esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Vehicle Detection and Edge Point Location
When the premises of the two ranging models are satisfied and the offset is 0, the normalized ranging error is:  
\[\frac{D_{e}}{D_{t}}=\frac{\left|D_{t}-D_{p}\right|}{D_{t}}=\left|1-\frac{D_{p} }{D_{t}}\right| \tag{20}\]  
where \(D_{p}\) is the vehicle distance calculated by the measurement model, and \(D_{t}\) is the true value. According to Equations (1) and (2), the magnitudes of _VMI_ and _HMI_ are exactly the same as the relative ranging error. When _VMI_ and _HMI_ are used as measuring indexes in the edge point location task, the influence of the positioning error on vehicle distance measurement can be intuitively revealed. The distribution curves depicting the errors of the MI\(-\)CNN network, with \(K=3\), are depicted in Figure 9. The vertical axis \"Frequency\" denotes the ratio of edge points falling within a specific error range to the overall count. On the other hand, the \"Fraction of Test Vehicles\" represents the ratio of the cumulative number of detected vehicles when the error index is below a certain threshold to the total number of vehicles. Upon analysis of the figure, it becomes evident that, when employing the height parameter as the reference for distance measurement, the errors in point localization are predominantly concentrated within the range of 0.02 to 0.03. With _VMI_ = 0.05, the ratio of the cumulative edge points is 0.95. When the width parameter is used as a reference, errors are mostly concentrated between 0.03 and 0.035. With _HMI_ = 0.05, the cumulative ratio is 0.94. Based on the above experimental results, the point coordinate errors calculated by the MI\(-\)CNN network are mostly below 0.05, which can provide a valid input for the next distance measurement model.
# Efficient Parallel Processing of Big Data on Supercomputers for Industrial IoT Environments
## 3 System Architecture
For the EPI project team developing the Rhea-GPP processor, our findings provide crucial guidance for ensuring optimal compatibility with industrial IoT workloads. For system architects designing edge-cloud industrial deployments, we demonstrate how ARM's energy efficiency can enable the sustainable scaling of distributed systems. Furthermore, our work informs the development of hybrid HPC-DISC architectures that combine Spark's data parallelism with HPC's computational density. Our experimental methodology employs a testbed designed to emulate real-world industrial edge-HPC scenarios, where ARM-based nodes (equipped with V1 processors) represent either edge computing clusters or fog computing layers. Within this framework, we investigated SVE-enabled Spark processing for representative industrial workloads, including K-means clustering (for equipment anomaly detection) and windowed stream aggregations (for production line monitoring). The performance metrics we examined focus on three critical dimensions: vectorization efficiency (comparing SVE against scalar ARM instructions), energy consumption per operation (particularly relevant for edge deployments), and end-to-end latency (crucial for quality-of-service sensitive control applications). The results of this evaluation provide practical guidance for multiple constituencies. Industrial system designers implementing ARM-based edge solutions will benefit from our performance benchmarks and optimization recommendations. The EPI development team can apply our findings to refine the Rhea-GPP processor for industrial IoT workloads. These results provide a foundation for refining Spark's execution engine to better exploit ARM vector instructions, with direct relevance to performance-critical tasks in manufacturing analytics, including the stream aggregation and batch processing of telemetry data. By systematically exploring the intersection of ARM-based HPC architectures and industrial big data processing, this work makes significant strides toward realizing the cloud-edge continuum vision that underpins modern smart factory initiatives. Our findings directly support the Industry 5.0 objectives of human-centric, sustainable production by demonstrating how advanced computing architectures can be effectively deployed in industrial environments without compromising energy efficiency or operational responsiveness. This research not only provides immediate practical benefits for system designers and developers but also establishes a foundation for future investigations into heterogeneous computing architectures for industrial applications.
## 5 Conclusions  
This study provides crucial insights into the spatial and temporal dynamics of soil water content (SWC) in coal mining and non-coal mining areas, revealing the significant effects of mining activities on soil health and moisture retention. Our findings demonstrate distinct differences in SWC levels and their relationships with environmental factors, underscoring the complexity of mining-induced disturbances.  
In coal mining areas, SWC showed higher mean values in topsoil but exhibited greater temporal variability because mining activities often cause significant distances to soil structures and can modify the natural soil horizons by mixing them or creating artificial layers, compared to non-coal mining regions. At deeper layers, however, SWC was lower in coal mining areas, indicating that mining activities have altered moisture dynamics throughout the soil profile. The increased spatial and spatiotemporal variability in these regions suggests heightened sensitivity to moisture fluctuations, posing challenges for effective water management.  
The relationships between SWC and environmental factors, such as topography and soil chemistry, were generally stronger in coal mining areas. Notably, altitude and nutrient availability (e.g., nitrogen and phosphorus) showed significant correlations with SWC, suggesting that mining has not only affected the physical structure of the soil but also its chemical properties, impacting its capacity to retain moisture.  
In contrast, non-coal mining areas exhibited more stable SWC dynamics, likely due to the preservation of natural soil structures and processes. This stability highlights the importance of conserving non-mined landscapes, which serve as vital benchmarks for understanding and managing disturbed environments.  
Conceptualization, Y.J. and H.Z.; methodology, Y.J. and Y.C.; software, Y.C. and H.D.; validation, Y.J., Y.C. and J.Y.; formal analysis, Y.J.; investigation, Y.J.; data curation, Y.J. and H.Z.; writing--original draft preparation, H.Z.; writing--review and editing, Y.J., J.Y. and H.D.; visualization, Y.J.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported financially by the Natural Science Foundation of Shanxi Province, China (202203021221167), National Natural Science Foundation of China (32371971), and School-level Educational Reform Project (JG-202212).  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
The authors declare no conflict of interest.
## 5 Comparison of Different Decomposition Methods
### Quantum Shannon Decomposition  
In [22], another way of using the CSD from Section 5.6, called Quantum Shannon Decomposition (QSD), is introduced. The decomposition of a two-qubit gate is shown in Figure 10.  
The start of the decomposition is the same as in Section 5.6, but the \(L\) and \(R\) matrices are decomposed using eigenvalue decomposition. This is shown in Figure 11. The resulting matrices are unitary gates applied to one less qubit than the starting unitary. This leads to the circuit in Figure 10, where the \(D\)-matrix is implemented as a multi-controlled \(R_{z}\) gate.  
Quantum Shannon Decomposition is applied recursively until the final one-qubit gates can be implemented with ZYZ decomposition. This means that only the multi-controlled rotation gates contribute to the number of CNOTs, each of which requires \(2^{n-1}\) CNOT gates for a single step of the recursion of an \(n\)-qubit gate. This leads to a total CNOT count of \(3/4\cdot 4^{n}-3/2\cdot 2^{n}\) for this decomposition method.  
There are two optimizations that can be implemented on top of this implementation of Quantum Shannon Decomposition. The first is to stop the recursion at two-qubit gates and translate those as in Section 5.4. The second optimization is to implement the central multi-controlled \(R_{z}\) gate using CZ gates rather than CNOTs, of which one can be absorbed into the neighboring multiplexer. This results in one fewer CNOT gate at each level of the recursion. With these two implementations, the CNOT count comes to \({}^{23}/48\cdot 4^{n}-3/2\cdot 2^{n}+4/3\)[22].
## 4 Simulation and Experimental Results
### Experimental Results  
The experiments were conducted on a laboratory drive consisting of a 5.5-kW four-pole induction motor, three-phase IGBT inverter, diode rectifier, dSPACE DS1103 control platform, and a loading dynamometer. The nameplate data and the used machine model parameters are given in Table 1 in Appendix B. The sampling time of the control algorithm was set to 50 \(\upmu\)s. Since the used PTC does not utilize a modulator, the transistors' switching frequency was not constant, averaging around 2 kHz. Table 2 shows a detailed comparison of current THD and electric power consumption for a delta and star winding connection. The input electric power was measured by NORMA 4000. The current THD was obtained from oscilloscope R&S RTH1004 using the current probe Tektronix A622. The THD was calculated as the ratio between the root mean square (RMS) of all distortion products (i.e., all the harmonics except the fundamental one) and the signal's total RMS. According to Table 2, the star-connected machine exhibited lower current THD in all achievable operating points, leading to lower power consumption. The difference grows with increasing flux reference and decreasing load. Figures 11 and 12 show the plotted dependence of the line current THD and consumed power, respectively, for the speed 500 RPM and reference stator flux 1.7 Wb. The THD difference decreased with increasing load, while the power difference increased slightly with the load. The saved power varied between 120 and 200 W, with an average value of approximately 145 W. The reason the THD decreased with increasing load is that the ratio of passive and active voltage vectors and their distribution differed at various loads. As for the power differences, it was expected that the most significant portion of the saved energy comes from the reduced iron losses that were THD-dependent [12; 30] (distorted currents create distorted flux density distribution in the iron core). Even when substantial circulating currents were present, the contribution of the ohmic losses was expected to be less significant (in the range of Watts units). Figure 10: Online change of the winding topology both in the motor model and control model—delta (0.3–0.4 s), star (0.4–0.5 s), delta (0.5–0.6 s).
# Trimodal Watch-Type Wearable Health Monitoring Device
## 3 Experimental Results
### Optical Phase Testing  
The IR sensors used in IR thermometers are optical sensors that measure the intensity of IR radiation emitted by the body. This intensity is directly proportional to the temperature of the emitting object, according to the Stefan-Boltzmann law. The electrical signal from the sensor's detector is converted into corresponding ADC code, which is then processed to determine the body temperature. This process involves calibration based on known temperatures, which is crucial for ensuring accurate readings. Medical-grade IR thermometers are specifically calibrated and designed for precise temperature measurements on human skin, typically offering a precision of approximately 0.3 \({}^{\circ}\)C. They are non-invasive and can quickly measure body temperature without direct skin contact, making them suitable for various medical applications.  
To measure body temperature using the developed device, the IR sensor ADC code is calibrated against the human body temperature range (32-42 \({}^{\circ}\)C). This calibration is performed using an experimental setup where a hot plate's temperature is increased from 32 to 42 \({}^{\circ}\)C in 1 \({}^{\circ}\)C increments. The body temperature testing results are displayed in the GUI and the app as shown in Figure 8. The hot plate temperature is measured simultaneously using both the proposed device and a medical-grade IR thermometer (GoodBaby, Nanjing,  
Figure 7: (a) Aqueous phase testing results; (b) calibration curve.  
China). Based on the results of multiple experiments, a calibration curve of the IR sensor ADC versus human body temperature is obtained, as shown in Figure 9.  
After calibration, the hot plate temperature is increased from 32 to 42 \({}^{\circ}\)C in 1 \({}^{\circ}\)C increments using the same experimental setup. The temperature measurements are taken simultaneously with both the proposed device and the medical-grade IR thermometer. Error rates are calculated to evaluate the accuracy of the developed device. The comparison of measured temperatures between the developed device and the medical-grade IR thermometer is shown in Figure 10.  
Figure 8: Optical phase (body temperature) testing results (**a**) Windows GUI; (**b**) Android app.  
Figure 9: IR sensor ADC code versus body temperature calibration.  
mometer, along with the corresponding error rates, is illustrated in Figure 10. During the experiments, it is observed that the device provides a temperature measurement accuracy of 0.3 \({}^{\circ}\)C, which meets the guidelines for medical-grade IR thermometer.
## 2 Overview of Blockchain Technology
### Supply Chains  
Blockchains enable data sharing in supply chains where stakeholders can track the origin and movement of products with reduced risk of unethical behavior. It is possible to keep track of operations using a dispersed blockchain and the movement of goods along the supply chain [120]. A practical illustration of supply chain management with blockchain is cold chain management [121]. A systematic literature review on blockchain applications suggests that blockchain has been extensively used in supply chain management as a business application for identifying and tracking products, sharing information among stakeholders, facilitating supply chain decisions, etc. [122].  
Networking  
For decentralized data sharing, network administration, and network security, blockchains have applications in networking, particularly in Internet of Things (IoT) networks and Unmanned Aerial Vehicle (UAV) networks. By assisting network intrusion detection systems, creating network security records, preventing unauthorized access, protecting data integrity, etc., blockchains may be utilized to ensure network security. To automate decision-making and enforce network regulations, smart contracts can be used. For example, TRUCON is a platform built on the blockchain for trustworthy data exchange that has traffic management capabilities for the web of automobiles, where stationary devices serve as complete nodes and moving automobiles serve as portable nodes [123]. IoTChain leverages blockchain infrastructure to offer permitted customers safe accessibility to IoT assets. Multicast teams are established for these individuals on the public blockchain [124]. UASTrustChain is a trust management framework for UAV networks where observers maintain the trust score of UAVs in a secure and reliable blockchain ledger to detect abnormal behaviors [125]. Moreover, the integration of blockchain and the Internet of Things (IoT) has resulted in a new paradigm called Blockchain and the Internet of Things (BIoT), in which there are intellectual cores such as data security and privacy, applications, frameworks, etc. [126].
## 5 Numerical Results
### Results for an OFDMA Signal Detection
Still referring to Figures 5-8, the AND decision combining scheme has shown to be the worst among all fusion rules, as happened with the detection of subcarriers or sub-bands of general wideband signals. However, the performance gap from the best result was reduced when comparing with the results in Figures 1-4. Also notice that the majority-voting has gained the best results among the decision combining rules, whereas it was the second better result in the case of detecting general wideband signals. This reinforces the statement that the AND, OR and majority-voting rules can alternate their performance ranking in different system parameters or scenarios. The major difference between the sample fusion and the eigenvalue fusion strategies is the amount of data sent to the FC. For the parameters used in the numerical results for the OFDMA signal, we had the following situation: The primary signal was made up with \(n=KN=20\ 0=1,200\) samples in the time domain. Therefore, the number of samples sent to the FC in the sample fusion scheme was \(mKN=6\times 1,200=7,200\). With \(K^{\prime}=5\) subcarriers in each subchannel, the order of the covariance matrices was \(K^{\prime}\times K^{\prime}=5\times 5\). Thus, the total number of eigenvalues sent to the FC was \(mK^{\prime}P=6\times 5\times 4\ =120\). Considering that sample values and eigenvalues are digitized with the same number of bits, the required amount of data in the case of the eigenvalue fusion is \(mKN/mK^{\prime}P=mKN/m(K/P)P=N=60\) times less than in the case of sample fusion. Obviously, this reduction in the volume of data sent to the FC must be traded with the increased complexity of the CRs due to the need of local estimation of the eigenvalues. A general analysis of the volume of data sent to the FC and the complexity tradeoff is made in the next section, also considering the sensing of general wideband signals.
# Communication Technologies in Emergency Situations
## 2 Emergency Communications Systems
However, any one of the emergency communication systems mentioned so far has its limitations, which may limit their operation in an emergency: (i) satellite communications may be unavailable in certain areas; (ii) mobile communications may be also unavailable or spoiled during the emergency; (iii) all of them, with the only exception of satellite, may fail to communicate in rugged terrain with no line of sight or may suffer from fading due to destructive interference from several wave reflections; (iv) besides, they require high transmission power and the deployment of a much larger number of repeaters to cover the potentially large area of an emergency; and (v) they are all managed by third-parties, limiting the governance by FRs. Indeed, during an emergency, conventional wireless infrastructure, cellular or mobile, may collapse and become unavailable. Additionally, mobile phones can stop working in a few hours if people cannot recharge their batteries due to power outages. Disruption of communication infrastructure may be caused directly by damage to cables and cellular towers, or indirectly through shutdown of power and water. The unreliability of fixed communication infrastructure in disaster situations is well documented [5; 6; 7]. In such scenarios, an on-demand communication infrastructure that can quickly recover communications in the disaster area is of critical need to coordinate emergency response operations. Public safety networks are perceived as mission critical. They are required to be dependable, resilient, and secure, while satisfying other strict requirements concerning network coverage, system accessibility, and end-to-end performance. These crucial operational demands are the primary drivers for the design and engineering of the \"public safety grade\" network [8]. Emergency situations require reliable broadband communications systems which are able to transmit relevant information from the disaster site to the decision makers and send feedback to first responders regarding potential dangers or decisions. In fact, the ITU-T Y.1271 recommendation [9] proposes the basic requirements, features, and concepts for emergency telecommunications to restore a state of normality to avoid further risk to people or property. A key factor in designing a robust communications system with applications to emergency response is the development of a quick, easily deployable, and mobile infrastructure providing voice and data communications, available within the first 24 h, the most critical phase for crisis operations [10]. Common well-known requirements that ECS must meet are described in Table 1[11].
# Study of Global Navigation Satellite System Receivers' Accuracy for Unmanned Vehicles
## 1 Introduction
Using the same formula, the accumulated IMU error will increase to 1 m for 10 s.  
As the GNSS-only error is higher than the fusion of GNSS and IMU data for navigation [25], especially in urban environments due to multi-path signal interference and signal loss, it is essential to increase the GNSS accuracy and update rate as the long GNSS gaps reduce positioning accuracy distinctly [26]. There are multiple benefits of Kalman filters in UAV applications, such as position, altitude and velocity estimation, GNSS and IMU data fusion, and smoothing the sensor output orientation in the space, in terms of roll, pitch and yaw gyro-stabilized platforms in the navigation system, etc. Previous researchers [27] found that the accuracy of a Kalman filter used in an INS/GPS integration algorithm is increased by estimating the measurement covariance matrix, based on measurement data from GPS and the DOP parameters, which are functions of the diagonal elements of the covariance matrix of the parameters, expressed in a global or a local geodetic frame. As position accuracy mainly depends on the number of processed satellite signals, the number of satellite systems used, the number of frequency bands used (especially in urban environments) and antenna parameters, the current study will investigate the influence of different parameters on the localization by analyzing the dilution-of-precision (DOP) parameters. The comparison will be made for the following cases:  
* Reception of satellite signals from a single GNSS system. * Reception of satellite signals from multiple GNSS systems. * Analysis of DOP parameters of L1 and L1/L5 GNSS receivers. * Analysis of DOP parameters of each GNSS receiver with active patch and passive helical antennas. * Comparison of DOP parameters for GNSS receivers from one manufacturer, but from a different production generation, to evaluate the number of tracking satellites during the time.
## 3 Results
### Effects of CWP on Concrete Microstructure
#### 3.4.3 Thermogravimetric Analysis (TGA)  
[PERSON] et al. [52] used TGA to track mass loss stages, linking them to hydration products like C-S-H, CH, and ettringite. The results are shown in Figure 16. TGA confirmed a progressive reduction in CH content with increased curing days, supporting the XRD findings that CWP's pozzolanic activity of CWP refines the microstructure by consuming CH and producing additional C-S-H formation.  
Figure 15: XRD patterns of CWP-cement pastes. (**a**) XRD patterns at days (**b**) XRD patterns at 28 days (**c**) XRD patterns at 56 days [52].  
Figure 15: _Cont._  
Figure 16: TG curves of CWP-cement pastes. (**a**) TG curve at 7 days (**b**) TG curve at 28 days (**c**) TG curve at 56 days [52].  
[PERSON] et al. [56] indicated, through TGA analysis, distinct mass loss phases: bound water evaporation and CH decomposition. The largest mass loss corresponded to the release of bound water, affirming CWP's hydration activity. With a higher CWP content, more CH was consumed at later stages, highlighting TGA's ability to measure the pozzolanic reactivity of CWP in the concrete microstructure.