included RTMP streaming (top left), sending video frames through Kafka (top right), and a direct connection using web sockets (bottom). The latter was finally selected, as it yields the lowest latency and requires no intermediaries.
# A Practical and Efficient Node Blind SignCryption Scheme for the IoT Device Network
## 3 The Proposed Scheme
### Unsign-Cryption Phase  
* When \(BS\) received these cipher-text messages from \(DS_{i}\), it can decrypt by the following operations: \[\begin{split} C_{3}^{*}&=C_{3}^{\prime\prime\prime}* \gamma^{-1}\\ t&=(C_{3}^{*})^{2}*(\lambda)^{-4}\\ &=C_{3}^{-4}*\beta^{-4}\\ t^{*}&=t*y_{1}\end{split}\] (7)After \(BS\) has computed this signature \(t\) from the above equation, it forwards \((t^{*},z,\hat{z})\) to the node \(DS_{i}\) and allows the \(DS_{i}\) to decrypt \(t^{*}\) and un-blinds this signature \(t\) as follows: \[t =t^{*}*y_{1}^{-1}\] \[S_{R} =t*\beta^{4}\] \[=C_{3}^{-4}*\beta^{-4}*\beta^{4}\] \[=C_{3}^{-4}\mod n\]
* After \(DS_{i}\) summarizes the above equation, we conclude that the node \(DS_{i}\) has the final signature \(\sigma_{R}=(S_{R},C_{1},C_{2},C_{3})\), where \(S_{R}^{4}=C_{3}=H_{1}(C_{1},C_{2},\gamma,t,\hat{z},m)\). Then, the node \(DS_{i}\) can forward the sign-cryption signature \(\sigma_{R}\) and cipher-text messages \((C_{1},C_{2},C_{3})\) to the receiver \(R\) of the Internet host.
* Once the receiver \(R\) has obtained this sign-cryption signature \(\sigma_{R}\) and cipher-text messages \((C_{1},C_{2},C_{3})\) from \(DS_{i}\), it can perform the following steps: \[r^{*} =D_{sk_{R}}(C_{1})\] \[m \overset{?}{=}C_{2}\oplus H_{1}(r^{*})\] (9) \[C_{3} \overset{?}{=}H_{1}(C_{1},C_{2},r,\hat{z},m)\] \[S_{R}^{4} \overset{?}{=}C_{3}\]
## 2 Materials and Methods
### Preparation of Film  
The film-forming solution was prepared in three steps as shown in Figure 1. In the initial step, 2 wt.% of corn starch was dissolved in 50 mL of distilled water for 30 min at 50 \({}^{\circ}\)C. In the 2 nd step, gelatin was added to the solution and stirred for 30 min at 75 \({}^{\circ}\)C. In the final step, 3 wt.% of glycerin was added as a plasticizer to each kind of film solution and was constantly stirred for 30 min via a magnetic stirrer to generate solutions. To investigate the effect of papaya on gelatin films, 8:10 \(w/w\) g papaya was added to the G-2 solution before plasticization to generate G/PP-1 and G/PP-2 papaya/gelatin blended film solutions. To investigate the effect of defatted soy protein on papaya/gelatin films, 2:4 \(w/w\) defatted soy protein was added to G/PP-2 to produce G/PP/SP-1 and G/PP/SP-2. A total of six formulations was obtained, as shown in Table 1.  
Edible films were formed utilizing a doctor blade (ZAA 2300, Zehntner Testing Instruments, Switzerland) for forming films on 210 mm \(\times\) 297 mm transparent PET substrates (Melinex(r) ST504, DuPont Teijin Films UK Ltd., Middlesbrough, UK). The solution was dried for 24 h at 40 \({}^{\circ}\)C. The films were peeled off and free-standing films were obtained (see Figure 1). These films were stored in an inert atmosphere until the samples were further characterized.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **Composition** \\ \hline G-1 & Gelatin (6 wt.\%) + Corn Starch (2 wt.\%) + Glycerin (3 wt.\%) \\ G-2 & Gelatin (9 wt.\%) + Corn Starch (2 wt.\%) + Glycerin (3 wt.\%) \\ G/PP-1 & Gelatin (9 wt.\%) + Papaya (8 wt.\%) + Corn Starch (2 wt.\%) + Glycerin (3 wt.\%) \\ G/PP-2 & Gelatin (9 wt.\%) + Papaya (10 wt.\%) + Corn Starch (2 wt.\%) + Glycerin (3 wt.\%) \\ G/PP/SP-1 & Gelatin (9 wt.\%) + Papaya (10 wt.\%) + Sop Protein (2 wt.\%) + Corn Starch \\  & (2 wt.\%) + Glycerin (3 wt.\%) \\ G/PP/SP-2 & Gelatin (9 wt.\%) + Papaya (10 wt.\%) + Soy Protein (4 wt.\%) + Corn Starch \\  & (2 wt.\%) + glycerin (3 wt.\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of samples along with the sample identification acronyms.
## 5 Parameters Study
### Influence of Shear Stiffness  
The influence of shear stiffness on the acceleration spectrum was subsequently investigated. The information about the composite beam was the same as the previous Jiubao northern approach bridge. Only the shear stiffness of the composite beam was analyzed with \(k_{s}\) = 10 Mpa, \(k_{s}\) = 100 Mpa, \(k_{s}\) = 1000 Mpa, and \(k_{s}\) = 10,000 Mpa. The vehicle mass \(m_{v}\) was 1200 kg, the vehicle stiffness \(k_{v}\) was 500 kN/m, the vehicle damping ratio \(\xi_{v}\) was taken as 0.2, and the vehicle speed \(v\) was 5 m/s. The acceleration spectrums for the four shear stiffness values are shown in Figure 16. The results indicate that, as the shear rigidity increased, the peak point gradually shifted to the right. This suggests that the inherent frequency of the bridge gradually increased, which is consistent with the structural mechanical property. In the range of 10-1000 Mpa, the spectrum amplitude of the first four frequency peaks remained relatively constant, but at 10,000 Mpa, the spectrum amplitude of the fourth-order frequency slightly decreased. Higher-order frequencies (e.g., the fifth, sixth, and seventh frequencies shown in Figure 16a) significantly decreased in spectrum amplitude as the shear stiffness increased. This indicates that higher-order frequencies of the bridge are more easily excited when the shear stiffness is low.