esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 6 System Testing and Applications
### System Application  
The system was applied to the monitoring of a highway slope in Fujian Province, China. The monitoring system was deployed along a 30 km mountainous section of a two-lane expressway. This area is characterized by average slope gradients of 28-45\({}^{\circ}\) and bedrock outcrops accounting for 60% of the monitored slopes. The region experiences an annual average rainfall of 1600-2200 mm. Such environmental conditions are favorable for the occurrence of landslides. As this section has a daily traffic volume of 5000-8000 vehicles, deformation monitoring is necessary.  
Six monitoring stations were deployed (Figure 17), each integrating GNSS (Global Navigation Satellite System) displacement sensors, weather sensors, and high-definition cameras. The deployment of the slope monitoring system requires avoiding electromagnetic interference and ensuring a clear line of sight for GNSS antennas. Sensors are fixed using concrete foundations or grouted boreholes; cables are shielded with armored conduits and waterproof seals; equipment housings meet IP66 standards; and corrosion-resistant materials are selected for coastal areas. The system utilizes 4G for data transmission and solar-powered lithium batteries for power supply. Core sensors meet stringent precision requirements (GNSS horizontal accuracy \(\leq 1\) cm, inclinometer resolution 0.001\({}^{\circ}\)). A monitoring database and system software are established to realize slope monitoring, environmental monitoring, and video surveillance, thereby enhancing the safety protection level of monitored objects.  
Until now, this section shows that the monitoring system operates stably, with monthly displacement velocities in the monitored area ranging from \(-1.0\) to \(2.1\) mm/month. The results indicate the stability of the monitored region. Specifically, monitoring stations 1-6  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement Dimension** & **Method** & **Tool** & **Delay Results (Mean \(\pm\) SD)** \\ \hline End-to-end delay & \
\begin{tabular}{c} Timestamp \\ synchronization \\ \end{tabular} & Network debugger &
\begin{tabular}{c} No-load: 215 \(\pm\) 12 ms \\ Load: 380 \(\pm\) 45 ms \\ \end{tabular} \\ \hline Network transmission delay & ICMP RTT testing & Ping tool & 100–150 ms \\ \hline Load response delay & Pressure testing & Data monitoring platform & Delay growth rate: 76.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: System latency test results.  
recorded average deformation rates of 0.8 mm/month, 1.3 mm/month, 2.1 mm/month, \(-0.8\) mm/month, \(-0.5\) mm/month, and \(-1.0\) mm/month, respectively.
# Framework for Developing an Information Technology
## 2 State of the Art
### IT Maturity Frameworks
In [13], describes how the smart city's ICT adoption maturity model (SCIAMM) defines 5 domains (e-government strategy, public innovation, data, infrastructure and services), 15 key domain areas (KDA), and 48 critical variables (CVs). This model was applied in Colombia, using public data from the Ministry of ICT (MinTIC) for Colombia's seven largest cities, and presented results such as the strong correlation between the presence of national programs and the improvement of local KDA. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Concept** & & **Outline** \\ \hline Strategic Intent & \multicolumn{2}{c}{Successful smart cities have a strategic roadmap on how investment in data and digital technologies enable service reform and partner collaboration. An effective strategy focuses on delivering improved outcomes aligned to the city’s strategic priorities} \\ \hline Data & \multicolumn{2}{c}{Successful smart cities make effective use of their data assets to secure better outcomes. They invest in system-wide data capture, integration and analytics capabilities. Open data underpins their commitment to transparency and innovation} \\ \hline Technology & \multicolumn{2}{c}{Successful smart cities invest in open, flexible, integrated and scalable ICT architectures that enable accelerated service innovation, such as the provision of automated and real time dynamic response capabilities} \\ \hline Governance and Service Delivery Models & \multicolumn{2}{c}{Successful smart cities adapt the traditional organizational model of delivery to realize the opportunities of data and digital technologies. They invest in system-wide partnership models focused on shared outcomes} \\ \hline Stakeholder Engagement & \multicolumn{2}{c}{Successful smart cities make the best use of data and digital technologies to invest in enhanced openness and transparency. Stakeholder engagement and stakeholder ownership of service reform is central within a smart city. Smart cities are proactive in improving take up of digital services while supporting the digitally excluded} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Dimension maturity IDC. Proposed Framework for Developing an IT Maturity Model for Smart City Services in Emerging Economics  
The framework proposed to develop an IT maturity model for smart city services, consists of three components Figure 1, a conceptual model of smart city service, IT dimensions, indicators and IT maturity levels. The framework components are designed to be adopted in a practical and contextualized way for the implementation of smart city services. The framework components are described below.
# Road to Efficiency: V2V Enabled Intelligent Transportation System
###### Abstract  
Intelligent Transportation Systems (ITSs) have grown rapidly to accommodate the increasing need for safer, more efficient, and environmentally friendly transportation options. These systems cover a wide range of applications, from transportation control and management to self-driving vehicles to improve mobility while tackling urbanization concerns. This research looks closely at the important infrastructure parts of vehicle-to-vehicle (V2V) communication systems. It focuses on the different types of communication architectures that are out there, including decentralized mesh networks, cloud-integrated hubs, edge computing-based architectures, blockchain-enabled networks, hybrid cellular networks, ad-hoc networks, and AI-driven dynamic networks. This review aims to critically analyze and compare the key components of these architectures with their contributions and limitations. Finally, it outlines open research challenges and future technological advancements, encouraging the development of robust and interconnected V2V communication systems in ITSs.  
V2V communications; intelligent transportation systems; connected vehicles; autonomous vehicles; cellular vehicle-to-everything +
Footnote †: journal: _electronics_
# A Road Truncation-Based Location Privacy-Preserving Method against Side-Weight Inference Attack
## 1 Introduction
Specific query users are distributed on high-density roads with high probability, so attackers infer that users are located on high-density roads with high probability. The specific query user is more likely to lie on the road with more users. Based on the background knowledge, the attacker obtains the user's distribution information and infers which road the user is on with high probability, namely side-weight inference attack, which reduces the performance of location privacy protection. However, the user distribution is ignored. Motivated by this, we investigate the issue of location privacy preservation for the non-uniform distributed road network. In this paper, we propose a privacy preserving method based on road truncation, considering non-uniform distributed road network. In our scheme, the road section with high user density is designed to be truncated to reduce the density and then added to the anonymous set. As a result, the divergence between different road sections decreases, which is harder for the adversary to track the user. On the premise of privacy protection, we also consider reducing the query cost of the algorithm. In \(k\)-anonymity based schemes, the location information uploaded by the user is an area containing several users and roads, which affects the quality of LBS. Our aim is to enable the LBS to protect users' location privacy while keeping the location query service relatively efficient. In summary, the contributions of this work are as follows:  
* We investigate the issue of location privacy preservation for non-uniform distributed road network. We also consider the cost function and formulate the object as a mixed integer programming problem. * We propose a privacy-preserving scheme that achieves location privacy protection based on road truncation. The proposed scheme meets the demand of privacy protection for non-uniform distributed road network at a low cost. As a result, the proposed scheme provides good quality of LBS. * We analyze the scheme in terms of privacy and efficiency with theoretical analysis and the simulation results show that our scheme works well with both privacy and efficiency. The proposed scheme achieves \(k\)-anonymity and \(l\)-diversity and resists side-weight inference attack. Meanwhile, the proposed scheme maintains a low cost level. The rest of this paper is organized as follows. Section 2 reviews the related works of location privacy preserving schemes. In Section 3, we give some preliminaries and explain how privacy leakage happens. In Section 4, the motivations and aims are described and the problem is formulated. Then, we introduce our proposed scheme. Privacy analysis and numerical results are shown in Section 5 to evaluate the performance of the proposed scheme. Finally, we present the conclusion in Section 6.
## 2 Proposed Algorithm
### Interval Type-2 Fuzzy System  
Fuzzy systems are used in numerous proposals about clustering for sensor networks as it behaves properly with the inherent characteristics of these types of networks. This convenience is mainly due to the ability of the fuzzy systems to work on systems with ambiguous, vague, or incomplete inputs [25]. For example, many measurements of a sensor network can be considered incomplete or imprecise.  
In other situations, the nodes cannot afford to achieve an effective and complete knowledge about their surroundings (other nodes in the network or the environment) because it usually involves the exchange of too many messages. This will saturate the wireless channel and accelerate the depletion of their batteries. In addition, when a node analyzes its own data, non-precise information can be found due to the tolerance of the probes. This happens when evaluating the remaining energy or the distance to the BS that the node computes with the RSSI of different signals.  
In our proposal, we use an Interval Type-2 Fuzzy System (IT2 FS) because these types of systems work better than Type-1 ones when the measurements are specially inaccurate or vague [26]. One of the features that makes them suitable to cope with the uncertainty of WSNs is that Type-2 fuzzy systems can incorporate that uncertainty into their fuzzy sets as can be seen in Section 2.1.1. The block diagram of an interval Type-2 fuzzy system, as the one employed in our approach, is presented in Figure 2.  
The operation mode of the structure shown in Figure 2 is as follows:  
1. The crisp values of the inputs are fuzzified with interval Type-2 fuzzy sets that are stored in the Knowledge Base (KB).
2. Fuzzified inputs are bound together through the rules found in the KB.
3. The inference process binds the inputs, the rules, and the outputs.
4. The interval Type-2 fuzzy is reduced to a Type-1 set by the type-reducer block.
5. The last step is to obtain the output. In our case, the highest and lowest values of the interval are used, each one in a different stage of the process.  
Following a distributed clustering approach, each node runs its IT2 FS independently and it obtains the output based on its own measurements. To allow an effective deployment and reduce the computational cost of the nodes, the complete solution space of the IT2 FS is sampled. Thus, the node only has to quantify the inputs and it gets the right output from a table stored in its memory. This method is much faster than the execution of the inference engine, and we have checked that the error that it introduces is negligible.  
Once the main process is described, the system has to be adapted to a specific application (clustering in this case). Therefore, we present the design of the input variables and the layout of the fuzzy sets in the next section.
## 5 Discussion
### Imaging Performance Evaluation  
Figure 19 displays the azimuth profiles of strong scattering areas from three imaging sets, located at 340 m east, 270 m east, and 270 m east, respectively, corresponding to the position of the red dotted line in Figure 17(a1-a3). The blue dashed line in the figure represents the azimuth profile before ionospheric scintillation error compensation, showing significant defocusing and low peak energy. In contrast, the solid red line is the compensated azimuth profile, which is clearly well-focused. To enhance the clarity of the curves in the north direction, the receiver was used as the origin of the coordinates, and only a range of 400 m was intercepted. The actual imaging range extended 800 m in the northward (as indicated in Figure 17). The effectiveness of the proposed modified BP method based on two-step phase compensation has been preliminarily verified.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(N_{a}\) & \(N_{in}\) & \(N_{x}\) & \(N_{y}\) & \(d\mathbf{x}\) & \(dy\) \\ \hline
300,000 & 16 & 300 & 160 & 5 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings of the proposed algorithm.  
According to the analysis in Section 4.3, the three groups of experiments had different strong scattering points because of using different transmitters (GPS satellites). Through incoherent superposition, the fusion results of the three experimental data sets can be obtained. Figures 20 and 21 display the results before and after compensation for ionospheric scintillation errors, respectively. Overlaying Figure 21 with the optical image of the area (from Google Earth), the effect is shown in Figure 22. Clearly, the strong scattering area of the SAR image roughly coincided with the target position of the optical image. Some strong scattering areas included the swimming pool about 270 m to the east, the gymnasium about 340 m to the east, the front edge of the new main building about 470 m to the east, the office building of Datang Telecom about 780 m to the east, Peking University Third Hospital about 1270 m to the east, etc. In addition, the metal fences of playgrounds and basketball courts formed a dihedral angle with the ground (about 100 m and 185 m eastward) at those experimental moments, which also caused strong electromagnetic scattering.  
Figure 19: Comparison of azimuth profiles of strong scattering points. (**a**) First set of experiments in the morning; (**b**) second set of experiments at noon; (**c**) third set of experiments in the evening.  
Figure 20: Incoherent accumulation results of three SAR images (Before phase error compensation).
## 3 Results and Discussion
### Soil Profile  
Based on the data from CPTu and SPT-T, the geotechnical profile of the experimental field was created. Figure 12 shows the soil profile along the measured area. Clayey and loamy soils can be found up to the first 2.5 m, and from 2.5 m to 5.8 m, medium sands with clays were encountered. From 6 m to 25 m, a homogeneous profile of dense and medium sands can be found, except near the SPT-1 region, where stiff clay was encountered at the end of the borehole. According to Figure 4, the three CPTu boreholes generally show good correspondence. The distinction between the profiles is limited to the variability in the resistance of the sand layer in the first horizon and the position of the clay layer. In this regard, it was possible to tentatively delineate the three horizons shown in Figure 12 up to 8 m, in correspondence to CPTu data and SPT. From a depth of 8 m, where cone tests were not conducted, the soil horizons were classified solely using the SPT. These horizons mostly correspond to sands, with some areas containing cohesive soils. These horizons result from the approximation of the behavior of the analyzed soils.
## 3 Data Collection
### Ultrasonic Dynamic Modulus  
Ultrasonic non-destructive tests of the asphalt concrete samples were carried out by a ZBL-U510 non-metal ultrasonic detector, as shown in Figure 1. The instrument adopts ultrasonic pulse technology and includes measuring functions such as pile measurement, strength measurement, non-compaction area, cavity detection, crack depth measurement, etc. In this study, the ultrasonic defect detection function was used due to its ability to capture information of defect size and its orientation, such as the location and size of the structural crack that was not reflected on the pavement surface. An ultrasonic wave is able to conduct such an analysis since the wave signal generated by the transducer is reflected by defects and the back surface of the sample.  
The initial parameters of the detector for the ultrasonic test were set at a voltage of 500 V, a probe frequency of 50 kHz, and a temperature of 21.1 \({}^{\circ}\)C. The instrument was zeroed first in order to remove the zero sound (which introduces a time delay of sound wave propagation) followed by the formation of the waveform. In order to ensure better coupling between the transducer probes and the upper and lower surfaces of the sample during the test, butter was applied as a coupling agent at the contact surface of both sides of the test specimens to ensure measurement accuracy. Acoustic information, such as wave velocity, amplitude, waveform, and frequency, was recorded.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mix Type** & \begin{tabular}{c} **Density**, \\ **(g/cm\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} **E\({}^{\star}\)1, Lab** \\ **(MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Wave Velocity** \\ **(km/s)** \\ \end{tabular} &
\begin{tabular}{c} **E\({}^{\star}\), Calculated** \\ **(MPa)** \\ \end{tabular} & **Poisson’s Ratio** \\ \hline AC-13 & 2618 & 5400 & 3.096 & 18620 & 0.3 \\ SMA-13 & 2552 & 4685 & 2.992 & 16951 & 0.3 \\ AC-20 & 2539 & 7029 & 3.197 & 19255 & 0.3 \\ AM-20 & 2346 & 4063 & 2.716 & 12841 & 0.3 \\ \hline \hline \end{tabular}  
* E*—dynamic modulus, which is mathematically defined as the maximum dynamic stress divided by the peak recoverable strain.  
\end{table}
Table 1: Comparison of laboratory-measured and calculated dynamic moduli.  
Figure 1: Ultrasonic detector and measurement setup.
# IoT and UAV Integration in 5G Hybrid Terrestrial-Satellite Networks
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPPThird-Generation Partnership Project  
8 PSK8-ary Phase Shift Keying  
AMQPAdvanced Message Queuing Protocol  
Figure 8: IoT device-UAV-satellite and 5G architecture.  
BPSK  
Binary Phase Shift Keying  
CSMA  
Carrier Sense Multiplier Access  
CSMA/CA  
Carrier Sense Multiplier Access with Collision Avoidance  
CSS  
Chirp Spread Spectrum  
CDMA  
Code Division Multiple Access  
CoAP  
Constrained Application Protocol  
CN  
Core Network  
DBPSK  
Differential Binary Phase Shift Keying  
DQPSK  
Differential Quadrature Phase Shift Keying  
DVBS2  
Digital Video Broadcasting-Satellite-Second Generation  
DDS  
Direct Distributed Service  
DSSS  
Direct Sequence Spread Spectrum  
eMBB  
enhanced Mobile Broadband  
FDMA  
Frequency Division Multiple Access  
FSK  
Frequency Shift Keying  
GFSK  
Gaussian Frequency Shift Keying  
GPSK  
Gaussian Phase Shift Keying  
gNB  
gNode-B  
ICN  
Information-Centric Networking  
ISM  
Industrial, Scientific, and Medical band  
IoT  
Internet of Things  
KPI  
Key Performance Indicator  
LoRaWAN  
Long-Range Wide Area Network  
LPWA  
Low Power Wide Area  
LTN  
Low Throughput Network  
M2M  
Machine to Machine  
MEC  
Mobile Edge Computing  
MQTT  
Message Queue Telemetry Transport  
NB-IoT  
Narrow-Band Internet of Things  
OQPSK  
Offset Quadrature Phase Shift Keying  
OFDMA  
Orthogonal Frequency Division Multiple Access  
OSS  
Orthogonal Sequence Spread Spectrum  
PCA  
Priority Channel Access  
QAM  
Quadrature Amplitude Modulation  
QoE  
Quality of Experience  
RPMA  
Random Phase Multiplier Access  
RN  
Relay Node  
SCADA  
Supervisory Control And Data Acquisition  
TDMA  
Time Division Multiple Access  
UAV  
Unmanned Aerial Vehicles  
UE  
User Equipment  
UPF  
User Plane Function  
XMPP  
eXtensible Messaging and Presence Protocol
# Development and Operation of Track Condition Monitoring System Using In-Service Train
## 5 Improvement in Train Position Accuracy Based on Yaw Angular Velocity
### Map-Matching Based Train Position Identification  
The map-matching technique used to estimate the train position is shown in Figure 10. We created a database (DB) that associates the latitude and longitude measured in advance on the target route with the distance from the starting point, and each point in the DB was connected through a straight line.  
Figure 8: Changes in maximum lateral acceleration in a section between 1.05 and 1.15 km.  
Figure 9: Changes in maximum lateral acceleration in a section between 1.4 and 1.5 km.  
We searched for the DB point that was the closest to the position measured by the onboard sensing device (DB_B) for the DB and determined the DB section with the shortest perpendicular length between the DB section before and after the point (DB_A and DB_C) as well as the measurement position (GNSS position). We calculated the distance from the aforementioned DB point to the intersection of the DB section and perpendicular line via linear interpolation. The distance obtained by adding the distance of the DB point to the distance above is defined as the distance converted from the GNSS position.