esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Pigment Analysis
#### 3.1.3 Mineralogical and Chemical Compositions of the Red Topcoat
In particular, the distinctive diffraction pattern of kaolinite was only found in samples taken from Heddal (Figure 6d), confirming the FT-IR results (Table S13, See Supplementary Material). Additionally, trace amounts of other minerals were identified, such as lizardite [Mg3 Si2O5(OH)4]. This mineral belongs to the kaolin-serpentine group and is similar in structure to kaolinite, except for the octahedrally coordinated Mg ions that are settled in the octahedral sheet. For these samples, the presence of lizardite can be considered as an impurity in the original material or due to ongoing alteration processes mostly occurring on the extremities of the wall. The significant amount of lizardite is attributable to degradation phenomena affecting kaolinite in H1a and H2b, which both contain weddellite in the basecoat (see Section 3.1.2). XRD analysis (Figure 6c,d) confirmed the presence of hematite in both sites, supporting the EDS results (Table S6, see Supplementary Material). The main difference between the XRD data of these two churches is in the relative amounts of such pigment; the semi-quantitative analysis of patterns suggests that greater amounts of red pigments were present in samples from Eidsborg (Figure 6c), where only traces of hematite were identified in Heddal's samples (Figure 6d). This is probably related to the way the analysis was performed. Generally, the red layer of the Heddal samples is thinner, with the contributing factors from the previous layers being more relevant. The final effect is that of a dilution. Additionally, minium was individuated in samples from both churches (Figure 6c,d); only traces of Pb3O4 and hematite were found in samples H1d, H2a and H2b, while samples E2d and E3a showed considerable relative amounts of minium (-33.6%), associated to other Pb-oxides such as Litharge and Massicot (tetragonal and orthorhombic PbO, respectively). Red ochre and minium have previously been identified in the pictorial layer of 1600-1700s and medieval stave church wall paintings, respectively [1]. However, combining minium and hematite was not mentioned in [1], although stave church artists during this period were known to mix pigments. Finally, cellulose from the wooden substrate was the only compound observed for E2c (Figure 6c). This sample was extremely thin, not permitting the separation of the pictorial surface layer from the substrate (Figure S1, see Supplementary Material). Consequently, both the XRD pattern and the FT-IR spectrum acquired on the surface of the paint layer only showed cellulose contributions.
# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## 1 Introduction
One of the 3D methods, mean shift, a classical 2D clustering approach that can easily be adapted to 3D scenarios, has drawn considerable attention for direct 3D point cloud segmentation [24; 25]. Mean shift had been successfully used in computer vison and image processing for mode-seeking in feature space. The mode is the maxima of a density function, and is located iteratively by shifting the weighted mean determined by a kernel, hence the name mean shift. The kernel can be easily expanded into 3D, so the mean can be calculated directly from the 3D points. It has shown promising results for different types of tree conditions, such as multi-layered temperate [24] and tropical forests [26], mixed-species urban trees [27], and boreal coniferous forest [28]. However, there are a few factors to be considered, such as the kernel shape, size, and weight, to better implement it to segment trees. As an in depth analysis of the kernel function and weighting are inadequate from the literature, this paper aims at providing a detailed assessment of the mean shift algorithm for ITD from airborne lidar data to clarify the influence of the variations to the performance.
## 2 System Model and Problem Statement
### Problem Statement  
In the highly dynamic maritime search and rescue environment with poor communication link quality, how to design a novel low-delay and energy-efficient opportunistic routing protocol to realize real-time and efficient forwarding of MSR data packets from source node to sink node.
# Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification
## 1 Introduction
In the case of fault diagnosis of electrical machines, the knowledge about the physical system may limit the number of necessary features. Furthermore, in engineering practice, there is still considerable ambiguity concerning the selection of the most suitable classifier. Based on the above briefly summarized antecedents and state-of-the-art applications, this research on the development of new condition monitoring and diagnostic methodology for electrical machines and drives is focused into the applicability of improved versions of multi-label classification machine learning approaches for multiple fault detection under noisy conditions and also the simultaneous determination of fault severity. The contribution of this work is to reveal the most suitable multi-label classification methodology for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. For this purpose, the performance of various multi-label classification models is compared. Some observations indicate that numerous attributes result in wide parameter space. In our view of the physical model approach, we highlight the important features to be extracted. Current and vibration signals are acquired under normal and fault conditions on an experimental set-up. To enable fast training set preparation, we propose a semi-synthetic database based on a small number of measurements. The rest of this paper is organized as follows: in Section 2, we briefly introduce the latest multi-label classification methods, and we derive a new methodology for multi-fault diagnosis and severity assessment for rotating electrical machines and drive systems. Section 3 is devoted to the description of the experimental setup as well as the description of feature extraction and dataset preparation. Section 4 presents the results of our investigations on the multi-label classification methods. After, in Section 5 we draw some pertinent conclusions.
# Revealing Development Trends and Key 5G Photonic Technologies Using Patent Analysis
## 1 Introduction
Thus, this study focused on patent data and observed the key areas and trends in the technological development of key technologies using network analysis. In summary, this study differs from previous research on 5G photonic technology and the related market. In particular, a comprehensive examination was conducted of the layout of 5G technologies and an overall picture of photonic technology in 5G was presented from a macro perspective, focusing on the establishment of a technology network model and the analysis of technology development trends. This can provide a valuable reference for the government, researchers, and industry professionals.
# Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet
## 4 Experimental Results and Analysis
### Attention Comparison Experiment  
Due to our enhancements in attention, particularly based on ECA-Net, the effectiveness of the proposed attention module in this study was validated by comparing it with other attention modules. Senet [45], CBAM [46], ECA-Net, and CPCA attention were integrated into the model, and attention comparison experiments were conducted. The experimental results are depicted in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **DoU Loss** & **Attention** & **CGM** & **ODS** & **OIS** \\ \hline Baseline & & & & 0.907 & 0.917 \\ \hline \multirow{3}{*}{CG-UNet} & ✓ & & & 0.924 & 0.929 \\  & & ✓ & & 0.922 & 0.928 \\ \cline{1-1}  & ✓ & ✓ & & 0.926 & 0.930 \\ \cline{1-1}  & ✓ & ✓ & ✓ & **0.934** & **0.938** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiment results.  
Figure 5: \(\alpha\) coefficient experiment results.  
Figure 6 illustrates the experimental results of replacing our proposed attention module with other algorithms. Senet calculates channel attention through the fully connected layer. CBAM adds spatial attention based on Senet, resulting in a 0.2% increase in the F-score. ECA-Net substitutes the calculation of channel attention with efficient convolution operations, enhancing the model's edge detection capabilities while reducing the number of parameters. CPCA enhances the model's attention to spatial content by separately extracting spatial attention from features weighted by channel attention. The attention module proposed in this study, which is based on ECA-Net and incorporates the advantages of the above attention modules, achieved the most favorable recognition results, with the F-score reaching 0.934. Moreover, the parameters of CG-UNet were superior to those of CPCA, being reduced by 0.7 M.
## 3 Data Analysis and Model Training
### Data Processing
#### 3.2.4 Data Splitting  
The prepared data were split into a training set (85% of the data) and a validation set (15%). The training set was used to train the LSTM model, while the validation set was employed to evaluate and fine-tune the model performance.
## 4 Results and Discussion
### Performance of Prediction Model
Applicability and usability can be judged in the building where the actual system is being used, and the faster the calculation speed, the higher the applicability to the field. It can be seen that the computation time of the predictive model using artificial neural networks is the shortest at 31 s. It took 1 min 20 s for the support vector machine, 2 min 8 s for the random forest, and 2 min 22 s for the K-nearest neighbor. In the case of the support vector machine, the difference in operation time from other machine-learning models was not large, but as training data increased, limitations on memory and operation time appeared. Among the four machine-learning models, the artificial neural network model showed the shortest computation time, and it was confirmed that the performance prediction was possible with a small computation speed even with an increase in the number of data, so it was highly likely to be applied in the field.
# Evaluation of Resilient Modulus and Rutting for Warm Asphalt Mixtures: A Local Study in Iraq
###### Abstract  
Warm-Mix-Asphalt (WMA) allows aggregates to be coated with asphalt binder at a temperature lower than the Hot-Mix-Asphalt (HMA) temperatures by using additives that make the asphalt mixtures more workable and the asphalt binder viscosity reduced. Due to the cost and environmental advantages of WMA, it is now more frequently employed in the mixtures of asphalt pavement. WMA with two percentages of zeolite (0.3% and 0.5%) is used in this research. The optimum asphalt content of WMA mixtures has a lower value compared to the HMA mixture. According to Marshall Stability's findings, the HMA has a higher value of stability, whereas the WMA mixtures for both percentages of zeolite have a lower value of stability. WMA mixture with 0.5% zeolite has a higher Marshall Flow value, which is followed by zeolite at 0.3% and the lower value of flow was for HMA. The resilient modulus for both HMA and WMA was determined. The findings indicate that, for the two percentages of zeolite, the resilience modulus of the WMA mixtures was lower than that of the HMA. Additionally, the findings indicate that the WMA has a high potential resistance against rutting that competes with the HMA mixture.  
warm asphalt mixtures; hot asphalt mixtures; Marshall stability; flow; resilient modulus; rutting +
Footnote †: journal: Applied Sciences  
Accepted: 9 December 2022  
Published: 14 December 2022
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles
### Conductive Polymers
#### 2.1.2 Polyaniline (PANI)  
PANI has become an influential resource for conductive textiles because of its facile manufacturing techniques, biocompatibility [21], porous nature [22], thermal stability up to 420 \({}^{\circ}\)C [23], catalytic behavior [24], distinct adaptation methods for acquiring diversified behavior, various colorful forms and their interconversion [25], and its transparent medium [26]. Although the application of PANI was initially for use as writing ink, it was first acknowledged by [PERSON] with his research group (researchers of the School of Industrial Physics and Chemistry of the City of Paris) as an electroconductive material during the late 1960s [7]. The polymer molecules of PANI are incorporated with poly-conjugated chains derived from reversible phenyl rings and nitrogen-based groups. The profound delocalization, polarization, and one-dimensional characteristics of \(\pi\)-electron clouds of poly-conjugated networks have led to the outstanding structural, optical, and electroconductive dispositions of PANI.  
Numerous versatile polymerization techniques, such as oxidative polymerization, direct and inverse emulsion polymerization, interfacial polymerization, solution polymerization, seeding polymerization, metathesis polymerization, and self-assembling polymerization, have been adapted to synthesize PANI [27]. Customarily, three forms of PANI, exhibited in Figure 2, can prevail, thereby relying on their extent of oxidation and degree of protonation. These are:  
1. Leucomeraldine (\(\mathrm{C_{6}H_{4}NH}\))\({}_{n}\)--100% reduction level
2. Emeraldine (\(\mathrm{[C_{6}H_{4}NH}]_{2}\mathrm{[C_{6}H_{4}N}]_{2}\))\({}_{n}\)--50% oxidation, 50% reduction
3. Pernigraniline (\(\mathrm{C_{6}H_{4}N}\))\({}_{n}\)--100% oxidation level  
Only the Emeraldine form of PANI can be applied directly to the textiles to impart conductivity among these three categories; however, the pristine emeraldine retains a lower conductivity level and limited processability [25].  
Though the potential growth of PANI-based conductive textiles has escalated drastically in recent years, the inherent toxic nature and lower conductivity of PANI than metallic particles, even compared to other intrinsic conductive polymers (PEDOT, PPy) are still the major handicaps for commercializing PANI-based conductive materials. Notable research projects related to ascertaining the environmentally friendly and cost-effective synthesis process of PANI-based conductive textiles have been reported [29, 30].
## 3 Results and Discussion
### Comparison of Animation Rendering Results for 3D Models Generated Using Three Algorithms  
In this subsection, we present the development of a mobile AR interactive display system for the white-lipped deer, implemented using the widely adopted Unity3D platform [21]. A customized skeletal rig, adapted from a standard tetrapod framework, was applied to each of the three white-lipped deer models within Blender. Interactive animations--including walking, running, foraging, and kneeling--were created using inverse kinematics (IK) techniques to ensure realistic motion and adaptability across different models [22; 23; 24; 25; 26]. These animations were seamlessly integrated into the Unity3D AR environment, establishing a robust foundation for performance benchmarking. To comprehensively assess the efficiency of the 3D model constructed using the TPOT-based physically rendered fur simulation algorithm, comparative performance tests were conducted alongside models developed using the traditional texture mapping and particle hair simulation approaches. The animation sequences were rendered in both Blender and Unity3D AR environments on Android mobile devices to evaluate rendering quality and system resource demands. The experimental results are summarized in Table 2, providing quantitative evidence to support the effectiveness and applicability of each algorithm and offering a theoretical basis for future research and optimization. By analyzing the experimental data presented in Table 2, it is evident that the TPOT-based fur simulation algorithm demonstrates rendering efficiency and system resource utilization comparable to that of the traditional texture mapping method across both platforms. Although the particle hair algorithm exhibits superior performance, with GPU rendering efficiency 22.3% higher and CPU computational efficiency 71.5% greater, our approach achieves a well-balanced trade-off between visual realism and computational efficiency. Figure 5: Modeling workflow for white-lipped deer using the particle hair simulation algorithm. To further emphasize the robust performance of the proposed algorithm on mobile platforms, we conducted an additional test by increasing the number of white-lipped deer rendered within the same Unity Android AR environment. Using a ten-second head-down grazing animation as a benchmark, we computed the average frame rates corresponding to varying numbers of models. The results are illustrated in Figure 6.  
The average frame rate results on the Unity Android AR platform indicate that the white-lipped deer model generated using the proposed TPOT-based algorithm achieves runtime performance comparable to that of the traditional texture mapping method while significantly outperforming the particle hair approach. This demonstrates the method's distinctive advantage in maintaining high visual fidelity with minimal performance over
## 3 Contextual Received Signal Strength Approach
### CRSS Approach
#### 3.4.2 From Spatial Data to WSN  
Applying this analysis from spatial data, in a sensor network, the random point is the random node (sensor) [35]. Thus, the pdf of the distance, \(\beta>0\), of a node to its nearest neighbor is  
\[f(\beta)=2\pi\lambda\beta e^{(-\lambda\pi\beta^{2})}. \tag{8}\]  
Moreover, the probability that this distance is less than or equal to the communication range R is then expressed as  
\[P(\beta\leq R)=\int_{\beta=0}^{R}f(\beta)d\beta=1-e^{(-\lambda\pi R^{2})}. \tag{9}\]  
1. In a one-dimensional case \(N\) nodes are randomly uniformly deployed on a distance \(d_{max}=  
for \(N>>1\) and \(\frac{(x_{2}-x_{1})}{d_{max}}<<1\), this solution can be approximated with a Poisson distribution, with \(p=\frac{x_{2}-x_{1}}{d_{max}}\), and keeping the density \(\lambda=\frac{N}{d_{max}}\) constant:  
\[\begin{split} P(\xi=v)&=\frac{(Np)^{v}}{v!}e^{-Np}\\ &=\Big{(}\frac{N\frac{(x_{2}-x_{1})}{d_{max}}}{v!}\Big{)}^{v}e^{-N \frac{(x_{2}-x_{1})}{d_{max}}}\\ &=(\frac{\lambda(x_{2}-x_{1})}{v!})^{v}e^{-\lambda(x_{2}-x_{1})} \end{split} \tag{11}\]  
Thus, the probability that a node has \(v\) neighbors within its communication range \(R\), is the same as the probability in Equation (11), but the distance interval is on \(2R\) such as  
\[P(\xi=v)=\Big{(}\frac{\lambda 2R}{v!}\Big{)}^{v}e^{-\lambda 2R}. \tag{12}\]  
(ii) In two-dimensional deployment  
\(N\) nodes are deployed randomly on surface \(A\), providing a realistic representation of random node placement in practical scenarios. Under this assumption, the average number of nodes per unit area is denoted by \(\lambda=N/A\). This allows us to compute the probability \(P(\xi=v)\) that any subset of \(v\) nodes from the total \(N\) nodes falls within a specific sub-area \(A_{0}\) of the system plane \(A\), delimited by the communication range \(R\) of a node, with \(A_{0}=\pi R^{2}\); hence, analogously to (11), \(P(\xi=v)\) is obtained as  
\[\begin{split} P(\xi=v)&=\frac{(\frac{A_{0}}{A_{0}}N )^{v}}{v!}e^{-\frac{A_{0}}{A}N}\\ &=\Big{(}\frac{\lambda A_{0}}{v!}\Big{)}^{v}e^{-\lambda A_{0}}\\ &=\Big{(}\frac{\lambda\pi R^{2}}{v!}\Big{)}^{v}e^{-\lambda\pi R^ {2}}.\end{split} \tag{13}\]
## 1 Introduction
On these conditions, the proposed DMCAP model significantly classifies real-time multi-channel attributes based on SNIR, entropy, malicious events, and other nonlinear distortions. The contributions of the proposed DMCAP model has the benefits such as optimal energy utilization rate, optimal link establishment rate, minimal routing delay, and maximum secure throughput rate compared with existing techniques. Compared with other existing techniques, the proposed model was specially developed for WUGSN. Furthermore, the proposed model was modelled to predict multi-channel quality metrics for reducing the impacts of channel uncertainties in WUGSN. The results provided in Section 4 illustrate the benefits of proposed model against existing techniques practically. On the basis of research motivation, the manuscript has notable research works that contribute to the channel evaluation and attribute assessment model in Section 2. Section 3 of this manuscript explains the technical contributions and system design of proposed DMCAP model. Section 4 provides the experiment details and performance evaluation. This section shows the implementation details, network configuration parameters, channel configurations, and results. Finally, Section 5 summarizes the crucial contributions of the research work with appropriate future philosophies.
# On the Stability of Graphene-Based Aqueous Dispersions and Their Performance in Cement Mortar
## 2 Materials and Methods
### Suspension Stability Characterisation  
The suspension stability was studied by means of the SMLS technique, using a Turbiscan LAB(r) analyser (Microtrac Formulation, Toulouse, France). This instrument analyses samples with a volume of about 30 mL. During the analysis, an infrared light source (wavelength \(\lambda=880\) nm) scans the sample vertically, while two sensors collect the transmitted (T) and backscattered (BS) light at different heights. By comparing these profiles, the Turbiscan LAB(r) can track changes in the sample over time and compute the Turbiscan Stability Index (TSI). The TSI (a dimensionless number) is calculated at time \"t\" by summing up all temporal and spatial fluctuations within a designated area:  
\[TSI(t)=\frac{1}{N_{h}}\sum_{i_{l}=1}^{t_{max}}\sum_{z_{i}=z_{min}}^{z_{max}}| BST(t_{i},z_{i})-BST(t_{i-1},z_{i})| \tag{1}\]  
where \(N_{h}=(z_{max}-z_{min})/\Delta h\) is the number of height positions in the selected area; \(z_{max}\) and \(z_{min}\) are, respectively, the upper and lower selected height limits; \(t_{max}\) is the last time \(t\) at which the TSI is calculated; \(BST\) is the signal under consideration (its value is \(BS\) when \(T<0.2\%\) and \(T\) otherwise).  
Based on the TSI values, suspensions are classified into five categories, each corresponding to a different quality grade. (**A+** The Turbiscan(r) detects no notable destabilisation, maintaining visual stability. An A+ ranking denotes the highest level of stability. **A** No visually apparent destabilization is detected at this early stage, either through migration or size variation. **B** The Turbiscan(r) detects higher variations indicating the beginning of destabilization, yet visual cues are largely absent (\(>\)90%); **C** Significant destabilization is evident, characterized by notable sedimentation, creaming, broad particle size shifts, or minor phase separation. Though not always visible, caution is advised, necessitating careful monitoring of samples in this category. **D** Severe and conspicuous destabilization is evident, likely observable through substantial sedimentation, creaming, phase separation, considerable particle size or colour changes, signalling a visual failure.)