esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This paper proposed a probabilistic framework that could be adopted for robust stability and performance analysis of VSCs for power system applications. It provided the desired statistical information that could better reflect a system's performance under different uncertainties and disturbances.  
In addition, it provided the risk of system instability or the likelihood of a specific situation, which is of utmost importance for power system design and utilization.  
The proposed probabilistic framework was applied to an LCL-filtered grid-connected VSC to evaluate the applicability of the proposed methodology and broaden the available knowledge on the robustness of VSCs. The obtained results showed that a grid SCR could vigorously limit a system's stability and performance; hence, under the lower grid SCR, PDFs of stability indices became wider and moved to the unstable area, meaning there was a higher sensitivity in the critical mode to grid inductance variations and smaller stability margins. It was also concluded that, although stability and performance were not affected by the inverter power level in a strong grid, it was remarkably affected in a weak grid. In weak grids, the stability margin might not be sufficient under lower inverter power levels. Moreover, it was found that the PLL bandwidth selection was a compromise between the fast dynamic response and stability margin. Lower PLL bandwidths resulted in thinner PDFs, meaning lower coupling between the VSC and grid, but the transient response was slow and the stability margin might not be adequate. Higher PLL bandwidth results in faster transient response and broader PDFs, revealing more coupling between the grid and VSC and higher sensitivity to grid inductance variations. In summary, based on the proposed probabilistic sensitivity assessment, a system's dynamic behavior under different system uncertainties and disturbances was entirely investigated. Moreover, the main phenomena and reasons behind each observation were fully explained.  
Conceptualization, H.G.-K., P.D., M.N. and F.B.; methodology, H.G.-K.; software, H.G.-K.; validation, H.G.-K.; formal analysis, H.G.-K. and P.D.; investigation, H.G.-K., P.D., M.N. and F.B.; resources, P.D. and F.B.; data curation, H.G.-K.; writing--original draft preparation, H.G.-K.; writing--review and editing, H.G.-K., P.D., M.N. and F.B.; visualization, H.G.-K.; supervision, F.B., P.D. and M.N.; project administration, H.G.-K.; funding acquisition, F.B. and P.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Reliable Power Electronic-Based Power System project at AAU Energy, Aalborg University, Aalborg, Denmark, as a part of the Villum Investigator Program funded by the Villum Foundation.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Comparison of Results
### Experiments and Results for the Benchmark Control Problem
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Minimum** & **Maximum** & **Average** & **Std.** & **Experiments** \\ \hline Results of GAs optimization FLC & \(4.08\times 10^{-1}\) & \(5.70\times 10^{-1}\) & \(4.37\times 10^{-1}\) & \(5.01\times 10^{-2}\) & 29 \\ Type-1 [47] & \(4.08\times 10^{-1}\) & \(5.70\times 10^{-1}\) & \(4.37\times 10^{-1}\) & \(5.01\times 10^{-2}\) & 29 \\ \hline Results of GAs optimization FLC & \(4.11\times 10^{-1}\) & \(4.01\times 10^{-1}\) & \(4.01\times 10^{-1}\) & \(3.25\times 10^{-3}\) & 30 \\ Type-2 [47] & \(4.08\times 10^{-1}\) & \(5.70\times 10^{-1}\) & \(4.40\times 10^{-1}\) & \(4.89\times 10^{-2}\) & 37 \\ \hline Ga optimization FLC Type-2 [48] & \(3.99\times 10^{-1}\) & \(4.11\times 10^{-1}\) & \(4.01\times 10^{-1}\) & \(3.40\times 10^{-3}\) & 37 \\ \hline ASRank + CONVCONT [49] & \(2.90\times 10^{-4}\) & - & \(1.31\times 10^{-2}\) & - & 30 \\ \hline S-ACO [49] & \(9.82\times 10^{-2}\) & - & \(1.20\times 10^{-1}\) & - & 30 \\ \hline _Resulted ghost_ PSO optimization FLC Type-1 [50] & \(1.51\times 10^{-1}\) & \(1.90\times 10^{0}\) & \(2.44\times 10^{-1}\) & \(3.79\times 10^{-1}\) & 21 \\ \hline _Resulted ghost_ PSO for optimization del FLC Type-2 [50] & \(1.60\times 10^{-1}\) & \(1.61\times 10^{-1}\) & \(1.61\times 10^{-1}\) & \(2.56\times 10^{-4}\) & 16 \\ \hline OFDE with noise FLC & \(3.95\times 10^{-4}\) & \(1.50\times 10^{0}\) & \(8.78\times 10^{-2}\) & \(2.68\times 10^{-1}\) & 30 \\ \hline OFHS with noise FLC & \(2.00\times 10^{-4}\) & \(1.26\times 10^{0}\) & \(2.37\times 10^{-1}\) & \(3.07\times 10^{-1}\) & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Comparison of results with other metaheuristics. FLC Type-2  
The hypotheses would be as follows: \(H_{o}:\ \mu_{1}\ \geq\mu_{2}\) and \(H_{a}:\ \mu_{1}\ <\mu_{2}\ (\textit{Claim})\). In this case, we can say that there is enough evidence for all values below the critical value Z or -1.645. Analyzing the results of the statistical tests for both methods used (OFDE and OFHS), we can note that, in all cases, our proposal has significant statistical evidence of being the better method when compared to the three methods of literature.
###### Abstract  
The Unit Commitment Problem (UCP) is a critical component of power market decision-making and is typically formulated as Mixed Integer Programming (MIP). Given the complexity of solving MIPs, efficiently solving large-scale UCPs remains a significant challenge. This paper presents a hybrid Graph Neural Network (GNN)-Linear Programming (LP) framework to accelerate the solution of large-scale Unit Commitment Problems (UCPs) while maintaining the quality of solutions. By analyzing variable stability through historical branch-and-bound (B&B) trajectories, we classify MIP variables into dynamically adjustable stable and unstable groups. We adopt an MIP formulation that includes multiple types of binary variables--such as commitment, startup, and shutdown variables--and extract additional information from these auxiliary binary variables. This enriched representation provides more candidates for stable variable fixation, helping to improve variable refinement, mitigate suboptimality, and enhance computational efficiency. A bipartite GNN is trained offline to predict stable variables based on system topology and historical operational patterns. During online optimization, instance-specific root LP solutions refine these predictions, enabling adaptive variable fixation via a dual-threshold mechanism that integrates GNN confidence and LP relaxations. To mitigate suboptimality risks, we introduce temporally flexible fixation strategies--hard fixation for variables with persistent stability and soft fixation allowing limited temporal adjustments--alongside a GNN-guided branching rule to prioritize unstable variables. Numerical experiments demonstrate that jointly fixing commitment, startup, and shutdown variables yields better performance compared to fixing only commitment variables. Ablation studies further validate the importance of hard fixation and customized branching strategies, especially for large-scale systems.  
unit commitment acceleration; mixed integer programming; fast solution; neighborhood search; graph neural networks +
Footnote †: journal: Applied sciences  
Article
# A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN
###### Abstract  
At present, the interaction mechanism between the complex indoor environment and pseudolite signals has not been fundamentally resolved, and the stability, continuity, and accuracy of indoor positioning are still technical bottlenecks. In view of the shortcomings of the existing indoor fingerprint positioning methods, this paper proposes a hybrid CSI fingerprint method for indoor pseudolite positioning based on Ray Tracing and artificial neural network (RT-ANN), which combines the advantages of actual acquisition, deterministic simulation, and artificial neural network, and adds the simulation CSI feature parameters generated by modeling and simulation to the input of the neural network, extending the sample features of the neural network input dataset. Taking an airport environment as an example, it is proved that the hybrid method can improve the positioning accuracy in the area where the fingerprints have been collected, the positioning error is reduced by 54.7% compared with the traditional fingerprint positioning method. It is also proved that preliminary positioning can be completed in the area without fingerprint collection.  
CSI fingerprint; ANN; pseudolite; ray tracing; indoor positioning +
Footnote †: journal:  
00 000-0001-4000-0000][PERSON], [PERSON], [PERSON], [PERSON], [PERSON]  
CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN. _Future Internet_**2022**, _14_, 235. [[https://doi.org/10.3390/614080235](https://doi.org/10.3390/614080235)]([https://doi.org/10.3390/614080235](https://doi.org/10.3390/614080235))
# A Method for the Rapid Propagation of Emergency Event Notifications in a Long Vehicle Convoy
## 3 Simulation Environment and the Convoy VANET System
### Convoy VANET System
#### 3.4.1 Senders  
Every node contains at least one sender: the vehicle state (VS) sender. This sends information analogous to a BSM: position, velocity, and heading. This information is sent every 100 ms plus a random delay between 0.01 ms and 0.50 ms. The start time for every node's sender is randomly offset by a value between 0 s and 1 s. The randomness on the timings is mainly to prevent transmission collisions due to the perfect synchronization that is possible in a simulation. See the paragraph regarding the routing output function in Section 3.4.3 for additional information regarding the VS message transmissions.  
The lead node contains an additional sender: the SM sender. An SM is a set of data that we are trying to propagate quickly, such as indicating a hard braking maneuver. The sender is initialized with a schedule of SMs to send at various times. For the tests described below (Section 4) a new SM is sent every 5 s starting at 20 s into the simulation. Upon starting an SM sending event, the SM sender will attempt to broadcast the SM every 10 ms, only stopping on the 10 th transmission. However, the routing protocol will determine if the SM needs to actually be sent (see Section 3.4.3).
## 7 Results  
### Memory Footprint  
The system memory comprising SRAM and flash is segmented into various regions, with allocations to both secure and non-secure environments, as illustrated in Table 4. The flash memory mapping for the secure world is determined by the SM, which enforces a predefined memory configuration that cannot be modified by the user. This mapping includes the SM and STuROT firmware images, the ITS file system, and the ITS provisioning area. The SRAM memory required for the SM is 140 kB and the flash memory footprint is 360 kB. Additionally, a shared buffer (NS/S) area is allocated for data exchange between the non-secure application and the SM. This buffer is sized to a maximum of 16 kB to optimize the hashing time of the TFLite model buffer, which is processed in chunks of 12 kB.  
The flash memory for the non-secure environment primarily contains the application components, with the TFLite interpreter occupying the largest portion of 242 kB. The size of the ML models varies depending on their hyperparameters and quantization settings. The difference in size between quantized and non-quantized models is approximately three to four times smaller for the quantized versions, offering a significant reduction in memory usage. Nevertheless, apart from the benefit of reduced memory usage, quantization comes with a penalty in model accuracy [43].  
The SRAM footprint of the model refers to the memory allocated for tensors, which includes the input, output, and intermediate data used during model inference. The size of this memory area depends on the target architecture and must be explicitly defined. To determine the optimal memory allocation, we use an open-source divide-and-conquer algorithm [44]. This helps in efficiently sizing the tensor memory required for the model's execution, considering the constraints of the target system.  
The STuROT supports dual-slot boot for both the SM and non-secure firmware images, enabling over-the-air firmware updates. This dual-slot configuration effectively doubles the available flash memory. With a total of 2 MB of flash memory, the SM and application components occupy 718.2 kB, leaving 1281.8 kB for the ML model in a single-slot setup. However, in the dual-slot configuration, this available memory is divided, leaving only 640.9 kB for an ML model. As shown in Table 4, the AE-32, AE-64 (both quantized and non-quantized), and AE-128 quantized models can fit within this available flash memory in the dual-slot configuration. However, larger models, such as the non-quantized AE-128, exceed the available memory limits.
# Relative Positioning in Remote Areas Using a GNSS Dual Frequency Smartphone
## 1 Introduction
[PERSON] & [PERSON] (2020) [8] showed, using a dual frequency smartphone, accuracies of 5 cm for GPS L1 solutions using a reference station a few kilometers away with a survey time of around one hour. Other authors developed works with smartphones with other proposes, [PERSON] et al. [9] had done experiments to assess the observations quality and performance of GNSS standalone positioning with dual frequency smartphones, while [PERSON] et al. [10] demonstrated the impact of real-time regional ionospheric correction model can have in the smartphone positioning and [PERSON] et al. [11] analyzed the antenna limitations in a smartphone-based GNSS positioning approaches. The authors [PERSON] & [PERSON] [12] recently published a work about the opportunities and perspectives that the use of GNSS smartphones can be employed in different types of applications in the areas of mapping, navigation, cadastral, and in a wide range of works with georeferencing needs. Some of these works present results of differential positioning using very short baselines and, in general, are based on single frequency measurements, in our experiments we want to assess a long baseline survey methodology with the dual frequency. In this work, we want to show that dual frequency smartphones can be used as low-cost GNSS surveying tools for relative phase positioning in remote areas, with decimeter accuracy even with distances to a reference station of several hundred kilometers. This is of interest in some areas of the world, such as Africa, South America, or the Antarctic,where the networks of reference stations are sparse and using some of the common GNSS positioning techniques may be difficult, namely the real-time positioning mode (RTK), which relies on short baselines between the rover and the reference station and requires the use of more expensive high-grade geodetic equipment. Looking to these challenges and new opportunities we demonstrate in these work that, even using long distance reference stations, positioning with accuracies better than 30 cm is possible using a smartphone. As could be expected, with the increase of the baseline length the GNSS solutions, obtained from relative positioning, show a decrease in quality. A way to mitigate that is to use dual frequency observations provided by some smartphones, namely the Xiaomi Mi8. This device allows to record Galileo and GPS dual frequency (E1/L1 and E5a/L5), code, and phase measurements, which can then be explored for quick ambiguity resolution, cycle slips correction, and minimization of ionospheric errors. As we rely on the use of dual frequency measurements, this work only focuses on the use of the Galileo and GPS systems, discarding the GLONASS and BeiDou because there were no smartphones in the market recording double frequency GLONASS or BeiDou phase measurements. This paper is structured in five sections. After this introduction, the experimental setup adopted is described in Section 2 and the signal analyses in Section 3, while Section 4 shows the results obtained and Section 5 present the conclusions.
## 3 Results and Discussion
### DP590, 780, and 980 Steels
Therefore, it exhibited a lower hardness than the RZ (Figure 2). In the TZ, the peak temperature was slightly below Ac\({}_{1}\), resulting in the formation of tempered martensite (Figure 3c). Figure 4 shows the weld cross-section of the DP780 steel welds by zone. The microstructure of the DP780 BM has a finer grain size of the ferrite matrix and a higher volume fraction of martensite compared with that of the DP590 steel (Figure 4b). The RZ is rapidly cooled after heating above the Ac\({}_{3}\) temperature, causing martensitic transformation, increasing the martensitic volume fraction, and making it harder than the BM (Figure 4e). Because the PRZ has a peak temperature between Ac\({}_{1}\) and Ac\({}_{3}\), it partially transforms to austenite during heating. Subsequently, during cooling, bainite, martensite, and a polygonal ferrite structure were formed (Figure 4d). In the TZ, the peak temperature was lower than Ac\({}_{1}\); hence, a tempering effect can be observed, resulting in grain growth and tempered martensitic formation (Figure 4c). Figure 2: Hardness profiles for DP590, DP780, and DP980 steels. Figure 5 shows the weld cross-section of the DP980 steel welds by zone. Compared with the previously observed DP590 and DP780 steel sheets, the microstructure of the DP980 steel has a finer grain size of the matrix ferrite, a larger volume fraction of martensite, and a higher carbon content, resulting in a relatively higher hardness than BM (Figure 5b). The RZ microstructure has a fully martensitic structure after being heated above Ac\({}_{3}\) and rapidly cooled for martensitic transformation (Figure 5e). In the PRZ, as shown in Figure 5d, the peak temperature is between Ac\({}_{1}\) and Ac\({}_{3}\), and only partial phase transformation occurs, but most of the observation region is austenized and recrystallized as a martensitic structure during cooling. In the TZ, since the peak temperature is lower than Ac\({}_{1}\) and because of the local tempering of the metastable martensite, the hardness decreases (Figure 5c). Figure 4: Microstructure of the DP780 steel (F: ferrite; B: bainite; PF: polygonal ferrite; M: martensite). (**a**) Joint micrograph; (**b**) BM; (**c**) TZ; (**d**) PRZ; (**e**) RZ. Figure 3: Microstructure of the DP590 steel (F: ferrite; M: martensite). (**a**) Joint micrograph; (**b**) BM; (**c**) TZ; (**d**) PRZ; (**e**) RZ.
# LoRaWAN Network for Fire Monitoring in Rural Environments
## 3 LoRa Technology Overview
### LoRaWAN
#### 3.2.2 Regulation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **LoRa** & **Wifi** & **ZigBee** & **SigFox** & **NB-IoT** \\ \hline \multirow{4}{*}{Frequency} & 868 MHz (EU); & & 868 MHz (EU); & 868 MHz (EU); & \\  & 915 MHz & 2.4 GHz and 5 & 915 MHz & 915 MHz & 915 MHz & Depends on the frequency \\  & (USA); 433 & GHz & GHz & MHz (Asia); 2.4 MHz (Asia) & 433 & licensed to LTE \\ \hline \multirow{2}{*}{Standard} & IEEE802.15.4g, & \multirow{2}{*}{IEEE802.11} & \multirow{2}{*}{IEEE802.15.4} & \multirow{2}{*}{SigFox (Owner)} & \multirow{2}{*}{3 GPP Standard} \\  & LoRa Alliance & & & & \\ \hline \multirow{4}{*}{Coverage} & 5 km (urban),20 km (rural) & 50 m (indoor), & & 10 km (urban), & 1 km (urban), \\  & & 40 km (outdoor, & as a function of & 40 km (rural) & 10 km (rural) \\  & & the visibility) & & & \\ \hline \multirow{4}{*}{Modulation} & LoRa, FSK, & BPSK, QPSK, & & & QPSK, OFDM \\  & GFSK & 16 QAM, 64 QAM, 256 QAM, 1024 QAM & BPSK, OQPSK & BPSK, GFSK & (DL, SC-FDMA (UL) \\ \hline \multirow{2}{*}{Power consumption} & \multirow{2}{*}{Low} & \multirow{2}{*}{High} & \multirow{2}{*}{Medium-Low} & \multirow{2}{*}{Low} & \multirow{2}{*}{Low} \\  & & & & & \\ \hline \multirow{4}{*}{Theoretical Data Transfer Rate} & \multirow{2}{*}{22 kbps (LoRa), (GFSK)} & 2.4 Gbps (IEEE802.11 ax, 2 streams with 1024 QAM) & 250 kbps at 915 MHz & 20 kbps at 915 MHz & 20 kbps at 2.4 \\  & & 2.4 Gbps (IEEE802.11 ax, 2 streams with 1024 QAM) & 250 kbps at 915 MHz & 20 kbps at 915 MHz & 20 kbps \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Price of end \\ devices \\ \end{tabular} } & \multirow{2}{*}{3–5 \(\epsilon\)} & \multirow{2}{*}{3–5 \(\epsilon\)} & \multirow{2}{*}{2–5 \(\epsilon\)} & \multirow{2}{*}{\(>\)2 \(\epsilon\)} & \multirow{2}{*}{\(>\)20 \(\epsilon\)} \\  & & & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Price of** \\ **Gateway/** \\ **Base Station** \\ \end{tabular} } & \multirow{2}{*}{20–600 \(\epsilon\) Base } & \multirow{2}{*}{40–1000 \(\epsilon\)} & \multirow{2}{*}{4000 \(\epsilon\) Base } & \multirow{2}{*}{15000 \(\epsilon\) Base } \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of technologies used in IoT.
## 3 Bridge Element Maintenance Decision Optimization Model
### Sequential Decision Making Model for Bridge Project-Level Maintenance Decisions  
#### 3.2.1 Bridge Element State Sets  
The state set represents all the possible states of a bridge's elements, and it also corresponds to the technical condition levels of the elements. Since a definition of element technical condition levels is not provided in \"Standards for Technical Condition Evaluation of Highway Bridges\" (JTG/T H21-2011) [26], this paper refers to the classification boundaries of bridge element technical conditions as are defined in JTG/T H21-2011. Moreover, this paper defines five states for bridge elements that correspond to five technical condition levels, as shown in Table 1.  
At time \(T\), the ratio of the number of elements in each state to the total number of elements forms the state distribution vector \(D_{T}\).  
\[D_{T}=\bigg{\{}\frac{C_{1}}{C},\frac{C_{2}}{C},\frac{C_{3}}{C},\frac{C_{4}}{C },\frac{C_{5}}{C}\bigg{\}}. \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Element Technical Condition Score** & \multicolumn{5}{c}{**State Set**} \\ \hline \multirow{3}{*}{EBCI} & State 1 & State 2 & State 3 & State 4 & State 5 \\ \cline{2-6}  & [95; 100] & [80; 95] & [60; 80] & [40; 60] & [0, 40) \\ \hline \hline \end{tabular}
\end{table}
Table 1: SDP state set.  
With respect to the elements of the vector \(\frac{C_{1}}{C}\), \(C_{i}\) represents the number of elements in state \(i\) at the current year and \(C\) represents the number of elements at the current year.
# Multi-Timescale Control of Variable-Speed Wind Turbine for Inertia Provision
## 4 Simulation Results
The frequency nadir is 49.73 Hz, and the maximum RoCoF is \(-\)0.17 Hz/s, which is not as satisfactory as the results of the previous case. Finally, the speed recovery control bandwidth was designed as 1 Hz, which is much faster than the inertia response. Figure 15 displays the simulation results. It is observed that the wind turbine rotor speed quickly recovers to 300 rad/s, even when it is subjected to the load change disturbance. Therefore, the mechanical energy of the turbine rotor does not effectively contribute to power grid frequency regulation. The scenario is similar to Figure 12, with a frequency nadir of 49.73 Hz and a maximum RoCoF of \(-\)0.2 Hz/s. Figure 14: Simulation results when the DFIG system provides inertia support and the speed recovery control bandwidth is 0.1 Hz. (**a**) Grid frequency; (**b**) grid RoCoF; (**c**) rotor angular speed. (**d**) DC-link voltage. Figure 15: Simulation results when DFIG system provides inertia support and the speed recovery control bandwidth is 1 Hz. (**a**) Grid frequency; (**b**) RoCoF; (**c**) rotor speed; (**d**) DC-link voltage. To summarize, Table 2 compares the simulation results. It is found that the maximum frequency error and maximum RoCoF for Case 2 are comparatively small. At the same time, the maximum rotor speed and DC-link voltage error for Case 2 are comparatively large. The DFIG for Case 2 satisfies the multi-timescale control principle proposed in this paper, whereas other cases do not. As a result, the mechanical energy stored in the rotor can be effectively released to support frequency regulation. This explains why the rotor speed error for Case 2 is larger and the frequency error/RoCoF are smaller.