esa-sceva/satcom-chunk-collection huggingface dataset:
# Diamond-Like Carbon (DLC) Coatings: Classification, Properties, and Applications
## 5 Conclusions  
DLC coatings have received more attention for various enhancements in distinct material properties since the last century. A viable approach for presenting and characterization of applications related to DLC coatings has been attempted. DLC coatings have quite alluring wear behavior that can be customized by modifying the C sp\({}^{3}\)/sp\({}^{3}\) proportion.  
Classifications of DLC coatings based on different considerations, while simultaneously providing their applications in a wide array of industrial sectors such as biomedical, marine, automobiles, and aerospace engineering, has been provided so that the reader understands the viability and importance of the DLC coating process. This work further highlights that much work needs to be done to develop DLC coatings by mixing composite material additives, which should be cost-effective.  
The current literature shows that DLC coatings are one of the most promising coating processes for fulfilling the increasing demands where certain properties such as lower friction, good tribological behavior, and thermal characteristics are required. DLC coatings have importance in the hybrid industry, from clinical to automotive. Some of the fundamental difficulties with DLC coatings are in choosing the correct types of films for a particular application, and adhesion with other materials. More future investigation is expected to discover new/reformed DLC coatings with a combination of various influencing parameters and implementation of new methodologies.  
Conceptualization, D.K.R.; A.K.; A.B. and P.L.M.; methodology, D.K.R.; A.K. and P.L.M.; writing\(-\)original draft preparation, D.K.R.; A.K.; A.B. and P.L.M.; writing\(-\)review and editing, D.K.R. and P.L.M.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Distributed Extended Kalman Filtering Based Techniques for 3-D UAV Jamming Localization
## 4 Proposed Distributed Extended Kalman Filter Based 3D Jammer's Localization Schemes
Next, we can define the following Jacobian matrices as:  
\[\mathbf{A_{k}}=\frac{\partial\mathbf{f}(\mathbf{x_{k-1}},\mathbf{u_{k}})}{ \partial\mathbf{x_{k-1}}}\Big{|}_{\tilde{\mathbf{x}}_{\mathbf{k-1}|\mathbf{ k-1}}}\quad\text{and}\quad\mathbf{H_{k}}=\frac{\partial\mathbf{h}(\mathbf{x_{k}}, \mathbf{u_{k}})}{\partial\mathbf{x_{k}}}\Big{|}_{\tilde{\mathbf{x}}_{\mathbf{ k}|\mathbf{k-1}}} \tag{12}\]  
the conventional EKF algorithm can be summarized, as follows:  
\[\widehat{\mathbf{x}}_{\mathbf{k}|\mathbf{k-1}}=f(\widehat{\mathbf{x}}_{ \mathbf{k-1}|\mathbf{k-1}},\mathbf{u_{k}}) \tag{13}\]  
\[\mathbf{P_{k|k-1}}=\mathbf{A_{k}}\mathbf{P_{k-1|k-1}}\mathbf{A_{k}^{T}}+ \mathbf{Q_{k}} \tag{14}\]  
\[\mathbf{K_{k}}=\mathbf{P_{k|k-1}}\mathbf{H_{k}^{T}}(\mathbf{H_{k}}\mathbf{P_{ k|k-1}}\mathbf{H_{k}^{T}}+\mathbf{R_{k}})^{-\mathbf{1}} \tag{15}\]  
\[\widehat{\mathbf{x}}_{\mathbf{k}}=\widehat{\mathbf{x}}_{\mathbf{k}|\mathbf{k- 1}}+\mathbf{K_{k}}(\mathbf{z}_{\mathbf{k}}-\mathbf{h}(\widehat{\mathbf{x}}_{ \mathbf{k}|\mathbf{k-1}})) \tag{16}\]  
\[\mathbf{P_{k|k}}=\mathbf{P_{k|k-1}}-\mathbf{K_{k}}\mathbf{H_{k}}\mathbf{P_{ k|k-1}} \tag{17}\]  
where \(\mathbf{K_{k}}\) is the Kalman gain and \(\mathbf{P_{k|k}}\) is the state error covariance matrix estimate at time instant \(\mathbf{k}\). Next, we propose our distributed EKF-based two different methods for the 3-D Jammer's Localization. These are presented in the ensuing sub-sections.
## 3 Methods
### High-Resolution Albedo Estimation  
Surface albedo can be obtained directly from Top of Atmosphere (TOA) reflectance without requiring atmospheric correction [43]. The apparent surface albedo was separated from the inherent surface albedo, and the empirical relationship between the simulated surface albedo and TOA reflectance was built based on extensive radiative transfer simulations under a variety of atmospheric conditions. To mitigate errors from nonlinearities, a new statistical relationship was extended to generate albedo measurements from the spectral bands and three broadbands [18], including the visible (300-700 nm), near-infrared (NIR; 700-3000 nm), and total shortwave ranges (300-3000 nm), based on the following linear equation:  
\[\alpha_{\lambda}=\sum\rho_{\mathrm{i}}^{TOA}.c_{\mathrm{i}}+c_{0} \tag{8}\]  
where \(\alpha_{\lambda}\) is the surface albedo for the spectral range of \(\lambda\), \(\rho_{\mathrm{i}}^{\mathrm{TOA}}\) is the TOA reflectance for spectral band i, and c\({}_{\mathrm{i}}\) and c\({}_{0}\) are the regression coefficients.  
The direct estimation approach simulates TOA reflectance from radiative transfer information. Here, the sensor spectral response functions obtained from the USGS and the MODIS BRDF database were used as inputs for the 6S radiative transfer code, aerosol types, water vapor, ozone, and CO\({}_{2}\). The linear regression coefficients for each of the geometrical combinations were precalculated and stored in LUTs for operational use. TOA reflectance was simulated using the regression models in the LUT. Cloud-free observations were selected from the quality control document, then the TM surface albedo was obtained from TOA reflectance data. The direct estimation approach has been successfully applied to Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) sensors [18]; extensive validations have also been carried out at SURFRAD, AmeriFlux, BSRN, and GC-Net sites. The direct estimation approach can generate reliable surface albedo estimates with accuracy of 0.022 to 0.034 in terms of RMSEs over snow-free surfaces [44; 45]. The direct estimation approach has also been used in OLI and Gaofen-1 satellite (GF-1) surface albedo inversion [46; 47].
# Evolution towards Smart and Software-Defined Internet of Things
## 2 IoT Classification and Applications
### Heterogeneous Network IoT  
Heterogeneous networking is the revolution in the field of device connectivity. Heterogeneity is a very prominent feature that makes the machine-to-machine (M2M) communication possible with varying requirements of those machines. Different devices are working under different technologies and using different protocols. IoT brings all these diverse requirements under an umbrella and makes communication possible with these variants. The heterogeneous networking made billions of devices talk to each other in an efficient way which gives rise to new horizons of interconnectivity including the Internet of vehicles (IoV) and the Internet of everything [46; 47; 48].
## 1 Introduction
### Contributions  
In this survey, we primarily focus on the MAC protocols that are applicable to IBSN. Although numerous MAC protocols are available in the literature, the majority of them are designed for BSN and WSN. To the best of our knowledge, there is so far no research paper that explores the impact of these MAC protocols specifically for IBSN. In this context, the main contributions of this paper are:  
* Identifying requirements of MAC protocols for IBSN,
* Providing a taxonomy for the existing MAC protocols based on the WuR technology,
* Identifying the research challenges in the design of MAC protocols for IBSN.  
The rest of the paper is organized as follows. We first explain the characteristics of IBSN in Section 2, with respect to the general strategies of IBSN and elucidate its difference from conventional BSN. In Section 3, we present the traditional medium access mechanisms and their working principle concerning the IBSN. In Section 4, we categorize the existing MAC mechanisms based on their use of WuR and present a taxonomy followed by a comparison between the access mechanism in the classified order. An evaluation of MAC protocols based on the requirements of IBSN is presented in Section 5. Eventually, we present the open research challenges in designing MAC protocols for IBSN in Section 6, followed by a concluding remark from our study which is included in Section 7.
## 1 Introduction
To the best of our knowledge, complex network analysis techniques have rarely been applied to the field of feature selection for rolling bearings. In this work, we present a novel approach to the feature selection issue in the context of composite fault diagnosis. First, we draw upon incremental association Markov blanket (IAMB) discovery to mine the causal relationship among signal features and construct the complex network to express the causal relationship among composite fault features. Then, we model feature selection as the identification of key nodes in the network. In this case, we draw upon nodes' centrality indicators of the complex network to quantify the importance of different composite fault features. We also propose threshold selection criteria to determine the number of features in the optimal feature subset. At last, we experiment on the accuracy of composite fault diagnosis to verify the effectiveness of our approach. Our method reduces the number of features from 20 to 10. Meanwhile, the accuracy of composite fault types is increasing by 4% (that is, the value is up to 94%). The remaining part of the article will be as follows: Section 2 introduces the dataset of composite faults and theories relevant to our approach. Section 3 elaborates on the selection procedures in the causal feature network. Section 4 discusses the test experiments and the results. Section 5 offers a conclusion of our research.
## 3 Results and Discussion
### Descriptive Analysis Evaluation of FS-GRSS, FS-CLVR and FS-TMR Butters by Trained USA Panelists  
The results of the descriptive analysis (DA) undertaken by trained USA panelists are listed in Table 3. There was no significant (\(p>0.05\)) difference perceived for cooked/nutty, milkfat, and salty taste, between all three butters. Grassy was rated significantly (\(p<0.05\)) higher in FS-GRSS butter, followed by FS-CLVR butter. However, it was not detected in FS-TMR butter, corresponding to results from [PERSON] et al. (2020) [41], who reported significant difference in grassy/hay perception of bovine skim milk powder, produced from pasture versus indoor TMR diets, as assessed by a descriptive panel. [PERSON] et al. 2013 [42] identified that grassy intensity was higher from milk produced by pasture and correlated to the higher levels of the aldehyde pentanal. The attribute stale was not perceived in any of the butters, and the levels of saltiness perception were similar (Table 3). Panelists found a significant (\(p<0.05\)) difference in the color intensity of the three butters, in the following order: FS-CLVR \(>\) FS-GRSS \(>\) FS-TMR. Mothball flavor was noted in FS-CLVR butter but not in FS-GRSS or FS-TMR butters. This is a feed specific flavor that has been documented in previous studies (butter, cheese, dried ingredients) manufactured from pasture feeding, and appears to be specific to certain types of pastures [25; 43].
# Borrow from Source Models: Efficient Infrared Object Detection with Limited Examples
## 4 Experiments
### Comparison of SMG-C and SMG-Y  
We compare two instantiations and their baseline methods in Figure 9. It is notable that SMG-Y outperforms SMG-C but YOLOv3 is inferior to CenterNet. In other words, the gap between SMG-Y and its baseline is larger in comparison with SMG-C. To be specific, SMG-Y achieves 77.0% mAP, which is 7.6% higher than its baseline when trained on a full FLIR. In contrast, SMG-C obtains 75.6% mAP, exceeding its baseline by 4.5%. We attribute this phenomenon to the fact that three different BSMs are added in SMG-Y to receive knowledge from different scales, and only one BSM is inserted in SMG-C.  
Additionally, the data dependency for a detector can be reflected in the performance degradation when we reduce the training examples, which is also the slope of the curves in Figure 9. The decline of CenterNet is less than that of YOLOv3 due to the different principles between two frameworks: one is anchor-free and the other is anchor-based. We observe that the curves of both SMG-Y and SMG-C are smoother than their baselines. For example, a slight reduction in mAP can be witnessed in SMG-Y while its baseline accuracy drops dramatically, which indicates that SMG is an efficient strategy to decrease the data dependency for an infrared detection network.  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Dataset** & **Method** & **mAP (\%)** \\ \hline \multirow{2}{*}{IAT} & Baseline & 92.5 \\  & SMG-Y & 97.8 \\ \hline \multirow{2}{*}{IAT-1/2} & Baseline & 88.3 \\  & SMG-Y & 96.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Detection results of SMG-Y on the IAT benchmark.  
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**mAP (\%)**} & \multicolumn{3}{c}{**AP (\%)**} \\ \cline{3-6}  & & & **Person** & **Car** & **Bicycle** \\ \hline \multirow{2}{*}{FLIR} & Baseline & 69.4 & 74.5 & 84.4 & 49.2 \\  & SMG-Y & 77.0 & 78.5 & 86.6 & 65.8 \\ \hline \multirow{2}{*}{FLIR-1/2} & Baseline & 64.9 & 68.5 & 82.1 & 44.0 \\  & SMG-Y & 75.4 & 76.9 & 86.7 & 62.7 \\ \hline \multirow{2}{*}{FLIR-1/4} & Baseline & 56.2 & 61.2 & 78.3 & 29.1 \\  & SMG-Y & 74.5 & 76.6 & 84.4 & 62.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detection results of SMG-Y on the FLIR benchmark.  
Figure 8: Visualization results on FLIR-1/4.
## 2 Materials and Methods
### Experimental Plan  
The crack inclination angle refers to the angle between the crack plane and the horizontal plane. The crack inclination angle refers to the angle between the crack plane and the horizontal plane. Pre-existing cracks with inclination angles of 0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\) were selected for the experiment. The specimen numbering format used is S-\(\alpha\), where S represents the fine-grained sandstone specimen, and \(\alpha\) represents the inclination angle of the pre-existing crack. In addition, various parameters of each specimen were measured using devices such as vernier calipers and ultrasonic velocity meters, and the results are shown in Table 1. The schematic diagram and loading scheme of the splitting test are shown in Figure 2a,b, respectively. In Figure 2, 2\(a\) represents the length of the crack, \(\alpha\) represents the angle between the crack and the horizontal direction, \(R\) represents the radius of the disc specimen, \(t\) represents the thickness of the disc specimen, and \(P\) represents the vertical line load.  
Figure 1: Rock specimens: (**a**) Intact circular specimens. (**b**) Circular specimens with pre-existing crack.  
The splitting test was conducted on a WDW-50 microcomputer-controlled universal testing machine in the Mining Disaster Monitoring Laboratory at the University of Science and Technology Beijing. The loading was displacement-controlled at a rate of 0.5 mm/min. To fix the circular specimen, a pre-load of 0.2 kN was applied to the specimen before loading in the vertical direction until the specimen failed. An acoustic emission detection system was used to monitor the entire process of rock instability and failure in real time during the entire test. The acoustic emission acquisition equipment used was the DS5 Full-Information Acoustic Emission Analysis System manufactured by Beijing Soft Island Company. To avoid noise interference, the monitoring threshold of the test was set to 40 dB, and the sampling frequency was 3 MHz. Figure 3 depicts the experimental system layout.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Specimen Number**} & \multicolumn{2}{c}{**Diameter/mm**} & \multirow{2}{*}{**Thickness/mm**} & \multirow{2}{*}{**Quality/g**} & **Wave** \\ \cline{2-2} \cline{5-6}  & **Upper Surface** & & & **Lower Surface** \\ \hline S-0 & 100.16 & 99.84 & 50.20 & 942.6 & 2336 \\ S-30 & 100.10 & 99.80 & 50.30 & 943.1 & 2381 \\ S-45 & 99.72 & 99.70 & 50.32 & 941.8 & 2296 \\ S-60 & 99.82 & 99.82 & 49.82 & 941.5 & 2375 \\ S-90 & 99.80 & 99.82 & 50.30 & 942.3 & 2278 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters of the rock specimens.  
Figure 2: Splitting test of circular specimens with pre-existing cracks: (**a**) Schematic diagram of circular specimen loading. (**b**) Loading scheme for circular specimens.
# Automatic Generation of Guidance for Indoor Navigation at Metro Stations
## 3 Materials and Methods
### Considerations for Implementations of Rules
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rule Id** & **Rule** & **Description** \\ \hline
3-1 & Change in the floor & Add a direction if floor change is detected. Provide detailed information using attributes of _Indoor Installation_. \\ \hline
3-2 & Change in the room (logical space) & Add a direction based on _Space Connection_ appearance. The direction is also added for two different consecutive _Interior Spaces_. \\ \hline
3-3 & Go straight & If an angle between two consecutive _Path Edges_ in route trajectory is close to 180 degrees, generate the ‘Go straight’ direction. Based on the tests, a tolerance of 30 degrees calculated at 15 m from the decision point was determined. \\ \hline
3-4 & Turn left/right & If an angle between two consecutive _Path Edges_ in route trajectory is close to  more than 50 degrees, generate the ‘Turn left/right’ direction. \\ \hline
4-1 & Combine multiple floor changes & If the route goes through many levels by one _Indoor Installation_ combine it as a single floor change. \\ \hline
4-2 & Remove useless  ‘Go straight’ & Remove consecutive ‘Go straight’ directions unless some of them are created for decision points marked as ‘Need direction here’, which indicates the important point that requires a navigation direction. \\ \hline
4-3 & Change two turns into & Two consecutive turns are changed into one turnaround. Additional conditions must be fulfilled; for instance, the distance between turns does not exceed 10 m. \\ \hline
4-4 & Replace ‘turn left/right’ with landmark-based direction & Each ‘Turn left/right’ direction is attempted to be changed. The algorithm verifies if (1) the orientation of a user body is defined—the preceding edges led through a corridor, etc.; (2) a landmark is available for the decision point. \\ \hline
4-5 & Delete directions with & Remove consecutive directions based on the same landmark. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Selected rules of the algorithm for automatic generation of navigational directions; rules 3-1 to 3-4 are used in stage 3, and rules 4-1 to 4-5 in stage 4.
## 2 Materials and Methods
### GPS and LEO Data  
In order to investigate the performance of the proposed STG cycle slip detection and repair method, the analysis of our paper is based on the GPS products (provided by the International GNSS Service (IGS)), LEO precise orbit products (GOCE, GRACE-A, and Jason-3 satellites provided by the European Space Agency (ESA), Jet Propulsion Laboratory (JPL), and CNES, respectively), and GOCE satellite GPS observations provided by the ESA. Here, we tested GOCE satellite phase observations in November 2009, with a 1-Hz sampling rate.
## 4 IETF 6 LoWPAN Working Group (IPv6)
### Leveraging upon 6 LoWPAN to Realize the IoT  
As described in the previous sections, the core specifications of 6 LoWPAN are RFC 4944, RFC 6282 and RFC 6775. As 6 LoWPAN is part of a full protocol stack, it is used in combination with different protocols, from the physical to the application layer. Further standardization work and research efforts are therefore focusing on, amongst others, improvements to the core specifications, extensions for non-IEEE 802.15.4 networks and the adoption of 6 LoWPAN in practical use cases.
# Spatiotemporal Assessment and Correction of Gridded Precipitation Products in North Western Morocco
## 3 Results and Discussion
### Assessment Based on Rainfall Intensity
These findings are consistent with a previous study conducted in Saudi Arabia [85], which also assessed the performance of different precipitation products from the GPM-IMERG system in an arid region with diverse topography and varying rainfall intensities. The study reported similarly low correlation coefficients for all products, with improvements observed only for the highest-intensity class. The analysis of rainfall intensity categories and their corresponding frequencies revealed that the GPM models were able to reproduce frequencies comparable to the ground observations, while ERA5 struggled to accurately capture the frequencies for each class across all rain gauges (Figure 12). This suggests that the GPM models better reflected the distribution of rainfall intensities in the study area compared to ERA5. Figure 12 also indicated that the class (\(0\leq\mathrm{T}<1\)) was predominant in the study area, representing over 80% of the precipitation. This highlights the importance of accurately capturing low-intensity precipitation, as it constitutes a significant portion of the total rainfall in semi-arid and arid regions. It is worth noting that as the intensity of precipitation increased, the errors in the precipitation products also increased. This suggests that the models had more difficulty accurately representing higher-intensity rainfall events. This finding aligns with the general understanding that estimating high-intensity precipitation is more challenging due to the complex dynamics and localized nature of such events.
# Federated Learning for Cybersecurity: A Privacy-Preserving Approach
## 4 Experimental Setup and Evaluation  
To validate the effectiveness, security, and scalability of the proposed privacy-preserving FL framework, a comprehensive experimental setup was established. This section presents the environment configuration, datasets employed, evaluation metrics considered, and the sequential phases followed during experimentation. Moreover, expected outcomes and a practical case study application are discussed to highlight the real-world applicability and advantages of the proposed architecture.  
The experimental design replicates realistic IoT cybersecurity scenarios and integrates advanced privacy-preserving mechanisms, ensuring that the results provide a thorough assessment of the system's performance under both normal and adversarial conditions.