esa-sceva/satcom-chunk-collection huggingface dataset:
# Flexible Coated Conductive Textiles as Ohmic Heaters in Car Seats
## 3 Metal-Coated Electrically Conductive Heating Textiles  
Electrically conductive fabrics and yarns are the key components of heat-producing textiles. The incorporation and coating of conductive material (metals and carbon-based) are used to fabricate electrically conductive textiles. The choice of metal-coating strategy for the manufacturing of conductive fabrics is critical in terms of processing parameters that tend to be associated with economic attractiveness and determine the reliability, flexibility, physiological, and electrical properties of end e-textiles [25]. In particular, there are two methods for producing conductive textiles. The first method involves weaving, knitting, or embroidering conductive fibres, threads, or yarns into fabrics. Nevertheless, these techniques are highly complex, and because the fabric is hard and inflexible due to the implanted electrical fibres or threads, it may not be comfortable to wear [26]. The typical method entails coating the surfaces of fabrics with robust electrically conducting materials (conductive nanoparticles, metals, and conductive metals, etc.) using a variety of coating techniques in order to give the textiles their electric properties throughout the finishing process. The incorporation of different metals on surface of the fabric (a process known as metallization) has drawn considerable interest among the various techniques employed to attain conductivity for their distinctive properties in offering multifunctional performance, including EMI shielding, antimicrobial activities, fungicidal activity, electrical properties, and ohmic heating [9], in addition to a lustrous metallic visual appeal for aesthetic reasons. The most popular techniques for metallizing textiles can be subdivided as follows (Figure 2) [27]:  
(i) Metallic inks are printed on surfaces using a lithographic technique, inkjet, screen printing, and stencils;  
(ii) Deposition (electrochemical, chemical and physical vapour, electroless);  
(iii) Spray coating (thermal, plasma-assisted cold, etc.).  
Figure 2: Schematic presentation of different metal-coating techniques to obtain electrically conductive textiles, namely, electroless plating. Reproduced with permission from [[PERSON]], [Electroless copper deposition: A critical review]; published by [Elsevier], [2018] [27].  
The appropriate method is determined by several parameters, including the form and type of textile material, operating temperature, metal type, and end application, which is also related to the required thickness of the layer of conductive material [27].
# Texture-Guided Graph Transform Optimization for Point Cloud Attribute Compression
## 5 Conclusions  
In this paper, we propose a texture-guided graph transform optimization scheme for point cloud attribute compression. We formulate the attribute transform coding task as a joint graph optimization problem, considering both the decorrelation capability of the graph transform and the sparsity of the constructed graph. Additionally, we integrate a Hilbert-based point cloud reorganization and uniform clustering approach, facilitating our inter-cluster attribute prediction and intra-cluster prediction on segmented local clusters to eliminate spatial redundancy and extract texture priors. Moreover, we devise a joint rate-distortion-sparsity optimization scheme for constructing the underlying graph structure within each cluster. This optimization process is guided by both geometry structure and texture priors, aiming to achieve optimal coding performance. These methodological advancements present promising solutions for efficient point cloud attribute compression. Experimental results show the proposed scheme outperforms the state of the art with significant BD-BR gains, surpassing G-PCC by 31.02%, 30.71%, and 32.14% in BD-BR gains for Y, U, and V components, respectively. Furthermore, ablation studies conducted on key modules within our scheme validate their effectiveness, further reinforcing the significance of our proposed approach. Conceptualization, Y.S., F.S. and W.G. ; methodology, Y.S., F.S. and S.L. ; software, Y.S., F.S. and G.L. ; validation, W.G., S.L. and G.L. ; formal analysis, W.G., S.L. and G.L. ; investigation, W.G. ; resources, S.L. ; data curation, G.L. ; writing--original draft preparation, Y.S. ; writing--review and editing, W.G., S.L. and G.L. ; visualization, Y.S. and F.S. ; supervision, W.G. ; project administration, S.L. ; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (No. 62172021).
## 4 Mid-Layer Software EMIDE for User-Friendly Application Development Environment
### New MIROS Mid-Layer Software EMIDE
#### 4.3.2 Discussion  
In some software like the Atmel OTAU (over-the-air-upgrade) [35], the application is not decoupled from the system, and the monolithic software image (commonly larger than 100 KB) needs to be updated for the WSN reprogramming. The reprogramming cost of this software is high and the reprogramming success probability is low. In the article [35], it is computed that in a network with 100 routers, the entire OTAU reprogramming process will take approximately 7 h to be completed for a non-secure network, 11 h to be completed for a secure network, and 14 h to be completed for a high-security network. However, if the EMIDE is applied, only the application code needs to be reprogrammed, and the reprogramming process can be completed quickly within several minutes (just hundreds of bytes need to be updated). As a result, both the reprogramming energy cost and time cost can be decreased. Another advantage of using the EMIDE is that the application code will not be built together with the system code, thus the application project becomes simple and can be reprogrammed remotely from the Internet by the webpage.
# ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna
## 6 Results and Evaluation
### Parameter Analysis
#### 6.4.5 Antenna Rotation Speed  
The rotary table in the experiments is automatic. It can rotate at a constant speed by turning on the switch. There are three speeds: 3 degrees/s, 4 degrees/s, and 6 degrees/s. During each rotation, about 200-500 data are collected for each tag. It is obvious that, the slower the speed, the more complete the collected data is, which is more conducive to positioning. We tested different rotation speeds. The results show that there was no difference among them, so ANTspin can extract RSSI peaks at faster rotation speeds. As long as the antenna does not rotate at very high speed, the data it collects is basically complete, and the peak of the RSSI data is easy to extract. Accordingly, ANTspin has a large tolerance range for antenna rotation speed, which can meet the needs of most real-world applications.
## 2 Materials and Methods
### ACL Modeling and Properties  
As illustrated in Figure 2, this dataset includes markers located on both acromia, pelvis, bilateral thighs, bilateral knees, bilateral shanks, and multi-segment feet. This study employed an OpenSim full-body musculoskeletal model adapted from a recent study [40], we included updates on the mediolateral translations, adduction-abduction (AA) rotation, and internal-external (IE) rotation at both knees for this study. The hip joint was modelled as a ball-and-socket joint with three degree-of-freedoms (DOFs) in relation to the \(x\)-axis, \(y\)-axis, and \(z\)-axis. The ankle was modelled as a pin joint with dorsiflexion and plantar flexion in relation to the \(z\)-axis. Previous research has confirmed that ACL is fundamentally viscoelastic [43], with its tensile properties varying with strain rate or loading rate. In this study, the ACL was modeled as a non-linear, elastic, passive soft tissue [44], which was inserted into two fixation tunnels in the femur and tibia, as shown in Figure 2. The superior part of the ACL extends deep into the intercondylar space, while its inferior part attaches to the anterior tibial meniscus. The two primary fiber bundles of the ACL, namely the anteromedial (AM) and posterolateral (PL) bundles, were considered as a single entity in the model, as they were presumed to possess comparable characteristics and aligned orientation. The material properties of the ACL were established based on the criterion that the passive strains of the ACL during flexion-extension (FE), IE rotation, and AA rotation limits were below 15% [45]. The maximum strain was constrained to be less than 2.5-3.0% at the bone-ligament junction, which was the area of contact between the ligament and the bone. The contact area between the ligament and bone was considered, with the starting strain set at a very low value of 0.2% or less at isometric force. The ACL strain was found to be affected by the kinematic function of the knee, meaning that it was restricted by the prescribed limitations of the knee's degrees of freedom. It should be noted that the constant stiffness was only applicable in the region of lower strains (8-10%), with higher strains exhibiting increased stiffness according to the Gaussian law [46].
## 5 Conclusions  
In the present study, the sensory quality of Korla pear fruit from 12 orchards in Circum-Tarium Basin in two consecutive seasons was ranked by using the fuzzy logic model. The ranks of sensory quality attributes indicated that'mouth feel' and 'aroma' have great contribution to the fruit quality of Korla pear. Hexanal, (_E_)-2-hexenal, nonanal, p-limonene, (_Z_)-3-hexen-1-yl acetate and hexyl acetate were identified as the major volatiles in Korla pear. Notably, l-(+)-tartaric acid, hexanoic acid, trans-limonene oxide and 2,2,4-trimethyl-1,3-pentanediol diisobutyrate were found to be negatively correlated with the sensory similarity values.  
**Supplementary Materials:** The following are available online. Table S1: Sensory scores for pear samples and the corresponding triplets; Table S2: Preferences given by panelists and corresponding triplets for relative weightage; Table S3: Overall triplets of pear samples; Table S4: Values of overall membership function of pear samples; Table S5: Similarity values of quality attributes for Korla pear; Table S6: Primary metabolites detected in the pulp of Korla pear by using GC-MS; Table S7: Volatiles detected in the peel of Korla pear by using HS-SPME-GC-MS; Table S8: Seven volatile compounds uniquely detected in specific orchards in 2018; Table S9: List of potential chemical markers.  
**Author Contributions:** Study design, J.X. and J.W.; financial support, C.W., J.X. and J.W.; sample collection, Z.T., C.W., and J.W.; data collection, S.X. and H.Z. ([PERSON]); data analysis, H.Z. ([PERSON]), S.X. and Y.L.; figures, Y.L.; literature search, Y.L.; writing, X.X.; writing, Y.L. and J.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the XCCC South Xinjiang Key Industry Support Plan (2017 DB006-2).  
**Conflicts of Interest:** The authors declare there is no conflict of interest in this study.
# Force Analysis of Circular Diaphragm Wall Based on Circular Cylindrical Shell Theory
## 7 Results and Discussion
### Circumferential Stress of Circular diaphragm Wall  
The circumferential stresses of the circular diaphragm wall obtained through the CCSTCM, FENSM and site monitoring are shown in Figure 11, and they are minus, which represents compressive stress. From Figure 11, we can observe that the circumferential stresses of the circular diaphragm wall obtained through both the CCSTCM and FENSM increase first and then decrease with the increase in depth in different working conditions. The maximum circumferential stress increases gradually with the increase in excavation depth, and the position of the maximum circumferential stress declines gradually and then tends to be stable with the increase in excavation depth. The TCR values are slightly larger than the NSR values. Except for Working Conditions 4 and 5 (W 4 and W 5), in other working conditions, the circumferential stresses of the circular diaphragm wall obtained from the site monitoring increase first and then basically decrease with the increase in depth. The maximum circumferential stress increases first and then decreases slightly with the increase in excavation depth, but the fluctuations are obvious. Combined with Table 4, it can be further determined that the extremum of the circumferential stresses obtained from the site monitoring is 2.973 Mpa, which occurs 29 m from the top of the circular diaphragm wall in the ninth working condition (W 9). The extremum of the maximum circumferential stresses obtained from the CCSTCM is 2.521 Mpa, which is 84.8% of the SMR and occurs 32 m from the top of the circular diaphragm wall in W 15. The extremum of the maximum circumferential stresses obtained from the FENSM is 2.375 Mpa, which is 79.9% of the SMR and 94.2% of the TCR, and occurs 32 m from the top of the circular diaphragm wall in W 15 also. Generally speaking, the variation rules of the circumferential stresses obtained by the three methods are the same and basically in accordance with those of the radial displacements, and the SMR values are slightly larger than the TCR and NSR values and closer to the TCR values, which further illustrates that the proposed CCSTCM and the numerical calculation model established are reasonable and applicable. However, the extremum of the maximum circumferential stresses obtained from the site monitoring occurs 29 m from the top of the circular diaphragm wall in W 9, which is quite different from the position and working conditions of the extremum of the maximum radial displacements. The reason for this could be related to many factors, such as the perpendicularity deviation of the wall, the thickness deviation of the wall, or the mud added to the joint in the actual construction process of the circular diaphragm wall. Another possibility is that the monitoring points of the circumferential stress set were insufficient at Section A, so there was a certain deviation for the TCR.
## 2 System-Level Design
### System Components
#### 2.1.1 Dsi-24  
The centerpiece of this work is the DSI-24 [18], which is a high-performance wireless EEG headset, designed for advanced neurophysiological research and brain-computer interface (BCI) applications [20; 21; 22; 23]. This high-end instrumentation features 21 scalp electrodes, plus three additional sensors for reference and ground, ensuring high-quality data acquisition with minimal noise interference. The captured EEG waveforms are sampled at 300 Hz, providing precise temporal resolution of 3.3 milliseconds necessary for capturing rapid neural dynamics. A 16-bit analog-to-digital converter (ADC) dictates the amplitude resolution of the captured EEG traces. Furthermore, the DSI-24 is notable for its user-friendly design, allowing for quick setup and real-time monitoring, which is essential for both laboratory and clinical environments. It is compatible with various software platforms, facilitating seamless integration with existing research workflows [24; 25; 26].
## 3 Results  
### Optimization Performance  
Table 2 shows a summary of the optimization results for the different experiments. For all three experiments, cost function values were largely reduced, with some exceptions in the 'fapar' experiment, where the initial cost functions were already very small (AU-DaS, 0% reduction in the cost function value, FR-Pue 1.38% reduction, and US-Wkg 7.74% reduction). In general, for all experiments, the optimization was successful, with gradients close to zero. In the'sm+fapar' experiments, the cost function values were reduced by 16.37% to 69.28% in the case of single-site assimilation, and it was reduced by 49.49% in the case of multi-site assimilation.
## 5 Simulation Results
### Impact of Mobility Model  
Figure 6a-d show the effect of the mobility model on the network performance. According to the NFC model, we know that the node encounter probability depends highly on the mobility model of both the vehicles and the UAVs. It can be observed from these figures that the protocol performance is significantly reduced when the network nodes adopt the random movement mobility model to plan the trajectory. That is because in the random movement mobility model, the network nodes (UAVs and vehicles) move randomly and always follow the paths defined by the map data. However, in the SPMBM movement mobility model, the network nodes choose a random point on the map and then follow the shortest route to that point from their current location. This result indicates that optimizing the network node movement mobility model is more useful for improving the network performance comprehensively.
## 4 Results  
### Model Calibration and Validation  
In the context of a car-following scenario, variations in parameters significantly impact the vehicle's motion state, and the driving behaviour adapted to the traffic scenario differs accordingly. In this paper, for the car-following model involving multiple vehicles within a safety potential field, the ITAOA was employed for parameter calibration owing to its superior convergence speed and optimisation accuracy, which are more in line with the calibration requirements of this model. Actual car-following data, specifically from the acceleration and deceleration processes on urban roads, were selected for calibrating the model. The calibration results of the MIDM-SPF model are presented in Table 3, with the key parameters \(\gamma\) and \(\mu\) being 0.37 and 0.29.  
To evaluate the proposed model in this paper effectively, the IDM was also calibrated using the same dataset. The final trajectory results of both the MIDM-SPF and the IDM were compared, as illustrated in Figure 5. Figure 5a,b display the differences between the estimated trajectories of the leading and following vehicles and the actual data for both models. In these figures, the blue line signifies the actual trajectory of the following vehicle in the car-following scenario, the green dashed line represents the trajectory estimated using the IDM, and the red solid line depicts the trajectory based on the MIDM-SPF model.  
From the comparison, both the MIDM-SPF and IDM models did not perfectly match the real data. However, the estimated trajectory trends and errors of both models were within a reasonable range. To further compare the models' effectiveness, key metrics such as the mean squared error, mean absolute error, and goodness of fit for these models on the dataset are summarised in Table 4. Evidently, the MIDM-SPF model demonstrates higher accuracy in terms of error precision compared to the IDM, with its goodness of fit being 2.3% higher than that of the traditional model. This finding suggests that the fitting performance of the improved MIDM-SPF model is better than the original IDM.  
Figure 4: Comparison of convergence curves of different functions: (**a**) F1; (**b**) F2; (**c**) F10; (**d**) F11.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Description** & **Boundaries** & **Value** \\ \hline a\({}_{0}\) & Maximum acceleration & [0.1,5] & 2.2 \\ b & Comfortable deceleration & [0.1,5] & 1.4 \\ s\({}_{0}\) & Minimum headway & [0.1,10] & 3.6 \\ T & Safe time headway & [0.1,5] & 1.5 \\ \(\lambda\) & vehicle potential field strength coefficient & [0.1,1] & 0.11 \\ \(\gamma\) & The coefficient of the safety potential field & & \\  & for the leading vehicle & [0.1,1] & 0.37 \\ \(\mu\) & The coefficient of the safety potential field & & \\  & for the following vehicle & [0.1,1] & 0.29 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calibration results of MIDM-SPF model parameters.
# Pathological Digital Biomarkers: Validation and Application
## 3 Validation of Pathological Digital Biomarker  
Tumor biomarkers are used for diagnosis and management of tumors in patients. Because there are so many various types of tumor biomarkers now, it is crucial to pick the right one. This is a topic that is directly related to the patient's treatment outcomes, hence scientifically validated biomarker testing should be performed. Despite the enormous number of publications on tumor \"biomarkers\" in recent years, the lack of clinically usable biomarkers appears to be largely due to the lack of a well-defined validation method for bringing a newly found \"biomarker\" into the clinic [14].  
Preamalytic, analytic, and clinical validation and a demonstration of clinical utility are the four important processes in moving a novel pathological digital biomarker from a laboratory to daily clinical use [15]. The validation process of a pathological digital biomarker is comprehensively illustrated in Figure 1. The sample collection and processing step evaluates preanalytical factors that may alter biomarker concentration measurement, and this step is essential for all biomarkers utilized clinically, not just cancer biomarkers. Analytical validity, clinical validity, and clinical utility are three crucial semantic concepts proposed for biomarker research [16]. Analytic validity refers to the ability of biomarker tests to accurately and reliably quantify biomarkers in patient samples. Clinical validity refers to the ability of a tumor biomarker test to divide patients into two or more distinct groups with statistical significance based on biological or clinical results. For tumor biomarker tests to be clinically useful, they must either improve clinical outcomes or demonstrate that clinical results are the same at low cost or toxicity.
# Skin Lesion Segmentation Using Image Bit-Plane Multilayer Approach
## 3 Adopted Techniques  
In the conceived method, the singular value decomposition (SVD) method was used for dermoscopic image preprocessing stage aimed at reducing the noise inherently present on skin lesion image. The recourse to SVD, instead of a standard low pass filtering technique, was explained by its capability of preserving patterns and correlated information and discarding spatially uncorrelated terms (such as noise and quantization) without affecting significantly the global image quality. Therefore, the proposed procedure was data adaptive.  
The SVD technique performed the factorization of a matrix into three components as follows [40; 41].  
\[A=U\times S\times V^{T} \tag{1}\]  
where \(A\) is the original data, \(U\) and \(V\) orthonormal matrices, and \(S\) a diagonal matrix, respectively.  
Matrix \(A\) can be decomposed into three matrices, as indicated in Equation (1). \(V\) is the matrix of coefficients able to orthogonalize \(A\) into the product of \(U\) and \(S\), the matrix of \"singular values\".  
The columns of \(U\) and \(V\) are indicated as the left and right singular vector matrices of \(A\), respectively, while the values along the diagonal of \(S\) are arranged in descending order. Such values represent the \"importance\" of each singular vector in \(U\) in describing the entire image.  
The SVD aim is to store the most correlated information present in the image in the first singular vectors; the lower singular values associated to singular vectors can be related to uncorrelated information and noise.  
The SVD capacity of pulling out the correlated components of an image and exploiting the image information into mutually uncorrelated components, sorted by their specific energy, has been exploited for noise reduction in dermoscopic images.