esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Localization of Non-Cooperative Targets in Non-Coplanar Rendezvous Processes
## 2 Distributed Localization Method
### Measurement Model  
The state model is established in an inertial system; however, the system's observations are based on the LOS angles of the target, measured by the tracking satellite in its own local coordinate system. Therefore, it is necessary to transform the system's observations into the inertial system. Assuming that the local coordinate system of the tracking satellite coincides with the camera's coordinate system, the observations in the inertial system can be obtained through a coordinate transformation.  
In centralized state estimation, the observations from multiple satellites are collected at a fusion center, increasing the dimensionality of the observation vector. In contrast, in distributed state estimation, each satellite performs local filtering and maintains an independent observation model. The measurement equation of each tracking satellite is composed of two angles in LOS observation. According to the geometric relationships shown in Figure 1, the measurement equation for each satellite is as follows:  
\[\mathbf{Z}_{i}=[\alpha_{i},\beta_{i}]^{T}=h_{i}(\mathbf{X}_{i})+\mathbf{v} \tag{5}\]  
\[h_{i}(\mathbf{X})=\left[\begin{array}{c}\arctan\!\frac{z_{t}-z_{i}}{\sqrt{(x_{t }-x_{i})^{2}+(y_{t}-y_{i})^{2}}}\\ \arctan\!\frac{y_{t}-y_{i}}{x_{t}-x_{i}}\end{array}\right] \tag{6}\]  
where subscript \(i=1,2,3\) represents different tracking satellites; subscript \(t\) represents the target; \(x,y,z\) are the lengths of the position vectors projected along the three axes of the J2000 coordinate system; and \(v\) denotes the measurement noise.  
By linearizing non-linear state Equation (6) at the operating point \(\hat{\mathbf{X}}_{k,k-1}\), the first-order linearized measurement equation is obtained as follows:  
\[\mathbf{Z}_{k}^{i}=\mathbf{H}_{k}^{i}\mathbf{X}_{k}^{i}+\mathbf{v}_{k} \tag{7}\]  
\[\mathbf{H}_{k}^{i}=\frac{\partial h_{i}(\mathbf{X}_{i})}{\partial\mathbf{X}_{i}}\Big{|}_{ \mathbf{X}_{i}=\mathbf{X}_{k-1}^{i}}\]  
\[=\left[\begin{array}{ccc}-\frac{(x_{t}-x_{i})(z_{t}-z_{i})}{\rho_{i}^{z_{t} }\rho_{i}^{z_{t}}}&-\frac{(y_{t}-y_{i})(z_{t}-z_{i})}{\rho_{i}^{z_{t}}\rho_{i} ^{z_{t}}}&\frac{(x_{t}-x_{i})^{2}+(y_{t}-y_{i})^{2}}{\rho_{i}^{z_{t}}}\\ -\frac{(y_{t}-y_{i})}{\left(\rho_{i}^{xy}\right)^{2}}&\frac{(x_{t}-x_{i})}{ \left(\rho_{i}^{xy}\right)^{2}}&0\end{array}\right] \tag{8}\]  
\[\left\{\begin{array}{c}\rho_{i}^{xy}=\sqrt{\left(x_{t}-x_{i}\right)^{2}+(y_ {t}-y_{i})^{2}}\\ \rho_{i}=\sqrt{\left(x_{t}-x_{i}\right)^{2}+\left(y_{t}-y_{i}\right)^{2}+ \left(z_{t}-z_{i}\right)^{2}}\end{array}\right. \tag{9}\]
# Satellite Attitude Determination and Map Projection Based on Robust Image Matching
## 3 Method
### Finding Matched Feature Points from Satellite and Base Images
#### 3.2.1 Feature Descriptor  
Extracting robust feature points with fast computation is a fundamental problem in image processing, and many feature extraction algorithms have been proposed (e.g., [10; 23]). In this study, feature points are identified by the SURF feature descriptor, which robustly extracts features from different views of the same scene. Moreover, SURF has been implemented in an open source framework, OpenCV [24]. Note that our proposed scheme operates on any feature detector.  
SURF provides three quantities for each feature point; the position (to sub-pixel order), the effective scale of each feature (in pixel units), and a 64- or 128-dimensional vector characterizing the texture surrounding the point (called a descriptor or descriptor vector). In this study, we used the 128-dimensional descriptor, which is considered to be more distinctive [7]. The descriptor vector is calculated from the brightness gradient of the texture with various pixel scales. Since SURF descriptors take similar values if feature points are similar to each other, they can be used to discover corresponding points in different images by measuring the distances of the 128-dimensional descriptors. Since each element in the 128-dimensional descriptors is normalized and ordered according to its represented direction, SURF is robust to scaling, orientation, and illumination changes; that is, even if different images include the same point from different views, SURF can detect that point in all images with similar descriptor vectors, where the similarity is measured by the distances of the 128-dimensional descriptors. The similarity of descriptors for a given point is essential for the rough matchings described in the following subsection.
# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
## 2 Materials and Methods
### Performance Metric  
To evaluate the performance of the proposed approach, the developed algorithm was compared against a non-blockage version of the path planner, where the UAV could freely fly to any area on the map. The total number of successful links divided by the distance traveled was used as the performance metric to evaluate the different paths. This assumed that a successful link was obtained between two points if direct line of sight was possible with both points. This is exemplified in Figure 4 for both successful and unsuccessful links.
# A Traffic Equilibrium Model for Multi-Modal Networks with Uncertain Demands
## 3 Generalized Travel Cost Function
### Physical Links
#### 3.1.3 Transport Mode 3 (Subway)  
In the absence of congestion, the travel time on the physical links for transport mode 3 is assumed to be fixed:  
\[t_{l}^{3}=t_{l}^{2^{0}} \tag{10}\]  
where \(t_{l}^{3^{0}}\) (min) is the free-flow travel time of mode 3 on physical link \(l\), and is equal to the average travel speed of the subway.  
By applying a crowd discomfort function similar to (4), the crowd discomfort of mode 3 on physical link \(l\) can be expressed as follows:  
\[u_{l}^{3}=\gamma u^{3^{0}}t_{l}^{3} \tag{11}\]  
where \(u^{3^{0}}(min^{-1})\) is the crowd discomfort per time unit.  
Similarly to (5), the travel fare of mode 3 is  
\[m_{l}^{3}=\pi m^{3^{0}}h_{l} \tag{12}\]  
where \(m^{3^{0}}\) (km\({}^{-1}\)) is the travel fare of mode 3 per unit distance.  
Combining the above, the travel cost of transport mode 3 on each link is established as follows:  
\[c_{l}^{3}=\left(\lambda_{t}+\lambda_{u}u^{3^{0}}\right)t_{l}^{3^{0}}+\lambda_{ m}\pi m^{3^{0}}h_{l} \tag{13}\]
###### Abstract  
Aim: This study compares the microhardness, water sorption and solubility of nano-zirconia-reinforced biodentin groups to commercially available biodentine. Materials and Methods: Samples were prepared by adding different percentages of nano zirconia to biodentin. Four groups were categorized as follows: group 1 used Biodentin as the control group, group 2 used 10% nano zirconia and 90% Biodentine, group 3 used 20% nano zirconia and 80% Biodentine, and group 4 used 30% nano zirconia and 70% Biodentine. A Vickers microhardness tester was used to measure the microhardness of the groups according to ISO 4049/2000. The water sorption and solubility of the divided groups were assessed using disk-shaped molds. The second weight was measured on an electronic weight machine after two weeks, and values were recorded. FTIR analysis was performed. Vickers microhardness, water sorption and solubility were analyzed using a one-way ANOVA, and for multiple comparisons, a post hoc [PERSON]'s HSD test with a significance level of \(p<0.05\) was performed. Group 3 had the highest mean microhardness measurement among all groups. The microhardness values for all groups in descending order are as follows: group 3, group 2, group 1 and group 4 (\(p=0.008\)). Group 2 showed the lowest mean of water sorption compared among all groups. Group 2 has the lowest mean of water sorption, followed by group 3, group 1 and group 4 (\(p=0.002\)). Group 4 showed the highest water sorption among all groups. Group 4 exhibited the highest solubility mean among all groups. Group 1 had the lowest mean of solubility, followed by group 2, group 3 and group 4 (\(p=0.000\)). The FTIR spectra of different types of biodentin showed the absorbance peaks of the precipitates of each modified biodentin. The absorbance peaks fell between 474.63 cm\({}^{-1}\) and 3438.33 cm\({}^{-1}\). Conclusion: Biodentin is a competitive biomimetic material, but it lacks hardness and has more solubility. Thus, adding nano zirconia improved microhardness and reduced water sorption. The solubility of 20% nano zirconia added to biodentin was almost close to biodentin but with no significance. The 20% nano-zirconia-modified biodentin showed overall better properties compared to biodentin. The 20% nano-zirconia-modified biodentin can be used in deep cavities as a single restorative material instead of multi-layered restorations to increase longevity without microleakage and failure of restoration.  
Keywords: biodentine; biomimetic materials; microhardness; nano zirconia; restorative +
Footnote †: journal: Applied Sciences
## 2 Background
### WBASN Components  
The WBASN's node structure contains various modules, including an energy source, a processor, memory, a transceiver, a sensor, actuators and an operating system [50; 51; 52]. Figure 5 shows the structure of the WBASN node.  
(1) Energy Source In WBASNs, a small battery size limits the energy source and allows very low-power levels in order to increase the lifetime of the sensor node.  
(2) Processor The function of the processor is to manage all the computation activities. Various companies make microcontrollers for WBASNs. In this context, MSP430 from Texas Instrument (TI) is an example; it is considered the world's most popular ultra-low-power microcontroller with a 16-bit microcontroller platform. The speed of this processor varies from 8 to 15 MHz.  
Figure 5: Generic sensor node structure.  
Memory capacities vary in WBASNs, e.g., a node with MSP430 contains up to 64 KB RAM with up to a maximum of 512 KB flash memory.  
Transceiver  
A transceiver is a component that can transmit and receive data. Usually, WBASNs consist of a CC2420 chip that is useful for low-power data communications.  
Sensors  
Generally, the sensing module contains various sensor nodes that are used to monitor the physiological parameters of the human body.  
Actuators take action against the data received from the sensors, e.g., upon receiving some critical data regarding diabetes, the actuators can inject insulin.  
TinyOS is a popular open-source operating system used in WBASNs. As TinyOS's design supports low-power communications, it is suitable for WBASN devices.
## 3 Results and Discussion
### Effect of pH  
Metal-chelate formation and its stability are two critical factors for CPE. pH plays an important role in the formation of metal-chelate complex and occasionally in its extraction [29]. Extraction percentage relies on the pH at which complex formation is carried out. The pH effect was studied within a pH range from 2.0 to 10.0 and the results were presented in Figure 2. At pH >6, the recovery of Pb\({}^{2+}\) ions decreases due to the formation of hydroxide precipitates of lead or plumbate ions. At lower pH, the extraction of Pb decreases as the acidity of the solution increases. This may be attributed to the competition between H\({}_{3}\)O\({}^{+}\) with Pb\({}^{2+}\) for the reaction with DLNL. Since the maximum recovery was obtained at pH 6, this pH value was selected for all subsequent measurements for lead, resulting in the relative standard deviation (RSD) of 1.9%.
## 1 Introduction
Elements of virtual reality and serious games make the rehabilitation process more engaging and entertaining [3]. This type of monitoring allows for the early detection of deviations in exercise performance or the patient's condition, which can prevent injuries and complications [6]. By reducing the number of visits to a clinic, the burden on medical institutions and the resources required from patients are also diminished [10]. While these individual technologies are powerful, their true potential is unlocked when they are integrated into a single, cohesive system. The convergence of these technologies is leading to the creation of holistic AI-assisted platforms that represent a new paradigm for personalized, data-driven care. A conceptual model for such a platform is illustrated in Figure 1.  
The architecture in Figure 1 is built on the synergy of three key technological pillars: sensor-based Internet of Things (IoT) devices, machine learning (ML), and Large Language Models (LLMs). IoT devices, such as EMG sensors, address the challenge of remote monitoring by continuously collecting objective physiological and behavioral data. This data is then fed into machine learning algorithms, which form the core of the intelligent model, to assess progress, detect anomalies, and personalize the rehabilitation plan in real-time. Finally, Large Language Models provide an intuitive interface for patient interaction, solving the issues of a low engagement and lack of clear instructions by delivering personalized feedback, motivation, and support through virtual assistants or chatbots. This integrated feedback loop creates a flexible, scalable, and remotely controlled rehabilitation environment capable of empowering patients and reducing the burden on caregivers and the healthcare system. The goal of this review article is to explore innovative medical rehabilitation systems with a focus on the use of AI, ML, and sensor technologies to create scalable platforms. This review presents an analysis of existing technologies, including personalized recovery through AI, the use of the IoT for patient monitoring, and the role of virtual assistants and chatbots powered by Large Language Models in patient support, and methods of integrating various technologies to create effective rehabilitation platforms that can adapt to the individual needs of patients in real-time.
# An Audio Watermarking Algorithm Based on Adversarial Perturbation +
## 3 The Proposed Methodology
### Whitening Layer  
The features output by the neural network possess many nice properties, but there is no guarantee that these feature elements are mutually independent. During watermark embedding, the loss function \(\mathcal{L}\) closes the distance between the feature and the watermark, so the elements of the feature will be modified. If the elements are correlated, the modification of one element will affect the other elements and the watermark may fail to embed.  
On the other hand, the dimensionality of the output features of the pre-trained network is fixed. However, the dimensionality of the watermark is variable with different scenarios. Retraining the model for each scenario is tedious and inefficient, while aligning the watermark by complementary zeros may waste embedding capacity and be ambiguous.  
To solve the above problems, this paper introduces principal component analysis (PCA) to whiten the features of PTM [58]. The features of all the audio in the training set \(\{\mathbf{o}_{ptm,i}\}_{i=1}^{N}\) are used as samples, and their covariance matrix \(\mathbf{S}\) is computed.  
\[\mathbf{S}=\frac{1}{N-1}\sum_{i=1}^{N}(\mathbf{o}_{ptm,i}-\mathbf{o}_{ptm})( \mathbf{o}_{ptm,i}-\mathbf{o}_{ptm})^{T} \tag{4}\]  
where \(\mathbf{o}_{ptm}\) denotes the mean of the features \(\{\mathbf{o}_{ptm,i}\}_{i=1}^{N}\) and the superscript \({}^{T}\) denotes the transpose.  
An eigenvalue decomposition is applied to the covariance matrix \(\mathbf{S}\) to obtain the eigenvalues \(\lambda_{i}\mathbf{s}\) and the corresponding eigenvectors \(\mathbf{v}_{i}\mathbf{s}\). The eigenvectors are sorted  
Figure 5: A training scheme for contrastive learning models. Different augmentations of the same audio end up with similar features and representations, but with less similarity than that of another audio sample.The PTMs have shared weights, and so do the projection operators.  
descending by their eigenvalues, and then the first \(k\) eigenvectors \(\{\mathbf{v}_{i}\}_{i=1}^{k}\) and eigenvalues \(\{\lambda_{i}\}_{i=0}^{k}\) are selected to calculate the parameters of the whitening layer \(\mathbf{W}_{wl}\).  
\[\mathbf{W}_{wl}=\text{diag}(\sqrt{\lambda_{1}},\sqrt{\lambda_{2}},\cdots,\sqrt{ \lambda_{k}})(\mathbf{v}_{1},\mathbf{v}_{2},\cdots,\mathbf{v}_{k}) \tag{5}\]  
It is noted that the eigenvectors are involved in Equation (5) as column vectors. The operation of the whitening layer is  
\[\mathbf{o}_{wl}=f_{wl}(\mathbf{o}_{ptm})=\mathbf{W}_{wl}^{T}\mathbf{o}_{ptm} \tag{6}\]  
where \(\mathbf{o}_{wl}\) is the output of the whitening layer and \(\mathbf{o}_{ptm}\) is the output of the pre-trained module.  
The whitening layer keeps the elements of \(\mathbf{o}_{wl}\) statistically independent from each other, which avoids the interference among elements during the watermark embedding. Moreover, it can be noted that \(\mathbf{o}_{wl}\) has \(k\) elements. Hence, when the dimensionality of the watermark changes, only the eigenvectors in the matrix \(\mathbf{W}_{wl}\) need to be increased or decreased without training the PTM again.
# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 3 Research on Car-Following Behavior in the V2X Environment
### Applications of the Car-Following Models
#### 3.3.3 Evaluating Energy Consumption and Emission
With the results of the above works, the positive effects of various sources of information on energy consumption and emission in the V2X environment have been confirmed. These effects are caused by the optimization exerted by the information on the car-following behavior, as we discussed in the previous sections. These results also prove that it is vital to further develop the V2X technologies and incorporate the information in modeling the car-following behavior in the V2X environment. Furthermore, it should be pointed out that the car-following models are the theoretical basis of the aforementioned works. Thus, modeling the car-following behavior is key and basic.
## 3 Results
### Storage Tank Pressure Optimization
\begin{table}
\begin{tabular}{c c c c} \hline
**Level** & **P\({}_{\text{high}}\) [Bar]** & **P\({}_{\text{medium}}\) [Bar]** & **P\({}_{\text{low}}\) [Bar]** \\ \hline \multicolumn{4}{c}{} \\ \hline 1 & 46.7 & 46.88 & 46.87 \\ 2 & 46.68 & 46.74 & 46.67 \\ 3 & 46.75 & 46.68 & 46.7 \\ 4 & 46.87 & 46.7 & 46.76 \\ Delta & 0.1871 & 0.1982 & 0.1984 \\ Rank & 3 & 2 & 1 \\ \(\sum\) Delta & 0.5837 & & \\ w (\%) & 48.82 & & \\ \hline \multicolumn{4}{c}{} \\ \hline \multicolumn{4}{c}{} \\ \hline 1 & 19.46 & 19.48 & 19.46 \\ 2 & 19.47 & 19.46 & 19.48 \\ 3 & 19.47 & 19.48 & 19.47 \\ 4 & 19.49 & 19.49 & 19.48 \\ Delta & 0.0274 & 0.0353 & 0.0192 \\ Rank & 2 & 1 & 3 \\ \(\sum\) Delta & 0.0819 & & \\ w (\%) & 6.85 & & \\ \hline \multicolumn{4}{c}{} \\ \hline \multicolumn{4}{c}{} \\ \hline \multicolumn{4}{c}{} \\ \hline \multicolumn{4}{c}{} \\ \hline \multicolumn{4}{c}{} \\ \hline \multicolumn{4}{c}{} \\ \hline \multicolumn{4}{c}{} \\ \hline \multicolumn{4}{c}{} \\ \hline \end{tabular}
\end{table} 
Table 7: The S/N ratios and the weight factors calculated for the n\({}_{\text{Vehicle}}\), m\({}_{\text{Average}}\), and W\({}_{\text{Comp}}\). during storage pressure optimization. The weighting factors calculated for each output parameter, as shown in Table 7, are taken into account, leading to the formulation of the objective function given in Equation (15). \[\text{GRG}=0.4882\;\text{GRC}_{\text{nvehicle}}+0.0685\;\text{GRC}_{\text{m Average}}+0.4433\;\text{GRC}_{\text{WComp}} \tag{15}\]  
The last column of Table 8 ranks the calculated GRG values. Based on these rankings, the optimal result, which represents the simulation design that maximizes the objective function, is achieved with 250 bar (P\({}_{\text{high}}\)), 145 bar (P\({}_{\text{medium}}\)), and 70 bar (P\({}_{\text{low}}\)), corresponding to simulation 4. As a result, the number of vehicle tanks that can be filled in 24 h is nVehicle = 229, with an average gas mass of mAverage = 9.43 kg, and the compressor's work per mass filled is WComp = 0.1898 kWh/kg. Taguchi analysis was conducted to assess the influence of control factors on the obtained GRG, with the results presented in Table 9. Analyzing the Delta and Rank values in the table reveals that the parameters influencing the objective function the most are Pmedium, followed by Phigh and Plow.
# Provenance and Paleo-Environment of the Late Carboniferous Bauxite Formations in Southern Shanxi
## 5 Discussion
### Iron-Aluminum-Coal Structure  
The physicochemical environment plays a pivotal role in determining the mineral composition and rock-forming elements of aluminum-bearing strata. In nature, aluminum is primarily found in its trivalent form, making its geochemical behavior largely independent by redox conditions [57]. The solubility of aluminum silicate minerals is closely related to acidity and alkalinity: at pH \(<\) 4, these minerals dissolve into Al\({}^{3+}\), while at pH \(>\) 10, they dissolve into AlO\({}_{2}\). However, since the natural pH typically ranges from 5 to 9, primary aluminum minerals are minimally affected. During the weathering of source rocks, aluminum is transformed into various mineral components without being carried away by aqueous solutions [58]. Instead, it may be transported as terrestrial clastic material, suggesting that the accumulation of aluminum in sedimentary basins is primarily due to the in situ weathering of aluminum-rich source rocks and the clastic transport of high-aluminum materials.  
During weathering, the mobility of Fe and Si is significantly influenced by acidity and alkalinity levels. When pH levels exceed 8, Si dissolves rapidly, whereas Fe remains largely insoluble, resulting in the removal of Si from primary aluminosilicate minerals while most Fe is retained. When 5 \(<\) pH \(<\) 8, the dissolution rate of Si is in the range of 10-20 times greater than that of Fe, leading to significant Si removal and minimal Fe dissolution from primary aluminosilicate minerals, with most Fe and stable Al remaining. At pH \(<\) 5, both Si and Fe solubility increase, but Fe dissolves faster than Si, resulting in substantial Fe removal and limited SiO\({}_{2}\) retention in primary aluminosilicate minerals [58]. This phenomenon signifies that alkaline and neutral conditions are more conducive for de-siliconizing while retaining Fe. The La/Y ratios further confirmed that the digenetic environment of bauxite and its substrate tend towards alkalinity. The XRD spectra demonstrating the mineral composition of illite - kaolinite in Y9, Y10, and Y11 suggest that the iron-rich clay rocks of the bauxite substrate were originally deposited in a neutral-alkaline environment. This environment promotes de-siliconization and iron retention during the in situ weathering of source rocks, which, combined with the intermediate-acidic igneous characteristics of the source rocks, likely elucidates the high iron content in the bauxite substrate.  
During the late digenetic stage of bauxite formation, the depositional environment shifted to a marine-terrestrial interaction or freshwater swamp environment. Subsequently, the overlying beds transitioned to sand-mudstone or coal seams, signaling the end of bauxite metallogenesis and the onset of coal formation, thereby forming typical iron-aluminum-coal structural layers [59].