esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Influence Factors of Green FPAR and Vegetation Indices
There is a positive relationship between leaf absorptance and green FPAR. Thus, canopy PAI, the proportion and optical properties of canopy components have impacts on FPAR\({}_{\text{PAV}}\), but impacts of these factors are different. To compare impacts of three factors, we selected FPAR\({}_{\text{PAV}}\) values in different scenarios including three PAI values, two proportions and six tree species (Table 1). The maximum difference in FPAR\({}_{\text{PAV}}\) between six tree species was 0.0228 (Oak and Juniper) which occurred when the proportion of green leaves was 98% and PAI is 1. The maximum difference (Oak and Juniper) was only 0.0199 when the proportion of green leaves was 50%. However, when PAI increased from 1 to 4, green FPAR increased by at least 0.4039 (Juniper). It seems that leaf absorptance has less impact on green FPAR than PAI. The possible reason is that there is small difference in the absorptance of green leaves between tree species. When the proportions of green leaves in the canopy varied from 50% to 98%, FPAR\({}_{\text{PAV}}\) increased by more than 0.1 (PAI was 7), and the increments were large as PAI was 4, more than 0.2. The impacts of the proportion of canopy components were less than those of PAI, but more than leaf absorptance in the PAR region. For four our vegetation indices, they varied with canopy structure. They increased with rising plant area index and the proportion of green component in the canopy. The theoretical basis for these vegetation indices lies with the red-NIR contrast of vegetation spectral reflectance signatures. In the red region, live vegetation has a strong absorption. In the NIR regions, leaves exhibit large scattering (reflecting and transmitting) but branches have no transmission. Consequently, the absorption of solar light in the red region will increase and the reflectance will decrease when plant area index rises. However, the light scattered in the NIR region also increase and the reflectance increase with the increase of \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Tree Species** & & **98\%** & & & **50\%** & \\ \cline{2-7}
**PAI** & **1** & **4** & **7** & **1** & **4** & **7** \\ \hline Alder & 0.4536 & 0.8798 & 0.9566 & 0.2541 & 0.6619 & 0.8409 \\ Aspen & 0.4495 & 0.8754 & 0.9523 & 0.2558 & 0.6645 & 0.8398 \\ Oak & 0.4678 & 0.8891 & 0.9628 & 0.2622 & 0.6725 & 0.8497 \\ Hemlock & 0.456 & 0.878 & 0.9533 & 0.2525 & 0.6577 & 0.8374 \\ Juniper & 0.445 & 0.8669 & 0.9415 & 0.2516 & 0.6555 & 0.8298 \\ Spruce & 0.4531 & 0.8797 & 0.9568 & 0.2537 & 0.6614 & 0.8406 \\ \hline \end{tabular}
\end{table} 
Table 1: Green FPAR of different species in give scenarios. Figure 8: Absorptance of different tree species. plant area index. This makes four vegetation indices increase. If there are more green leaves, the reflectance in the red region rises but in the NIR region has opposite result. Non-green components can increase the magnitude of NIR absorbance because of its negligible NIR transmittance [31].
# Content Adaptation and Depth Perception
## 6 Potential Applications
### Text Overlay: Subtitles  
SliceView can be used as a video player with some advantages when overlaying content such as subtitles. We used a VLC Media Player with settings configured to play synched videos and subtitles at different areas of the screen, which will be projected onto different layers.  
In general, black backgrounds are used with white subtitles, occluding the original image behind them. Sometimes, subtitles are displayed with partial opacity. Professionally made subtitles render specific phrases at different coordinates to avoid occluding important information, but most subtitles are always rendered centred at the bottom. When using SliceView, the whole video is always visible, as subtitles are rendered in a different layer (see Figure 15). The viewer can change its point of view and focal point to reveal areas.  
Figure 14: User playing to Ghosts’N GobinsTM videogame sliced on multiple layers.  
Figure 13: Masking the foreground silhouette in black in background layers. (**a**) observed from the front. (**b**) gaps are revealed when SliceView is observed from a different angle. (Megadrive Sonic the HedgehogTM).  
Figure 15: (**a**) Traditional subtitles on a regular screen. (**b**) Using SliceView to render subtitles at different depths, enabling the user to change the focal point to reveal partially occluded areas.  
Subtitles could be developed to be rendered at different depths, visually representing the distance from the speaker, or superimposing different phrases when two or more speakers are arguing on the scene.
## 2 Materials and Methods
### Element Generation and Convergence Test  
The study result was affected by the number of elements, so the mesh convergence test was used to achieve the most advantageous number of elements. The maximum equivalent von Mises stress (EQU) was used to investigate the least possible number of elements. The result of maximum EQV established on the miniplates from the NM Low-Z model and HE model is shown in Figure 3. While the Low \(Z\) model graph begins to stabilize at elements from 400,000 to 500,000, the HE model's graph tends to be more stable in the element range from 300,000 to 400,000. The point at which both models are stable, and overlap was chosen to avoid bias regarding the significant degree of difference elements between the two models. The number of elements and nodes in the NM Low-Z model was 407,716 elements and 102,792 nodes, while the HE model consisted of 411,427 elements and 104,965 nodes, as shown in the convergence graph (Figure 3).  
Figure 3: The graph shows maximum EQV results for convergence test. In total, 407,716 elements (102,792 nodes) and 411,427 elements (104,965 nodes) were selected for NM-Low \(Z\) and HE models, respectively.
## 4 Polyphenol-Based Systems and Their Unveiling Potential for Wound Care
### Polyphenol Encapsulation Techniques
Nanofibers have numerous advantages, such as the possibility to use mild processing conditions and controlled biodegradability (seconds to months) as carriers in a solid dosage. In addition, it is possible to control the architecture of the fiber at macroscopic levels and manufacture core-shell morphologies, multilayer structures, and various three-dimensional geometries [89; 90; 91]. Liposomes are colloidal particles consisting of a bilayer membrane system formed by the self-assembling of phospholipids [92]. Normally, this technique is used to protect the bioactive compound from digestion in the stomach, leading to the enhancement of its bioactivity and bioavailability. As an example, curcumin was encapsulated in liposomes in order to overcome the poor absorption of the polyphenol from the gastrointestinal tract after oral administration. The results of the study revealed that curcumin enhanced gastrointestinal absorption by liposome encapsulation [93; 94]. Likewise, liposomes were revealed to be great candidates for the delivery of kaempfer and quercetin [95]. Coaervation is also a technique used for the encapsulation of polyphenols, which involves the interaction of oppositely charged polyelectrolytes in aqueous form. However, coaervation is more expensive than the liposome method [93]. On the other hand, inclusion encapsulation is widely used to improve the water solubility of polyphenols. Generally, cyclodext are the most used compounds as encapsulating material. Evidence of the effectiveness of the inclusion technique in encapsulating polyphenols and improving their solubility, including resveratrol [96; 97], curcumin [98], chlorogenic acid [99], and kaempfer [100], has been reported by a number of researchers. Each encapsulation method has its own advantages and limitation, which should be considered according to the application and the purpose of the final product (Table 2). Independently of the encapsulation method used, the selection of an appropriate encapsulating material is extremely important to achieve the required final characteristics, such as a desirable encapsulation efficiency and particle stability. An ideal choice of the wall material should respect the following criteria [81,102]: (i) the encapsulating agent should have a low viscosity and should provide good protection to the core material, (ii) the properties of coated material (porosity and solubility), (iii) the encapsulation efficiency, toxicity, and microscopic properties of the surface of the particles, (iv) the compatibility between both materials, i.e, the encapsulating material should not react with the core, (v) the microencapsulation process, (vi) the desired size for the particles, and (vii) the required application of micro- or nanoparticles and economic factors.
###### Abstract  
This paper discusses the issue of the influence of cartographic Terrestrial Laser Scanning (TLS) data conversion into feature-based automatic registration. Automatic registration of data is a multi-stage process, it is based on original software tools and consists of: (1) Conversion of data to the raster form, (2) register of TLS data in pairs in all possible combinations using the SURF (Speeded Up Robust Features) and FAST (Features from Accelerated Segment Test) algorithms, (3) the quality analysis of relative orientation of processed pairs, and (4) the final bundle adjustment. The following two problems, related to the influence of the spherical image, the orthoimage and the Mercator representation of the point cloud, are discussed: The correctness of the automatic tie points detection and distribution and the influence of the TLS position on the completeness of the registration process and the quality assessment. The majority of popular software applications use manually or semi-automatically determined corresponding points. However, the authors propose an original software tool to address the first issue, which automatically detects and matches corresponding points on each TLS raster representation, utilizing different algorithms (SURF and FAST). To address the second task, the authors present a series of analyses: The time of detection of characteristic points, the percentage of incorrectly detected points and adjusted characteristic points, the number of detected control and check points, the orientation accuracy of control and check points, and the distribution of control and check points. Selection of an appropriate method for the TLS point cloud conversion to the raster form and selection of an appropriate algorithm, considerably influence the completeness of the entire process, and the accuracy of data orientation. The results of the performed experiments show that fully automatic registration of the TLS point clouds in the raster forms is possible; however, it is not possible to propose one, universal form of the point cloud, because a priori knowledge concerning the scanner positions is required. If scanner stations are located close to one another in raster images or in spherical images, Mercator projections are recommended. In the case where fragments of the surface are measured under different angles from different distances and heights of the TLS, orthoimages are suggested.  
TLS registration; the cartographic projection of the TLS; quality assessment; SURF; FAST; orthoimages; spherical; Mercator
## 3 Results & Discussion  
### Materials Characterization  
The morphology of the successful LbL fabrication of the PANI@CNT/CNC SPME MN was verified by SEM. Figure 2a shows the tip of the CNT/CNC coated MN, where the rough surface of the CNT/CNC composite coating is visible. Magnification of the CNT/CNC coated MN reveals a physically entangled porous interwoven network structure between the CNTs and CNCs (Figure 2b), held together by strong hydrogen bonding interactions. The magnified SEM image of the PANI@CNT/CNC SPME MN is shown in Figure 2c, where the rough and porous morphology of the electrically conductive PANI matrix is evident.  
To further verify the successful LbL assembly of the PANI@CNT/CNC SPME MN, Raman spectroscopy was performed following each stage of development. Two sharp peaks at 1350 and 1585 cm\({}^{-1}\) were observed in both CNT/CNC and PANI@CNT/CNC SPME MNs (Figure 3), corresponding to the D- and G-bands of the CNTs, respectively. The D band is generally attributed to the disordered carbon structure of the CNTs, while the G band corresponds to its C-C bond stretching [25; 26; 28]. The Raman spectra for the PANI@CNT/CNC SPME MN exhibited a small, sharp peak around 1032 cm\({}^{-1}\) (Figure 3), which was attributed to both the C-H bending of the benzenoid structure, and the C=N stretching in quinoid structure of PANI [29]. Moreover, the intensity of the D- and G-band peaks decreased after fabrication of PANI (Figure 3). These results confirm the successful assembly of the PANI@CNT/CNC SPME MN. These results from characterization of the PANI@CNT/CNC SPME MN were comparable to a similar needle platform involving PANI coating reported elsewhere [26]. This attests to the reproducibility in the fabrication of the conductive polymer coatings with the PANI@CNT/CNC SPME MN platform.  
Figure 3: Overlaid Raman spectra for (i) PANI@CNT/CNC and (ii) CNT/CNC SPME MNs.  
Figure 2: SEM images of (**a**) CNT/CNC coated MN; (**b**) magnified CNT/CNC coated MN; and (**c**) magnified PANI@CNT/CNC SPME MN.
## 1 Introduction
### Organization  
The remainder of this paper is organized as follows: Section 2 introduces the considered system model, which includes the opportunistic transmission model with selfish nodes and the encounter probability analysis model. In Section 3, this paper elaborates the proposed opportunistic transmission scheme. The simulation results are presented in Section 4. Finally, this paper concludes our paper in Section 5.
# A Deadbeat Current and Flux Vector Control for IPMSM Drive with High Dynamic Performance
###### Abstract
Although the aforementioned studies [21; 22] addressed the voltage and current limit issues to improve dynamics, they were still heavily linked to motor parameters. In the advanced hybrid DB-DTFC proposed in [23], the voltage command and flexible time duration were directly calculated based on torque and flux linkage errors considering voltage saturation. Having improved dynamic performance and torque ripple, compensation to the time delay and current limitation are not considered. A combination of deadbeat predictive torque control (DB-PTC) and duty ratio calculation for voltage, based on stator flux linkage vector in the stationary frame, was introduced with benefits for reducing computation and switching loss [24]. Nonetheless, these methods have been mostly applied to surface-mounted PMSM (SPMSM) to reduce complexity and it may still be a challenge for an implementation in IPMSM. On the other hand, a control algorithm for high efficiency, such as maximum torque per ampere (MTPA) [25], has usually been implemented for IPMSM drive. Since electromagnetic torque is typically used as an input command for the DB-DTFC or DB-DFVC schemes, it would be complex to determine flux linkage and/or load angle references from torque and to restrict the output current. In this work, a deadbeat current and flux vector control (DB-CFVC) method is proposed, where a simple DB flux vector control in the stationary frame with the \"reinforcedphase angle command\" concept is introduced. A practical and robust hybrid flux linkage observer is also developed. As a result, the dynamic performance is enhanced, the complexity of tuning the control/observer parameters is mitigated, and the quality of torque and flux is improved. Unlike other DB-DTFC or DB-DFVC, the input command of the proposed method is current magnitude, and the voltage command is determined by a deadbeat control based on stator flux linkage vectors in the stationary frame with improved current prediction for the one-step delay compensation algorithm. Therefore, the current magnitude can easily be restricted in an allowable range, resulting in a robust control strategy. Furthermore, differing from a previous study [17], the proposed method exploits the advantages of directly controlling the stator flux linkage vector. By using the reinforced phase angle command, the phase angle reference of the stator flux linkage vector can be accurately calculated based on the enhanced quality of load angle command considering actual speed error and voltage limit. They contribute to boosting dynamic response and simultaneously avoiding voltage command saturation. Finally, to confirm the effectiveness and reliability of the proposed method, simulations and experiments on an IPMSM drive are conducted to validate the proposed method, wherewith a comparison with other deadbeat control methods is made.
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 2 Background on Federated Learning  
FL is an ML setting where multiple entities (clients) collaborate to solve a problem under the coordination of a central server or service provider. Each client's raw data are stored locally and not exchanged or transferred. Focused updates intended for immediate aggregation are used to achieve the learning objective. These updates are narrowly scoped to contain the minimum information necessary for the specific learning task. Aggregation is performed as early as possible at the centralized point; this is the main difference between FL and fully decentralized peer-to-peer (P2P) learning methods, where aggregation is not performed at all [6]. Two main variants of FL exist: cross-device and cross-silo FL. The first variant is distinguished by a considerable number of clients, reaching up to \(10^{6}\). At any given moment, only a subset of the clients is accessible, and direct indexing of the clients is not feasible. Cross-silo FL is constituted by a limited number of disparate organizations (up to \(10^{2}\)). All clients are consistently accessible, and each is identified by a unique name that enables direct system access. In both settings, data are generated locally and remain decentralized: a central Orchestration Service/Server (OSS) organizes the training but never sees the raw data produced by the clients.  
There exist two main variants of data distribution across the clients: horizontal and vertical distributions. In the case of horizontal FL, also called homogeneous FL, user training data share the same feature space but have a different sample space (e.g., captures of CCTV across different buildings). In the vertical FL variant, known as heterogeneous FL, user training data share the same sample space but have a diverse feature space (e.g., personal data of customers shared by different companies, each having a subset of features).  
One of the most prominent domains where FL has been adopted is healthcare, largely due to its intrinsic capacity to safeguard privacy. For instance, FL setting have been considered for COVID-19 detections [7] and cancer diagnoses [8].
# Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser
## 3 Proposed Fog-Based CDN Framework
### Fog-Based CDN Framework Components
#### 3.1.5 Fog Browser  
The Fog Browser is a web browser installed on edge devices such as PCs, laptops, routers, IoT devices, or any programmable device. The main functionality of the Fog Browser is to enable the end device to discover nearby fog nodes and seamlessly retrieve the available resources from those fog nodes. The Fog Browser can work jointly with any existing browser as an extension or plugin. It can be perceived as an underlying layer for the browser. With low-specification edge devices, command line HTTP browsers such as cURL or wget can be extended with fog browser functionalities.
# Dynamical Networks Modelling Applied to Low Voltage Lines with Nonlinear Filters
## 4 Results, Discussion and Perspectives
### Discussions, Perspectives and Conclusions
Studies on system robustness are present in theories about complex systems [33; 34], along with several methods and measurements describing complex network [35], graph and dynamic communities [36], social network analysis [37], just to cite a few. However, these measurements are mainly dealing with the network \"structure\", not the processes. Here we argue that both, in our model, must be considered within the iterative computation, bringing each node and its connection parameters together to calculate the system dynamical evolution. The nonlinear application presented is an example of this approach. Our future research will focus on a model to measure the system sustainability based on the _structure_ and the _processes_ framework, as outlined in Figure 15.  
The author acknowledges [PERSON] for her contributions with discussions, ideas and several suggestions for the final version of this paper, and The University of Bristol and Unisul Universidade do Sul de Santa Catarina for their administrative support. The authors declare no conflict of interest. Figure 15: The system as a static network structure with dynamical processes running on top of it. The dynamical approach considers reflected signals (time-step \(t\)) and incident signals (time-step \(t+1\)), dependent on the connections and node functions \(f_{n}\).
# Failure Warning at the End of Service-Life of Lead-Acid Batteries for Backup Applications
## 2 Linear Superposition-Voltage Aging Model
### Linear Aging Filter of Internal Resistance Based on Contacting Resistance of EIS
\begin{array}{c}\hat{r}_{\Omega,k}^{-}\\ \hat{\alpha}_{k}^{-}\end{array} \right]=\Phi_{\Omega,k}\left[ \begin{array}{c} \hat{r}_{\Omega,(k-1)}\\ \hat{\alpha}_{(k-1)}\end{array} \right] \tag{31}\]  
\[P_{k}^{-}=\Phi_{\Omega,k}P_{(k-1)}\Phi_{\Omega,k}^{T} \tag{32}\]  
\[K_{k}=P_{k}^{-}H_{\Omega}^{T}\big{[}H_{\Omega}P_{k}^{-}H_{\Omega}^{T}+M_{ \Omega}\big{]}^{-1} \tag{33}\]  
Step 3. Updating the estimation with measurement of the deep-discharge:  
After measurement of the deep-discharge, \(r_{\Omega,k}\) and \(\alpha_{k}\) is calculated in Equation (20). In Equation (34), state vector estimation (\(\hat{r}_{\Omega,k}\) and \(\hat{a}_{k}\)) is updated with measurement (\(r_{\Omega,k}\) and \(\alpha_{k}\)). In Equation (35), \(P_{k}\) is updated before jumping back to Step 2. \[\left[ \begin{array}{c}\hat{r}_{\Omega,k}\\ \hat{\alpha}_{k}\end{array} \right]=\left[ \begin{array}{c}\hat{r}_{\Omega,k} ^{-}\\ \hat{\alpha}_{k}^{-}\end{array} \right]+K_{k}\left[\left[ \begin{array}{c}r_{ \Omega,k}\\ \alpha_{k}\end{array} \right]-H_{\Omega}\left[ \begin{array}{c}\hat{r}_{ \Omega,k}^{-}\\ \hat{\alpha}_{k}^{-}\end{array} \right]\right] \tag{34}\]  
\[P_{k}=[I-K_{k}H_{\Omega}]P_{k}^{-} \tag{35}\]  
Step 4. Increment \((k+1)\) and go back to Step 2. Until battery failure, the algorithm returns to Step 2. In this paper, the sampling interval between \(k\) and \((k+1)\) is limited by period of accelerated aging. However, in future applications, the sampling of EIS can be shorter than the deep-discharge counterpart.
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 2 Theoretical Background
### Challenges in Mitigating RFI
#### 2.4.4 RF Site Master  
An RF Site Master [11] is a portable device used to test the integrity of cable and antenna systems. It identifies issues such as signal degradation or physical damage that could compromise the system's ability to handle interference effectively. These tools are particularly useful for maintaining ground-based navigation aids like ILS.