esa-sceva/satcom-chunk-collection huggingface dataset:
# Explainable Artificial Intelligence: Advancements and Limitations
## 1 Introduction
We analyse the advantages and limitations of a variety of methodologies across these categories. This review is intended to serve both practitioners and researchers: for end users, it provides guidance on selecting the most appropriate XAI technique for their specific use case; for researchers, it highlights current gaps and challenges in the field, potentially inspiring new directions for future work. We then focus on one specific dimension, i.e., _explanation modality_, which involves the subcategories _visual_ and _conceptual_. We argue that the modality of explanations is the most crucial aspect, especially for visual tasks, and the future of explainability depends on the ability of methodologies to provide both visual and conceptual explanations. Although this review focuses on XAI for architectures processing static data, explaining models such as LSTMs [36], GRUs [37], and Transformers [38] has also become increasingly important as countless real-world applications employ these models for sequential data processing. In this direction, many works have either adapted existing XAI methodologies to temporal data or developed novel ones specifically for them. TimeSHAP [39], for instance, extends KernelSHAP [40] to explain the predictions of recurrent neural networks (RNNs) [41]. Similarly, explainable attention-based methods--e.g., RETAIN [42] or Temporal Fusion Transformer (TFT) [43]-- aim to embed interpretability within the model architecture by highlighting key timesteps or variables directly via learned attention weights. Despite these advances, translating dynamic model behaviours into human-centric insights is still a significant challenge for dynamic XAI. In this survey, we do not delve into the dynamic XAI or propose solutions to its challenges, as we reserve this for a separate, more detailed future work. This paper is structured as follows. Section 2 discusses the black-box nature of convolutional neural networks (CNNs) and provides a brief history of their development. Section 3 introduces the overarching categorisation of XAI methodologies, laying the foundation for the detailed discussions that follow. Visual explanation techniques are covered in Section 4, while concept-based approaches are reviewed in Section 5. In Section 6, we explore the close relationship between XAI and the field of weakly supervised semantic segmentation (WSSS), highlighting key methodologies and their shared objectives. Finally, Section 7 offers concluding remarks and perspectives on future directions.
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 2 Related Research and Background
### Video Processing  
For resources to be managed properly in the IoT, data must be processed and stored efficiently by filtering and data reduction. Among various stages of video processing, motion detection and compression can be utilized for data management. Compression is used for reducing the size of any data, which can be videos, still images, audio or speech. The basic principle of video compression methods is that they need to reduce as much redundant data as possible. For video, compression involves [29; 30] exploitation of spatial, temporal and color space redundancies. Temporal redundancy is the similarity between successive frames of a video sequence and spatial redundancy is the correlation of neighboring pixel values in the same natural image [31]. Inter-frame coding using motion compensation is adopted for exploitation of temporal redundancies and transform coding algorithms such as the Discrete Cosine Transform (DCT) [32] reduce spatial redundancies, while operating in the frequency domain. Color spaces are converted to a more suitable color space to exploit color space redundancies. Furthermore, quantization and entropy coding is used in the standardized hybrid codecs [30]. The main building blocks of a video are images, formatted as video frames. Individual images are compressed for efficient transfer, post-processing, and storage purposes. Image compression algorithms also exist in their own right, especially Joint Photographic Experts Group (JPEG) compression. JPEG is an image compression codec [33] intended principally for 'lossy' compression of natural images created through digital photography [34]. As JPEG is a lossy compression method, the image quality is traded against the size of the compressed data. Steps in JPEG compression include subdivision of the image into 8 \(\times\) 8 blocks, shifting the gray levels of the image, then applying the DCT on a block-by-block basis, after which quantization of the coefficients occurs, followed by zigzag ordering of those coefficients and lastly performing entropy coding. The JPEG decoder works [33] in the reverse order and in place of DCT, it applies Inverse Discrete Cosine Transform (IDCT). JPEG encoding is used in this current paper's proposal and serves as a stepping-stone in achieving the goals of this study. Some related video compression techniques in the literature are given in the following Section, followed by a review of related techniques.
## 3 Results
### Comparison between Geoelectric Maps (R3) and Corresponding Satellite Maps (R2)  
The observation of the MCARI2 index (Figure 8) during the cultivation of corn shows a very homogeneous distribution of all correlation values, oscillating from \(-1\) to \(+1\) but also assuming values around \(0\).  
In the condition of uncultivated soil, there are very few correlations close to \(0\), with higher densities of correlations close to \(0.90\) and \(-0.60\). In the period of tritticale cultivation, the correlations are mainly concentrated towards the value of \(-1\). Regarding the NDWI index, in the period of corn cultivation, greater densities of correlations were identified at  
Figure 8: In the left column is the distribution of correlation values between the maps produced with the geoelectric method and with the MCARI2 spectral index. In the left column is the distribution of correlation values between the maps produced with the geoelectric method and with the NDWI spectral index.  
Figure 7: Distribution of the correlation values between maps produced with sensors and with the geoelectric method.  
\(-1\) while presenting some correlations close to +1. In the period of uncultivated soil, the values appear to be homogeneously distributed between \(-1\) and +1. On the other hand, dense values of correlations were identified in the triticale growing period with values close to +0.80.  
Summarizing, the results showed that in the period in which the summer crop (corn) was present, the correlation between the sensor grid and the satellite maps was greater. In the period when the soil was not cultivated, the correlation between the sensor grid and the mapping with the ARP method was greater. Finally, in the period in which triticale was grown, the correlations between the ARP method, satellite maps, and the sensor grid were greater.  
The analysis of variance (ANOVA) showed significance (\(p\)-value \(<\) 0.001) in the comparison between sensors and satellite maps (R1-R2) for both spectral indices and for the corn condition; between sensors and ARP (R1-R3) the condition of soil and triticale were similar but different compared to corn (Table 2). Finally, the comparison between ARP and satellite maps (R3-R2) showed statistically significant differences in both indices for the triticale condition. All comparisons, except for R1-R2 (MCARI2 used), showed statistical significance in case of rainy events greater than 40 mm, which occurred in the previous five days, also showing first-order interaction factors with this condition. This indicates that the results are influenced by the presence of water in the soil.
# Boosting Second Harmonic Radiation from AlGaAs Nanoantennas with Epsilon-Near-Zero Materials
## 3 Conclusions  
We performed a theoretical analysis to show how to enhance the collection of the second harmonic signal generated from AlGaAs resonators. The possibility of enhancing optical nonlinear effects by introducing an epsilon-near-zero substrate has been demonstrated. More precisely, in this work we have shown that it is possible to enhance the second harmonic efficiency and directivity by exploiting simultaneously the strong electric field enhancement induced inside the structure at the FW and the ENZ condition at the SH wavelength. By designing a cylindrical nanoantenna optimized over an ENZ substrate we obtained a second harmonic conversion efficiency on the order of \(10^{-3}\) for a pump intensity of \(I_{0}=1.6\) GW/cm\({}^{2}\). These results offer the opportunity to re-think the design of novel nonlinear photonic metadevices, with improved capabilities for on-chip communication, information processing and sensing.  
Conceptualization, D.R., M.A.V., C.D.A.; Methodology, D.R., M.A.V., C.D.A.; Investigation, D.R., M.A.V., C.D.A.; Writing - original draft preparation, D.R., M.A.V., C.D.A.; Writing - review and editing, D.R., M.A.V., C.D.A.  
This research was partially funded by the Rita Levi Montalcini Program and by the Erasmus Mundus NANOPHI Project--Education, Audiovisual and Culture Executive Agency (EACEA) (5659/002-001).  
The authors declare no conflict of interest.
## 1 Introduction
In this paper, a modified layered fault tree model is proposed, aiming to distinguish and separate the two different power supply modes of smart grids, namely grid-connected mode and islanded mode. The focus in this paper concentrates on the load points within a potential islanded local framework of a specific smart grid architecture. In order to find the weak parts of the system and improve the design and using, revised importance measures for the components are presented with integration of load priority. Based on the layered fault tree model, a hierarchical Monte Carlo simulation procedure for reliability evaluation is also proposed, which integrates the state transition process of different power supply modes. With the standardized structure, advanced automation and communication infrastructures of smart grids, the procedures proposed can be accomplished. Fault tree analysis is good at reflecting the logical relation among system failure, structure of the system and the failures of components. Compared with currently existing methods, the proposed model and reliability evaluation process integrate power adequacy assessment into system failure logic, providing a comprehensive insight into system and its failure. With the layering procedure and inadequacy judgment function introduced, the fault tree model can also be greatly simplified and unitized compared to the conventional fault tree model of great complexity. But it should also be pointed out that the evaluation of the fault tree may need huge amount of computation, considering the dynamic aspect of DGs and loads as well as the increasing scale of system. The paper is organized as follows: Section 2.1 gives a brief introduction of fault tree analysis. In Section 2.2, the procedure of layered fault tree construction and inadequacy judgment function are presented. Section 2.3 introduces the revised importance measures for the components in a local framework of smart grids. The corresponding simulation procedure is revealed in Section 2.4. In Section 3, the proposed procedure is tested on a case of the FREEDM system. Finally, the conclusions are presented in Section 4.
# Biomass and Cellulose Dissolution--The Important Issue in Renewable Materials Treatment
## 3 Dissolution of Biomass and Its Components
### Deep Eutectic Solvents (DESs) in Biomass Processing
#### 3.7.2. Hemicelluloses Dissolution in DESs  
Some deep eutectic solvents can also be useful in the dissolution of hemicelluloses. Because of bonds connecting lignin and hemicelluloses, lignin removal is often followed by hemicellulose extraction. By applying a solvent with a basic hydrogen bond acceptor and cholinium lysine with urea as the hydrogen bond donor, it was possible to extract approximately 62% of hemicelluloses from poplar wood [145]. Similar results (the removal of 63.3% hemicelluloses) were achieved in the process with DES consisting of choline chloride and monoethanolamine [146].
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 8 Future Work  
This paper studied the link budget for one satellite through its visible time and compared to ground base stations. In the future, various satellite constellations can be studied, adding more indicators to the entropy-weight method for handovers. These indicators can comprise capacity, data speed, and the remaining service time. More link budget parameters, like the bit error rate, can be introduced, which would delve into channel modeling for LEO SATCOM systems.  
Conceptualization, K.M. and S.A.-R.; methodology, K.M.; software, K.M.; validation, K.M. and S.A.-R.; formal analysis, K.M.; resources, K.M. and S.A.-R.; data curation, S.A.-R.; writing--original draft preparation, K.M.; writing--review and editing, S.A.-R.; visualization, K.M.; supervision, S.A.-R.; project administration, K.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study's data are contained within the article.  
The authors declare no conflicts of interest.
# A Novel Dual-Wavelength Method for Evaluating Temperature Effect in Fiber-Optic SPR Sensors
## 2 Material and Methods  
### The Model of Fiber-Optic SPR Sensor  
A typical model of an SPR-based fiber-optic sensor can be constructed with the structure shown in Figure 1. A small portion of the cladding is firstly removed and the exposed fiber core is then coated with a metal layer. Therefore, an analyte medium (dielectric analyte) is surrounded on the metal layer that has been detected. When a beam of light is inserted into the optical fiber, the metal-dielectric interface supports an electromagnetic wave called the surface plasmon wave (SPW). In the SPR-based fiber optical sensor model, the temperature effects on SPR can be equivalent to the changes in the physical parameters of the sensing structure, i.e., the metal layer and fiber core. Therefore, combining the phonon-electron scattering along with electron-electron scattering (in the metal layer) and phenomena of thermo-optic (in the fiber core) with the multi-layer matrix method, a complete theoretical model can be constructed to investigate the multiple temperature effects on a fiber-optic SPR sensor.
## 3 Optimum Design of SOE
### Comparison of Three Optimum SOEs  
Figure 21 shows the dependency of optical efficiency on the incidence angle deviation for the CPV unit with the three optimum SOEs, including the KOD II, KFTS II and SP II. Table 4 also summarizes the optical performances of the CPV unit with KOD II, KFTS II and SP II SOEs.  
According to the simulation results shown in Figure 21 and Table 4, the KOD II significantly extended the acceptance angle to 1.7\({}^{\circ}\), which is much higher than those obtained from KFTS II (1.1\({}^{\circ}\)) and SP II (0.7\({}^{\circ}\)). The KFTS II will lead to a best uniformity of irradiance distribution under normal incidence, while the SP II has the largest non-uniformity. Although the SP II SOE exhibits a best optical efficiency when there is no incidence angle deviation, the lowest acceptance angle will limit its practical application.  
After considering the three factors of choosing suitable SOE, _i.e._, high acceptance angle, high optical efficiency and high uniformity of irradiance distribution, the KOD II type SOE was selected as the best SOE and broadband solar spectrum is performed in the following section.
# Effectiveness of Collagen Membrane in the Treatment of Schneiderian Membrane Perforation
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Pre op**} & \multicolumn{2}{c}{**Post op**} & \multicolumn{1}{c}{**Follow-Up**} \\ \cline{2-5}  & & \multicolumn{2}{c}{**Diff. (M \(\pm\) SD)**} & \\ \hline
**Group 1 – Group 2** & \(-0.84\pm 0.69\) & \(1.12\pm 0.89\) & \(0.34\pm 0.78\) \\
**Group 1 – Group 3** & \(0.19\pm 0.72\) & \(11.11\pm 0.81^{*}\) & \(8.00\pm 0.73^{*}\) \\
**Group 1 – Control** & \(-1.45\pm 0.67\) & \(-1.05\pm 0.75\) & \(-0.22\pm 0.65\) \\
**Group 2 – Group 3** & \(1.04\pm 0.74\) & \(9.99\pm 0.96^{*}\) & \(7.66\pm 0.96^{*}\) \\
**Group 2 – Control** & \(-0.61\pm 0.72\) & \(-2.18\pm 0.84^{*}\) & \(-0.56\pm 0.91\) \\
**Group 3 – Control** & \(-1.65\pm 0.82\) & \(-12.72\pm 0.67^{*}\) & \(-8.23\pm 0.78^{*}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean difference between groups according to measurement time. (*: [PERSON] post hoc test (\(p=0.05/3\)).) \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**G**} & **Perforation** & **Repair** & **N (site)** & **Grafting of BS (site)** & \multicolumn{2}{c}{**Implant (Patient/implant)**} & \multicolumn{1}{c}{**Complication**} \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Weakened} & \multirow{2}{*}{CM} & \multirow{2}{*}{15} & \multirow{2}{*}{simultaneous} & \multirow{2}{*}{15} & Simultaneous & 14/34 & 1 \\  & & & & & Delayed & 1/2 & 0 \\  & & & & & None & & 0 \\ \hline \multirow{2}{*}{2} & Small & \multirow{2}{*}{CM + FA} & \multirow{2}{*}{11} & \multirow{2}{*}{simultaneous} & \multirow{2}{*}{11} & Simultaneous & 1/2 & 0 \\  & & & & & Delayed & 8/23 & 1 \\  & & & & & None & 2 & 0 \\ \hline \multirow{3}{*}{3} & Large & \multirow{3}{*}{CP} & \multirow{3}{*}{12} & delayed & \multirow{3}{*}{1} & Simultaneous & 0 & 0 \\  & & & & & Delayed & 0 & 0 \\ \cline{1-1}  & & & & & None & 1 & 0 \\ \cline{1-1}  & & & & & Simultaneous & 6/13 & 1 \\ \cline{1-1}  & & & & none & 11 & Delayed & 4/9 & 1 \\ \cline{1-1}  & & & & & None & 1/0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary regarding progress of repair according to perforation size. (All complications were periimplantitis during follow-up period. Abbreviations; CM: Collagen membrane, FA: Fibrin adhesive, CP: Collagen plug, G: Group, BS: Bone substitute). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Pre op**} & \multicolumn{1}{c}{**Post op**} & \multicolumn{1}{c}{**Follow-up**} & \multicolumn{1}{c}{**Source: F(p)**} \\ \hline
1 & \(4.08\pm 2.55\) & \(14.94\pm 3.00\) & \(13.00\pm 2.40\) & \multirow{2}{*}{Group: 52.97 (\(<\).001)} \\
2 & \(4.92\pm 2.55\) & \(13.81\pm 3.61\) & \(12.65\pm 3.43\) & \multirow{2}{*}{Time: 203.67 (\(<\).001)} \\
3 & \(3.88\pm 2.21\) & \(3.82\pm 2.19\) & \(4.99\pm 2.59\) & \multirow{2}{*}{Group*Time: 11.67 (\(<\).001)} \\ Control & \(5.53\pm 3.29\) & \(16.00\pm 3.63\) & \(13.22\pm 4.09\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Bone height from crestal bone at the planned implant placement site (mm).
# Mobile Robot Path Planning Based on Fused Multi-Strategy White Shark Optimisation Algorithm
## 3 Improved White Shark Optimisation Algorithm
### Third-Order Bessel Curve Optimisation Strategy  
The above optimisation strategy makes the planned paths better and the search efficiency is greatly improved, but there are still problems such as the paths not being smooth enough, i.e., it is easy to cause problems such as the turning slope of the mobile robot is too large in the process of driving, and the paths need to be smoothed.  
Third-order Bezier [22] curves demonstrate significant advantages in path smoothing techniques: compared to higher-order polynomial curves, they enable flexible trajectory adjustments using only four control points, with intuitive parameter tuning and local modifications that do not affect the global path, balancing flexibility and controllability. Compared to methods like polyline-arc transitions, they inherently possess C\({}^{2}\) continuity, avoiding curvature discontinuities and ensuring path smoothness. Compared to optimization-based smoothing techniques, they do not require iterative solutions, have low computational complexity, and exhibit linear time growth, making them suitable for real-time scenarios. Additionally, it faithfully preserves key nodes of the original path, avoiding safety risks caused by excessive smoothing, and is easily integrated with discrete path generation algorithms for simple and efficient implementation. These features make it an optimal solution in fields such as robot navigation and autonomous driving, balancing smoothness, real-time performance, and safety.  
\[D(t)=\sum_{i=1}^{n}\begin{bmatrix}n\\ i\end{bmatrix}p_{i}(1-t)^{n-i}t^{i} \tag{13}\]  
\[b(t)=p_{0}(1-t)^{3}+3p_{1}t(1-t)^{2}+3p_{2}t^{2}(1-t)+p_{3}t^{3} \tag{14}\]  
where D(t) is a Bessel curve expression; b(t) is a third-order Bessel curve; t (0,1); \(p_{i}\) is a given path point, i = 0, 1, \(\ldots\), n. And the third-order Bezier curve is defined by four control points: the starting point P\({}_{0}\), the end point P\({}_{3}\), and the intermediate control points P\({}_{1}\) and P\({}_{2}\).  
This equation ensures that the curve satisfies C\({}^{2}\) continuity at the splice points, eliminating the sharp corners commonly found in traditional grid path planning. By adjusting the position of the intermediate control points, the curve shape can be locally fine-tuned to achieve a smooth transition while retaining the key nodes of the path. Additionally, its cubic polynomial structure ensures uniform curvature changes, avoiding the curvature discontinuity issues associated with lower-order curves, and its computational complexity is only O(n), making it suitable for real-time applications. As t keeps changing continuously, a complete smooth mobile robot travelling path can be depicted.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 4 Performance Metric and Simulation Setup
### Generation of Simulation Environment  
The target location is downloaded from the Open Street Map (OSM), which refers to the realistic scenario of Rourkela. The.osm file generated is interfaced to SUMO to get the mobility.tcl with details of each node (vehicles), including the number of vehicles, position, speed, and direction.  
The simulation is conducted in NS-3 with the mobility.tcl file. To evaluate the environment, the 802.11p MAC/PHY, frequency of 5.9 GHz, is kept constant for sending Basic Safety Messages (BSM). The network performance evaluation is done by varying propagation models, routing protocols based on simulation time, and the number of nodes (vehicles). A snapshot of the realistic scenario with the simulation SUMO scenario and NetAnim generated is depicted in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulator** & **NS-3.29, SUMO** \\ \hline Radio Range & DSRC \\ MAC Protocol & 802.11p \\ Routing Protocols & AODV, DSDV, OLSR \\ Propagation Model & FRIS, Two Ray Ground, Log-Distance, Nakagami \\ Channel Type & Wireless \\ Vehicle Speed & Random \\ Transmission range & 145 m \\ Transmitting Power & 20 dBm \\ Data Rate & 2048 bps \\ Packet Size & 200 Bytes \\ Simulation time & 300 s in SUMO \\ Simulation time & 20, 40, 60, 80, 100, 120 (s) in NS3 \\ Number of Nodes & 20, 30, 40, 50, 60, 70 \\ Frequency & 5.9 GHz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters setting.