esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Test Analysis of Subsurface Cavity and Ground Collapse Due to Broken Pipe Leakage
## 2 Testing Program  
### Testing Apparatus  
The relevant parameters were selected based on the dimensional analysis method of the second theorem of similarity, and the similarity ratio between the experimental materials used in this model experiment and the actual prototype materials was set to 10. Figure 1 shows the test model box. It was a soil box with a length of 1 m, a width of 0.2 m and a height of 0.8 m. It was fixed and reinforced with a steel frame as the outer frame. For convenience of observation, the soil box was designed as a semi-structure and its body was made of transparent acrylic board. On the left and right sides of the model box at a height of 35 cm, a circular pipe with 5 cm in diameter was installed; then, the test pipe was inserted through the reserved holes on the left and right sides to simulate an underground pipeline. On the front of the model box, gridlines were drawn every 5 cm from a height of 20 to 60 cm to observe the cavity initiation and evolution processes.  
As shown in Figure 2, the indoor model test apparatus included a test model box, a water tank, a pressure pump, and a filtering water tank. Water could be circulated during the experiment using this apparatus, thereby saving water.  
For the development of an underground cavity caused by the pipeline breakage and ground collapse, measuring points were laid on the soil pressure box in the test, as shown in Figure 3. The soil pressure measurement system comprised three miniature soil pressure boxes with a range of 200 kPa, three miniature soil pressure boxes with a range of 500 kPa, and a data collection instrument. The soil pressure boxes were numbered 201#, 202#, 203#, 501#, 502#, and 503#. Taking the vertical centerline of the pipeline crack as the axis, miniature soil pressure boxes with vertical distances of 5 cm and 10 cm above the pipeline were symmetrically arranged; the horizontal spacing was 15 cm, and a miniature soil pressure boxes with a range of 500 kPa was placed on the horizontal plane of 5 cm. A 3D  
Figure 1: Schematic of the model box.  
Figure 2: Experimental equipment.  
laser scanner was used to scan the ground at the beginning and end of the experiment, and the results were post-processed using Geomagic software.
## 2 Experimental Procedures  
We used a vacuum ion-coating machine in the pretreatment of the p-type doped conductive silicon wafer (resistivity 5 \(\times\) 10\({}^{-4}\)\(\Omega\)-cm) and quartz wafer (resistivity \(>\)100 M\(\Omega\)-cm). WTi alloy films and \(\sim\)600 nm thick W were deposited. In the deposition process, Ar (99.99%) was passed in the chamber as sputtering gas. The target material used for the deposition of W film was 470 \(\times\) 110 \(\times\) 8 mm\({}^{3}\) pure W target, and the target material used for the deposition of WTi alloy film was W and Ti splice targets of the same size, and their schematic diagram is shown in Figure 1a. In the sputtering process, there are fewer Ti atoms near the upper end of the splicing target, but more Ti atoms near the lower end of the splicing target. Therefore, by changing the clamping position of the matrix, WTi alloy films with different Ti contents can be obtained. The specific deposition parameters are shown in Table 2. Figure 1b shows an image of a stainless-steel mask used for the WTi film deposition. The mask size was 30 \(\times\) 30 mm\({}^{2}\). Figure 1c shows an image of the patterned WTi alloy film sensor. The line width was \(\sim\)200 \(\mu\)m. The W and WTi alloy film thicknesses were kept at 591\(\sim\)664 nm (Table 2) to minimize the impact of thickness on film resistivity. The initial film resistance at RT was in the range of 55.8\(\sim\)73.8 \(\Omega\).  
Figure 1: **(a)** Schematic diagram of the W-Ti segmented target, **(b)** image of the stainless-steel mask using for the WTi film deposition, and **(c)** image of the patterned WTi alloy film sensor.  
Using scanning electron microscopy (SEM, FEI Nova NanoSEM 430, Hillsboro, OR, USA), the surface and cross-section morphologies of the W and WTi thin film were observed. The phase structure and crystallinity of the films were analyzed by X-ray diffraction (XRD, Bruker D8 advanced, Bruker, MA, USA). An AFM was used to characterize the surface morphology and roughness of the thin films. The TCR value was calculated using the formula TCR = \(\Delta\)R\(/\)(R\({}_{0}\)\(\cdot\)\(\Delta\)T), where R\({}_{0}\) is the resistance at temperature 0 \({}^{\circ}\)C and TCR is the temperature coefficient of the resistance. During the measurements, an ice-water (deionized water) mixture condition was used to measure the R\({}_{0}\). The sample was heated by a vacuum heating system, and the resistance was measured with a resistance tester to calculate the TCR. For the chamber temperature was measured by using a commercial Pt1000 thermal resistor (class A accuracy, TCR 3.851 \(\times\) 10\({}^{-3}\) K\({}^{-1}\)). The influence of Ti content on the thermal resistance of Ti-W alloy thin film was studied by changing the content of Ti in the WTi alloy thin film by fixing other technological parameters.
## 4 Conclusions  
This work proposes a data network infrastructure for Large Geographical Area Aerial Surveillance Systems, supporting several Heterogeneous Smart Services (HSS) from several independent public and private entities. The proposed data network infrastructure and its services are the basis for future work following the authors' previous developments. The intention is to offer a review of the scientific work and technologies currently offered commercially, which have been integrated in order to accomplish the presented data network infrastructure.  
The Data Network Infrastructure for Heterogeneous Smart Services (DNIHSS) is supported by several autonomous tethered aerostat airships (ATAA) deployed to work as the physical support infrastructure of the wireless data network nodes. Although several HSS operate over the same network infrastructure, the DNIHSS guarantees their independence and security. The DNIHSS components are leveraged by the latest developments in Artificial Intelligence (AI), Edge AI, Blockchain, and Internet of Things (IoT).  
Several goals are determined for the proposed DNIHSS in order to ensure independence and security to the supported HSS, such as service type agnostic; hardware and software agnostic; support for several HSS at the same time guarantying its security and independence; automation of maintenance and operations; simplification of components and services with separation of concerns; and visibility and transparency over maintenance and operation to assure the HSS services security.  
An Edge AI infrastructure is proposed for the implementation of the DNIHSS Edge AI services. These services can execute autonomous and offline operation. The Edge AI Agents (EAIA) have as simplified goals and tasks as possible, implementing the proposed DNIHSS goal of separation of concerns on services and components. By reducing the EAIA responsibilities, a reduction and simplification of decision tasks are achievable. The several simplified EAIA collaborate with each other by means of high-level commands to accomplish complex autonomous tasks and decisions.  
To ensure the visibility and transparency over operation, maintenance, and security, a Blockchain service is used. The Blockchain services is introduced in the center of the data message exchange modules of the architecture.  
In order to better illustrate the operation and interaction of EAIA services, an example of surveillance operation is illustrated. This operation example illustrates the interaction of several EAIA to accomplish the complex task of autonomous sea traffic surveillance and identification. The steps of identifying objects in the sea and differentiating them are accomplished by different EAIA services that cooperate with each other.
###### Abstract  
Topics concerning autonomous navigation, especially those related to positioning systems, have recently attracted increased research attention. The commonly available global positioning system (GPS) is unable to determine the positions of vehicles in GPS-shaded regions. To address this concern, this paper presents a fuzzy-logic system capable of determining the position of a moving robot in a GPS-shaded indoor environment by analyzing the chromaticity and frequency-component ratio of LED lights installed under the ceiling. The proposed system's performance was analyzed by performing a MATLAB simulation of an indoor environment with obstacles. During the simulation, the mobile robot utilized a fuzzy autonomous navigation system with behavioral rules to approach targets successfully in a variety of indoor environments without colliding with obstacles. The robot utilized the x and y coordinates of the fuzzy positioning system. The results obtained in this study confirm the suitability of the proposed method for use in applications involving autonomous navigation of vehicles in areas with poor GPS-signal reception, such as in tunnels.  
vehicle-navigation system; GPS-shaded areas; vehicle-positioning system; chromaticity +
Footnote †: journal: Sensors  
1 The School of IT, Information and Control Engineering, Kunsan National University, Gunsan-si 54150, Korea; [EMAIL_ADDRESS]  
2  
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## 2 Materials and Methods
### Cell Segmentation
#### 2.4.2 Randomized Hough Transform for Ellipses  
For the realization of an accurate segmentation, able to identify the different types of patterns and to separate any overlapping cells, in this work, a geometrical method was applied for the initialization of the final step, based on an active contour model. In fact, the cells can be approximated to ellipses and then searched for geometrical figures; the identification of the equivalent ellipse allows the definition of the center and dimensions for the generic cell. Many methods have been developed for the identification of geometric figures, certainly among the most performing is the Hough transform.  
Ellipse detection is an important class of computer vision algorithms because an ellipse is the 2D projection of a circle in an image, from a 3D scene. Circular and elliptical shapes are often present in both nature and constructed environments, such as those found in a factory or a city, and computer vision systems can use information about the circles or ellipses in a given scene, to perform some useful task. For this reason, many methods have been developed for the identification of elliptical objects in images [33; 34; 35].  
Due to the many parameters involved in the detection of ellipses, the various methods developed often limit their recognition dimensions, or search for sub-images. A complete search would be computationally not feasible. The traditional approach for ellipse detection using the Hough technique is similar to a line or circle detection. This approach is not only memory expensive but also computationally intensive. In this work, for an ellipse detection, a randomized Hough Transform with result clustering is used [36]; the method uses only a one dimensional accumulator for ellipse voting, and reducing algorithm complexity. In order to be processed by the actual ellipse detector, a preprocessing phase, composed of the following steps, is used:  
1. noise reduction;
2. grey-scale transform;
3. edge detection;
4. binarization.  
In order to reduce the calculation time and memory usage, in this work, the randomized Hough transform was applied to the sub-images containing the ROIs obtained from the pre-segmentation. Figure 4 shows an example of the result of the transformation applied on a sub-image containingtwo contiguous cells. As can be seen from the Figure, the method identifies the two ellipses that best characterize the two contained cells.
# A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications
## 3 Introduction to GNSS
### Pseudorange  
The pseudorange represents the distance which the GNSS receiver measures between the satellite and the receiver's antenna by measuring the travelling time, \(\Delta T\), the signal takes to propagate from the satellite (emission time) to the receiver (reception time) antenna. This value multiplied by the light speed gives the apparent range \(P=c\cdot\Delta T\) between them. This measurement \(P=c\cdot\Delta T\) is what we know as pseudorange or pseudodistance and it is an \"apparent range\" between the satellite and the receiver that does not match with its geometric distance due to, among other factors, synchronism errors between receiver and satellite clocks. Taking explicitly into account possible synchronism errors between these clocks, the travelling time between transmission and reception is obtained as a difference of times measured in two different clocks or time scales: the satellite (\(t^{S}\)) and the receiver (\(t^{R}\)).
## 2 Structures, Synthesis, and Magnetic Properties of Fe\({}_{3}\)O\({}_{4}\) Nanoparticles
Magnetic anisotropy is a crucial factor contributing to the effect of the size and geometry on magnetic properties. In this context, magnetic anisotropy is related to magnetocrystalline anisotropy and shape anisotropy, which correspond to the preferential magnetization direction of the crystals and the departure from sphericity of particle shapes, respectively [2]. The magnetic anisotropy of nanoparticles can be higher than the value obtained from the crystalline and shape anisotropy due to enhanced surface anisotropy [2]. Furthermore, shape anisotropy is also a vital factor to determine the strength of the magnetic properties of magnetic NPs [2; 5]. Finally, highly crystalline structures can significantly enhance the magnetic properties of NPs, even NPs with comparable sizes and shapes [46; 47]. Enhanced crystallinity was also proposed as the reason for increasing the magnetic properties of cubic versus spherical Fe\({}_{3}\)O\({}_{4}\) NPs [42]. These contributing factors, driven by the size and geometries of Fe\({}_{3}\)O\({}_{4}\) NPs, underpin the fundamental phenomena that rationalize or predict the magnetic properties when the size and geometries of Fe\({}_{3}\)O\({}_{4}\) NPs are varied. Due to the strong effects of the geometry and size on the properties of NPs, we will summarize the advances in the synthesis of Fe\({}_{3}\)O\({}_{4}\) NPs with different geometries and sizes. For a particular geometry, we will cover the synthesis routes for different size ranges and compare the important properties (e.g., saturated magnetization M\({}_{\text{S}}\) and coercivity H\({}_{\text{C}}\)) of these NPs. We focus on three geometries, spherical, cubic, and rod, and highlight recent advances in the synthesis of other sophisticated geometries, such as 2D hexagonal/triangular shapes, multiarmed structures, octahedrons, and hollow structures. We focus on methods with high efficiency, good control of geometry uniformity, narrow size distributions, and the ability to tune the size of particles.
# Set-Point Control of a Spatially Distributed Buck Converter
## 6 Discussion  
The current investigations on the control of the distributed buck converter can be seen as a proof of concept, which, for several reasons, is not yet ready for a technical application. For example, the coaxial cable used here is comparatively expensive and not suited for power electronics. The short length of the transmission line results in a high switching frequency. In a further realization, the coaxial cable could be replaced by twisted pair cable. If the load is operated at a much larger distance from the power supply, such as in power grid applications, we would obtain a significantly lower switching frequency.  
The control law (21) used in the simulation is easy to implement and fulfills the control goal as shown in Figure 7. The control laws (12) and (13) require the spatial integral of current deviation along the transmission line. These integrals can be computed in a simulation, but not directly be obtained in a practical realization. On the one hand, one could investigate how this integral can be estimated by means of observers, see [41; 42]. On the other hand, it should be possible to replace the spatial integration by a time integration, because the solution of the telegrapher's equation is a superposition of travelling waves. In addition, a more advanced control strategy could include an identification of the transmission lines parameters, cf. [43].  
There are two main approaches to control distributed systems, namely early and late lumping. In early lumping, the system is discretized first and the controller is designed afterward for the (finite dimensional) approximation. This approach can lead to an unstable control loop if important system properties are lost by the approximation. In the late-lumping approach, the controller is designed for the (infinite dimensional) distributed  
Figure 7: Signals of the simulated distributed buck converter with the control feedback (21) for a desired output voltage \(v_{d}=6\) V.  
system. Then, the controller is discretized for the implementation. The control strategy suggested in Section 5.3 can be interpreted as late lumping approach.  
In the article, the converter was examined exclusively with resistive load. For practical applications, e.g., for electric drives or power grids, an inductive load with ohmic losses should be investigated [9; 10]. In particular, voltage ripples caused by switching converters may reduce the durability of power grids and the attached electrical drives [15; 16; 44; 45].  
Further research could also be devoted to extend the concept of a (spatially) distributed buck converter to other switched DC-DC converter topologies such as boost and buck-boost converters, as well as to DC-AC, AC-DC, and three-phase power converters [1; 2].
# SoK: Delegated Security in the Internet of Things
## 6 Discussion
### Access Control and/or Authorisation Delegation  
In this domain, all approaches focus on delegating decision-making functions. Specifically, the RC system delegates access control and authorisation to a delegate, which then determines whether a third party is granted access. Most approaches either delegate these functions to a single device or utilise blockchain as a delegate. Additionally, some delegates incorporate monitoring mechanisms to track the activity of third parties and respond accordingly. For secure data transmission, the TLS protocol is predominantly employed.  
The overall architecture of protocols related to this domain is illustrated in Figure 11, and the summarised results of their security evaluation are presented in Figure 12.
# Analysis and Correction of Antenna Pattern Effects in AMAO Spaceborne SAR Images
## 3 Antenna Pattern Effects in Images
At the reference squint angle, Equation (19) becomes  
Figure 4:1where \(T\) is the target exposure time. In this case, the integration interval is symmetric about zero, so the integral of linear modulated AAP is mostly canceled out, and a centroid offset arises from AAP asymmetry. At this point, a convenient relation is proposed to analytically analyze the antenna pattern effect on radiometric intensities in AMAO SAR images. It explicitly contains two factors: one is an elevation-dependent factor derived from the EAP, the other is an azimuth shift of the signal centroid from the reference point. These two independent factors lead to 2D radiometric variations in AMAO SAR images. Figure 5 shows the 2D effect of the antenna pattern in the image. The maximum variation occurs at the swath edge along the azimuth, and the minimum appears near the reference squint angle.
## 4 Results
### Receiver Operating Characteristic Curve  
Figure 5 displays the results of the ROC analysis. The IPLFCS achieved the highest AUCs (0.795(0.635-0.955)) in distinguishing NC from SCD when compared with ALFF (0.644 (0.471-0.817)), ReHo (0.616 (0.443-0.789)), and FC (0.689 (0.482-0.896)). In distinguishing NC1 from MCI1, the AUC of the IPLFCS reached 0.815 (0.656-0.973), whereas the  
Figure 4: Linearities between IPLFCS and SUVR and clinical scores in different groups. **(a)** Correlation analysis between IPLFCS and FC in the SCD1. **(b)** Correlation analysis between IPLFCS and FC in the MCI1. **(c)** Correlation analysis between IPLFCS and FC in the AD1. **(d)** Correlation analysis between IPLFCS and FC in the SCD2. **(e)** Correlation analysis between IPLFCS and FC in the MCI2. **(f)** Correlation analysis between IPLFCS and FC in the AD2. **(g)** Correlation analysis between IPLFCS and FC in the AD2. **(g)** Correlation analysis between IPLFCS and SUVR of LMTG in Cohort 1 with PET. **(h)** Correlation analysis between IPLFCS and SUVR of LMTG in Cohort 2 with PET. **(i)** Correlation analysis between IPLFCS of LMTG and SCD-9 scores in the SCD1 group. **(j)** Correlation analysis between IPLFCS of LMTG and AVLT recognition scores in the SCD1 group.  
AUCs of the ALFF, ReHo, and FC were 0.680 (0.496-0.864), 0.754 (0.543-0.965), and 0.771 (0.593-0.949). In distinguishing NC1 from AD1, the IPLFCS (0.900 (0.694-1.000)), ALFF (0.867 (0.664-1.000)), ReHo (0.861 (0.637-1.000)), and FC (0.917 (0.789-1.000)) all showed good classification performance.  
In distinguishing NC2 from SCD2, the AUC of the IPLFCS was 0.762 (0.596-0.929), whereas the AUCs of ALFF, ReHo, and FC were 0.732 (0.575-0.888), 0.686 (0.494-0.877), and 0.729 (0.535-0.923). In distinguishing NC2 from MCI2, the AUC of the IPLFCS was 0.780 (0.678-0.882), whereas the AUCs of ALFF, ReHo, and FC were 0.676 (0.543-0.808), 0.691 (0.565-0.818), and 0.701 (0.579-0.823). In distinguishing NC2 from AD2, the IPLFCS (0.831 (0.702-0.961)), ALFF (0.794 (0.659-0.928)), ReHo (0.809 (0.679-0.938)), and FC (0.823 (0.690-0.965)) performed well.