esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 SF-PPP Methodology
### Uncombined Model  
Unlike the GRAPHIC model, the raw pseudorange and carrier phase observations are utilized directly in the uncombined approach, while the slant ionospheric delays are estimated as random walk process parameters. In this way, the slant delay and receiver clock offset parameters are tightly coupled; thus, a priori constraint on the ionospheric parameters is a significant issue in improving both estimation accuracy and convergence. Explicitly, a priori ionosphere information provides approximate values for the epochwise slant delays and is then inserted into observation equations along with its variance information, which can be formulated as:  
\[Ion_{r,f}^{s}=I_{r,f}^{s}, \tag{4}\]  
\[I_{r,f}^{s}=40.28\times\frac{m\cdot VTEC}{f^{2}}, \tag{5}\]  
\[\sigma_{ion}^{2}=\sigma_{ion\_k}^{2}/\left(4\cdot\sin^{2}e\right), \tag{6}\]  
where \(Ion_{r,f}^{s}\) is the ionospheric parameter with an approximate value \(I_{r,f}^{s}\) derived by VTEC (vertical total electron content) and mapping function \(m\), and \(\sigma_{ion\_k}^{2}\) is the variance of \(I_{r,f}^{s}\) at \(k\)-th epoch.  
In this study, the GIM model provided by IGS was adopted when implementing both the GRAPHIC and uncombined models. The GIM can provide global ionosphere VTEC values over a 2 h temporal resolution and has a spatial resolution of 2.5\({}^{\circ}\) and 5\({}^{\circ}\) in latitude and longitude, respectively. The RMS of the GIM model is about 2-8 TECUs (total electron content units) [35]. Considering the accuracy limitation of GIM products, the slant delay observations \(Ion_{r,f}^{s}\) are assigned with varying variances. Explicitly, \(\sigma_{ion\_k}^{2}\) is assigned with a small value at the beginning stage of PPP processing to speed up estimation convergence as it can help decrease parameter correlations (such as the correlations between receiver clock errors, ambiguities, and ionospheric delays). However, \(\sigma_{ion\_k}^{2}\) is then gradually increased as the initial ionospheric parameter values suffer errors from the GIM model; by loosening the ionospheric parameters constraints, the estimation precision could be improved [29]. The \(\sigma_{ion\_k}^{2}\) can be expressed as:  
\[\sigma_{ion\_k}^{2}=\sigma_{ion\_0}^{2}+\alpha(k-1)\Delta t, \tag{7}\]  
where \(\sigma_{ion\_0}^{2}\) is the initial variance, set as 1.6 m\({}^{2}\) (standard deviation about 1.28 m); \(\alpha\) is the variance varying rate with time, set as 1 m\({}^{2}\)/min; and \(\Delta t\) is the elapsed time.
## 1 Introduction
* To promote the security performance and harvest more power, the AN is injected designedly into the transmit beamforming at the femtocell sink node. To achieve secrecy enhancement, a joint design problem is formulated for the MSC maximization while considering the fairness among multiple M-SNs. The original problem is non-convex and hard to solve directly. * In order to reduce the performance loss of the rank relaxation from the SDR technique while solving the non-convex max-min program [38], we apply SCA technique, first-order Taylor series expansion and SPCA method, and then the recast problem can be converted to an SOCP problem [39] which can be directly solved to achieve a near-optimal solution. Moreover, we propose a novel SCA-SPCA-based iterative algorithm, while its convergence property of the proposed algorithm is proved. Compared with our previous papers [23; 24], major additional works and results incorporated in these papers are summarized in the following. (1) This paper has extended the problem of AN power maximization to both perfect and imperfect CSI cases, which introduces substantial changes in the analyses. (2) An SPCA-based iterative algorithm has been proposed to solve the problem such that the computational complexity is largely reduced compared with the 1-D search method used in the previous work. The rest of this paper is organized as follows: In Section 2, we presents the HWSN system model and formulates the MSC maximization problem. Section 3 solves the non-convex problem and provide the proposed SCA-SPCA-based iterative algorithm. Section 4 outlines the simulation results. Finally, section 5 concludes this paper. _Notation:_ Lower-case letters mean scalars, bold-face lower-case letters are denoted by vectors, and boldface upper-case letters are used for matrices. \(\mathbb{C}^{M\times L}\) indicates the space of \(M\times L\) complex matrices. \(|\cdot|\) defines the absolute value of a complex scalar. \(\|\mathbf{A}\|_{F}\) describes the Frobenius norm of the matrix \(\mathbf{A}\), \(\text{tr}(\mathbf{A})\) indicates the trace of \(\mathbf{A}\). \(\text{E}\{\cdot\}\) describes the mathematical expectation. \([x]^{+}\) equals \(\max\{x,0\}\).
# Tribological Analysis of Steels in Fuel Environments: Impact of Alloy Content and Hardness
## 1 Introduction  
In combustion engines, high-pressure fuel systems play a pivotal role in increasing efficiency and power. These systems operate in harsh conditions, subjecting their vital components to extreme forces, temperatures, and pressures. Achieving consistent and reliable performance is of utmost importance and demands efficient lubrication mechanisms that can combat wear and prevent potential failures. However, traditional lubrication methods face significant challenges when dealing with the unique dynamics of these systems. For diesel systems running on jet fuel and for increasing the pressures of gasoline injection systems, the relatively low viscosity of the fuels limits the effectiveness of conventionallubrication pathways. This limitation leads to increased wear rates, particularly in the realm of reciprocating contacts within the fuel system, especially over extended periods, as recent research has confirmed [1; 2; 3; 4]. Scuffing failure is also expected in this context.  
With the incorporation of coatings and fuel additives to improve lubrication, the vulnerability of sliding steel surfaces to wear and degradation remains a persistent concern, as documented in numerous studies [4; 5; 6; 7; 8; 9; 10; 11]. Many fuel pumps are fabricated from AISI 52100 tool steel or similar alloys, which is renowned for its impressive strength and wear resistance [3; 12; 13; 14; 15; 16]. The gradual shift towards low-viscosity fuels like ethanol and gasoline-ethanol blends has further complicated matters. 52100 steel, while highly regarded for its wear resistance, exhibits higher wear rates and an increased susceptibility to scuffing failure when exposed to these alternative fuels [3]. This dilemma necessitates a re-evaluation of its reliability in fuel delivery systems. To navigate this challenge, it becomes important to explore innovative strategies for material enhancement, aligning our efforts with the contemporary sustainability goals that call for fuel systems that are both robust and eco-friendly.  
This research endeavors to shed light on the tribological performance of steels with different alloying elements and hardness when subjected to two distinct counter-bodies (alumina and 52100 steel) in two chemically different low-viscosity fuel environments, providing valuable insights for addressing the pressing challenges faced by high-pressure fuel systems in modern combustion engines.
## 3 Results and Discussion  
### UV-Vis Absorption  
Because the UV-vis absorption shows different intensities along with the gradient direction of the IPN, it can be used for detecting the gradient structure. As shown in Figure 3, after the specimen was fractured (along the direction of thickness) and well-polished, 1/8 h, 1/2 h and 7/8 h regions were selected for testing (characterizations of the gradient structure through the infrared absorption spectrum can be found in Figures S5 and S6).  
As shown in the top left insert figure, the peaks of 214, 318 and 368 nm can be assigned to the maximum absorption of acylamino groups, the R bond of ketone groups and the ether bonds in PU, respectively. Peaks at 203 and 230 nm should be ascribed to the maximum absorption E\({}_{2}\) bond and the triethylene conjugated structure of benzene rings in EP [39; 40; 41]. Besides, the absorption band between 470 and 560 nm may be attributed to the conjugated structures between tertiary amine and the benzene rings, which explain the tawny color of cured EP.  
As shown in Figure 3, the chosen regions show similar absorption in the UV band. In addition, compared to PU and EP, the new peak of 277 nm in IPN could be assigned to the inter-molecular interaction between acylamino group in PU and the alkoxy groups in EP. According to the absorption curves of h/8, h/2 and 7h/8 regions, we can find that the absorption intensities between 200 and 400 nm decrease from the bottom side to the top side of the sample. This indicates that the percentage of PU in the IPN composite is reducing along the gradient direction. In addition, the absorption of  
Figure 3: Ultraviolet absorption spectrum (UV-Vis) absorption spectra of the chosen regions in the gradientinterpenetrating polymer network (IPN). The top left insert is the absorption spectra of PU and EP. Here “h” represents the thickness.  
benzene ring (203 nm) in the chosen regions of the sample implies that the layer near the top side has the maximum content of EP.  
In contrast, the absorption intensities in the range of visible light are increasing along the gradient direction from the bottom side to the top side of the sample. In addition, the increasing absorption between 400 and 600 nm (the range of blue-green light) makes the gradient IPN composite show gradual loss of tawny color.
## 2 Energy Efficiency Landscape
### Energy Consumption  
The power consumption of communication satellites primarily includes fixed and variable power usage. The fixed power encompasses the operating system power consumption \(P_{\text{OS}}\), the basic operational power consumption \(P_{\text{base}}\), and the laser alignment platform power \(T_{\text{z}}\), which are considered constants over the long term. Variable power consumption is associated with the satellite's role as a routing node in data processing and transmission tasks, varying with the node's throughput. The total traffic \(F_{i}\) through the satellite includes all inter-satellite and ground-to-satellite link traffic, represented by \(S_{i}\) for the inter-satellite links and \(G_{i}\) for the ground links. The total traffic passing through the satellite can be expressed as follows:  
\[F_{i}=\sum_{(i,j)\in S_{i}}F_{(i,j)}^{ISL}+\sum_{(i,j)\in G_{i}}F_{(i,j)}^{ULL} \tag{2}\]  
Figure 1: Illumination energy effects on satellite constellation.  
The forwarding of satellite data involves accessing routing tables, sending data to buffer areas, and modulating the data onto transmission links, with power consumption linearly related to data volume, denoted as \(P_{skr}\)[26]. The directional links are represented by \(S_{i}^{+}\) and \(S_{i}^{-}\), indicating the flow of data. The power consumption coefficients, \(\rho_{ISL}^{s}\) for laser link transmission and \(\rho_{ISL}^{r}\) for reception, along with \(\rho_{ULDL}^{s}\) and \(\rho_{ULDL}^{r}\) for ground-to-satellite links, are determined by factors such as link length, bit error rate, and signal frequency and are considered constants.  
\[P_{skr}=\sum_{(i,j)\in S_{i}^{+}}\rho_{ISL}^{s}F_{(i,j)}^{ISL}+\sum_{(i,j)\in S _{i}^{-}}\rho_{ISL}^{r}F_{(i,j)}^{ISL}+\sum_{(i,j)\in S_{i}^{+}}\rho_{ULL}^{s}F_ {(i,j)}^{IDL}+\sum_{(i,j)\in S_{i}^{-}}\rho_{ULL}^{s}F_{(i,j)}^{ISL} \tag{3}\]  
The power consumption for processing data streams by the processor is denoted by \(\xi_{i}(F_{t})^{\alpha}\), where \(\xi_{i}\) and \(\alpha\) are constants dependent on the processor model [27]. The operational power consumption of the satellite is given in Equation (4). The variable \(x_{ij}\) represents the operational mode of the optical terminal \(j\) on satellite \(i\), with 1 indicating normal mode and 0 indicating sleep mode. \(N_{i}\) represents the number of optical terminals on satellite \(i\), and the sum \(\sum_{j=1}^{N_{i}}x_{ij}T_{a}\) represents the total fixed power consumption of the optical terminals. During sleep mode, the power consumption reduces to \(\varphi\) times (\(\varphi<1\)), maintaining only the basic power consumption, as shown in Equation (5).  
\[P_{i}^{work}=P_{const}+P_{variable}=P_{OS}+\sum_{j=1}^{N_{i}}x_{ij}T_{a}+P_{ base}+P_{skr}+\xi_{i}(F_{i})^{\alpha} \tag{4}\]  
\[P_{i}^{slexp}=\varphi P_{OS}+P_{base} \tag{5}\]
# MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data
## 4 Results
### Experimental Results  
#### 4.2.1 Jilin-1 Dataset  
We visualized the results after segmentation, as shown in Figure 8. We displayed eight sets of typical images to observe the segmentation differences among various models. The red rectangular boxes highlight the areas we focused on for comparison. Sets 1, 2, 3, and 6 demonstrate that the edges of the segmentation targets subjected to MFFNet are regular and continuous. In contrast, networks such as UNet, UNet++, PSPNet, and DeepLabV3+ tend to misclassify or omit boundaries, resulting in irregular geometries at the edges. Sets 4 and 7 show that when segmenting buildings of different sizes, MFFNet can better delineate smaller structures than other networks, which often confuse multiple small targets. Set 8 reveals that MFFNet still achieves commendable results when segmenting very small targets in a satellite image. This indicates that MFFNet can achieve excellent segmentation results for buildings of various sizes and accurately delineate the edges of structures.  
We evaluated the segmentation results of various networks using the assessment metrics. MFFNet outperforms the comparison networks in MIoU, achieving an MIoU of 89.69%, which is 3.03%, 3.58%, 4.63%, and 2.72% higher than those values obtained via UNet++, UNet, PSPNet, and DeepLabV3+, respectively. The accuracy value reaches 97.05%, which surpasses UNet++, UNet, PSPNet, and DeepLabV3+ by 1.59%, 1.39%, 1.65%, and 0.93%, respectively. The precision, recall, and F1-score values reached 94.66%, 94.25%, and 94.82%, respectively, all of which are superior to the values of the comparison networks. As illustrated in Table 1, MFFNet achieves the best results across all evaluation metrics, including the Kappa metric. MFFNet exhibits exemplary segmentation performance when applied to the Jilin-1 building dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MIoU** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Kappa** \\ \hline UNet++ & 86.66\% & 95.46\% & 92.91\% & 92.70\% & 92.85\% & 90.21\% \\ UNet & 86.51\% & 95.66\% & 92.81\% & 92.53\% & 92.68\% & 90.05\% \\ PSPNet & 85.06\% & 95.40\% & 91.26\% & 92.16\% & 91.81\% & 89.06\% \\ DeepLabV3+ & 86.97\% & 96.12\% & 93.01\% & 92.74\% & 92.99\% & 90.48\% \\ MFFNet & 89.69\% & 97.05\% & 94.66\% & 94.25\% & 94.82\% & 92.63\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Segmentation Results on the Jilin-1 Dataset.  
Figure 8: Comparison of experimental results on the Jilin-1 dataset. Each row represents a set of the same image segmented by different models, with the red rectangular boxes highlighting areas of particular interest.
# Detecting Traffic Incidents Using Persistence Diagrams
## 1 Introduction
### Main Contributions  
Our main contributions in this paper are two-fold. First, we introduce a novel machine learning algorithm that uses TDA for detecting anomalies in time-series data. This is done by establishing a baseline of typical data distribution to serve as a reference through the method of bagging, and deviations from the reference for various data points are measured using the bottleneck distance from TDA. This procedure is repeated multiple times to reduce the variation that naturally arises from random sampling. The algorithm requires selection of several hyperparameters. Second, we address the problem of identifying incidents in traffic data that consists only of traffic counts. Our data set does not include any functional data, such as velocity, nor does it include video feed which are the most common data sources used in traffic incident detection problems.
## 1 Introduction
This study aims to enhance antimicrobial loading, specifically vancomycin, by incorporating the drug during the BG synthesis process. This approach is expected to promote stronger chemical interactions between vancomycin molecules and the BG matrix, potentially improving loading efficiency and releasing kinetics. When BG is loaded with any drug, chemical bonds are expected to form between BG and the drug of choice based on their chemical structures. The interactions will strongly affect the encapsulation efficiency of the drug and subsequent release. For this study, vancomycin was chosen for the reasons previously stated. When BG is loaded with vancomycin there is a high potential of hydrogen bonds forming between the two. This is primarily due to the presence of multiple functional groups in vancomycin's structure, such as amide, carboxyl, and hydroxyl groups [18], as shown in Figure 1a, which can interact with the silanol groups (Si-OH) on the surface of the BG, as shown in Figure 1b. These hydrogen bonds are stronger than simple physical adsorption forces, resulting in vancomycin being held more tightly within the BG matrix compared to surface adsorption alone. This study aims to establish the ability of BG to load drugs during synthesis and its bioactivity and biocompatibility in vitro when it is synthesized without thermal treatment. BG is a versatile biomaterial that can be used in various applications including bone regeneration and drug delivery. BG properties such as porosity surface chemistry and degradation rate are highly influenced by the synthesis method. These properties can be tailored differently based on the application, enabling the customization and the creation of application-specific biomaterial.
# Multi-Source Information Fusion Fault Diagnosis for Gearboxes Based on SDP and VGG
## 4 Experiments
### Analysis of Experimental Results  
In this experiment, 500 sets of experimental data were selected, and each set of data consisted of 1024 points. In addition, the experimental results showed that the accuracy of each sensor was improved after the implementation of DS fusion. However, when two sensors were fused with each other, the accuracy was not as high as the accuracy achieved when fusing three sensors at the same time. Additionally, simultaneous fusion of three sensors improved the accuracy by 0.05%, 0.31%, and 0.58% compared to the fusion of sensors 1 and 2, sensors 1 and 3, and sensors 2 and 3, respectively. The experiments showed that the use of the DS fusion method can compensate for the disadvantage of incomplete data collection inherent to using a single sensor and improve the results of gear tooth fault diagnosis. All in all, the fusion algorithm is important for the field of fault diagnosis.
## 3 Experimental Results and Discussion
### Comparison with State-of-the-Art
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Model/Study** \\ **Reference** \\ \end{tabular} } & \begin{tabular}{c} **k-Fold Cross-** \\ **Validation/Dataset** \\ **Partitioning** \\ \end{tabular} & \begin{tabular}{c} **Augmentation/** \\ **Smote** \\ \end{tabular} & \begin{tabular}{c} **Classification** \\ **Technique** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ **(\%Avg.)** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **(\%Avg.)** \\ \end{tabular} & \
\begin{tabular}{c} **F-Score** \\ **(\%Avg.)** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy** \\ **(\%Avg.)** \\ \end{tabular} \\ \hline
[9] & 5-fold & \(\surd\) & SVM + BoW & \(\times\) & \(\times\) & \(\times\) & 91.14 \\ \hline
[44] & 5-fold; 10-fold & \(\surd\) & CNN & \(\times\) & \(\times\) & \(\times\) & 91.43 \\ \hline
[45] & \(\times\) & \(\times\) &
\begin{tabular}{c} Capsule \\ Networks \\ (CapsNet) \\ \end{tabular} & \(\times\) & \(\times\) & \(\times\) & 89.56 \\ \hline
[46] & 5-fold & \(\times\) & CapsNet & 92.67 & 94.67 & 93.33 & 92.60 \\ \hline
[6] &
\begin{tabular}{c} 68\% training and \\ 32\% in test \\ \end{tabular} & \(\surd\) & CNN & 96.06 & 94.43 & \(\times\) & 96.13 \\ \hline
[43] & 5-fold & \(\surd\) & GA based CNN & \(\times\) & \(\times\) & \(\times\) & 94.20 \\ \hline
[47] & \
\begin{tabular}{c} 5-fold; 70\% \\ training and 30\% in test \\ \end{tabular} & \(\times\) &
\begin{tabular}{c} Hybrid \\ PCA-NGIST + \\ RELM \\ \end{tabular} & \(\times\) & \(\times\) & \(\times\) & 94.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison with other studies for brain tumor categorization using CE-MRI dataset. Good discriminating features exploiting diversity between the competing classes is the source of high prediction accuracy and low false negatives. These results demonstrate that the proposed UL-BTD framework outperforms state-of-the-art techniques for brain tumor classification problems. The proposed method has the highest detection rates of (99.18%, 98.86%, and 99.67%), and F-measure of (0.99, 0.97, and 0.99) for glioma, meningioma, and pituitary tumors, respectively. The reason for this lies in the preprocessed images fed to the UL-DLA for extracting dynamic features using GLCM based extremely discriminant features and then fine-tuned SVM for the brain tumor prediction system. No preprocessing of tumor region or segmentation is required rather, the rescaled images are directly used for feature extraction. The low prediction time per test image (11.69 ms) makes it suitable as a portable algorithm in developing countries on low-budget conventional PCs. Due to its low detection time, it can be used during surgical procedures for the detection of tumors [54] as the finely tuned algorithms require fewer resources for their implementation.
## 2 Materials and Methods
### Characterization  
Fourier transform infrared (FTIR) spectra were measured using an FTS-3000 spectrophotometer (Digilab Co. Ltd., MA, USA) within the range of 4000-400 cm\({}^{-1}\) with a resolution of 4 cm\({}^{-1}\) using the KBr pressed disk technique. X-ray diffraction (XRD) measurements were carried out with a Bruker D8 Advance X-ray diffractometer (Stadi P, Karlsruhe, Germany) with Cu Ka radiation (\(\lambda\) = 0.1540 nm) over a range of 20 = 1.5 to 12\({}^{\circ}\) at a scan rate of 2\({}^{\circ}\)/min. The basal spacing reflection of the sample was computed by applying the Bragg's law, and the voltage and current of the X-ray tubes were 40 kV and 30 mA, respectively. The morphology of the sample was determined by transmission electron microscopy (TEM, JEM-2100, JEOL Ltd., Tokyo, Japan) at an accelerating voltage of 200 kV. The samples were diluted with ethanol and then dispersions were placed onto a copper TEM grid and analyzed. The nitrogen adsorption and desorption analysis was performed using a Micromeritics ASAP 2420 instrument (Micromeritics, GA, USA). The Brunauer-Emmett-Teller (BET) surface areas and the Barrett-Joyner-Halenda (BJH) pore volumes of the clay samples were calculated for further confirmation of modified reaction. The chemical compositions of samples were determined by X-ray fluorescence spectrometry (XRF, Axios \({}^{\text{max}}\), PANalytical, the Netherlands). Elements lighter than oxygen could not be detected. Thermogravimetric analysis (TGA) was carried out on a thermogravimetric analyzer (TGA, STA409 PC, Bavaria, Germany) at a heating rate of 10 \({}^{\circ}\)C/min under a  
Figure 1: Schematic representation of the acid activation, ion exchange and silylation reactions of Mt samples. (**A**) The acid activation of Mt was performed under the treatment of HCl; (**B**) the TTPC surfactants were intercalated into the interlayer spacing of H-Mt; (**C**) the \(\gamma\)-MPS molecules were grafted onto the exterior surfaces and / or the broken edges of the H-Mt; (**D**) the H-P-Mt was modified with \(\gamma\)-MPS molecules to obtain the H-P-Si-Mt; **E**) the H-Si-Mt was intercalated with TTPC surfactants to obtain the H-Si-P-Mt sample.  
nitrogen atmosphere between 30 and 800 \({}^{\circ}\)C. The differential thermogravimetry (DTG) curves were obtained from the data of TGA.