esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Interpretation
### Bitumen Bulk Geochemical Parameters  
Through the organic matter extraction experiment on the 32 source rock samples of the Hengtongshan Formation, it was found that the EOM content was relatively high, at 512.15-3589.26 mg HC/g TOC, with an average of 1490.80 mg HC/g TOC, indicating good organic abundance. The results of the EOM component separation experiment on the source rock samples and six shale oil samples are shown in Table 3 and Figure 5. The EOM from the shale samples has the same characteristics as that from the shale oil samples. In these samples, the content of saturated hydrocarbons is the highest. The content of saturated hydrocarbons extracted from the shale samples is 40.01-72.3%, with an average of 55.34%, and the content of saturated hydrocarbons in the shale oil samples is 54.71-82.84%, with an average of 69.8%. The average content of aromatic hydrocarbons in the EOM from the shale samples is almost equal to the average content of NSO, with values of 21.91% and 22.75%, respectively. The average content of aromatic hydrocarbons in the shale oil samples is higher than the average content of NSO, with values of 20.99% and 9.21%, respectively. These indexes are widely used in hydrocarbon potential evaluations of source rocks [15]. Therefore, most of the samples from the Tonghua Basin appear to be a rich source of oil and have the potential to produce a wealth of naphthenic oil.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Bitumen Extraction and Chromotographic Fractions**} & \multicolumn{6}{c}{**Chromotographic Fractions of Bitumen Extraction**} \\ \cline{3-11}
**Sample ID** & **Depth (m)** & \multicolumn{6}{c}{**(ppm of Whole Rocks)**} & \multicolumn{6}{c}{**HCG/EOM**} & \multicolumn{6}{c}{**(EOM wt.\%)**} & \multicolumn{6}{c}{**TOC**} \\ \cline{3-11}  & & **EOM** & **Sat.** & **Aro.** & **NSO** & **HCs** & & **Sat./EOM**  
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample ID**} & \multirow{2}{*}{**Depth (m)**} & \multicolumn{6}{c}{**Bitumen Extraction and Chromatographic Fractions**} & \multicolumn{6}{c}{**Chromategraphic Fractions of Bitumen Extraction**} \\  & & \multicolumn{6}{c}{**(ppm of Whole Rocks)**} & \multicolumn{6}{c}{**HC/EOM**} & \multicolumn{6}{c}{**(EOM wt.\%)**} & \multicolumn{6}{c}{**TOC**} \\ \cline{3-11} \cline{3-11}  & & **EOM** & **Sat.** & **Aro.** & **NSO** & **HCs** & **Sat./EOM** & **Aro./EOM** & **NSO/EOM** & **HCs** & **(wt.\%)** \\ \hline TO-1 & 289.45 & 1339.35 & 865.32 & 333.65 & 140.38 & 1198.97 & 0.90 & 64.61\% & 24.91\% & 10.48\% & 89.52\% \\ TO-2
# Low-Cost Solution for Kinematic Mapping Using Spherical Camera and GNSS
## 2 Materials and Methods
### Ground Control Points and Checkpoints  
For the purposes of georeferencing and accuracy evaluation, several ground control points (GCPs) and checkpoints were established at each test site. The GCPs were physically stabilized using a surveying spike with a hole for the precise placement of a pole tip. Each point was marked by a high-contrast 10 \(\times\) 10 cm checkerboard target, a rigid plate, or fluorescent spray, depending on surface conditions.  
In addition to artificially marked points, several checkpoints were selected from spatially well-defined features such as corners of fences, buildings, or sidewalks, where precise identification in the images was possible.  
All GNSS measurements, both from the low-cost GNSS system and the reference Trimble R2 receiver (Trimble Geospatial, Dubai, United Arab Emirates), were recorded in the ETS89 coordinate system (realization of ETRF2000), as defined by the CZEPOS network.
# Anti-Jamming Power Control Algorithm for Wireless Communication Systems Based on MPC
## 2 System Description and Modeling Approach
Assuming the system sampling time is \(t_{k+1}-t_{k}=T\), according to the zero-order hold (ZOH) principle [15], Equation (3) is further discretized as:  
\[\left\{ \begin{array}{l}x(k+1)=\mathrm{A_{m}x}(k)+\mathrm{B_{m}u_{1}}(k)\\ y_{1}(k)=\mathrm{Cx}(k)\end{array} \right. \tag{7}\]  
where \(A_{m}=e^{A(t_{k+1}-t_{k})}=e^{A\tau}\), \(B_{m}=\int_{t_{k}}^{t_{k+1}}e^{A(t_{k+1}-\tau)}Bd\tau=-A^{-1}e^{(t_{k+1}-\tau) }|_{t_{k}}^{t_{k+1}}B=A^{-1}(A_{m}-I)B\). \[u_{1}(k)=u(k)-P_{l}(k) \tag{8}\]
## 3 The Consecutive Iteration Based on Signal Cancellation (SCCI) Algorithm
### Adaptive Variable Convergence Factor
#### 3.2.2 Analysis of the Parameters' Impact on Performance  
A simulation analysis of the impact of \(\alpha\) and \(\lambda\) on the convergence performance in Equation (9) was conducted. In assuming that the input signal x(n) and noise v(n) are both zero-mean Gaussian white noise, with variances of 1 for x(n) and 0.01 for v(n), the system has a sampling point of 500, and each learning curve is the statistical average of 100 independent simulations. The learning curves for varying parameters \(\alpha\) and \(\lambda\) are shown in Figure 3.  
Figure 3a shows the learning curves for \(\alpha\) values of 0.5, 0.8, and 1.5 with \(\lambda\) set to 2000. Analyzing Figure 3a, we can see that as \(\alpha\) increases from 0.5 to 1.5, the algorithm's convergence speed becomes faster, but the steady-state error also increases correspondingly. When \(\alpha\) = 0.5, the algorithm reaches a steady state after about 150 iterations; when \(\alpha\) = 1.5, it reaches a steady state after about 50 iterations, but with a larger steady-state error. Through simulation verification, the algorithm performs best when \(\alpha\) is around 0.8.  
Figure 3b shows the learning curves for \(\lambda\) values of 20, 200, 3000, and 40,000 with \(\alpha\) set to 0.8. Analyzing Figure 3b, we can see that as \(\lambda\) increases, both the steady-state  
Figure 3: The relationship between the mean square error (MSE) and the number of iterations. (**a**) The impact of parameter \(\alpha\); (**b**) the impact of parameter \(\lambda\).  
error and the convergence performance of the algorithm gradually improve. When \(\lambda\) is set to 3000 or 40,000, the convergence speed and steady-state error are similar. To reduce the computational complexity of the system and improve the algorithm's running speed, simulation verification shows that the algorithm performs best when \(\lambda\) is around 3000.
# Experimental Study on the Desalination of Waste Leachate Using the Combined Freezing Method
## 3 Results and Discussion
### The Desalination Effect of Different Desalination Methods on Waste Leachate
#### 3.2.1 Research on the Desalination Effect of the FGM on Waste Leachate
external environment of 28 \({}^{\circ}\)C, when the ice production rate is 40%, the salinity of the ice meltwater is 0.5%, and the highest salt rejection is 87.71%. As the ice production rate increased, the salt rejection gradually decreased to 82.55%, 64.86%, and 52.09%, respectively, until the ice production rate of 80%, when the salt rejection was 41.77%. This phenomenon was consistent with the study of [PERSON] et al. [29]. Average summer temperatures compared to average winter temperatures results in higher salt rejection. However, it was found that the color of the ice body gradually changed from yellow-brown to transparent at the winter temperature, but the color of the ice body remained brown and did not change much during the gravitational desalination process at the average summer temperature. We carried out the analysis and detection of this phenomenon and found that the TOC content of ice meltwater at an ambient temperature of 28 \({}^{\circ}\)C is 1.86 times that of 5 \({}^{\circ}\)C. This is mainly caused by organic residues. The salt is mainly a combination of anions and cations formed into compounds, the organic molecular structure is complex, and the molecular size is often larger, with higher ambient temperatures. To achieve a predetermined rate of ice in a short period, the formation of a brine channel is difficult to pass through the macromolecules of organic matter, and the result is that the residue in the ice body cannot be discharged promptly. Therefore, higher ambient temperatures are detrimental to the removal of organic matter for the same ice production rate. Considering the ice meltwater salinity, with the same ice production rate conditions, the summer ice meltwater salinity is lower than the winter but one cannot simply think that gravity desalination at higher temperatures has a better desalination effect; when at a certain temperature, the ice body will melt a large amount of water, which will reduce the salt rejection.
# A Novel Intelligent Method for Fault Diagnosis of Steam Turbines Based on T-SNE and XGBoost
## 4 Conclusions  
A model based on t-SNE and XGBoost was proposed to detect the early failure of steam turbines. The model with high accuracy was verified by the data of steam turbine units of thermal power plants in China.  
(1) The uncertainty problem of feature extraction in the unlabeled data set was solved using t-SNE and K-means. This method can distinguish fault data and normal data, and it has a certain foresight because it can distinguish the time when the fault occurs, which is earlier than the fault record of manual inspection, making it more suitable for practical application in fault diagnosis of steam turbines.  
(2) The problem of data imbalance caused by fewer fault records was solved by using the SMOTE algorithm, which is of great significance to the fault diagnosis of the steam turbine and other mechanical equipment with fewer faulty samples.  
(3) In the identification of new data, the accuracy and other indicators of the model based on XGBoost reached more than 97%, which shows that this method has high value in turbine fault diagnosis.  
**Author Contributions:** Conceptualization, L.Z. and X.W.; methodology, L.Z. and X.W.; software, X.W.; validation, Z.L., L.Z. and X.W.; formal analysis, L.Z.; investigation, X.W.; resources, L.Z.; data curation, L.Z.; writing--original draft preparation, X.W.; writing--review and editing, Z.L. and L.Z.; supervision, L.Z.; project administration, L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) of China (No. 311021013), National Natural Science Foundation of China (No. 51775037), and Fundamental Research Funds for Central Universities of China (No. FRF-BD-18-001A).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** The authors thank the National Center for Materials Service Safety for support of the simulation and data platform.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Material Properties, Loading, and Boundary Conditions  
FNFs with three cannulated screws with similar properties were analyzed by the finite element method under specified boundary conditions. All system parts fixed with cannulated screws are defined as homogeneous, isotropic, and linear elastic material. The cannulated screws are made of Ti6 Al4V material. The yield strength of the Ti6 Al4V material is known to be 885 MPa [13]. The mechanical properties of the cannulated screw and other parts of the system are shown in Table 1[14].  
The density value and modulus of elasticity of the femur bone fragments were generated from the HU value of the bone. The HU value is proportional to the absorbed portion of the X-rays sent to the bone from the tomography device used in the medical sector and is also a dimensionless value. The apparent density (\(\rho\)) and elasticity modulus (\(E\)) values were obtained by substituting the HU values of the femur bone fragments in Equation (1) [15]:  
\[\rho_{app}=a+HU(b) \tag{1}\]  
\[E\left(MPa\right)=a+(b)\rho_{app}{}^{c} \tag{2}\]  
The coefficients \(a\), \(b\), and \(c\) for \(\rho\) and \(E\) are calibration parameters. All parts of the FNF system assembled with cannulated screws were transferred to the ANSYS finite element analysis program, where the interaction of the parts with each other was ensured. The mesh structures of the parts consist of tetrahedral elements with 4 nodes. All femur bone parts have a mesh size of 2 mm, cannulated screws have a mesh size of 1 mm and the mesh size between the fracture site surfaces was taken as 0.7 mm (Figure 2b). In total, the modeled system consists of 1,498,882 nodes and 1,019,038 elements.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Components** & **Modulus of Elasticity** & **Poisson’s Ratio** \\  & **(E, GPa)** & **(v)** \\ \hline Cortical Bone & 16.8 & 0.3 \\ Cancellous Bone & 0.86 & 0.3 \\ Ti6 Al4V & 114 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of Cannulated Screws and Femur bone components.  
Figure 1: Geometric and parametric modeling of the TSC system used in FNFs. Determination of anatomical and mechanical axis (**a**), rotational and translational movements of the screws (**b**), and Pauwels angle (**c**).  
During the finite element analysis, the condyle part of the distal femur was held, and the distal movement of the bone was prevented (Figure 2a). During the analysis performed by taking into account the biomechanical studies of the lower extremities, the boundary conditions of the stance position from the gait phases of the person were applied. Three forces were applied on the proximal femur. These were 2460 N force applied to the femoral head, 1700 N force applied by the abductor muscle and 771 N force applied by the illiopsoas muscle [16] (Figure 2a). The coefficients of friction were 0.46 [17] between the fracture surfaces of the femoral neck, 0.23 [18] between the PFN fragments, and 0.3 [19] between the femur bone fragments and PFN.
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 2 Main Contributions  
Specifically, the points presented below are original contributions to this paper:  
1. Proposal of a framework that implements the NSSF service in vertical and horizontal models, where the handover decision is shared between the network edge and the user's equipment on network runtime;
2. Suitability of NSSF function originally from 5G core to network edge, to UE, or to 5G Radio Base Station (gNodeB). The deployment model is defined according to the institutional operating scenario;
3. Slice selection model implementation based on multicriteria decision techniques and machine learning, using hybrid algorithms;
4. Strategy formulation independent of packet marking type (e.g., SR-IPv6, MPLS, VXLAN, VPN, and GTP) for analyzing data flows and forwarding to available slices;
5. Defining approach for collecting QoS and Quality of Experience (QoE) metrics directly from the TCP/IP stack, and signaling the UE profile, without legacy infrastructure modifications;
6. Definition of network datasets characterization and acquisition models, in addition to decision matrices assembly in network runtime, using data analytics tools;
7. Integrated model specification and implementation with market orchestration tools such as ONAP, OSM, and OpenNESS;
8. Slice selection embedded application development for multistream mobile devices.
## 3 The PPBR Key Management Scheme
### Direct Key Establishment Stage  
The sink node broadcasts packets within its communication range while it moves along a fixed path. The information contains its own \(ID\) identifier, the polynomial ring and the key ring: MS \(\rightarrow\)*: hello(\(ID_{MS}\),\(ID_{PK}\),\(ID_{ki}\)). Sensor node \(S_{i}\) in the communication range matches the received packets with their own information. If \(ID_{PK}\) of the polynomial ring and \(ID_{Ki}\) of key ring can both be matched, the \(S_{i}\) will determine to share the keys with MS. Once MS and \(S_{i}\) at least have one shared polynomial and one shared key, they can establish the session key directly by the following process:  
1. If there are \(r\) shared keys in the key ring, then \(k=k_{1}\oplus k_{2}\oplus\ldots\oplus k_{r}\).
2. If there are \(R\) shared polynomials in the polynomial ring, then chooses a polynomial \(f_{k}(x,y)\) randomly and calculates: \(k_{p}=f_{k}(ID_{MS},ID_{i})=f_{k}(ID_{i},ID_{MS})\).
3. Finally, the direct session key between MS and sensor nodes can be calculated by the function \(H\): \(K_{MS-Si}=hash(k\,1\,|\,k_{p})\).  
In the session key transmission process, we can take the secure transmission method based on the ECC with public-private encryption. The ordinary nodes encrypt the shared key with the public key and the MS decrypts it with the private key, which can ensure the key cannot be decrypted in case the key is intercepted in the transmission process.
# Monitoring Water Area Dynamics in Kashgar (2003-2023) Using Multi-Source Remote Sensing Data
## 2 Materials and Methods
### Data
#### 2.2.3 Other Data  
This study utilized precipitation and temperature data from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset ([[https://doi.org/10.1038/sdata.2015.66](https://doi.org/10.1038/sdata.2015.66)]([https://doi.org/10.1038/sdata.2015.66](https://doi.org/10.1038/sdata.2015.66)), accessed on 11 March 2025) and the NASA Land Processes Distributed Active Archive Center (LP DAAC) ([[https://doi.org/10.5067/MODIS/MOD11A2.061](https://doi.org/10.5067/MODIS/MOD11A2.061)]([https://doi.org/10.5067/MODIS/MOD11A2.061](https://doi.org/10.5067/MODIS/MOD11A2.061)), accessed on 13 March 2025). Evapotranspirationation data were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A2 dataset ([[https://doi.org/10.5067/MODIS/MOD16A2.006](https://doi.org/10.5067/MODIS/MOD16A2.006)]([https://doi.org/10.5067/MODIS/MOD16A2.006](https://doi.org/10.5067/MODIS/MOD16A2.006)), accessed on 15 March 2025).
# New Candidate Multicomponent Chalcogenide Glasses for Supercontinuum Generation
## 3 Composition, Morphology, and Optical Properties: Glasses Towards Glass Ceramics
Secondly, the phase-separated morphology observed within the immiscibility dome leads to a unique nucleation behavior of crystalline phases upon a heat treatment. Due to an energetically unstable state, Pb-rich phases can be transformed into crystals upon heat treatment while Pb-deficient phases remain amorphous [54; 58; 60; 63; 64; 65]. These Pb-rich crystals have refractive indices far greater than those of their amorphous counterparts, making the effective linear and nonlinear refractive indices of glass-ceramic nanocomposites far greater than those of the parent glasses [59; 60; 66; 56]. Furthermore, we have shown that the size of resulting crystalline phases typically range from 50 to 200 nm, making the nanocomposites effective media with respect to incident electromagnetic wave in the MWIR and LWIR. This enables the nanocomposite to maintain its transparency throughout the IR. In these prior studies, secondary phases have been induced by thermal and/or laser plus thermal heating. Figure 2: (**a**) An approximated phase diagram of GeSe\({}_{2}\)-As\({}_{2}\)Se\({}_{3}\)-PbSe (GAP-Se) with an immiscibility dome illustrating two distinctly different inverse morphologies consisting of Pb-rich and Pb-deficient phases. (**b**,**c**) Linear profiles of atomic percentages for four constituents in glasses with 20 mol% and 40 mol% PbSe, respectively. How such material attributes would be impacted during film deposition or fiber drawing, for example, requires further consideration. Film studies in GAP-Se are ongoing for applications as gradient refractive index materials and other applications. To date, high quality (stoichiometric), low loss films of select GAP-Se glasses have been successfully made to thicknesses of 40 \(\upmu\)m and have shown induced index and dispersion modification following crystallization as optical composites. Fiber drawing of GAP-Se has not, to date, been carried out. Here, additional scattering loss (due to crystal growth during reheat/fiberization) would need to be optimized so as to enhance optical properties, but not scatter loss. Such reheating of bulk preforms to make fiber would need to be optimized since the control of nucleation and growth near the fiber draw temperature could result in unwanted crystallization that leads to increase in loss.
# Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection
## 3 Method
### Adaptive Interaction
To better utilize positional information during feature interaction, we implemented a multi-level optimization process for the position information \(P_{b}\). First, non-parametric positional mappings (e.g., positional encoding or hand-crafted position matrices) are used to preliminarily optimize \(P_{b}\), enhancing the critical target information. Then, using an MLP, we generate a set of transformation parameters from \(P_{f}\), which further optimizes the position information in \(P_{b}\), ensuring that each position accurately reflects its corresponding target characteristics. Furthermore, to extract semantic information from the feature maps and integrate it with positional information, we adopted both non-parametric spatial information mappings and MLP mappings. This method preserves the rich semantic information in the feature maps while efficiently merging it with the optimized \(P_{b}\), generating an adaptive vector. The adaptive vector is used to update the intermediate features derived from the ROIs after being transformed by the first set of matrix parameters. These updated features are then restored to their original shape by the second set of matrix parameters, generating the final output feature \(O_{f}\). This process allows ACPI to enhance feature representation while maintaining sensitivity to positional information. Figure 3: Structure of adaptive interaction. \(p_{f}^{{}^{\prime}}\) refers to the \(P_{f}\) updated by AAPI. The dashed box represents the ACPI calculating operation.
## 3 Results and Discussion
### Fourier Transform Infrared (FT-IR) Spectral Analysis  
To better understand the presence of metal-oxygen (M-O) in these materials, FTIR studies of MnCe\({}_{x}\)Fe\({}_{2-x}\)O\({}_{4}\) (x = 0.0, 0.1, 0.3, and 0.5) NCs were conducted, as shown in Figure 4. In the literature, the absorption band seen at a higher wavenumber (575 cm\({}^{-1}\)) is relegated to the metal-oxygen (M-O) bond in A-sites, while the absorption band seen at a lower wavenumber (437 cm\({}^{-1}\)) is the metal-oxygen bond in B-sites. In the 500-600 cm\({}^{-1}\) frequency range, all ferrites studied in this study have a broad, intense absorption band. Due to the statistical distribution of Mn\({}^{2+}\), Ce\({}^{3+}\) and Fe\({}^{3+}\) ions at A- and B-sites, band broadening is usually seen in inverse spinel ferrites. As a result, the higher wavenumber band corresponds to M-O vibrations in A-site, while the lower wavenumber band corresponds to M-O vibrations in B-site and the obtained results concur well with the literature on these kind of ferrites [44; 45; 46]. In the spectrum of MnCe\({}_{x}\)Fe\({}_{2-x}\)O\({}_{4}\), a broad peak approximately in the 3400-3500 cm\({}^{-1}\) region was designated due to the stretching vibration of the OH groups by the water molecule adsorbed on the catalyst surface during handling. Small peaks at 2926 and 2851 cm\({}^{-1}\) are associated with the C-H stretching vibration. The C-O stretching vibration on the samples is attributed to the small peaks at 1638 and 1412 cm\({}^{-1}\). The small peaks at 1012-1210 cm\({}^{-1}\) correspond to the vibration of spinel structure. The M-O stretching of octahedral (B-) sites is caused due to the bands at 437 cm\({}^{-1}\). The M-O stretching at the tetrahedral (A-) sites of the spinel is due to the band at 575 cm\({}^{-1}\).