esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Evaluation of Structural Safety for Aged Onshore Wind Foundation to Extend Service Life
## 2 Service Life Extension of Onshore Wind Foundation  
### Service Life Extension or Re-Use  
There are three methods to re-use the aged onshore wind foundation during or after the service life of the wind turbine [1,3,4]. Firstly, the service life of the onshore wind turbine is about 20 years. During the in-service life, the wind foundation will continue to receive turbine loads. As a result, unexpected damage to the foundation may occur during the in-service life, and in this case, it is desirable to continue using the damaged foundation appropriately by \"retrofit\" [5].  
Secondly, the service life of the wind turbine is about 20 years, but the wind foundation is designed to support the external force (wind load, etc.) of the return period of 50 years. Furthermore, it is known that the fatigue life of the concrete members such as the wind foundation is infinite. In this case, if it is possible to extend the service life of the aged wind turbine by replacing old components with new ones, it is possible to continue to use the aged foundation for an additional period of time, as presented in Figure 2a This method is called \"service life extension\" [6,7].  
Thirdly, after 20 years of service life of the existing wind turbine, it can be also re-used to support the new wind turbine. Compared to the decades ago when the existing wind farms were built, the development of wind turbine technology has led to a dramatic improvement in turbine power generation capacity [7]. In order to support these modern larger capacity turbines, aged foundations are retrofitted and re-used to support larger turbine loads, as presented in Figure 2b. This method is called \"partial repowering\" [3,4,8,9,10,11]. This is different from \"full repowering\", where all the existing foundations are removed from the same site and then all of the wind farm components, including the foundations, are reconstructed to fit large capacity turbines, as presented in Figure 2c.  
Figure 1: Life cycles of the onshore wind farm.  
In order to extend the service life or re-use an aged onshore wind foundation through the three methods described above, a structural safety evaluation using an engineering method is firstly performed on the wind foundation, and, if necessary, an appropriate structural retrofit should be made according to the result of the structural safety evaluation [8,9]. Against this background, the need for technology development has recently been raised. In this paper, for the case of \"service life extension\" with the same capacity for the wind turbine, a study was conducted on the methodology of structural safety evaluation to determine whether to extend the service life of the aged foundation.
### The Human Ankle Complex  
The choice to develop a foot prosthesis with a spherical joint as an ankle joint derives from the study of the human ankle complex, its structure and the movements it allows. The ankle joint complex consists mainly of three joints: the tibialolar or talocrural joint (formed by the tibia and the talus bones), the taloclancaneal or subtalar joint (formed by the talus and the calcaneus), and the transverse-tarsal or taloclancanoavalicular joint (articulation formed by three bones: talus, calcaneus and navicular). Some studies have suggested that the tibialolar joint has a geometry indicating that it does not only work as a hinge joint [52; 53], even if other studies have suggested that it is a uniaxial joint with the axis oblique to all three planes of motion of the foot-ankle system [53; 54]. Most of the dorsiflexion-plantarflexion of the ankle occurs in this joint [55]. The subtalar joint also has an oblique axis and thus generates a triplanar motion [55]. A study suggests that most of the ankle inversion-eversion takes place in the subtalar joint [56]. The transverse-tarsal joint is considered to be part of the same functional unit as the subtalar joint, considering that these two joints share a rotational axis [53; 56] and also contributes to the inversion-eversion of the ankle [55].
## 4 Experiment Results and Discussion
### Number of Reference Signals
The optimization problem in determining the number of required reference signals can be dependent upon the physical properties of a car, the specification, and the numbers/locations of reference sensors. The number of reference signals in this study is only relevant to the test car and may not be suitable for the other cars. Figure 8: Comparison of the averaged reduction levels against the number variation of the reference signals. Computer simulation (dashed line with triangles). Actual real-time control experiment (solid line with circles). The experiment results above clearly show that the arrangement of the reference accelerometers can be applied for the proposed ARNC algorithm when the ESG-cowl panel is used as a secondary actuator.
# Investigations of the Absorption Front in High-Speed Laser Processing Up to 600 m/min
## 1 Introduction
In [9], it was assumed that for processing speeds of about 840 m/min, the recoil pressure of the evaporated material at the absorption front accelerates the melt up the sidewalls and along the bottom of the resulting kerr to leave the processing zone. Here, the inclination angle of the front wall was determined via 3D measurements of the solidified end crater by laser scanning microscopy after switching off the laser power during the process. However, using this method, the real geometry of the absorption front can only be represented to a limited degree, due to the subsequent reflux of the molten material after the switch-off. In [3], investigations were carried out for processing speeds of up to 2700 m/min by means of a polygon scanner. Here, it becomes apparent that the inclination angle of the absorption front can be calculated using the ratio of spot size and ablation depth. It can be seen that the characterization and determination of the certain dimensions of the process zone in laser beam welding were examined for different processing speeds by means of various methods. However, detailed investigations of processing speeds up to 600 m/min are not yet available in the state of the art. In this study, investigations regarding the formation of the absorption front and the processing zone in laser processing with speeds from 1 m/s (60 m/min) to 10 m/s (600 m/min) were carried out. Besides the determination of the loss of mass, investigations on cross-sections and high-speed recordings were carried out. In addition, a camera setup was developed that enabled the processing zone to be observed through a glass plate. This approach provided characterizations regarding the keyhole and absorbing front using high-speed records at high processing speeds and small focal diameters. These aspects provided a detailed characterization of the processing zone and led to a better understanding of the resulting material removal mechanism in high-speed laser processing. Furthermore, a deeper insight into spatter formation and the behavior of the absorption front regarding the scanning speed was achieved.
## 2 Experimental Data
\[SSH_{GNSS-R}=MSS_{Sp\_area}+\epsilon_{altimetry}=SSH_{MSS}+\epsilon_{area}+ \epsilon_{altimetry} \tag{1}\]  
where \(SSH_{MSS}\) represents the sea surface height at the center point of the GNSS-R specular reflection, while \(\epsilon_{area}\) denotes the difference between the mean sea surface height \(MSS_{Sp\_area}\) within the GNSS-R altimetry resolution and \(SSH_{MSS}\). The difference \(\epsilon_{area}\) reflects the impact of GNSS-R altimetry resolution on the altimetry results and is referred to as the representativeness error in the [55]. Figure 5 provides an example of the simulated SSH altimetry results for an Operational track. Figure 5b shows the variations of the simulated Operational SSH altimetry results and the true SSH (using the MSS as the true SSH) along the red track in Figure 5a, while Figure 5c displays the differences between the simulated Operational SSH altimetry results and the true SSH. It can be observed that the simulated Operational SSH \(SSH_{Operational}\) follows a similar trend as the true sea surface height \(SSH_{MSS}\), but with an error term compared to the true SSH. As shown in Figure 5c, the error term corresponds to the simulated spaceborne GNSS-R interferometric altimetry error, which includes both representativeness errors \(\epsilon_{area}\) and altimetric errors \(\epsilon_{altimetry}\).
## 3 Measurement Set-Up and Prototype
### Scenarios
#### 3.3.2 Telecommunications Laboratory Tests  
The tests compare the LTE radio network and Non-Standalone (NSA) 5G. The objective is to analyse how the system works in an area with 5G deployment, demonstrating its improvement compared to 4G networks. Likewise, this scenario allows a comparison between SLA and regular users.  
Long-term measurements have been carried out, specifically 672 periods of 15 min. The collected data allows the correlations between the radio parameters and the latency to be found according to the signalling communication requirements based on UDP.
# Development and Operation of Track Condition Monitoring System Using In-Service Train
## 5 Improvement in Train Position Accuracy Based on Yaw Angular Velocity
### Position Correction Results and Track Condition Monitoring Results  
#### 5.5.1 Measurement of Car-Body Vibration  
We conducted an evaluation using the car-body vertical acceleration obtained from October 2020 to September 2021 at a regional railway, where onboard sensing devices were installed in in-service trains. The sampling frequency was 82 [Hz], and the operating data were used for 130 trials.
# CANPerFL: Improve In-Vehicle Intrusion Detection Performance by Sharing Knowledge
## 5 Experiments and Results
### Data Processing and Classifier Architecture  
Building on our previous work [6], the proposed model in this study uses only CAN IDs as features. In particular, we stacked a series of continuous CAN IDs represented in binary format to form a matrix that captures the temporal and spatial structure of the CAN ID sequence. This CAN IDs matrix is then fed into a convolutional neural network (CNN) for classification. To balance accuracy and computational efficiency, we designed a compact Resnet architecture, which is shown in detail in Figure 5. It is important to note that the primary goal of this study is to test the performance of the proposed framework CANPerFL. Therefore, we did not focus on feature engineering or apply a novel model architecture. Instead, we employed an efficient feature extraction method and a well-known deep learning model to streamline the process.  
After being preprocessed, the dataset is then spitted into three non-overlap subsets including training, validation, and test sets. However, we observed that the class distribution in the raw dataset does not match with the real environment, which means there are large attack samples over the normal samples. The ratio between the total number of attack samples over the total number of the dataset is defined as the attack density. Then, a data sampling process (Figure 6) is performed to ensure an attack density in the experimental environment, in which the attack density is uniformly chosen in range \([0.2,0.3]\). Table 3 shows the details of training data having approximately 50k samples in total after sampling. While the deep learning model is developed by Python 3.9 and Pytorch 1.12, the Flower framework [43] is used for demonstrating the federated learning scheme. In addition, all the experiments run on a server provided with 32 Intel(R) Xeon(R) Silver 4108 CPUs @ 1.80 GHz, a memory of 128 GB, and an Nvidia Titan RTX 24 GB GPU.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Total CAN ID** & **Data Generation (s)** \\ \hline BMW & 135 & 876 \\ Kia (Old) & 47 & 2086 \\ Kia (New) & 84 & 2630 \\ Tesla & 69 & 3128 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data variance on CAN from different vehicles.  
Figure 5: Model architecture used for local IDS.
# On Finding the Right Sampling Line Height through a Parametric Study of Gas Dispersion in a NVB
## 2 Materials and Methods
### CFD Validation for Gas Dispersion
#### 2.2.3 Turbulence Models and Solver Details  
Two turbulence models, the k-epsilon realizable model and the k-omega SST model, were compared in this study. All the simulations were run in the steady state (time independent). The same as [PERSON] et al. [13], the pressure velocity coupling scheme was set to coupled, getting better convergence from our experience. The spatial discretization schemes were kept standard (ANSYS, 2019), which means second-order upwind for the momentum and the energy, first-order upwind for the turbulent kinetic energy and the specific dissipation rate, and second-order for the pressure. As suggested by the Fluent user guide, convergence was considered as being achieved when the residuals were below \(10^{-3}\) overall and below \(10^{-5}\) for energy.
## 4 Conclusions  
We have successfully developed a flow cell coupled DLS set-up that allows to monitor the size of nanoparticles in real-time during bottom-up liquid phase synthesis. Through the use of a modulated 3D cross correlation DLS instrument, we were able to accurately analyze dispersions comprising silica nanoparticles in water/alcohol mixtures. Through Stober synthesis, we prepared small and large silica nanoparticles with a radius of approx. 20 nm and 150 nm, respectively. Both dispersions could be accurately analyzed using our novel flow cell coupled 3D cross correlation DLS set-up at silica concentrations of approx. 2.25 wt-% and flow rates up to 17 mL\(\cdot\)min\({}^{-1}\). Furthermore, we demonstrated that the particle size could be accurately monitored in real-time during Stober synthesis of the silica spheres starting from TEOS as molecular precursor. This was confirmed by conventional off-line DLS and TEM analyses. Thus, the newly developed flow cell coupled DLS set-up enables the study of nucleation and growth of nanoparticles in bottom-up liquid phase synthesis, which provides rational input for process optimization. Furthermore, when coupled to a production sized reactor, it can help to minimize the number of off spec batches since it provides real-time information that enables rational process intervention. We expect to validate the set-up for sol-gel syntheses of nanoparticles other than silica (e.g., titania, zirconia) and redox synthesis for the production of metallic nanoparticles starting from (complex) metal salts.  
This work was supported by the European Commission and is part of the EU project COPILOT which has received funding from the European H2020 program under grant agreement n\({}^{\circ}\) 645993. Solliance and the Dutch province of Noord Brabant are acknowledged for funding the TEM facility.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] contributed to the interpretation of the analyses results; [PERSON] and [PERSON] wrote the manuscript. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.  
Figure 5: High angle annular dark field (HAADF)-STEM images of (**a**) large silica particles and (**b**) small silica particles.
## 4 Case Study Analysis
### Sensitivity Analysis  
To further quantify the impact of key system parameters on user satisfaction, in this study we conducted sensitivity analyses of the maximum battery capacity limit (SOCmax) and maximum water storage limit (Water max). The corresponding simulation results are presented in Figure 14.  
As shown in the figure, user satisfaction increases with the enhancement of either SOCmax or Water max; however, the rate of increase gradually slows as the parameters grow, exhibiting a clear trend of diminishing marginal returns. Specifically, when SOCmax and Water max are in the lower range, user satisfaction is highly sensitive to parameter changes; as the parameters approach their upper bounds, the satisfaction levels off and further increases have a limited effect.  
In summary, it is recommended that in practical applications the upper limits of these parameters should be set reasonably by balancing system demand and economic considerations. This approach can enable optimal resource allocation and user satisfaction within the constraints of resource investment.  
Figure 14: Sensitivity analysis of user satisfaction with respect to SOCmax and Water max.
# Advances in Graphene Field Effect Transistors (FETs) for Annine Neurotransmitter Sensing
## 2 Problem Outline
### GFET Configurations
A lateral electric field is created across the graphene channel by applying a voltage to the coplanar gate, which modifies the carrier density. This configuration offers high sensitivity and exact control over the local electric field and enables more complicated device layouts on the same substrate, which is favourable for device miniaturisation [119]. Liquid-gated GFETs are designed to eliminate top-gate limitations by contacting the graphene channel directly with the gate electrode, which is submerged in a liquid electrolyte (e.g., PBS) [119]. Ions in the liquid create an electric double layer (EDL) at the graphene surface when a voltage is supplied to the gate electrode. The graphene channel can be effectively modulated at low gate voltages due to the high capacitance offered by graphene-electrolyte interface interactions. As ions directly affect the graphene's carrier density and conductivity, liquid-gated GFETs are frequently used in applications for biological molecule interaction and detection.
# Upper Limb Orthoses: Integrating Topology Optimization and 3D Printing for Custom Fit and Function
## 4 Design Process for Custom Limb Orthoses
### Addressing the TO Problem  
The software used for the TO iterations is HyperWorks. In Figure 8 are presented all the TO analyses that were completed in this research. The results obtained from solving each TO problem are then processed in HyperView. In each optimized wrist brace, design elements are generated that have a relative density of \(0.01\leq\rho\leq 1\). A way to visualize, understand and compare the results is to choose to display the elements that have a relative density value above some threshold. In this study, the value of the relative density that has been chosen as a limit is 0.5. This means that all elements presented in the design section (depicted in gray in Figures 9 and 10) of the topologically optimized wrist splints have a relative density value greater than or equal to 0.5 to 1 (0.5 \(\leq\rho\leq 1\)). All elements with density less than 0.5 have been removed in the post-processing stage in HyperView.  
In the first two load cases, regarding the radial deviation and wrist flexion, in which the effect of the discretization of the mesh (coarse and fine FE mesh) on the final optimal models of the splints is studied, it is observed that a finer mesh gives the final optimal designs more detail, while also leading to fewer imperfections/discontinuities in areas where a lot of material is removed. On the other hand, much faster computations can be achieved by creating a coarser mesh. This is not directly related to the number of iterations, which remain almost the same for both the fine and coarse FE meshes; however, this particular observation emerges based on the computational time each analysis takes.  
Figure 8: Topology optimization solutions.  
Figure 9: Optimized brace 1 (wrist flexion).  
Solving the TO problem determined above without applying any more constraints leads to wrist brace models with design flaws with elements that are scattered in the design space and are not connected at all to neighboring elements, thus creating several non-optimal microstructures, which are not easily implementable in construction (Figure 11).
# Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications
## 2 Techniques in Alzheimer's Disease Detection
### Deep Learning Models  
Deep learning (DL) is the process of training a computer to apply its experience to solve a specific problem given to it [22]. Current models in DL often treat large networks as a single object, which requires huge trainable parameters. To understand the structure of complex networks, the paper [51] proposed a method for improving DL that achieves greater efficiency as the level of segmentation deepens, it was proven that the method greatly improves the training speed and efficiency. DL models have demonstrated superior performance in many medical image analysis tasks, including AD diagnosis, due to their ability to capture nonlinear relationships and hierarchical features. In the diagnosis of AD, CT-based volumetric measurements correlate closely with MRI-based measurements, showing a comparable prognosis suggesting the possibility of using CT as a primary screening tool for the diagnosis of neurodegenerative diseases after further validation [52]. However, CT has rarely been used for tissue classification because the contrast between gray
# Calculation of View Factors for Building Simulations with an Open-Source Raytracing Tool
## 6 Discussion  
All the examples considered in this paper have analytical solutions and were chosen for that specific reason. As demonstrated in Section 5, setting a high value for ambient divisions will ensure that the result obtained is within desired tolerance limits (of say 0.5%). However, knowing whether the result is within tolerance is predicated on knowing the result of the calculation upfront.  
In non-standard cases where no analytical expressions exist and RADIANCE is being used to calculate the view factor, a convergence criterion can be defined by progressively increasing the ambient parameters between successive calculations. The result from every calculation can be compared with the results from the previous calculations to determine the variability in results. An example of this is shown through a chart in Figure 20, which compares the variation in view factor as a function of ambient divisions.  
The ambient divisions (black line) and calculated view factor (green line) shown here relate to the scenario presented in Section 3.1, i.e., view factor between differential planar element to finite parallel rectangle. The ambient division for the first iteration was set to 100 and increased by 50% for every subsequent iteration. So, for example, the second, third and final iterations had 150, 225, and 43,790 (rounded to nearest integer) ambient divisions, respectively. As highlighted by the red dotted line, the results stabilize beyond the ninth calculation where the ambient divisions are less than 5000.  
Figure 20: Variation of results with progressive increase in sampling controlled through ambient divisions.