esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Classifications of Routing Protocols in UAV Networks
### Location-Based Routing Protocol
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Ref.** & **Algorithm** & **Main Contribution** & **Advantage** & **Limit** \\ \hline \multirow{4}{*}{Hybrid routing} & \multirow{2}{*}{[47]} & \multirow{2}{*}{LADR} & Applications in UAV & Future UAV locations are estimated based on location and speed information of GPS-integrated UAVs & Depends on accuracy of UAV position and speed estimates; less effective in GPS noisy environments \\  & & & & & \\ \cline{1-1}  & \multirow{2}{*}{[48]} & \multirow{2}{*}{RTORA} & Applications in highly & Quickly re-establish a new line; shortens route re-establishment time & Route re-establishment costs are high; when there are many changes, requires updated network information \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._UAV (S) chooses the current nearest neighbor UAV or the UAV after a period of time t. To move to the UAV (D), the routing process is deployed through UAVs (M), (K) and (N). The experimental results show that the proposed ABPP scheme can effectively reduce the beacon cost and improve the packet delivery ratio. Communication between UAVs is unreliable and often disrupted due to the high mobility of UAVs. The ability to predict the behavior of UAVs allows the development of scenario-based routing protocols. However, data transmission requires a complete routing path, which is difficult to implement due to the prediction of possible disconnections. The improved OLSR routing protocol proposed in [54] allows location-based modification to address the high mobility of UAV networks by taking into account both energy efficiency and frequent topological changes. The proposed protocol takes into account not only energy efficiency by considering the remaining energy and node degrees of the UAVs but also frequent topological changes by incorporating the 3D position information of the UAVs to calculate the link expiration time. The simulation results using NS-3 to evaluate the performance between OLSR and existing standard protocols such as \"P-OLSR\" and \"Improved OLSR-ETX\" showed that improving OLSR helps increase packet delivery ratio by 8% and 13.4%, and average throughput by 4.46% and 19.67%. In addition, routing cost is reduced by 6.47% and 4%, and end-to-end delay is reduced by 18.99% and 25.10%. Table 4 summarizes the advantages and limitations of some prominent routing algorithms based on the location of nodes in the UAV network, including their classification and roles in specific applications. Figure 12: Working mechanism of prediction-based routing protocol.
# INSPIRE-SAT 7, a Second CubeSat to Measure the Earth's Energy Budget and to Probe the Ionosphere
## 4 UVSQ-SAT CubeSat Observations in Orbit and Future Observations (INSPIRE-SAT 7, Terra F)
### UVSQ-SAT Real Observations and INSPIRE-SAT 7 Simulated Observations
The implementation of a future constellation (Terra-F) requires a mastery of calibrations to guarantee the absolute determination of top of atmosphere global net radiation budget. Moreover, the different trends for the average ERA 5 model come from the fact that the satellite is on a Sun-Synchronous orbit at a constant LTDN, while the model is averaged over the whole month without distinction of local times. This last point underlines the importance of a satellite constellation placed in Sun-Synchronous orbits at different local times to provide a regular spatial and temporal coverage. In Figure 8, the simulation of the OSR and OLR trends for a two-CubeSats constellation (INSPIRE-SAT 7) illustrates the improvement obtained. In this simulation, the first satellite has the same orbital parameters as UVSQ-SAT with a Sun-Synchronous orbit at 21:30 LTDN. The second satellite has an equivalent orbit, but at 09:30 LTDN. The simulated trends are very close to the average ERA 5 model. Figure 9 shows the result of the reconstruction after a few iterations of the simulation in the case of OSR. It shows the temporally interpolated map of the ERA 5 model at the time of the measurement (top left), the averaged ERA 5 map (top right), the simulated map considering the field of view characteristics of the UVSQ-SAT sensors (bottom left) and the difference between the simulation and the averaged ERA 5 map (bottom left).
## 4 GPS Application for Garbage Trucks Modeled as a Travelling Salesman Problem  
An operation with substantial importance for the handling of everyday scheduling of a city's traffic is the routing of garbage trucks from their depots to every dustbin on their routes and back to their depots. The optimal routes correspond to minimum required transportation time and minimum distance. Finding optimal routes typically proves to be time-consuming, especially in the case of metropolitan cities with very dense road networks. However, by exploiting recent advances from the field of metaheuristics, it is possible to attain efficient, near-optimal solutions in a short amount of time for many practical cases. We take advantage of the performance improvement brought by a novel metaheuristic procedure based on VNS. Our novel GVNS, in combination with the minimal required computational time, can provide a significantly enhanced the solution for these kind of problems. The incorporation of the enhanced VNS procedure within the GIS will lower the system's response time and provide close to optimal solutions.  
GIS technology integrates common database operations such as query and statistical analysis with the unique visualization and geographic analysis benefits offered by maps [36; 37]. Among other things, a GIS facilitates the modeling of spatial networks (e.g., road networks) offering algorithms to query and analyze them. Spatial networks are modeled with graphs. In the case of road networks, the graph'sarcs correspond to street segments, whereas the nodes correspond to street segment intersections. Each arc has a weight associated with it, representing the cost of traversing it.  
A GIS usually provides a number of tools for the analysis of spatial networks. It generally offers tools to find the shortest or minimum route through a network and heuristic procedures to find the most efficient route to a series of locations. Such a problem is typically modeled as an instance of the TSP. Our implementation solves efficiently the TSP problem, finding near-optimal solutions for a range of small, medium, or large benchmark problems. Distance matrix calculation can be used to calculate distances between pairs of nodes representing origins and destinations, whereas location-allocation functions determine site locations and assign demand to sites. These capabilities of GIS for analyzing spatial networks enable them to be used as decision support systems for the districting and routing of vehicles [38; 39].  
Routing a garbage truck from its depot to each dustbin and back is modeled by finding routes for the travelling salesman problem. The GIS will be used to find the optimal routes corresponding to minimum required transportation time. The GIS can also present the driver with directions corresponding to the routes generated. These directions will be transmitted to the garbage truck. In a real-time system, the time performance of the routing function is of vital significance. Metaheuristics, like our implementation presented here, can guarantee that.
## 4 System Model
By integrating lightweight cryptographic techniques--Chebyshev chaotic mapping--it becomes possible to design authentication protocols that are both computationally efficient and resource-friendly. This is particularly beneficial for environments with constrained devices, such as the Internet of Things (IoT). The fusion of blockchain and lightweight cryptography paves the way for a next-generation identity authentication system characterized by high scalability, strong security, and decentralized autonomy. In addition, our proposed scheme exhibits strong scalability and can support large-scale device authentication (e.g., thousands of devices across multiple management domains). As the number of devices within or across domains increases, additional edge gateways can be deployed in the corresponding domains. These new edge gateways can quickly join the consortium blockchain network and synchronize with existing gateways. Once synchronized, they can operate in parallel to handle intra-domain and cross-domain authentication requests, thereby significantly enhancing the overall scalability of the system.
## 1 Introduction
As GRACE alone cannot provide the degree one coefficient changes, the authors of [23] computed degree one coefficients through the combination of GRACE and outputs from the ocean models in order to improve the accuracy of mass variations derived from GRACE data. Based on their method, monthly estimates of degree one coefficients (hereafter, we call them SW) were prepared for GRACE RL05 data. The authors of [24] improved degree one coefficient estimates by optimizing the processing choices (i.e., the truncation of spherical harmonic coefficients, the width of coastal buffer zone), and for the first time by including a gravitational self-consistent mass redistribution. Estimates of degree one coefficients following [24] are now provided in TN13 for GRACE RL06 data published by each data processing center. As the optimized method utilized GRACE spherical harmonic coefficients of degree two and higher and GRACE-determined \(C_{20}\) were replaced with SLR-based \(C_{20}\) prior to the estimation of the degree one coefficients, there were two versions of degree one coefficients (hereafter, we call them SUN11, SUN14) separately corresponding to SLR-based \(C_{20}\) estimates in TN11 and TN14 for GRACE RL06 data published by each data processing center. Besides, [25] also reported improved estimates of degree one coefficients (referred to as SV) by combining time-variable gravity and ocean model outputs. Currently, it is unknown whether these improved estimates of degree one coefficients would cause discrepancies in GRACE-derived mass redistribution. The differences among mass change caused by the improved degree one coefficients and those previously derived from [23] are also unknown. Consequently, it is worthwhile to study the potential influence of these estimates of degree one terms on GRACE-derived mass redistribution. In this study, we first compute mass change separately caused by degree one terms, SLR-based \(C_{20}\), and GRACE-determined \(C_{30}\) estimates during the GRACE mission span, as well as GSFC \(C_{30}\) since March 2012. We then quantify the impacts of these low degree spherical harmonic coefficients corresponding to GRACE RL05 and RL06 data on mass change over the Greenland ice sheet (GrIS) and the Antarctic ice sheet (AIS), respectively. We finally assess the total influence of these low degree spherical harmonic coefficients on polar ice sheet mass variations. Our study could provide a reference for quantifying the impacts of these low degree spherical harmonic coefficients on GRACE-derived polar ice sheet mass variations.
# An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry
## 3 Results
### Single-Track Comparisons  
The tuned model can be compared to single-track autocorrelations \(\rho(\tau)\) of matched-sample double-differences using the modeled error autocorrelation \(\widetilde{\rho}_{mod}\) as described in Equation (12). We compare three exemplar tracks in Figure 4.  
The single-track autocorrelation behavior shown in Figure 4 is also revealing of the limitations of this work. The single-track error autocorrelation for double-differenced data is highly variable. This may be due to both artifacts in the data (processing, quality control, insufficient data), as well as the real behavior of the observation. It is worth articulating that the autocorrelation of data is generally less stable with smaller datasets and at longer lags. With our quality control parameters, we flag out significant quantities of data, which  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tuning Parameter** & **Magnitude** & **Function** \\ \hline \(\alpha\) & 0.005 & Relative magnitude of uncorrelated error \\ \(\beta\) & 0.01 & Relative magnitude of endpoint correlated error \\ \(\gamma\) & 1 & Relative magnitude of nearby roll-off \\ \(\delta\) & 1 & Steepness of roll-off component \\ \hline \hline \end{tabular}
\end{table}
Table 2: The chosen magnitudes of model tuning parameters for \(R_{mod}\).  
Figure 4: Comparison of the single-track error autocorrelation behavior for three selected tracks. The autocorrelation of the NBRCS double-difference (solid lines), untuned modeled error correlation \(\widetilde{\rho}_{mod}\), and tuned modeled error autocorrelation \(\widetilde{\rho}_{mod}\) (dashed lines) for each of the three tracks are shown. Each track is painted a different color. This figure demonstrates the wide variability of single-track autocorrelations of double-differences, arising from the variability of the observable, the limited amount of data in a single track, and the challenges in quality controlling sufficient observation data. It is possible that the correlated error does vary this much from track to track. In contrast, both the tuned and untuned modeled error autocorrelation \(\widetilde{\rho}_{mod}\) are much more stable from track to track.  
decreases the stability of autocorrelations from track to track. We further note the difficulty in exploring the dynamics of this behavior in a statistical sense: our aperture of observation where two CYGNSS assets are in a near-overlap condition is quite rare, and as the mission progresses, the orbit planes of the satellites have drifted, making further matchup scenarios less representative. These behaviors may evolve differently day-to-day or as a function of observed wind speed, but the paucity of data in this configuration makes it challenging to establish sufficient baselines to test for significance.
# Digital Twin Conceptual Model within the Context of Internet of Things
## 8 Use Case
These stakeholders will have different privileges to monitor the cars, namely, individual car owner, local dealer, state dealer, country dealer, and car manufacturer, as shown in Figure 4. In order to validate the performance of the proposed model, some evaluation metrics that can be used are accuracy for predicting failures in the vehicular sensors in real time, total latency and throughput for end-to-end communication from physical space to virtual space of digital twin and vice versa, speed for the analytics' model in the DAVL stage, and scalability. Another use-case application for the proposed model is in the context of a smart healthcare patient monitoring system wherein the vitals of a mobile human can be monitored continuously from the physiological sensors attached to the human's body. These sensors as well as human avatars will be replicated in the virtual space as a digital twin. Additionally, data about the patient's health history will be collected and stored as electronic medical records (EMRs) and electronic health records (EHRs) in the virtual space of the proposed model unlike the BOM data in industrial use. This medical record dataset can be accessed from [24]. The collective data from real-time sensors and health history dataset in the virtual space of the digital twin will help in making accurate predictions on the status of a human's health while he/she is in motion. The proposed end-to-end digital twin conceptual model in a smart healthcare application is demonstrated in Figure 5. A corresponding processing and analytics' paradigm for a smart healthcare digital twin in DAVL layer is represented in Figure 6.  
Figure 4: Use-case analytics’ model for vehicular digital twin in DAVL. Figure 5: Use-case analytics’ paradigm for a smart healthcare patient monitoring digital twin in DAVL. It is worth mentioning that the evaluation metrics for both use case models will remain the same.
###### Abstract  
This paper highlights recent developments and challenges on beam steering characteristics of reconfigurable transmitarray antennas. It introduces the operating principle of beam forming/beam steering high gain transmitarray antennas to enable the user to opt for economical and high performance solutions. A transmitarray antenna typically consists of a source antenna and a phase transformation structure. The incident waves generated from the source antenna is tilted using the phase transformation structure in a desired direction to steer the beam. Moreover, the phase transformation structure alters the incident wavefront to a plane wavefront using phase change characteristics. In order to steer a beam to a specific desired angle, it can be divided into two methods. There is a method of applying a transmitarray with a variable transmission phase change or a method of changing the shape of the wavefront of the source antenna. This type of beam forming/beam steering high gain antenna has been mainly studied from the point of view of high efficiency, low profile, and low cost. Several solutions of transmitarray unit cells have been presented in the literature, using PIN diodes, varactors, MEMS switches, and microfluids enable electronics to realize reconfigurable characteristics of transmitarray antennas. This paper analyzes the characteristics of various beam steering high gain reconfigurable transmitarrays (RTA) and highlights the future opportunities and challenges of the structure design for transmitarray antennas. This paper also highlights the challenges and gaps in terahertz and optical frequencies related to future work due to the structure complexity and lack of components' availability. Moreover, the challenges and limitations related to multi-bit structures and dual-band requirements are presented.  
reconfigurable transmitarray; beam steering; beamforming; PIN diodes; varactor; MEMS; dual-band transmitarray; multi-bit transmitarray
# Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
## 3 Results
### Monitoring Technologies
#### 3.3.5 Data Logging and Event Recording Systems  
Data logging and event recording systems, such as electronic logging devices (ELDs) and event data recorders (EDRs), have been pivotal in monitoring professional truck drivers, enhancing safety, and improving compliance with regulations. These systems provide real-time data on driver behavior and vehicle performance, offering insights for both immediate interventions and long-term safety improvements.  
[PERSON] et al. [39] explored the impact of ELDs on Canadian long-haul truck drivers, finding that ELD use significantly reduced driver fatigue and stress by ensuring compliance with hours-of-service (HOS) regulations. Drivers using ELDs reported improved sleep quality and less difficulty unwinding after work, suggesting a positive influence on their overall well-being. However, challenges such as limited parking infrastructure and concerns about income reductions due to stricter adherence to HOS regulations were noted, highlighting operational constraints.  
[PERSON] et al. [40] demonstrated the safety benefits of ELDs in a U.S.-based study, where trucks equipped with ELDs experienced an 11.7% reduction in total crash rates and a 53% decrease in HOS violations compared to trucks without these devices. The study attributed these improvements to the system's ability to limit falsification of driving hours, thereby reducing fatigue-related risks.  
Furthermore, [PERSON] et al. [41] evaluated the integration of event data recorders (EDRs) with training and feedback programs in Brazil, finding significant reductions in unsafe driving behaviors, such as speeding, and improvements in fuel efficiency. The study highlighted that EDRs are most effective when combined with tailored feedback and continuous training, reinforcing safe driving practices. Over a 13-month period, the frequency of speeding events decreased by 94.4%, underscoring the potential of EDR systems to reshape driving habits when used in conjunction with active managerial oversight.  
[PERSON] et al. [42] examined unintended consequences of the ELD mandate in the United States. While the mandate effectively reduced HOS violations, it also led to offense displace ment, where drivers compensated for lost productivity by engaging in riskier behaviors, such as speeding. This phenomenon was especially pronounced among small carriers, suggesting that increased monitoring in one area may inadvertently shift unsafe practices to other dimensions.  
In conclusion, data logging and event recording systems provide a robust framework for improving safety and compliance in the trucking industry. However, to maximize their impact, it is essential to address systemic challenges, ensure equitable access, and integrate these technologies with broader safety initiatives. Through a balanced approach, these systems can significantly contribute to reducing crash rates and promoting safer driving behaviors among professional truck drivers.
# A Nested U-Shaped Residual Codec Network for Strip Steel Defect Detection
## 1 Introduction
the decoder, dilated convolutions with different dilation rates were used in a bridge to enlarge the receptive field and capturing multiscale context information. The decoder is the U-shaped residual network proposed by [PERSON] et al. [13], since the network is able to capture more contextual information from different scales without significantly increasing the computational cost. The encoder-decoder forms a nested U-shaped residual codec network. In addition, the introduction of a residual refinement network allows the model to capture defective objects with clear boundaries. Overall, the advantages of the proposed NURCNet can be summarized as follows:  
1. We designed a fully convolutional residual network with a lightweight attention mechanism as the encoder to fully extract multiscale defect features which pay more attention to defective objects;
2. We introduced a U-shaped residual network proposed by [PERSON] et al. [13] as the decoder to capture more contextual information from different scales without significantly increasing the computational cost; the encoder and decoder form a nested U-shaped residual codec network;
3. We proposed a residual refinement network, which is used to further optimize the coarse saliency map of the lack of boundary information output at the encoder-decoder stage;
4. The thorough evaluation of the proposed NURCNet on a challenging strip steel dataset [14] indicates that our model achieves state-of-the-art results in both regional positioning and boundary recovery. The rest of this paper is organized as follows: after presenting the related work in Section 2, Section 3 describes the proposed method; Section 4 presents the experimental results and, finally, Section 5 concludes this work.
## 5 Result of Experiment
### Data Model Development
#### 5.2.4 Target Data Model Design  
The target data model step defines the best data model for each segmentation unit. All three subsystems fit into a combination of the key-value and document-oriented data model.  
The identified target data model is shown in Figure 16. One customer can have many agreements and each agreement can contain many instructions. Customers, agreements, and instructions are saved as documents in separate collections. The relations between the customers and agreements and relations between the agreements and instructions were defined as collections and also stored in separate collections.