esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Seawater Channel Modelling Elements
### Scattering Phase Function Formats
#### 2.7.1 Petzold's Average Particle Phase Function  
One of these SPF formats is [PERSON]'s average particle (PAP) phase function, which can be applied in various fields, including atmospheric science and oceanography. Seawater channel modelling can represent the angular distribution of light scattering by particles suspended in the seawater column. This scattering affects the attenuation and propagation of light in underwater environments, which is crucial for tasks like remote sensing, underwater imaging, and communication. The PAP-SPF for seawater typically considers the  
Figure 6: PAP, HG, TTHG, HE, FF, and SS-SPF.  
scattering by suspended particles such as phytoplankton, detritus, and other particulate matter in the water. The PAP-SPF can vary depending on factors such as these particles' size distribution and composition. The function expression of PAP-SPF is as follows:  
\[\widetilde{\beta}(\theta_{s},g,\ \lambda)=\frac{1-g^{2}}{\left(1+g^{2}-2g\text{cos} \,\theta_{s}\right)^{\frac{3}{2}}}\ \ \ \ -1\ \leq\ \theta\ \leq 1 \tag{24}\]  
where \(g=-1\) corresponds to completely backward scattering, \(0\) corresponds to isotropic scattering, and \(1\) corresponds to forward scattering entirely. Petzold's data have limitations regarding its angular resolution and unavailability for a wide range of seawater types. As a result, researchers depended on empirical and analytical phase function models to evaluate the behaviour of UOWC models [16]. Figure 6 shows a family of curves for PAP-SPF compared to HG-SPF's. It is clear that PAP-SPFs overestimate the value of \(\widetilde{\beta}(\theta_{s},g,\ \lambda)\) by \(4\pi\) for all scenarios while the curve profile is the same.  
One of the observations we should remember is that Petzold's data have limitations regarding its angular resolution and unavailability for a wide range of water kinds. As a result, researchers depended on empirical and analytical phase function models to evaluate the behaviour of UOWC models.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 4 Infrared and Visible Image Fusion Dataset
### TNO Dataset  
The TNO dataset [84] is used to create fuzzy target detection algorithms, multi-spectral target detection and recognition algorithms, static and dynamic picture fusion methods, and color fusion algorithms. There are three different image sets that make up the dataset at the moment: the TNO image fusion dataset, the Kayak image fusion sequence, and the TRICLOBS dynamic multi-band image dataset.  
The TNO image fusion collection includes nighttime images in the enhanced vision (390-700 nm), near-infrared (700-1000 nm), and long-wave infrared (8-12 m) wavelengths from various military and surveillance scenarios, such as vehicle targets in rural settings. Three incoming kayaks are seen in the Kayak Image Fusion sequence, which combines registered visual, near-infrared, and long-wave infrared image sequences. The recorded visible (400-700 nm), near-infrared (700-1000 nm), and long-wave infrared (LWIR, 8-14 m) motion sequences are included in the TRICLOBS dynamic multi-band picture dataset for the dynamic monitoring of urban environments. Several photos from the TNO dataset are displayed in Figure 10.
# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 3 Preliminaries
### Fuzzy String Matching
#### 3.1.2 Jaccard Similarity  
Originally intended for set theory problems, the Jaccard similarity coefficient algorithm has found application in the measurement of the similarity of strings. By comparing the characters of two strings, the Jaccard algorithm identifies shared characters to determine their similarity. The formula for Jaccard similarity is given in Equation (3) [39],  
\[Jaccard(s_{1},s_{2})=\frac{|s_{1}\cap s_{2}|}{|s_{1}\cup s_{2}|} \tag{3}\]  
where, \(s_{1}\) and \(s_{2}\) denote the strings under comparison. The Jaccard algorithm computes the similarity score, and a higher value indicates a stronger match between the two strings.
# Study on Dominant Frequency Attenuation of Blasting Vibration for Ultra-Small-Spacing Tunnel
## 5 Data Analysis
### Nonlinear Relationship between ZCF and DF  
The relationship between the ZCF and the DF is not completely linear (as shown in Figure 12), and the frequency determined by the different methods deviates considerably. Particularly, the DFs are widely distributed below 10 Hz, in which range the ZCFs are almost absent. In the range of DFs higher than 10 Hz, it has a certain linear relationship with ZCFs. Obviously, the use of a single frequency value gives a poor description of the vibration frequency. As shown in Figure 11, although the frequencies obtained by different methods show a consistent law, there are different values of DF and ZCF when describing a specific vibration wave. This is because the DF describes the entire waveform, while the ZCF only describes the frequency of the cycle with the maximum amplitude. The ongoing revision of the Norwegian standard is considering an alternative approach by implementing a frequency-weighted filter that directly describes the damage potential at different frequencies [4].
# Nature-Inspired Optimization Algorithms for the 3D Reconstruction of Porous Media
## 2 Material and Methods
### Nature-Inspired Optimization Algorithms
#### 2.2.2 Particle Swarm Optimization (PSO)  
Particle swarm optimization (PSO) was proposed by [PERSON] and [PERSON] [25] as a new optimization method based on swarming theory, such as bird flocking and fish schooling. Similar to a genetic algorithm, a population of potential solutions (particles) is created randomly. Each particle ischaracterized by its position \(x\) and velocity \(u\). In each iteration, the velocity and position of the particle \(i\) are defined as follows:  
\[\mathbf{u}_{i}^{t+1}=\mathbf{u}_{i}^{t}+\alpha\varepsilon_{1}\big{(}\mathbf{g}^{*}-\mathbf{x}_{i} ^{t}\big{)}+\beta\varepsilon_{2}\big{(}\mathbf{x}_{i}^{*}-\mathbf{x}_{i}^{t}\big{)}, \tag{3}\]  
\[\mathbf{x}_{i}^{t+1}=\mathbf{x}_{i}^{t}+\mathbf{u}_{i}^{t+1}, \tag{4}\]  
where \(\alpha\approx\beta\approx 2\)[27] are the learning parameters; \(\varepsilon_{1},\ \varepsilon_{2}\in(0,1)\) are random numbers; \(\mathbf{x}_{i}^{*}\) is the current best of particle \(i\); and \(\mathbf{g}^{*}\) is the current global best of the entire population.  
From the above equations, it is obvious that the movements of the particles are guided by their own best-known position in the search space, as well as the entire swarm's best-known position. This algorithm is simpler than the GA since it does not apply any advanced operator; instead, some simple mathematical calculations are incorporated.
## 3 Results  
### The Performance of Geoman at the Site Scale  
#### 3.1.1 Model Evaluation  
According to the results of the 10-fold cross-validation, the predicted RE was in line with the observed RE (Figure 3). Most of the scattered points occurred approximately at the 1:1 line (R\({}^{2}\) = 0.87, RMSE = 0.39 g C m\({}^{-2}\) d\({}^{-1}\), MAE = 0.28 g C m\({}^{-2}\) d\({}^{-1}\)), indicating that Geoman can accurately estimate the RE value for northern China's grasslands.  
We further evaluated the model simulation for the seasonal dynamics of RE at the site scale. Results showed that Geoman can accurately capture the seasonal dynamics of RE in the seven grassland subtypes (Figure S1). The best agreement between the predicted and observed RE was for typical steppe and meadow steppe (R\({}^{2}\) = 0.97 and 0.95, respectively), followed by the alpine shrub meadow (R\({}^{2}\) = 0.91), alpine _Kobresia_ meadow (R\({}^{2}\) = 0.89), desert steppe (R\({}^{2}\) = 0.88), alpine swamp meadow (R\({}^{2}\) = 0.86), and alpine meadow steppe (R\({}^{2}\) = 0.86).  
The seasonal variation in the RE at all sites formed a unimodal curve, peaking in July or August. The model continued to perform well during the high value period (from July to August), the growing season (from May to September), and the low value period (from  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Grassland Type** & **KO** & **SH** & **SW** & **AS** & **MS** & **TS** & **DS** & **Total** \\ \hline Grid number & 471,226 & 79,952 & 104,752 & 65,294 & 147,111 & 368,673 & 239,440 & 1,476,448 \\ Percentage & 31.92\% & 5.42\% & 7.09\% & 4.42\% & 9.96\% & 24.97\% & 16.22\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The grid numbers for all grassland subtypes and their proportions in the total grid.  
Figure 3: The predicted RE vs. the observed RE at the site scale.  
December to January) in the non-growing season. Across the seven grassland subtypes, the alpine _Kobresia_ meadow, the alpine shrub meadow, and the meadow steppe had higher RE peaks, reaching 4-6 g C m\({}^{-2}\) d\({}^{-1}\) (Figure S1a,b,f), while the alpine meadow steppe and desert steppe had relatively low RE peaks of 0.5-2 g C m\({}^{-2}\) d\({}^{-1}\) (Figure S1d,f).
# UAV Formation Trajectory Planning Algorithms: A Review
## 4 Intelligent Algorithm
### Machine Learning Algorithm
#### 4.2.3 Deep Reinforcement Learning  
Deep Reinforcement Learning (DRL) algorithms combine reinforcement learning with deep learning. The optimization goal is obtained through reinforcement learning and environment exploration, the system operation mechanism is obtained by using deep learning, and the specific state characteristics and problem solving are obtained at the same time. Relying on the perception ability of deep learning, this method uses a certain strategy to map the current state into corresponding actions; even for high-dimensional raw data input, through continuous iterative learning, the optimal strategy for UAV formation trajectory planning can finally be obtained. Figure 24 is a deep reinforcement learning (DRL) model.  
[PERSON] et al. [127] proposed a trajectory control algorithm based on multi-agent Deep Reinforcement Learning, using multi-agent deep deterministic policy gradient (MADDPG) and a low-complexity method to optimize the UAV trajectory, which solves the problem that traditional dynamic algorithms include both integer variables and continuous variables in Mobile Edge Computing (MEC).  
[PERSON] et al. [128] proposed a constrained deep Q-network (cDQN) algorithm. The algorithm formulates the three-dimensional dynamic motion problem of the UAV under the coverage constraint as a constrained Markov decision process (CMDP). Then, it uses prior information to eliminate invalid actions in the deep Q network (DQN) to maximize the unmanned and real-time downlink connection capability between drones, solving the problem of low capacity of the drone formation communication system under coverage constraints.  
[PERSON] et al. [129] proposed a dual-deep Q-network (DDQN) based on a multi-agent reinforcement learning (MARL) approach, which transformed the trajectory planning problem into a decentralized partially observable Markov decision process (Dec-POMDP) and then solved it by the Deep Reinforcement Learning method optimizing Dec-POMDPto obtain the optimal trajectory, which solves the problem where the UAV formation is difficult to collect data in distributed IoT devices.  
[PERSON] et al. [130] proposed a multi-UAV trajectory planning method based on Deep Reinforcement Learning. This method uses the improved attention dynamic clustering algorithm to optimize the trajectory planning network model and then combines the Particle Swarm Optimization algorithm (PSO) and the Deep Reinforcement Learning (IA-DRL) algorithm to perform trajectory planning, which solves the slow convergence speed of traditional neural network algorithms.  
Table 7 summarizes what we surveyed about machine learning algorithms.
## 4 Experiment
### Clustering Results  
Table 2 presents the clustering accuracy of all of the algorithms on each of the three data sets, while Table 3 presents the normalized mutual information. The observations are as follows.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Size** & **Categories** & **Dimensionality** \\ \hline YALE & 165 & 15 & 1024 \\ ORL & 400 & 40 & 1024 \\ UMIST & 575 & 20 & 644 \\ COIL20 & 1440 & 20 & 1024 \\ PIE & 2856 & 68 & 1024 \\ \hline \end{tabular}
\end{table}
Table 1: Statistics of the datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **Kmeans** & **NMF** & **NCut** & **GNMF** & **Ours** \\ \hline YALE & 37.85 \(\pm\) 2.36 & 40.15 \(\pm\) 2.89 & 40.73 \(\pm\) 2.39 & 41.42 \(\pm\) 3.10 & 42.94 \(\pm\) 2.65 \\ ORL & 52.15 \(\pm\) 2.86 & 54.17 \(\pm\) 2.00 & 57.60 \(\pm\) 3.00 & 57.95 \(\pm\) 3.41 & 59.22 \(\pm\) 1.54 \\ UMIST & 40.71 \(\pm\) 1.92 & 41.12 \(\pm\) 2.71 & 41.37 \(\pm\) 1.74 & 44.50 \(\pm\) 2.59 & 50.16 \(\pm\) 1.16 \\ COIL20 & 63.19 \(\pm\) 4.85 & 63.25 \(\pm\) 3.17 & 70.19 \(\pm\) 2.80 & 75.92 \(\pm\) 2.79 & 78.03 \(\pm\) 1.70 \\ PIE & 24.22 \(\pm\) 0.85 & 51.08 \(\pm\) 2.27 & 66.60 \(\pm\) 2.14 & 75.61 \(\pm\) 3.32 & 77.81 \(\pm\) 2.33 \\ \hline \end{tabular}
\end{table}
Table 2: Clustering Accuracy on the 5 datasets (%).  
Firstly, NMF-based methods, including NMF, GNMF and \(\ell_{2,1}\)HNMFD, outperform the Kmeans method. This suggests the superiority of parts-based data representation for perceiving the hidden matrix factors.  
Secondly, Ncut and GNMF exploit geometrical information, and achieve more superior performance than Kmeans and NMF methods. This suggests that geometrical information is very important in learning the hidden factors.  
Finally, on all the data sets, \(\ell_{2,1}\)HNMFD always outperforms the other clustering methods. This demonstrates that by exploiting the power of Hessian regularization, group sparse regularization and discriminative information, new method can learn a more meaningful representation.
## 3 Methodology
### Validation and Comparison with Physical Methodologies  
The next phase of validation (starting from the qualification of the statistical models in the previous section) was aimed at verifying that the models with the best scores had the same or similar successes as those obtained in the predictions using the physical methodology [27; 69]. Starting from the hazard maps in terms of probability 0-1, a common probability threshold (70%) was established for all the models. This enabled discrimination in unstable areas from those that were not. This value was used to obtain the total area covered (TAC) and the ground failure capture (GFC). The total area covered (TAC) represents the percentage of the area predicted to be prone to landslides in relation to the total area, while the ground failure capture (GFC) expresses the actual percentage of landslides within the area predicted by TAC. The difference between GFC and TAC is a combined indicator that reflects the accuracy of the model's predictions. Higher values of this difference indicate more accurate predictions, as the predicted extent is smaller compared to the actual number of landslides. On the contrary, lower values of this difference suggest poorer predictions, as a larger extent prone to landslides is forecasted than what occurs. The coefficient used for the final validation was the success rate (SR) corresponding to the multiplication of GFC by the difference between GFC and TAC. The greater the amount of SR, the greater the coherence and accuracy between the prediction and the real position of the slope instabilities according to the actual inventory.
# Low Power Consumption Signal Detector Based on Adaptive DFSD in MIMO-OFDM Systems
## 3 Conventional Signal Detection Schemes  
### Maximum Likelihood Detection  
The ML detection scheme tries to look for the optimal \(\mathbf{\hat{X}}\) that has a minimum value of \(\left\|\mathbf{Y}-\mathbf{H\hat{X}}\right\|^{2}\). The general equation of ML detection is as follows,  
\[\mathbf{\hat{X}}_{ML} = \underset{\mathbf{x}\in\left|S\right|^{N_{t}}}{\arg\min}\left\{ \left\|\mathbf{Y}-\mathbf{HX}\right\|^{2}\right\}, \tag{3}\]  
where \(S\) denotes the set of constellation points of transmitter symbols. \(\left|S\right|^{N_{t}}\) denotes the space of transmission signal vector. The ML detection scheme has optimal error performance, so we considered using this detection scheme at the receiver. But, because the complexity of ML detection scheme increases in proportion to the number of \(\left|S\right|^{N_{t}}\), it is difficult to implement at the receiver.
## 2 Materials and Methods
### Morphometric Parameters  
In accordance with the study protocol, interdental regions in the anterior maxilla were assessed by identifying specific tooth pairings. The evaluated sites included the following: the space between central incisors (region 1), between the central and lateral incisors on the right (region 2) and left side (region 2\({}^{\prime}\)), and between the lateral incisor and canine on the right (region 3) and left side (region 3\({}^{\prime}\)), as illustrated in Figure 1.  
Assessment of IPP was performed using sagittal and axial slices of the CBCT:  
1. Sagittal cross-section:  
For each interdental region, four horizontal reference levels were defined on the sagittal CBCT sections (Figure 2), corresponding to vertical distances from the alveolar crest:  
* Level A--8 mm from the alveolar crest;
* Level B--6 mm from the alveolar crest;
* Level C--4 mm from the alveolar crest;
* Level D--2 mm from the alveolar crest.  
Figure 1: The interdental space of interest for mini-implant placement was marked in all regions of the anterior maxilla: regio 1—between the central incisors, regio 2—between the right central and lateral incisor, regio 3—between the right lateral incisor and canine, regio 2\({}^{\prime}\)—between the left central and lateral incisor, and regio 3\({}^{\prime}\)—between the left lateral incisor and canine.  
Axial cross-section:
* Buccopalatal depth (BPD)--the linear distance between buccal and palatal cortical plates (Figure 3).
* In region 1, due to the presence of the nasopalatine canal (NPC), BPD was specifically assessed from the buccal wall of the NPC to the buccal cortical surface of the anterior maxilla (Figure 4).  
Figure 4: This figure presents the buccalçodal depth in regio 1 (between central incisors), measured from the buccal wall of the NPC (marked green) to the buccal cortex of the anterior maxilla, at all examined levels.  
Figure 3: The definition of the morphometric parameters of interest on CBCT images of the interdental space of the anterior segment of the maxilla. Axial cross-section: Selected morphometric parameters for analyses: IDW (yellow) is the smallest mesiodistal interdental distance; BPD (green) is the distance between the cortical layer of the buccal to the palatal plate, measured perpendicularly through the middle of the IDW.  
Figure 2: Sagittal CBCT view with the marked levels of interest. The linear measurements were made at the levels of 8 mm (level A), 6 mm (level B), 4 mm (level C), and 2 mm (level D) from the alveolar crest.  
Interdertal width (IDW)--the narrowest mesiodistal distance between the roots of adjacent teeth (Figure 3). Measurements were performed at all four reference levels (A-D). For the purposes of this study, an IDW of \(\geq\)3 mm was considered clinically acceptable, based on the guideline that a minimum clearance of 1 mm is needed on each side of a 1 mm diameter mini-implant to prevent contact with neighboring anatomical structures [21] (Figure 5).
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 5 Results and Comparative Analysis  
### Scenarios with Homogeneous Nodes  
#### 5.1.1 Network Load  
Figure 1 presents a comparative analysis of eight network protocols--proactive (OLSR, DSDV), reactive (AODV, DSR, AOMDV, TORA), and hybrid (ZRP, GRP)--in terms of network load across different network sizes (20, 80, 200, and 500 nodes). The analysis reveals significant trends in network load as the network size scales. Initially, at 20 nodes, all protocols exhibit relatively low network loads, establishing a baseline for comparison. Proactive protocols show lower initial loads due to their constant route maintenance, making them more efficient for smaller networks. However, their performance becomes less optimal for very large networks due to the overhead of maintaining routing tables. As the network size increases to 80 nodes, reactive protocols experience a significant rise in network load due to the increased overhead of on-demand route discovery under moderate traffic conditions.  
As the number of nodes increases, more routing control packets are exchanged, leading to a higher network load. For 20 and 80 nodes, fewer control messages are needed due to lower network density. However, at 200 and 500 nodes, routing overhead rises exponentially due to frequent topology changes and route maintenance. Despite this increase, the network reaches a saturation point where adding more nodes does not significantly increase transmitted packets.  
The most substantial rise in network load is observed at 200 nodes, reflecting the scalability and adaptability of all protocols to higher node densities. Hybrid protocols, which combine the strengths of both proactive and reactive strategies, show balanced performance, making them most suitable for very high-density networks (500 nodes) where they can efficiently manage the increased load. At 500 nodes, the network load  
Figure 1: Network Load across the Four Scenarios.  
stabilizes across all protocols with minor variations, indicating their efficiency at high network densities. Notably, the GRP protocol shows a higher initial load but aligns with others at higher node counts, while OLSR maintains a consistent increase. This analysis highlights the efficiency and scalability of each protocol category, providing critical insights into their optimal operational conditions and potential performance constraints across various network scenarios. Table 5 summarizes the protocol suitability for the four different scenarios considering the network load.
# A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks
## 4 Design of Network Coding Routing Protocol  
In this section, we present the detailed design of NCRP, including the protocol design overview, initial routing construction, network coding design and routing update and maintenance.
# Hybrid Arctic Puffin Algorithm for Solving Design Optimization Problems
## 8 Three-Bar Truss Design Optimization Problem
### Fitness Function  
The overall fitness function that needs to be minimized combines the fitness function and the penalty for any constraint violations, as shown in Equation (55):  
\[\text{Fit}=f+\text{Penalty} \tag{55}\]  
Tables 7 and 8 show results from various optimization algorithms. APO-JADE achieved the best fitness value (BestF) of 1.339959, with design variables x1 at 6.017209 and x2 at 5.316636, indicating that it found the most optimal truss design. FOX closely followed APO-JADE with a fitness value of 1.339962, while Grey Wolf Optimizer (GWO) and SMA also performed well with slightly higher fitness values. Other algorithms like AVOA were competitive, achieving values close to APO-JADE. As the list progresses, algorithms like AO, HHO, SA, and ChOA have higher BestF values, indicating less optimal designs. Significant increases in BestF for optimizers like SCA, COA, OOA, WOA, BO, and GA show poorer performance compared to APO-JADE.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Optimizer** & **BestF** & **x1** & **x2** \\ \hline APO-JADE & 1.339959 & 6.017209 & 5.316636 \\ FOX & 1.339962 & 6.029312 & 5.305661 \\ GWO & 1.340021 & 6.049798 & 5.3041 \\ SMA & 1.339982 & 6.041642 & 5.29299 \\ AVOA & 1.339995 & 6.042115 & 5.319658 \\ HGS & 1.340285 & 5.946415 & 5.324573 \\ WSO & 1.339956 & 6.015926 & 5.309176 \\ DBO & 1.340462 & 6.07012 & 5.289151 \\ M