esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background  
### Bayesian Linearized Inversion (BLI)  
Given their ability to infer the spatial distribution in the subsurface, inversion procedures are essential for defining and predicting the physical properties of hydrocarbon reservoirs [27]. Beyond figuring out the parameter values for the model that fits the data the best, these prediction and modeling techniques also try to model uncertainty to improve the accuracy of the results [28]. The seismic inversion solution in a Bayesian context, where the procedure creates a relationship between the observed data \(\mathbf{d}\) and the modeling parameters, which are modified via a likelihood function \(p(\mathbf{d}|\mathbf{m})\). A posterior distribution \(p(\mathbf{m}|\mathbf{d})\) is used to derive this likelihood function. The equation can be written as follows:  
\[p(\mathbf{m}|\mathbf{d})\propto\alpha\;p(\mathbf{d}|\mathbf{m})p(\mathbf{m}), \tag{1}\]  
where the posterior distribution is represented by \(p(\mathbf{m}|\mathbf{d})\) and the likelihood function is \(p(\mathbf{d}|\mathbf{m})\); this function indicates the probability of obtaining the observed data based on the model parameters. The model parameters are represented by \(\mathbf{m}\), the observed data by \(\mathbf{d}\), the prior distribution of the geological model is \(p(\mathbf{m})\), and the normalization constant with values that tend to unity by \(\alpha\). Mathematically, this can be represented as:  
\[\alpha=\frac{1}{\int p(\mathbf{d}|\mathbf{m})\cdot p(\mathbf{m})}. \tag{2}\]  
[PERSON] theorem is utilized to incorporate prior knowledge regarding the model parameters, which imposes limitations on the solution and represents the uncertainty in the results. The likelihood function \(p(\mathbf{d}|\mathbf{m})\) incorporates the forward convolutional model \(g(\mathbf{m})\) inside the Bayesian framework. It serves as the initial reference point as established in the model described in [29]. The likelihood function quantifies the probability of observing the data \(\mathbf{d}\) given the model parameters \(\mathbf{m}\). This is accomplished by revising the initial distribution using the information that has been observed. The prior distribution functions, denoted as \(p(\mathbf{m})\), represent the initial knowledge or assumptions about the model parameters before any data are considered. Finally, the parameter \(\alpha\) acts as a normalization factor for the probability distribution \(p(\mathbf{m})\), guaranteeing that the resulting posterior distribution integrates with 1.
## 4 Experimental Results
### Texture Feature Analysis Using GLCM  
The Gray-Level Co-occurrence Matrix (GLCM) is a technique that analyzes the spatial relationships between pixels in an image and represents the results in a matrix. The GLCM is used to calculate key texture features such as the contrast, ASM, IDM, and correlation. In this experiment, the contrast, ASM, IDM, and correlation values were measured for both Korean and English texts by comparing original images with forged images created using the copy-move and insertion methods. The purpose was to analyze the differences in texture changes when document images are forged. Each metric represents the average value of the results obtained by applying the training data in Table 1, while the 'Difference Average' indicates the average difference between the values measured from the original and forged images. The method for calculating the Difference Average is expressed in Equation (7) as follows:  
\[\text{Difference Average}=\frac{1}{N_{B}}\sum_{i=1}^{N_{B}}\left|F_{O}^{i}(B)- F_{T}^{i}(B)\right| \tag{7}\]  
In Equation (7), \(N_{B}\) represents the total number of files within the pixel bin B, and \(i\) indicates the sequence within that bin. \(F_{O}\) denotes the GLCM results for each feature of the original image, while \(F_{T}\) represents the GLCM results for each feature of the tampered image. As shown in Figure 10, where the error bars represent 95% confidence intervals, the Difference Average value is larger for Korean in the contrast and correlation metrics when comparing the two languages, whereas it is larger for English in the ASM and IDM metrics. This can be attributed to the visual differences between Korean and English. Korean text has a complex syllable structure formed by combining consonants and vowels, resulting in densely packed characters. This complexity leads to frequent changes in grayscale values between pixels, causing significant variations in the differences between adjacent pixel grayscale values when forged. In other words, forgery significantly alters the contrast and spatial relationships between pixels in Korean text, leading to larger changes in these metrics. In contrast, English text has simpler character structures and wider spacing between characters, resulting in many uniform regions within the image. These uniform regions are more susceptible to noise or distortions when forged, causing a substantial reduction in overall uniformity. This has a significant impact on the IDM and ASM metrics, leading to higher Difference Average values for English. Korean, due to its complex structure, has inherently lower IDM and ASM values compared to English, which explains why the changes in these metrics are smaller for Korean during forgery.  
Figure 10: _Cont._
# Comparison of Lab vs. Backcalculated Moduli of Virgin Aggregate and Recycled Aggregate Base Layers
## 3 Results and Discussion
### FWD Test Results  
Figure 3 shows the interpreted results of the multi-load FWD testing on the test sections of this study. In Figure 3a, for Cells 185 and 186, higher \(E_{\text{FWD}}\) values were observed for the coarse RCA and fine RCA, with both achieving their highest values by the end of the last testing year (2022). The high layer modulus in both is attributed to cement hydration with presumably more retention of unhydrated cement in the fine RCA, causing it to have a higher value than the other RAB materials. Both the coarse and fine RCA also contained aggregates with higher angularity and, therefore, increased interlocking. It is noted that this observation agrees with the reviewed literature [13, 23].  
On the other hand, the backcalculated \(E_{\text{FWD}}\) values for the RCA + RAP base layer showed relatively lower values than those constructed with RCA. This is attributed to the lower aggregate interlocking due to the asphalt coating around the RAP aggregates. Also, the stiffening effect in the RAP aggregates due to asphalt aging was much smaller than the hydration process in the RCA aggregates, resulting in a relatively steady increase in the backcalculated \(E_{\text{FWD}}\) values over time in the RCA + RAP base layer. Finally, the lowest backcalculated \(E_{\text{FWD}}\) values were observed for section 188 with the VA base layer, which indicates it had the lowest aggregate interlocking rate compared to the RAB layers. Also, no significant stiffening effect over time was observed in the VA base layer, further validating the hydration and aging process in the RCA and RAP materials.  
Figure 2: Flowchart of this study. (1 in = 25.4 mm) [22].  
Figure 3b shows that the moduli of the different subgrades remained almost constant over the three years. Typically, sand showed a consistently higher value than clay loam.  
Finally, Figure 3c shows the distribution of the maximum surface deflection under the loading center. According to the layered elastic theory, surface deflection (as a system response) is a nonlinear function of pavement layers and their loading properties [27]. Therefore, the stiffness of the underlying layers, including the base and subgrade layers, is expected to affect the surface deflection basin adversely. In this regard, Cell 188, which had the VA base layer with the lowest base and subgrade moduli, showed the highest surface deflection values. On the other hand, Cell 186, with the fine RCA base layer and sandy subgrade, showed the lowest surface deflections.
## 6 Applications of Mathematical Models
### IsoSource Model  
IsoSource model, based on multisource linear mixing model (mass conservation model), is used to estimate the proportional contributions of n + 1 sources to a mixture. After setting the source increment (1-2%) and mass balance tolerance parameter (0.01-0.05%), IsoSource model is able to calculate the contribution ratio of different pollution sources in water samples through iterative method. The possible combinations for the contribution rate of pollution source can be obtained by Equation (10)  
\[\mathrm{Q=}\frac{(\frac{100}{l})+(s-1)}{s-1}=\frac{[(100/i)+(s-1)]!}{(100/i)!(s -1)!}, \tag{10}\]  
where \(Q\) is the quantity of combinations, \(i\) is source increment, and \(s\) is the number of pollution sources.  
The IsoSource model can verify all the possible combinations for the potential contribution ratio of each source. When the difference between the weighted averages of \(\delta^{15}\)N and \(\delta^{18}\)O of different sources and the compositions of \(\delta^{15}\)N and \(\delta^{18}\)O of test water samples are smaller than 0.1%, the solutions are considered possible. At present, the IsoSource model is mostly applied in the studies on atmosphere and plant water, and food web [84; 85; 86]. There are few studies that used IsoSource model to quantify the contribution ratio of pollution source to the nitrate of surface water. By using IsoSource model, [PERSON] et al. [87] successfully calculated the variation ranges of chemical fertilizer (23-78%), sewage (6-58%), and soil organic nitrogen (6-38%) in nitrate of surface water in the karst landform area of Guilin. [PERSON] et al. [88] illustrated the mechanism of nitrate source tracing through \(\delta^{15}\)N and \(\delta^{18}\)O. According to IsoSource model, a quantitative analysis on nitrate source at the mouth of Underground River was conducted. The contribution ratio of manure, soil organic nitrogen, NH\({}_{4}\)\({}^{+}\) in precipitation and fertilizer, and precipitation was 46.4%, 32.6%, 18.6%, and 2.4%, respectively. Different from SIAR model which needs programming, IsoSource model, characterized by simple and complete operation interface, is easy to use. Furthermore, it is able to calculate both the known contribution ratio and the potential contribution ratio of pollution source.  
Despite the fact that SIAR mixing model and IsoSource model are established based on mass- balance mixing model, the uncertainties in the results of SIAR model should be further studied. Besides, the reliability of IsoSource model needs to be further verified since its applications in the source identification of nitrogen in surface water are rare. Furthermore, if it is possible to consider the precipitation, rivers, groundwater, and soil water as a whole system, a hydrological model and a geochemical reaction path model can be established. The study of water cycle and nitrogen cycle could provide powerful evidence for identify nitrate source in water environment.
# Direct-Write Dewetting of High Melting Temperature Metals on Flexible Substrates
## 1 Introduction
The process is diagramed by Figure 1, where the dewetting event is separated into three main regions: heating, thermocapillary flow, and hole growth. The choice of laser wavelength, flux density, target material, and substrate material all play a large role in the viability of this technique to effectively create dewetted structures without damaging the underlying substrate. This is of particular importance when using polymer substrates with metal targets, since the melting point of the metal film is so high. In our previous examples of the direct-write dewetting of metals, the film targets were characterized by a relatively high absorptivity in the near-infrared range and melting points comparable to the softening/degradation points of polymers such as polytetrafluoroethylene (PTFE), polyimide (PI), and Par-C. Many of these polymers are used in the micro-electronics and MEMS industries for their mechanical flexibility, high temperature thresholds, and their barrier properties [22; 23; 24]. For applications that require a greater degree of thermal conductivity, electrical conductivity, bio-compatibility or heat resistance than low T\({}_{\text{m}}\) materials such as Bi or Sn can provide, it is more practical to use thin film compositions involving metals such as Ni, Au, Ag, or Cr. Our group has shown that layered substrates of Par-C deposited upon borosilicate glass are not damaged by near-infrared laser radiation light at relevant processing intensities [20]. Thus, they represent a viable choice in further investigations, especially in conjunction with their excellent biocompatibility for implantable micro  
Figure 1: The mechanism for direct-write dewetting using a focused near-infrared (NIR) laser radiation. electrode arrays [25; 26]. With a melting point of 1455 \({}^{\circ}\)C, nickel's thermal, electrical, and optical properties (Table 1) are typical of the Group VIIIB elements. Nickel thin films have been used in sensors [27] and electrochemical cells [28], and they exhibit a relatively high absorptivity in the NIR range. Conversely, Group IB elements, such as silver, are characterized by a relatively low absorptivity in the NIR range, while boasting the highest thermal and electrical conductivities of the transition metals. Nickel and silver films are representative of the extremes in high melting point and reflectivity, respectively, which are of a particular challenge to the laser dewetting on polymer substrates. Developing the direct-write dewetting of these two metals will prove essential to the progression of the technique to a wide variety of additional metals. In this regard, the effects of the higher melting temperatures were investigated with respect to any possible damage to the underlying substrate. Additionally, layered films were studied as a means to enhance the effects of NIR laser radiation and to bypass the high reflectivity of the silver thin films.
## 3 Experiments and Results  
To assess the sub-network architectures, their diagnostic performance is explored based on two different OCT datasets: a large-scale dataset and a small-scale dataset. Our classification algorithm is implemented in Keras and tested on a PC with Intel core i5-4590 CPU, 8 GB RAM and NVIDIA GTX 1070 GPU. Accuracy, sensitivity and specificity are measured using the confusion matrix for each classification result.  
Figure 4: (Left) DenseNet121 architecture. (Right) Dense_block, conv_block and transition_layer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DenseNet121** & **Total** & **Trainable** & **Non-Trainable** \\ \hline C5\_b2 & 5,063,620 & 5,007,556 & 56,064 \\ C5\_b4 & 5,294,916 & 5,235,972 & 58,944 \\ C5\_b6 & 5,543,108 & 5,481,028 & 62,080 \\ C5\_b8 & 5,808,196 & 5,742,724 & 65,472 \\ C5\_b10 & 6,090,180 & 6,021,060 & 69,120 \\ C5\_b12 & 6,389,060 & 6,316,036 & 73,024 \\ C5\_b14 & 6,704,836 & 6,627,652 & 77,184 \\ C5\_b16 & 7,037,508 & 6,955,908 & 81,600 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number of parameters in DenseNet121 sub-networks.
## 8 Challenges and Opportunities  
The reviewed articles highlight several persistent challenges in robotics simulation (summarized in Table 10). Computational efficiency and scalability remain a concern, as high-fidelity simulators demand significant resources [25], limiting real-time performance. Sim-to-real transfer limitations pose another challenge, as ensuring seamless deployment from simulation to physical systems remains complex [36]. Interoperability and standardization issues arise due to the lack of unified APIs and middleware, complicating system integration [38]. Additionally, latency and synchronization issues impact real-time control in distributed environments [39]. HRI modeling remains an open problem, as accurately simulating both physical and cognitive interactions is challenging [68]. Other concerns include learning curve and accessibility [65], where complex configurations hinder usability, and physics fidelity, where trade-offs exist between computational efficiency and physical accuracy. Finally, integration with robotics frameworks varies across simulators, requiring additional middleware for compatibility with established robotics ecosystems [65]. Despite these challenges, several advances present opportunities for improving robotics simulation. The integration of machine learning offers new possibilities for enhancing robotic adaptability, predictive capabilities, and real-time monitoring [22; 59]. AI-driven learning frameworks can improve decision making and perception in simulation environments, bridging the sim-to-real gap. Cloud and edge computing provide scalable solutions by offloading computational tasks, reducing hardware constraints, and enhancing real-time execution [96]. Additionally, high-performance simulators such as Unreal Engine and Isaac Sim support large-scale simulations with AI-driven frameworks and cloud integration, enabling more complex robotic applications [24]. Improved multi-robot and HRI modeling in simulators like Unity and Unreal Engine enhances the realism of dynamic multi-agent scenarios, making them particularly valuable for XR applications [21]. Finally, the development of more user-friendly interfaces and modular frameworks can lower the barrier to entry for researchers and developers, improving accessibility and accelerating the adoption of robotics simulation technologies.
## 2 The Model Backgrounds
According to the results of the aforementioned reference [6], the presence of a layer that covers the surface of the conductors, either of oxide from the material of the electrodes or any other conductive material, modifies the potential associated with each branch, and the global effect is equivalent to the substitution of the initial radius of the conductors by a Coated Electrode Equivalent Radius (CEER) whose expression is, in the case of oxidation with an oxide layer of thickness \(e\),  
\[r_{eq}=(r_{C}-e)^{\frac{\rho_{ox}}{\rho}}\cdot r_{C}^{-(\frac{\rho_{ox}}{\rho} -1)} \tag{3}\]  
while, for an electrode covered with a layer of material of thickness \(e\),  
\[r_{eq}=r_{C}^{\frac{\rho_{ox}}{\rho}}\cdot(r_{C}+e)^{-(\frac{\rho_{ox}}{\rho} -1)} \tag{4}\]  
In both cases, \(r_{C}\) represents the radius of the conductor, not oxidized or not coated, \(\rho_{ox}\) is the resistivity of the oxide layer that covers the electrode, \(\rho_{co}\) is the resistivity of the coated layer, and \(\rho\) the resistivity of the soil, which is assumed to be semi-infinite and uniform [6]. Although Expressions (3) and (4) are valid for single-layer soils, they will also be used to modify the radius of the conductors, even in multilayered soils, provided that such conductors are completely embedded in any of the soil layers. It should be mentioned  
Figure 1: A picture of the circuit-based model of a rod buried into the soil. that Expressions (3) and (4) represent a considerable simplification of the effect caused by the coating of the electrodes. The complex filtering mechanism of the current to the ground through the surface of the conductor and the interface that separates the covering layer from the ground is reduced to a geometric modification. A variable radius for the electrodes is relatively easy to implement in the calculation algorithms and does not significantly increase the computation time of the potentials. To finish this section, it is necessary to mention that the proposed model has been implemented using the commercial software MatLab, which is especially suitable for carrying out the necessary calculations.
## 4 Node Activity Scheduling
### Justification of Design Choices
#### 4.1.2 Choice of Vertex Coloring  
Considering three-hop coloring, we now discuss the respective merits of edge and vertex coloring. Edge coloring can increase the concurrency of transmissions. In fact, if a color is assigned to a vertex then no one-hop, two-hop or three-hop neighbors can use the same color in a collision free assignment, assuming immediate acknowledgement. In Figure 7, where the node color is given by the integer near the node, only node \(E\) can have the same color as node \(A\). With edge coloring, two three-hop neighbors can transmit or receive in the same slot if their corresponding nodes are at least two-hop away. For example in Figure 8, using edge coloring, where the link color is given by the integer on the link, node \(A\) can send a message to \(B\) while \(D\) three-hop away from \(A\) is sending a message to \(E\). This is not possible with vertex coloring, where \(D\) three-hop away from \(A\) cannot have the same color as \(A\), however it can receive data from \(E\) as illustrated in Figure 7.  
Edge coloring can assign a bandwidth amount proportional to the number of neighbors whereas with vertex coloring the assigned bandwidth is usually the same for all nodes. Edge coloring reduces energy lost in overhearing because only the sender and receiver are awake, unlike vertex coloring where all 1-hop neighbors of the transmitter are awake: they are potential receivers. However, if with edge coloring slots are not assigned carefully, a node can awake as many times as one-hop neighbors. Hence, the choice of contiguous slots would help in energy saving [61]. Moreover, edge coloring does not support broadcast communications. Nevertheless, broadcast messages are always needed for neighborhood discovery and route creation. Moreover, the MAC layer must maintain a buffer for each one-hop neighbor it has messages to send. Finally, the number of colors used to color all the edges of the network can be very important compared to the number of colors used to color all nodes in the network. That is due to the number of edges which is much greater than the number of nodes in the network. Consequently, delay can increase with the number of colors.  
In this work, broadcast messages are used to manage the network and build the neighborhood. For this reason, we choose the vertex coloring approach.  
Figure 8: Edge coloring.  
Figure 7: Vertex coloring.