esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Impact of CNCORS on BDS/GPS Joint POD
#### 3.1.4. Orbit Dilution of Precision Analysis
Therefore, this study adopts the following method to compute the ODOP value: (1) The satellite position of each epoch is computed, such as \(x_{k}^{j},y_{k}^{j},z_{k}^{j}\); (2) Whether the station involved in the computation is determined according to the actual observation of each station in each epoch (i.e., the original observation file). If the satellite is observed in a certain epoch, then the station is included in the computation of this epoch; if a satellite is observed by fewer than four stations, then the ODOP value of this epoch is discarded; (3) After obtaining the ODOP value for each satellite in each epoch, the average ODOP values of the MEO, GEO, and IGSO satellites are computed. The ODOP values for different strategies are computed, and the results are shown in Table 7. The results show that all ODOP values of strategy 1 are smaller than those of strategy 2, and that the respective average ODOP values of the GEO, IGSO, and MEO satellites are 14.3%, 14.5%, and 5.5% smaller. Therefore, the CNCORS strongly influence the geometry structure of BeiDou GEO and IGSO satellite orbit determination and only mildly influence the MEO satellites. The results of ODOP improvements match the improvements in the overlapping arc precision and SLR assessment accuracy of the BeiDou POD. In addition, the ODOP values of the GEO and IGSO satellites are improved to almost the same extent, but the accuracy improvement of IGSO satellite orbit determination was significantly higher than that of the GEO satellites, because IGSO satellites constantly move in the Asia-Pacific region. Such movement results in more ground tracking stations within the satellites' coverage, longer observation periods, more redundant observations, and, therefore, a higher orbit determination accuracy. However, GEO satellites are stationary relative to the Earth, and the relative observation geometry between the ground stations and a satellite in GEO is poorer. The accuracy of orbit determination is also low in itself; thus, the improvement of accuracy of orbit determination is lower than that of IGSO satellites.
## 3 Experimental Results and Discussion
### Spatial Distribution of EMPs  
To investigate the spatial distribution characteristics of the EMPs generated from the plasma associated with the laser filament-excited aluminum alloy target in air, we studied the EMP characteristics, which are discussed in the following two sections: the distribution of EMPs with different detection distances and that of EMPs with different detection angles.
# Study on the Seismic Effect of the Pebble Soil Site in the Zhongwei Basin
## 4 Results
### The Results of Comparison between the Pebble-Top Interface and Surface
As shown in Figure 12, under the same intensity of input, the amplitude of the acceleration response spectrum of the ground surface was generally higher than that of the pebble-top interface within 1 s, the amplitude difference was most obvious within 0.1-0.2 s, and the peak point of the acceleration response spectrum of the pebble-top interface lagged behind the surface of the site. Under different input intensities, the pebble bed top interface acceleration response spectrum showed a 0.1 s \"trough\" phenomenon, was significantly lower than the bedrock input response spectrum amplitude, and very rarely met the earthquake ground motion. The pebble bed top interface acceleration response spectrum curve showed a \"bimodal phenomenon\"; the second peak point was around 0.6 s; and within 0.24-0.4 s, the spectrum value was lower than the bedrock input. Based on the above summary, it can be concluded that the pebble-top interface has good damping and isolation effects. By analyzing the Fourier spectrum curves of the site surface and the pebble-top interface, it can be seen (Figure 13) that there is an excellent frequency in the pebble-top interface, which decreases with the increase in the input intensity in the range of 1.1-2.2 Hz. Compared with the pebble-top interface, the site surface has two superior frequencies, and the second superior frequency occurs in the high-frequency range of 5-7 Hz, and the Fourier amplitude in the range of 5-7 Hz is significantly higher than that in the pebble-top interface. It can be concluded that, under the action of an earthquake, the energy of the pebble-top interface is concentrated in the low-frequency range of 1.1-2.2 Hz, and the amplification effect is obvious. In the range of 8-10 Hz, the amplitude is lower than that of the bedrock input, and the effect of reducing and isolating earthquakes is obvious. The site's surface presents the phenomenon of double superior frequencies, the energy is concentrated in the high-frequency band, and the amplification effect is more significant. Figure 12: Comparison map of acceleration spectrum of pebble-top interface, site surface, and acceleration response spectrum of bedrock.
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 3 Materials and Methods
### Data Processing, Analysis and Validation  
Figure 3 shows the methods and the procedures used for the flood mapping.  
The analysis is divided into three main steps:1. The first step is the detection of the flooded area, which can be performed using a manual or a semi-automatic mapping approach: 1. The manual mapping consists of the direct visual interpretation of the images (SAR amplitude or color combinations of multispectral bands). In this case, the flooded areas were drawn manually directly on the georeferenced satellite images in QGIS software. 2. With the semi-automatic approach, we initially produced an automatic flooded area map in raster format. The map is extracted from SAR or multispectral satellite data using different methodologies such as band index, supervised classification or backscattering difference. In this step, we used an empirical threshold to detect flooded areas; for this reason, it is not a fully automatic approach. 2. A possible improvement of manual and automatic detection could be made using a cloud mask and permanent water body (from ancillary data or pre-flood images). The second step is map improvement and refinement. In this step, we also consider additional information such as (a) water depth model derived from DEM, (b) hillshade and aerial photos to detect the geomorphological features, and (c) ancillary data such as georeferenced photos or documents found on the web to have ground information about the flooded area extent. These data allow the creation of an improved final version of flooded area maps, manually drawn, both for the semi-automatic and manual approaches. 3. The third step is the flood map validation. This step is performed only when official flood maps or field survey maps are available. We used these maps to evaluate the quality of the flooded area maps and in particular the performance of semi-automatic mapping (flood ratio and not flood ratio).  
Figure 3: Flood mapping methodology used for this work.
## 3 Results and Discussion  
### Temperature and Soil Reflectance  
In this experiment, the spectra of calcium soil at five drying temperatures were collected. Figure 1 shows the raw spectra and the other five pretreated average spectra when soils were detected after 40 \({}^{\circ}\)C drying. The abscissa of the curve is the wavelength and the ordinate of the curve is the average spectral reflectance in Figure 1a-c.  
Figure 1a shows the raw spectra and Figure 1b shows the spectra after S-G smoothing pretreatment. With the increase of nitrogen concentration in the soil, the spectral reflectance of the soil decreases gradually, that is, the spectral absorbance of the soil increases gradually, which conforms to the Lambert-Beer law [37]. The spectral reflectance of the calcium soil decreases gradually near 1400 nm, which is caused by the vibration of O-H, and similar results also occurred in [PERSON] et al.'s research [11]. Figure 1c shows the spectra after MSC processing and Figure 1d shows the spectra with SNV pretreatment. These two methods make the different concentrations' average spectra concentrated, but the spectral reflectance still decreases obviously around 1400 nm. It is clear that the DT-treated soil spectra near 1650-1680 nm has a more obvious decline trend in Figure 1e. Figure 1f shows the spectra after 1 st-Der processing, and the spectral resolution is increased. There is a large fluctuation around 1650 nm and a similar result can be found in [PERSON]'s research [5].  
The relationship between drying temperatures and soil reflectance is further investigated and analyzed. Figure 2 shows the raw average spectra of different drying temperatures when the soil nitrogen concentration is 2.5 g/kg.  
Figure 1: Soil average spectra with different spectra pretreated after drying at 40 \({}^{\circ}\)C. (**a**) The raw spectra; (**b**) the spectra after Savitzky–Golay (S–G) smoothing pretreatment; (**c**) the spectra after multiplicative scatter correction (MSC) processing; (**d**) the spectra with standard normal variation (SNV) pretreatment; (**e**) the detrending (DT)-treated spectra; (**f**) the spectra after 1 st-Derivation (1 st-Der).  
According to Figure 2, with the increase of drying temperature, the reflectance strength of the spectra decreases gradually, indicating that the higher the drying temperature, the lower the spectral reflectance strength. Moreover, as the drying temperature increases, the soil spectral reflectance shows a decreasing trend around 1100 nm. The reason might be that medium and high temperature could stimulate the activity of soil urease as well as remove the influence of soil water [38]. Thus, the N-H bonds vibrate more obviously than the O-H bands.
## 3 System Outbound Process Modelling
### Modelling the Outbound Process of a Four-Way Shuttle System Based on Two Operation Strategies
#### 3.3.2 Serial Part Modelling  
If there are no idle shuttles at the target level, the serial operation strategy will be employed, constituting a semi-open-loop queuing network, as shown in Figure 6. The \"synchronisation node\" can be understood as the system receiving the pickup command. The pickup command, after entering the system and the shuttle for binding, is entered as a task in the queue of the waiting hoist node S1; if there is an idle hoist, it can complete the task, whereby a hoist picks up an idle shuttle. After completing the task at shuttle node S2, which is waiting to pick up the goods, the hoist picks up the shuttle and moves it to the target layer to pick up the goods; throughout the whole process, the shuttle is still in the task and is not released; therefore, the shuttle node is able to directly carry out the task of picking up the goods until the hoist receives the goods once they have been released. Finally, we consider hoist node S3, where the remaining operations are performed. Similar to node S2, the hoist is not released during the task, and therefore, any remaining operations do not need to wait.  
Similar to the parallel operation strategy, the Coxian distribution is used to approximate the general distribution of the service times instead. In the process of solving the semi-open-loop queuing network model, the use of more than two service nodes will cause a sharp increase in the state space, meaning that the model cannot be computed. Therefore, two nodes need to be aggregated into one, and as hoist node S3 does not need to be queued for direct service, hoist nodes S1 and S3 are combined into one, as shown in Figure 7.  
Figure 5: Open-loop queuing network using the parallel strategy.  
Figure 6: Semi-open-loop queuing network using the serial strategy.  
Figure 7: Integrated semi-open-loop queueing network.  
The whole system model is composed based on the probabilities of the two cases under the completed parallel operation strategy and the serial operation strategy.
###### Abstract  
Location Privacy Preservation (LPP) in Wireless Sensor Networks (WSNs) during the era of the Internet of things and smart systems is a critical element in the success of WSNs. LPP in WSN can be stated as: given a WSN with an adversary aiming to unravel the location of critical nodes of a WSN, the goal of the WSN manager is to enshroud the location of the critical nodes via routing and/or encryption mechanisms. Typical research in the LPP of WSN routing involves developing and/or estimating the performance of a fixed routing protocol under a given attack mechanism. Motivated by advancements in network softwarization, in this work, we propose an approach where the WSN manager as well as the WSN adversary can deploy multiple routing and attack mechanisms, respectively. Initially, the proposed approach is formulated as a repeated two-player zero-sum game. The formulation is further extended to handle multiple objectives and incomplete information in the game matrix. In this work, the multiple objectives are handled via the epsilon constraint method. The presence of incomplete information in the formulation is modeled as interval based uncertainty. To sum, the proposed formulation ultimately boils down to linear programming problems, which can be efficiently solved. Numerical case studies to showcase the applicability of the proposed approach are illustrated in this work. Finally, discussion on obtaining the required data from any given WSN, discussion and interpretation of the formulation's results, and future research direction of the current work is presented.  
Keywords:wireless sensor networks; location privacy preservation; game theory; incomplete information in payoffs +
Footnote †: journal: Sensors
# Global Regulatory Challenges for Medical Devices: Impact on Innovation and Market Access
## CE Marking
### United States of America
#### 4.2.3 Post-Market Surveillance  
The FDA's pre-market review process serves as an essential step in ensuring the safety of medical devices before they reach the market [65]. However, a robust post-market surveillance system is also essential to monitor device safety during clinical use and facilitate corrective actions like updating labelling, enhancing user training or removing devices from the market when issues arise [66]. The FDA's post-market surveillance includes mandated studies, adverse event reporting (or passive surveillance) that relies on unsolicited reports of adverse events from manufacturers, users, importers and active surveillance in real-world settings, which involves proactively collecting and analyzing data from large healthcare systems to verify or detect safety signals [55; 67].  
Passive surveillance mechanisms like the Medical Device Reporting (MDR) system identify significant adverse events and involves distinct reporting requirements for manufacturers, user facilities and importers. In 2014, the FDA mandated electronic Medical Device Reporting (eMDR) to expedite access to adverse event information, although user facilities may still submit paper MDR reports [68; 69].  
Active surveillance studies, such as post-market studies, such as \"522 studies\" and Post-Approval Studies (PAS), address gaps in pre-market data and respond to safety concerns. The FDA's 522 Post-Market Surveillance Studies program encompasses responsibilities for designing, tracking, overseeing and reviewing studies mandated under section 522 of the FFDCA to ensure they are conducted effectively, efficiently and with a simple approach [70]. The goal is to collect data that can uncover unforeseen adverse events or other critical information necessary to protect public health and answer surveillance questions. The PAS are mandatory for device approval and sponsors must employ valid scientific methodologies in study design and conduct these studies effectively, efficiently and with minimal burden. Failure to comply with any post-approval requirements may lead to the FDA's withdrawal of approval [67; 70]. Table 2 presents a compilation of the classification and regulatory requirements pre- and post-market for medical devices in the USA.
## 3 Results: Single-Lap Joint Validation by Applying FEA
### Numerical Model  
For comparison of results, finite element analysis is performed; 2D and 3D models are developed using Ansys software, considering the orthotropic properties of the laminate in each adherent. A 0.1 mm polyester resin interface layer is modelled between each layer of the laminates. The 3D model is analyzed with the ACP-Ansys module since it allows entering the properties of the adherents per layer; the adherents are modelled using shell elements and the adhesive using solid elements.  
For the 2D model, only the Static Structural module with plain strain configuration is used since it allows analyzing the behavior of laminates and interfaces in the tubular hybrid joint. Software tools for mesh quality analysis are used and the convergence of the stress results with the indicated mesh for the 2D and 3D models, is achieved using a mesh refinement as indicated below:  
* **Software:** Ansys Academic;
* **Module:** ACP Pre, ACP Post and Static Structural;
* **Meshing 3D:** Model size 600 mm, general mesh 4 mm, refinement to 1.5 mm on a 30 mm radius on pipe corners, and to 0.2 mm on the middle panel on a 10 mm radius. The adhesive has three elements through-thickness, (see Figure 7a);
* **Nodes:** 69,367;
* **Element:** Quadrilateral Dominant;
* **Meshing 2D:** Model size 210 mm, general mesh 0.7 mm and refinement to 0.15 mm on a 6 mm radius on adhesive ends. The adhesive has two elements through-thickness, (see Figure 7b);
* **Nodes:** 99,482;
* **Element:** Quadrilateral Dominant.  
* **Modeling**: The metal pipe is not considered in the validation.
* **Boundary conditions:** The panel is considered as being simply supported in the \((x,y,z)\) directions, at the ends of the panel (adherent 2). The tubular laminate (adherent 1) is considered as being free at its ends.
* Forces: \(\odot\) For the \(x\)-direction (\(T\)): \(\bullet\) The force is applied, taking into consideration the entire laminate surface of the reinforcement excluding the bonding tubular laminate over the panel. \(\odot\) For the \(y\)-direction (\(P\)): \(\bullet\) The force is applied on the side of the tubular laminate at a remote point in the center of the metal pipe. \(\odot\) For the z-direction (\(V\)): \(\bullet\) The force is applied, taking into consideration the entire laminate surface of the reinforcement excluding the bonding tubular laminate over the panel.
## 3 ARNNC of Single-Rod Pneumatic Cylinders
### ARNNC Compsator  
As shown in Figure 2, the three layers RBF NN, which contain an input layer, hidden layer, and output layer is used in the compensator. Input layer: \(x_{a}=[x_{1},x_{2},\cdots,x_{n}]^{T}\) is the input signals spreading directly to the hidden layer. Hidden layer: The input signals are activated by the RBF neurons with the following activation function:  
\[g_{j}(x_{a},c,d)=\exp(-\frac{\left\|x_{a}-c_{j}\right\|^{2}}{d_{j}^{2}})\ \ \ \ \ j=1,2\cdots r_{h} \tag{27}\]  
the activation function \(g_{j}\) is the Gaussian function for the \(j\)th neuron; \(c_{j}=\left[c_{j1},\cdots,c_{j\prime i}\right]^{T}\) is the center vector, \(d=\left[d_{1},\cdots,d_{r_{h}}\right]^{T}\), and \(d_{j}\) is the \(j\)th RBF's standard deviation. The output layer: the output of RBF is the linear combination of hidden layer neurons,  
\[h_{i}(x)=\sum_{j=1}^{rh}W_{ij}g_{j}(x_{a},c_{j},d_{j})\ \ \ \ \ i=1,\cdots,r_{o}. \tag{28}\]  
\(W_{ij}\) is the weight between the \(j\)th hidden neuron and the \(i\)th output. With regard to the ARNNC control strategy, \(\left[x_{1r},\dot{x}_{1r}\right]^{T}\) are chosen as the input signals of the input layer and the output of RBF is used to approximate the unmodeled dynamics, lumped disturbances \(\overline{f}_{1},\overline{f}_{a},\overline{f}_{b}\) and the uncalculated part \(\dot{p}_{u}\), and parameter estimation error \(\psi_{2}{}^{T}\overline{\theta}\), \(\psi_{3}{}^{T}\overline{\theta}\). The center vector \(c\) and standard deviation terms \(d\) will be selected due to the sensitive range of input signals and are fixed. It is clear the RBF NN is a nonlinear projection between the reference signal and unknown dynamics. \(g_{i}(x_{a},c,d)=\left[g_{i1}(x_{a},c_{1},d_{1}),\cdots,g_{i\prime h}(x_{a},c_{ rh},d_{rh})\right]^{T}\) is the activation function for hidden layerneurons, and \({W_{l}}^{\bigstar}={[W_{l1}}^{\bigstar},\cdots,W_{ihn}^{\bigstar}]^{T}\) for \(i=p,q\) are the optimal weight for RBF NN. According to the universal approximation property of RBF NN [31]  
\[p^{\bigstar} =-\frac{1}{\theta_{1}s_{a}}(W_{p}^{\bigstar}g_{p}(x_{a},c,d)- \varepsilon_{23})-\frac{1}{\theta_{1\min}\overline{s}_{a}}\lambda_{2s}s_{2}\] \[=-\frac{1}{\theta_{1}\overline{s}_{a}}(\hat{W}_{p}g_{p}(x_{a},c,d)- \widetilde{W}_{p}g_{p}(x_{a},c,d)-\varepsilon_{23})-\frac{1}{\theta_{1\min} \overline{s}_{a}}\lambda_{2s}s_{2}\ \, \tag{29}\]  
\[q^{\bigstar}=-(W_{q}^{\bigstar}g_{q}(x_{a},c,d)-\varepsilon_{33})-\lambda_{3s} s_{3}\] \[=-(\hat{W}_{q}g_{q}(x_{a},c,d)-\widetilde{W}_{q}g_{q}(x_{a},c,d)- \varepsilon_{33})-\lambda_{3s}s_{3}\ . \tag{30}\]  
\(\varepsilon_{23},\varepsilon_{33}\) can be any arbitrary small real positive number if choosing enough hidden layer neurons.
## Appendix A Theoretical Properties
Finally, Reference [5] showed that the traditional approach of MSA, i.e., \(\xi(k)=k\), satisfies (11)-(10) conditions, since:  
\[\sum_{k>0}\frac{1}{\xi(k)}=\sum_{k>0}\frac{1}{k}=+\infty \tag{11}\]  
\[\sum_{k>0}\left(\frac{1}{\xi(k)}\right)^{2}=\sum_{k>0}\frac{1}{k^{2}}=\frac{ \pi}{6}<+\infty \tag{12}\]  
In the case of the proposed generalised function, i.e., \(\xi(k)=1+((k-1)\cdot\eta)\), condition (10) is satisfied, and since for \(k\rightarrow+\infty\) we obtain \(\xi(k)\sim\eta\cdot k\), it is possible to show that:  
\[\sum_{k>0}\frac{1}{\xi(k)}\sim\sum_{k>0}\frac{1}{\eta\cdot k}=\frac{1}{\eta} \cdot\sum_{k>0}\frac{1}{k}=+\infty \tag{13}\]  
\[\sum_{k>0}\left(\frac{1}{\xi(k)}\right)^{2}\sim\sum_{k>0}\frac{1}{(\eta\cdot k )^{2}}=\frac{1}{\eta^{2}}\cdot\sum_{k>0}\frac{1}{k^{2}}=\frac{1}{\eta^{2}} \cdot\frac{\pi}{6}<+\infty \tag{14}\]  
i.e., all convergence conditions are satisfied for the proposed algorithm for each value of \(\eta>0\).