esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing Digital Twin-Based Collision Avoidance:
## 3 Evaluation of Results
### Communication Efficiency Metrics  
This section compares the communication flows necessary for effective collision detection. System latency measurements for both centralized and decentralized models, as outlined in Figure 3, are described by  
\[\tau_{d} =\frac{1}{f}+\tau_{\text{rtt}} \tag{2}\] \[\tau_{\text{ca}} =\tau_{\text{cw}} =\frac{1}{f}+\tau_{\text{rtt}}+\tau_{\text{edge}} \tag{3}\]  
where \(\tau_{d}\) indicates the system latency in decentralized communication, \(\tau_{\text{ca}}\) and \(\tau_{\text{cw}}\) represent system latency in centralized scenarios with location aggregation and warning-only approaches, respectively. \(f\) denotes the RTLS rate, \(\tau_{\text{rtt}}\) the RTT in the 5G network, and \(\tau_{\text{edge}}\) the edge communication and computation delay. The rate of Radio Protocol Data Units (R-PDUs) on the channel is then defined as  
\[m_{\text{ca}}=2 fn\qquad m_{\text{cw}}=f(n+2P_{w}) \tag{4}\]  
In this study, we leverage the Network Slicing capabilities of 5G networks for these applications, as discussed in [14; 15]. The continuous nature of network traffic for collision warnings necessitates bandwidth management to ensure availability for other critical applications within the local 5G network. With this consideration, we constrain the collision warning application to a maximum bandwidth usage of 2 Mbit/s. For data transmission, we employ the Constrained Application Protocol (CoAP), as outlined in the RFC 7252 specification [16]. CoAP is characterized by its minimal frame size, making it suitable for our bandwidth constraints.  
As per Table 2, the total packet size relating to the bandwidth limitation gives a maximal amount of 2 Mbit \(s^{-1}\) / 81 B = 3086 packets per second. Decentralized approaches with numerous trucks face transmission issues not aligned with bandwidth constraints, as demonstrated in Figure 5. Centralization scales transmissions linearly, enhancing performance. Either collision warnings alone can be transmitted, or location information can be selectively sent in dual systems to conserve bandwidth.  
Figure 4: Comparison of distance traveled between collision avoidance methods. Using zoning is shown to be better, while the dynamic braking method is the most efficient.
# PTA-Sync: Packet-Train-Aided Time Synchronization for Underwater Acoustic Applications
## 4 Conclusions  
This study proposed PTA-Sync, which adopts the one-way message exchange method for reducing the effect of propagation delay asymmetry as well as minimizing the message overhead for synchronization and delay time. To precisely track the propagation delay changes caused by the node's mobility, the change in propagation delay is continuously estimated using the reference node's position information and the moving node's 3D linear velocity vector, in addition to the transmission time difference and reception time difference information for each packet section transmitted continuously. The simulation results showed that PTA-Sync demonstrated more outstanding performance in relation to one-way communication-based schemes in terms of synchronization accuracy. Further research is verifying the performance of PTA-Sync through real sea experiments by applying the protocols to an underwater communication system. Furthermore, we will conduct research on a more advanced synchronization protocol by adding an algorithm that estimates the uncertainty of underwater propagation delay through a statistical method. The proposed protocol is expected to be practical in underwater acoustic applications with extremely long propagation delay. In addition, by increasing the accuracy of time synchronization, data quality or protocol accuracy can be improved in underwater acoustic networks.  
Conceptualization, A.-R.C.; Investigation, A.-R.C.; Validation, A.-R.C.; Writing--original draft preparation, A.-R.C.; Writing--review and editing, Y.C.; Visualization, A.-R.C.; Supervision, Y.C. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Time error measured at 30 s after synchronization versus \(Nr\) when \(V\_mean=1\) m/s, 2.5 m/s, 5 m/s, \(N\_ex=10\), \(I\_tx=1.2\) s, \(T=30\) s, \(\alpha=0.5\).  
**Funding:** This work was supported by a grant from the Endowment Project, \"Development of Core Technology for Cooperative Navigation of Multiple Marine Robots and Underwater Wireless Cognitive Network\", funded by the Korea Research Institute of Ships and Ocean engineering (PES4370).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 5 5G and the Edge Cloud
### Data/Video Analysis in the Edge  
In [49], the authors stated that large-scale video analytics could be the \"killer application\" of edge-clouds. The authors proposed a video analytics system called Rocket, which can operate real-time and has low resource cost. Although their system is designed for generic purposes, the initial application focuses on traffic planning. The rocket tries to estimate the video queries' resource-accuracy profile and optimize the video pipeline. The resource manager provides the access to all resources (CPU, GPU, network, etc.) through geodistributed executors to the query pipelines. To further reduce their system's resource demand, the authors developed additional application-level techniques such as intelligent frame selection and intelligent feed selection.  
Authors of [50] provide a highly dynamic system, that is, a modular based on a scalable architecture that relies on lightweight virtualization techniques. The proposed architecture consists of three main layers: sensing (edge devices), mediation (gateways), and enterprise layer (cloud). This architecture is validated through a typical smart home scenario, where different IoT end-devices communicate with the cloud through a gateway (GW), acting as a mediator, caching the data locally and lightening the edge-devices' resource usage.  
Firework [51] provides distributed data processing and sharing via a virtual shared data view and service composition for Internet of Everything (IoE) applications. Firework collects and concatenates data and predefined functions from multiple stakeholders and data owners to a virtual shared data set with the care of data privacy protection. This novel framework can split an application into \"subservices\" enabling users to subscribe for intermediate data and create new applications by leveraging existing \"subservices\". In addition, it provides an easy-to-use programming interface for both service providers and end users. The experimental results of [51] article demonstrate how Firework can reduce response latency and network bandwidth cost for a video analytics application compared to a cloud-centric solution.  
Figure 5: The OpenFog reference architecture: N-tier fog deployment [42].
# A Method for Autonomous Generation of High-Precision Time Scales for Navigation Constellations
## 5 Simulation Results and Analysis  
### Experimental Analysis of Time Scale Algorithm  
The experimental data in this paper used the 300-s precision clock difference data provided by the Center for Orbit Determination in Europe (CODE), and the data collection time is from 2220 weeks to 2226 weeks of GPS. Satellites G1, G3, G4, G6, G9, G11, G14, G18, G24, G27, G30 are rubidium clocks; satellite numbers are G2, G5, G7, G12, G15, G16, G17, G19, G20, G21, G22, G29, G31 are cesium clocks. In this paper, clocks are divided into two clock groups according to their types: rubidium clock group and cesium clock group. The two clock groups use the KPW time scale algorithm respectively, and use Equation (11) to assign weights. The two time scales obtained are TA(Rb) and TA(Cs), respectively, and the frequency stability of the single clocks in their respective clock groups are shown in Figures 4 and 5.  
Figure 4: Frequency stability of rubidium clock group.  
Figure 5: Frequency stability of cesium clock group.  
Figure 6: Timescales calculated for different \(\tau\).  
This paper intends to adopt the time-frequency steering algorithm, using TA(Cs) to drive TA(Rb), the short-term stability and long-term stability of the time scale after driving depend on TA(Rb) and TA(Cs), respectively. Therefore, the genetic algorithm is used to optimize the sampling interval \(\tau\), so that the stability of TA(Rb) is optimal before \(8\times 10^{4}\), and the stability of TA(Cs) is optimal after \(8\times 10^{4}\) s. In the end, the overall stability of the time-frequency steering algorithm is a relatively good time scale. Let the fitness function of the GA of the rubidium clock group be \(f(\mathrm{P})=\sigma_{TA(P)}(2\times 10^{4})\), the fitness function of the genetic algorithm of the cesium clock group be \(f(\mathrm{P})=\sigma_{TA(P)}(5\times 10^{5})\), and the optimal range of both be \([\;300\sim 1.5\times 10^{5}]\). The results of the genetic algorithm simulation are as follows:  
The red point in Figure 7 is the minimum fitness obtained by the genetic algorithm, and the two points correspond to 9300 and 132,659, respectively. The two red curves in Figure 7 are the stability of the time scale calculated using the minimum fitness value. It can be seen from the left figure of Figure 8 that when \(\tau=9300\), the frequency stability of TA(Rb) is the smallest in the interval [300-8 \(\times\) 10\({}^{4}\)], and it can be seen from the right figure of Figure 8 that when \(\tau=132,659\), the frequency stability of TA(Cs) is the smallest in the interval [\(8\times 10^{4}\)-\(1\times 10^{6}\)].  
Next, use the improved time-frequency steering algorithm in Section 4 for time-frequency steering, let TA(Cs) be used as the steering reference, and TA(Rb) be used as the signal to be steered. The specific simulation results are introduced in detail in Section 5.2.  
Figure 8: The frequency stability of the time scale of the fitness-optimal \(\tau\).  
Figure 7: Genetic algorithm fitness.
## 3 Results and Discussion
### Evaluation of Environmental Effects
#### 3.3.3 Environmental Effects of Triple-Axis Loading  
Table 7 shows the comparison of temperature stresses and coupling stresses in different seasons under triple-axis loading. From Table 7, the temperature-load coupling stresses show an increase, followed by a decrease in the depth direction in spring and winter seasons. The maximum depth is 4 cm below the road surface. It shows a gradual decrease in fall. Along the depth direction, the degree of influence of temperature stress on the coupling stress manifestation shows a gradual decrease in winter and a decrease followed by an increase in spring and fall. A minimum of 14% is reached at 9 cm. Overall, the degree of temperature influence on the road surface along the depth direction is 69.3%, 41.3%, 26.7%, and 23.7%, in that order.
## 4 Results and Discussion
For instance, although triple-constellation (GPS+GLONASS+BDS) PPP using CODE's COM ID provides a significant improvement over GPS-only and GPS+GLONASS, when the other triple-constellation (GPS+GLONASS+Galileo combination) was considered, no significant improvement has been achieved. * Similarly, quad-constellation (GPS+GLONASS+Galileo+BDS) PPP with GFZ's precise products provides the best results in all quad PPP solutions(Table 6). From this point of view, there is no doubt that additional satellites generally have improved the positioning performance; however, based on these results it is concluded that the PPP positioning accuracy greatly depends on the precise satellite orbit and clock products applied by the user. * As described above, the PPP coordinates were calculated using GrafNav software multi-pass strategy. In this approach, the coordinates are calculated as forward, then reverse, and then  
Figure 11: 2D position (**left**) and height (**right**) differences for Scenario 8 German Research Centre for Geosciences (GFZ)-GREC solution on the trajectory. forward again. Therefore, in this study, the effect of additional GNSS constellations on the PPP convergence time was not investigated. * The findings imply that kinematic multi-constellation GNSS PPP technique allows to reach cm- to decimeter-level of accuracy in a challenging dynamic environment.
## 6 Conclusions  
In this study, a simulated platform for top coal caving was developed, and coal and gangue images under the influence of dust were systematically collected. An improved DeeplabV3+ model for coal and gangue image segmentation was proposed, alongside a dehazing method that integrates dark channel prior and Gaussian weighting. This methodology effectively addressed the dehazing and segmentation of coal and gangue images affected by dust. The specific conclusions drawn from the study are as follows:  
1. A similar simulation platform for roof coal mining was established, and a coal gangue image collection scheme was designed. Considering the environmental characteristics of dust in fully mechanized mining faces, a method for creating different dust  
Figure 14: Underground coal gangue image de-fogging segmentation effect.  
concentration gradient environments in the laboratory was proposed, along with a corresponding image acquisition approach;
2. An improved DeepLabV3+ model for coal gauge image segmentation was developed. The original Xception backbone in DeepLabV3+ was replaced with EfficientNetV2, and an enhanced FPN feature pyramid network, along with the ECA attention mechanism, was introduced to improve the model's ability to extract boundary features in coal gangue images;
3. The improved coal gangue segmentation model outperformed the original model and other comparative models in multiple metrics. Compared to the original DeepLabV3+ model, the mean accuracy increased by 1.85%, the mean Intersection over Union (mIoU) improved by 0.92%, and the mean pixel accuracy increased by 1.05%. Additionally, the model's parameter count was reduced to 28.51 M, and the floating-point operations decreased by 31.97%;
4. A dual dark channel combined with Gaussian-weighted transmission estimation dehazing method was applied to study the effects of different dust concentrations on coal gangue image recognition. The recognition accuracy for coal improved by 6.8% to 71.8% after dehazing, and the accuracy for gangue improved by 5.8% to 45.8%. A dehazing segmentation application was also conducted on actual coal gangue dust images from underground coal mines, validating the model's practicality. This model can be effectively applied to coal gangue segmentation in dust-laden environments in coal mines, contributing to the advancement of smart mining technologies.
## 3 Results and Discussion  
### Specifics of Measurements on Selected Devices  
Three Raman instruments, defined as laboratory, modular, and handheld, were used in this work. Each of these selected devices has evident specificity associated with the Raman spectra measurement and related to their design, excitation wavelength, spectral resolution, and other properties. These differences are visible in the example of raw Raman spectra of a calf thymus DNA sample measured with these instruments and adjusted to a unified Raman intensity scale (Figure 2). The handheld Ahura FirstDefender instrument possessed a one-scan Raman spectrum of relatively high noise and low spectral resolution but with several well-resolved signals in the chosen working region of 600-1800 cm\({}^{-1}\). The portable i-Raman Plus instrument produces a Raman spectrum with more intense bands and higher signal-to-noise levels corresponding to the spectrum obtained with the Ahura instrument. Finally, the laboratory DXR SmartRaman instrument produces Raman spectra of the highest resolution and band intensity about noise but needs more measuring time to collect enough scans to obtain a spectrum of good quality. A high-fluorescence background occurred in all cases, and linear mathematics baseline correction was applied to obtain well-representative spectra.  
A size decrease in Raman spectrometers leads to a simplification of the design and, therefore, to a deterioration in the measurement parameters and the quality of the Raman spectra [26]. It is necessary to compare the spectra obtained on all these instruments to assess the analytical capabilities using the examples of the reference biological macromolecules, colon tissue, and colorectal polyps.
## 5 Conclusions  
This paper proposes an MPC method for NPC converters to accomplish multiple objectives using single reference voltages without using a weighting factor in a cost function. The reference voltages in the proposed MPC algorithm consist of a difference-mode term and a common-mode term in each phase. The difference-mode term and the common-mode term are responsible for sinusoidal source current synthesis and capacitor voltage balancing, respectively. By simply comparing the single reference voltage with all possible voltage candidates producible in the single-phase three-level NPC converter, an optimal state for the dc voltage balance as well as the sinusoidal source current control with unity power factor can be selected at every step. As a result, the proposed method is free from the multi-objective optimization issues and the empirical approaches to selecting proper weighting factors that accompany conventional MPC methods for NPC converters. The proposed method is easy to implement since it has a deterministic approach for capacitor voltage balance as well as sinusoidal source current control, guaranteeing fast dynamic performance. The proposed method was verified experimentally for a single-phase three-level NPC converter.  
Conceptualization, S.K.; methodology, S.K. and E.-S.J.; software, E.-S.J.; validation, E.-S.J.; formal analysis, E.-S.J.; investigation, S.K. and E.-S.J.; resources, S.K.; data curation, E.-S.J. and M.N; writing--original draft preparation, E.-S.J. and M.H.N.; writing--review and editing, M.H.N.; visualization, E.-S.J.; supervision, S.K.; project administration, S.K.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (2020R1A2C1013413).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Conventional Method** & **Proposed Method** \\ \hline Computational time (\(\mu\)s) & 5.262 & 5.263 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computational complexity comparison between the conventional and proposed methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Criteria** & **Conventional Method** & **Proposed Method** \\ \hline
**Weighting Factor Tuning Procedure** & **Yes** & **No** \\ \hline Steady-state & Source current THD (\%) & 2.99 &
\begin{tabular}{c} 2.89 \\ (slightly better) \\ \end{tabular} \\ performance & Capacitor voltage & Good & Good \\  & balancing & Fast & Fast \\  & Capacitor voltages & Fast & Fast \\  & balancing speed & & No \\  & Reference tracking & Fast & Fast \\  & Overshoot & No & No \\  & Computational time (\(\mu\)s) & 5.262 & 5.263 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summarized comparison between the conventional and proposed methods.
## 3 Background
### [PERSON] et al. introduced the FIRE model in [27], naming it after \"fides\" (Latin for \"trust\") and \"reputation\". We selected FIRE as a reference model for comparison with CA because it is a well-established trust and reputation model for open MASs that, like CA, adopts a decentralized approach. Additionally, FIRE represents the traditional trust management approach, where trustors select trustees, providing a contrast to the CA model, where trustees are not chosen by trustors. It consists of four key modules:  
* Interaction Trust (IT): Evaluates an agent's trustworthiness based on its past interactions with the evaluator.
* Witness Reputation (WR): Assesses the target's agent's trustworthiness using feedback from witnesses--other agents that have previously interacted with it.
* Role-based Trust (RT): Determines trustworthiness based on role-based relationships with the target agent, incorporating domain knowledge such as norms and regulations.
* Certified Reputation (CR): Relies on third-party references stored by the target agent, which can be accessed on demand to assess trustworthiness.  
IT is the most reliable trust information source, as it directly reflects the evaluator's satisfaction. However, if the evaluator has no prior interactions with the target agent, FIRE cannot utilize the IT module and must rely on the other three modules, primarily WR. However, when a large number of witnesses leave the system, WR becomes ineffective, forcing FIRE to depend mainly on the CR module for trust assessments. Yet, CR is not highly reliable, as trustees may selectively store only favorable third-party references, leading to the overestimation of their performance.
# Contextual Background Estimation for Explainable AI in Temperature Prediction
## 1 Introduction
### Limits of XAI
#### 1.2.1 Base Value
* If someone wanted to know why a model predicted an electrical load of 15 GW for next Wednesday, while it normally predicts 10 GW for Wednesdays at this time of year, a base value of 50 GW or 0 GW would provide no useful insight. * If a model predicted that tomorrow's temperature would be 25 \({}^{\circ}\)C, whereas the average temperature for the week was 18 \({}^{\circ}\)C, a base value of 0 \({}^{\circ}\)C would not enable explaining this difference effectively. As illustrated in Figure 3, all three examples mentioned above are regression-based problems with real-valued predictions. However, predicting real-estate prices can be conducted using a single vector of features \(\mathbf{x}\), where the features might include distance from the city center (\(x_{1}\)), crime rate (\(x_{2}\)), age of the estate (\(x_{3}\)), and so on. Figure 3: Schematic description of difference between (**a**) single-vector input and (**b**) time-series sequence input. Electrical load and temperature predictions are similar in nature as both rely on time-series data to capture seasonality and generate forecasts. They use features such as temperature (\(x_{1}\)), humidity (\(x_{2}\)), wind speed (\(x_{3}\)), and so on, but the key difference between predicting prices and those two problems is that they require a matrix of features (**M**) that represents each feature over a time period instead of one vector. This matrix, that is, **time-series sequence**, has rows representing different features and columns corresponding to time steps from the past, (\(t_{1}\) to \(t_{jmax}\)). For example, suppose that, to predict the temperature for the next day, the model needs to consider the temperature, humidity, wind speed, pressure, and precipitation from the past six days. In this case, the time-series sequence consists of five rows (\(i_{max}=5\)) and five columns (\(j_{max}=6\)), providing the dimensions of the time-series sequence as \(\mathbf{M}_{5\times 6}\).