esa-sceva/satcom-chunk-collection huggingface dataset:
# In Vitro Evaluation of a Non-Invasive Photoplethysmography Based Intracranial Pressure Sensor
## 3 Results  
The final result of the in vitro model is shown in Figure 5, where the main components of the closed-loop circulation circuit are depicted. From right to left, the figure presents the pulsatile pump (heart), reservoir, silicone vessels (arteries), intracranial chamber, and brain phantom.  
The predictive model appeared to satisfactorily explain the variance observed in the measurements, with coefficients of determination equal to R\({}^{2}\) = 0.90, R\({}^{2}\) = 0.97, and R\({}^{2}\) = 0.98 for the three datasets, respectively.  
Figure 6 shows the scatter plots between the non-invasive ICP predictions against the reference invasive ICP measurements. The predicted nICP showed high correlation with the reference invasive ICP, and the computed Pearson's correlation coefficients between the nICP and invasive ICP were r = 0.95 (\(p<0.01\)), r = 0.98 (\(p<0.01\)), and r = 0.98 (\(p<0.01\)), respectively, for Dataset 1, Dataset 2, and Dataset 3.  
Figure 4: PPG signals and features extracted.  
When comparing the residuals of the prediction error between nICP and ICP, the nICP demonstrated an acceptable difference from the reference measurements. In Dataset 1, the predicted nICP was observed to have a higher root-mean-square-error, equal to RMSE = 3.12 mmHg, whereas the measurements in Dataset 2 and Dataset 3 showed a lower error, respectively, of RMSE = 1.48 mmHg and RMSE = 1.45 mmHg.  
The Bland and Altman analysis of agreement provides a visual and statistical method to assess the agreement between two measures. Figure 7 displays the Bland and Altman plots between the predicted nICP and the reference invasive ICP measurement for all three datasets. Dataset 1 showed a low bias of \(-\)0.4 mmHg between the two measurements, but also displayed a wider interval of agreement of \(\pm\)6 mmHg. Datasets 2 and 3 showed both low bias and narrower deviation, with limits of agreements of \(-\)0.2 \(\pm\) 2.8 mmHg and \(-\)0.4 \(\pm\) 2.8 mmHg.  
Figure 5: Picture of the in vitro model.  
Figure 6: Scatter plots between predicted non-invasive intracranial pressure (nICP) and the reference invasive ICP measurements.
## 3 Simulation Verification of the Rock-Breaking Process by Disc Cutters
The rolling force values obtained from the DEM closely align with the experimental results, demonstrating high accuracy. However, the specific energy values derived from the DEM deviate slightly from the experimental results. This discrepancy may arise because, in the DEM simulations, rock fragmentation occurs when the tensile or shear stress on particles exceeds their respective strengths. This mechanism can produce fewer fragments during particle breakage, reducing the fracture area. Conversely, finite element method (FEM) simulations involve more complex stress states and crack propagation paths, resulting in more extensive fragmentation and larger fracture areas. Despite these differences, the DEM simulation results for rolling force and rock-breaking specific energy fall between the FEM and experimental results, closely aligning with the experimental data. This demonstrates that the calibrated DEM model effectively simulates the rock-breaking process by disc cutters. As such, the model provides a reliable tool for analyzing rock-breaking behavior under various tunneling parameters.
###### Abstract  
During rapid urbanization, it is necessary to increase soil permeability and soil porosity for reducing urban runoff and waterlogging risk. Woody plants are known to increase soil porosity and preferential flow in soil via living roots growth and dead roots decay. However, the primary results of dead woody plant roots on soil porosity and permeability have been discussed based only on the hypotheses or assumptions of different researchers. In this study, living and dead roots (decayed under natural conditions for more than 5 years) of Gansu poplar trees (_Populus gansuensis_) were selected. They were selected to compare the influence between living and dead roots on water infiltration rate and soil porosity in a cylindrical container (diameter = 20 cm, height = 66 cm) under laboratory conditions. Results indicated that the steady-state water fluxes at the bottom of the containers without roots (control), with living roots, and with dead roots were 54.75 \(\pm\) 0.80, 61.31 \(\pm\) 0.61, and 55.97 \(\pm\) 0.59 cm d\({}^{-1}\), respectively. Both living roots and dead roots increased the water infiltration rates in soil and also increased the water storage capacity of soil. The water storage capacities of soil without roots, with living roots, and with dead roots were 0.279, 0.317, and 0.322 cm\({}^{3}\) cm\({}^{-3}\), respectively. The results from SEM indicated that smaller pores (30-50 \(\upmu\)m) were in living roots and larger pores (100-1000 \(\upmu\)m) were in dead roots. The soil permeability was increased by living roots possibly due to the larger channels generated on the surface of the roots; however, water absorbed into the dead roots resulted in greater water storage capacity.  
soil permeability; soil porosity; water storage capacity; woody plant; desert poplar; _Populus gansuensis_
## 6 Conclusions
\begin{tabular}{l l} AWGN & Additive White Gaussian Noise \\ BER & Bit Error Rate \\ Bi-LSTM & Bidirectional Long Short-Term Memory \\ BN & Batch Normalization \\ CNN & Convolutional Neural Network \\ CSI & Channel State Information \\ DFE & Decision Feedback Equalizer \\ DNN & Deep Neural Network \\ DL & Deep Learning \\ GD & Greedy Detector \\ HDL & Hybrid Deep Learning \\ ISI & Inter-Symbol Interference \\ MC & Multi-Carrier \\ MMSE & Minimum Mean Square Error \\ ML & Maximum Likelihood \\ OFDM-IM & Orthogonal Frequency-Division Multiplexing with Index Modulation \\
16 QAM & 16-Quadrature Amplitude Modulation \\ QPSK & Quadrature Phase-Shift Keying \\ ReLU & Rectified Linear Units \\ SNR & Signal-to-Noise Ratio \\ SE & Spectral Efficiency \\ Tanh & Hyperbolic Tangent \\ ZF & Zero-Forcing \\ \end{tabular}
## 5 Applications Across Diverse Sectors
### Inventions in 3D Printing Technologies
#### 5.2.1 Robotics  
In the robotics sector, additive manufacturing has contributed to significant innovations and advancements. Here are a few important inventions and advances made by applying AM to robotics [46]:  
(1) _Multi-material and functionally graded robots_: AM allows for the creation of robots featuring complex designs and built-in parts from various materials. Robots can now possess a range of characteristics, such as different levels of hardness, built-in sensors, and moving parts, all in one piece. [PERSON] and colleagues used a 3D printing method called stereolithography to combine several materials with unique physical traits. This made it possible to build advanced tiny robots with specific magnetic properties for movement, offering the ability to craft detailed forms that can be customized [77].  
(2) _Soft robotics_: Soft robotics utilizes flexible materials to design robots capable of safe interaction with humans and delicate object manipulation. AM techniques, such as selective deposition, allow for the fabrication of intricate structures in soft robots. These structures can include embedded features like pneumatic networks, sensors, and actuators. [PERSON]'s research concentrated on grippers utilizing granular jamming, which is difficult to model. He introduced an innovative \"one-shot\" method that employs multi-material 3D printing to create complete grippers, encompassing both the membranes and grains, in just one printing session [78].  
(3) _Hybrid robots_: A new class of robots called soft and stiff hybrid robots has emerged as a result of advancements in hybrid robotics made possible by additive manufacturing. Integrating soft and hard elements, these robots manage to combine adaptability with structural support, making them capable of executing detailed tasks precisely and interacting safely with humans. Researchers at Harvard have succeeded in developing a hybrid robot that incorporates hard elements within a soft exterior, illustrating the capabilities of this approach [79].  
(4) _Modular and reconfigurable robots_: Additive manufacturing enables the fabrication of modular components, facilitating the assembly and reconfiguration of robotic systems for swift customization to different tasks and environments. Scientists are using 3D printing to create hybrid robots with swappable parts, increasing their flexibility and allowing for easy adaptation to different tasks. [PERSON] et al. developed GHEFT, a universal connecting system for modular robots that can change their shape, making it reliable, safe, even if parts fail, and able to connect well even when not perfectly aligned, which helps the robots rearrange themselves [80].
# A CNN-LSTM-Attention Model for Near-Crash Event Identification on Mountainous Roads
## 1 Introduction
The CNN-LSTM-Attention model utilizes the CNN layer to extract feature variables, captures the temporal correlation between feature variables through the LSTM layer, and employs the attention mechanism to assign different weights to each feature variable. 3. A naturalistic driving dataset collected on mountainous roads is used to evaluate the proposed method. It is demonstrated that the proposed process accurately and efficiently identifies near-crash events from the raw data. The proposed multilevel deep learning-based identification model demonstrates significantly better accuracy and response speed compared to benchmark models. The remainder of this paper is organized as follows. Section 2 summarizes the state of the art of near-crash event identification methods and outlines the overall framework of the proposed methodology. Section 3 introduces the details of collecting and processing naturalistic driving data. Section 4 presents the architecture and different components of the CNN-LSTM-Attention model. Section 5 reports the results of data processing and near-crash event identifications. Section 6 concludes the paper by summarizing the main findings and providing directions for future research.
## 4 Fatigue Damage Analysis
### Fatigue Damage Statistics  
The purlin distances of roof claddings were set from 600 to 1000 mm (600, 800, and 1000). When the stress time history of Typhoon Chantu was loaded, the hot spot stress time history of roof claddings was calculated according to the pressure records during the typhoon and the finite element analysis results. The stress time history was counted and analyzed using the rainflow statistics method. When the NELCS and the LHL sequence were loaded on the roof claddings, the wind loading P is the maximum value of the pressure  
Figure 8: Stress distribution of the finite element model when the purlin distance is 600 mm.  
Figure 9: Load–stress relationship of different purlin distances.  
record during the typhoon (4.24 kPa). The hot spot stress cycles were obtained based on finite element analysis results. Then, the fatigue damage accumulation of the roof claddings was counted in combination with the material _S_-_N_ curve, the Goodman formula, and [PERSON]'s linear damage accumulation rule. The fatigue damage calculation results are shown in Table 3.  
In Table 3, the fatigue damage accumulation obtained by loading the LHL sequence is more conservative compared with the fatigue damage calculation results based on the Typhoon Chantu pressure record. The results of NELCS in this study fall between the preceding two methods. Therefore, our results are more reasonable because NELCS not only considers the safety, but it is also not too conservative.
# The Applications of Magnetic Particle Imaging: From Cell to Body
## 3 MPI Tracers  
MPI detects the tracer rather than its surrounding environment, such as blood or biological tissue. Hence, the performance of the tracer directly affects the imaging quality of MPI. Therefore, research on MPI tracers is essential for the development of MPI [24; 34; 35]. At present, the most commonly used MPI tracers, such as Resovist and Feraheme, are not tailored to the MPI physics. Moreover, fewer tracers are designed for MPI according to its imaging principle. In terms of MRI, studies have shown that the contrast can shorten the T2 relaxation time of MRI. These nanomagnetic substances will add magnetic micro-gradients onto the external magnetic fields, resulting in the loss of phase coherence of resonance protons, which leads to signal voids in the MR image [17]. In terms of MPI, the device directly images the SPIONs in the region of interest; therefore, it does not produce background signal except where SPIONs are concentrated. The differences in imaging principles between the two techniques lead to differences in tracer requirements. Considering the tracer behavior and imaging principle, characteristics such as size, shape, and magnetization saturation of the SPIONs affect the MPI signal. Therefore, exploring and designing tracers suitable for MPI is an essential direction in MPI research [34; 36; 37; 38]. For example, [PERSON]'s research group at Stanford University used different iron oxide particles that were produced with a series of preparation times to explore the influence of preparation time on MPI tracers because those differences in time resulted in various iron oxides [3]. They demonstrated that Fe\({}_{3}\)O\({}_{4}\) rather than Fe\({}_{2}\)O\({}_{3}\) generated MPI signal. [PERSON] et al. of the University of Applied Science discussed the parameters that affect the performance of MPI tracers by modeling the particle dynamics of it [39]. According to their research, parameters such as diameter, shape, and magnetization saturation affect the particle dynamics, thereby influencing the MPI signal [34; 39]. [PERSON]'s group from the Chinese Academy of Sciences and co-author controlled the shape and size of SPIONs to design a tracer tailored to MPI physics. They designed a cubic SPION with an edge length of 22 nm, which has high sensitivity and high resolution [40].
# GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning
## 4 Results and Discussion
### Weld Fume Removal Using GAN  
In this section, real-time experiments related to weld fume removal are discussed. The datasets used for analysing the dehazing performance included nonhomogeneous fumes present over the plates. Additionally, to make the detection robust, plates were placed in different backgrounds to prepare the datasets. In this paper, DW-GAN was used for fume removal and was trained for 3000 epochs with an image size of 1600 \(\times\) 1200 pixels. The learning rate was chosen as 1e-4. The ground truth image used is shown in Figure 9a-f. The dehaze results for GAN are shown in Figure 9g-l, and for DCP they are shown in Figure 9m-r. The inference speed for DW-GAN was found to be 6.33 s, and for the DCP method it took 20.14 s for a single image.  
Figure 8: Real-time hardware setup of TAL BRABO manipulator for weld seam detection and tracing.  
Figure 9: Comparison of weld fume removal using (**a–f**) Ground Truth Image (**g–l**) DW-GAN and (**m–r**) DCP methods.  
The image histogram calculated from image 1 is shown in Figure 10a-c to compare the variations in pixels between ground truth and dehazed images using the GAN and DCP methods.  
The RGB variations in ground truth and dehazed image are plotted and, from the graph, it can be inferred that pixel values were better for the GAN images, which were similar to ground truth. Furthermore, to analyse the dehazing performance, _PSNR_, _SSIM_, _MSE_ and _NIQE_ scores for image 1 to image 4 are analysed and plotted in Figure 11a-d, respectively.  
Figure 11: Performance evaluation of weld fumes removal (**a**) PSNR (**b**) SSIM (**c**) MSE and (**d**) NIQE.  
Figure 10: Image histogram to show the comparison between (**a**) ground truth (**b**) DCP (**c**) DW-GAN.  
From the plot, it can be seen that _PSNR_ for GAN was high for all four images, which shows that image quality was better than DCP-based weld fume removal. The average _PSNR_ of GAN was obtained as 15.95 dB, which was higher than DCP. Similarly, _SSIM_ value for GAN-based weld fume removal was high, with an average value of 0.407, which implies better quality than the DCP method. Additionally, it was noted that the mean square error was less for the GAN-based dehazing method with a value of 1.69e+3. Hence, it can be concluded that the GAN method was more effective at removing the weld fumes than the conventional DCP method, and also preserved the image quality. Furthermore, the dehazed images were sent for training along with the other images for weld seam detection.
## 2 Material and Methods  
### Material and Samples Preparation  
The distribution of cold regions in China is shown in Figure 1. Xinjiang Province belongs to the shallow and medium-depth seasonal permafrost regions, which makes rock work in this region vulnerable to frost damage [4]. At the same time, the area is a base for open-pit coal mining in China, and winter workings are prone to landslides and other geological hazards [5]. Therefore, the red sandstone used for testing was obtained from an open-pit mine in Urumqi (Figure 1), which is structurally intact, uniformly granular, and brownish red in color. The primary mineral composition of the rock was determined by X-ray diffraction (XRD) (Figure 2), revealing that the sandstone is composed mainly of quartz (47.3%), feldspar (31.5%), and illite (9%), with minor amounts of calcite and chlorite.  
As shown in Figure 3, fresh red sandstone block was processed into standard samples with a diameter of 50 mm and a length-to-diameter ratio of 1 [24]. To reduce the influence of rock structure, all samples were taken from the same block and in the same direction. Moreover, both ends of the sample had to be ground to a tolerance of 0.02 mm, with no more than 0.1% rad from the perpendicular to the axis. Once the samples were machined, an ultrasonic detector was used to measure the P-wave velocity and select samples with similar P-wave velocity. In addition, five samples with a length and diameter of 50 \(\times\) 100 mm were selected for physical and mechanical tests, with the results listed in Table 1.  
Figure 1: Distribution of cold regions in China and sampling points for the red sandstone sample; the red circles refer to the approximate locations of the sandstone sampling.  
Figure 2: XRD result for the tested sandstone.  
To obtain the samples with different saturations (Figure 3), the selected samples were first dried in a drying oven at 105 \({}^{\circ}\)C for 48 h until their mass remained constant. The samples were then saturated with pure water for 24 h using a vacuum saturator. Thereafter, the samples were evaporated in a drying oven at 50 \({}^{\circ}\)C and weighed at intervals of 0.5 to 1 h to obtain the specified saturation [25]. The saturations for the test were set to dry (0%), 30%, 50%, 80%, and fully saturated (100%). Finally, considering the winter temperatures at the sampling site, the prepared samples were placed in a freeze-thaw chamber to freeze at \(-25\) \({}^{\circ}\)C for 12 h [4]. Following the methods described above, frozen samples with different saturations were obtained for SHPB tests at low temperatures.
## 2 The Development of the Fluoropolymer Industry from 1930s \(\sim\) 1990s
### 1960s
#### 1990s  
After the 1970s, little work was done on the traditional semicrystalline fluoropolymer materials, then in 1993 Hoechst invented THV by the copolymerization of TFE, HFP and VDF (Figure 12). Later, Hoechst granted 3M the rights for the production of THV by forming a joint venture--Dyneon in 1996. The new fluoropolymer is now known as Dyneon THV.  
Figure 11: The preparation of Hydlon AD.  
The preparation of THV is quite similar to the traditional procedures for PTFE, FEP and PVDF [38]. THV polymer is soluble in polar organic solvents and can easily form thin films by solution casting. The film obtained is transparent through UV, visible and near IR wavelengths. THV film also exhibited excellent adhesive ability to other polymers, making them easy to utilize in a very wide range of applications.  
THV fluoropolymers are processed in the range of 230-250 \({}^{\circ}\)C by general polymer processing equipments. THV can be processed by extrusion, blow-molding and injection.  
Most applications of THV are the multilayer constructions, where a thin layer of THV provides enhanced barrier properties to other layers. THV has excellent permeation resistance and chemical resistance to be used as fuel tubings due to its flexibility and the ease of processing. Similarly, THV is used for wire and cable insulation because of its flexibility and the flame resistance. THV can also serve as a flexible liner for chemical holding tanks.  
The low processing temperatures, high clarity, high flexibility and excellent adhesive properties made THV very competitive compared to other fluoropolymers.
# Predictive Maintenance Based on Identity Resolution and Transformers in IIoT
Secondly, the predicted sequences via the sequence prediction model are used by the state sensing model to execute pre-sensing, enabling the framework to sense the current facility condition and predict future ones, thereby creating a pre-emptive solution for real-time PdM decisions. The main contributions of this paper are as follows:  
* This paper presents a real-time predictive maintenance framework ITPM based on the transformer model and outlines its implementation. The ITPM consists of two main components: the state awareness model (SAM) and the sequence prediction model (SPM). These models work collaboratively to analyze the current sensor data of equipment and predict future states, enabling proactive maintenance decisions and preventing machine breakdowns. * The SAM utilizes a self-attention structure to evaluate the states of equipment in a production workshop. The input sequence comprises the data collected from sensors attached to the equipment. These data are fed into the model, which employs self-attention to weigh the significance of different parts of the input sequence. The output of the SAM is a classification category that reflects the states of equipment. * By leveraging the self-attention mechanism, the SPM can detect patterns and dependencies in the input data that may be difficult for other models to capture. The predicted sequences are subsequently utilized by a state-sensing model that performs pre-sensing to monitor the current condition of the facility and predict future states. The remainder of this paper is organized as follows. Section 2 presents the reference review of the existing techniques. Section 3 introduces the system's overview and identity resolution. Section 4 describes the proposed framework in detail. The evaluation setup and experiment design are presented in Section 5. Section 6 concludes this paper and discusses further work.