esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 8 Conclusions and Future Work  
The presented work compares the state-of-the-art SOC estimators based on Kalman filter variants, EKF and UKF, to the proposed data-driven PHM methodology. The latter outperforms the former ones consistently across ten different tested datasets, which reproduced different deployment scenarios and load conditions.  
The proposed approach contributes a solid framework to handle noisy measurements which are common in real-time systems limited by measurement device capabilities. By enriching the original dataset with their weighted moving averages selected across different time spans, it was shown that the SOC estimator can attain better results and eliminate inherent noise interferences.  
Another important aspect of the framework was to look carefully at the relevant features at the model tuning phase and propose, by using additive features contribution techniques, those which are meaningful for the process. That has the benefit of reducing  
Figure 7: Execution times for the selected methods.  
the computational cost of the system and provides insights on the system behavior by displaying the endogenous variables that are more impactful for the system operation.  
The results in this paper propose the use of a bidirectional LSTM as the most effective neural network for this application, which agrees with [14; 37; 38; 49].  
It is seen in Table 3 that the proposed data-driven PHM framework outperforms the state-of-the-art methods using Kalman filters for the tested datasets, by having a higher correlation with the real values, and by displaying a higher \(R^{2}\) (near the unit value) and lower MSE. For very stringent load profiles, as in the US06 and the neural network drive cycles, the UKF matches the PHM performance, but does not outperform it.  
For the SOC estimation error comparison, the PHM framework surpasses the ones reported in the literature [9; 10; 16] with mean absolute errors (MAE) below 2% for all methods; however, the data-driven PHM reached 0.40% and 0.47% for the reported drive cycles with the best MAE across the methods.
# An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs
## System Model
### Energy Model  
We use the radio model applied by [PERSON] and [PERSON] [25] where the transmitter divides the energy to run the radio electronics and power amplifier and the receiver also divides the energy only to run the radio electronics. In this model the energy usage for transmitting and receiving l bit of data will be as follows:  
\[E_{tx}=l(E_{elec}+\propto d^{n}) \tag{1}\]  
\[E_{rx}=l(E_{elec}) \tag{2}\]  
where \(l\) is the length of the transmitted/received message in bits. \(\propto\) denotes the energy dissipated by the op-amp in data transmission and n is the path loss exponent and depends on the specific propagation. For example, in free space, this value will be 2. \(d\) is the distance between the destination and source. Another parameter _Eelec_ denotes the electronic energy depending on the digital coding, filtering and spreading of the signal. The parameters are listed in Table 1.  
Figure 1: Clustered corona-based WSN (C is the corona number and \(\theta\) denotes the angle of each sector).
# Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services
## 4 Results
### QoE Quantization Performance
#### 4.4.1 Time Delay  
The sensitivity of QoE to time delay can be calculated by  
\[\Psi_{DL}=\frac{\partial QoE}{\partial DL}=-\alpha\beta ab\cdot\exp(-\beta\cdot QoS +b\cdot DL) \tag{16}\]  
We assume that \(DL\) lays in the range of \([0,1000]\), meaning the time delay is between 0 and 1000 milliseconds. The sensitivity curves of five services are drawn in Figure 4, and it can be seen that their absolute values have the following relation, \(\left\{\left|\Psi_{DL}|(\text{IM}),\left|\Psi_{DL}|(\text{VC}),\left|\Psi_{DL} |(\text{LA}),\left|\Psi_{DL}|(\text{OG}\right)\right.\right\}>\left\{\Psi_{DL} (\text{LO}),\left|\Psi_{DL}|(\text{MV}),\left|\Psi_{DL}|(\text{LV})\right. \right\}>\left\{\left|\Psi_{DL}|(\text{OV}),\left|\Psi_{DL}|(\text{OA}),\left| \Psi_{DL}|(\text{FS}),\left|\Psi_{DL}|(\text{WB}\right)\right.\right\}\right.\right.\) Also, note that in the range \([0,400]\) ms of \(DL\), the on-demand video service and on-demand audio service curves are approximately coincident, which lead to the following conclusions.  
1. The values of all the curves in the entire time delay analysis domain (0 to 1000 ms) are less than 0, indicating that the increase of time delay has a negative effect on all QoEs of the network services, and will lead to the attenuation of QoE.
2. The QoE of IM, VC, LA, and OG is the most susceptible to time delay, followed by LO, MV, and LV. Live services are most sensitive to time delay, whereas other traffic, like OV, OA, FS, and WB services, are the least affected, which correctly matched with our intuitive experience. For instance, web pages opening one second earlier will lead to the same reading experience as the one opened a second later, but people will certainly feel bad for the buffer time while watching live video.
3. In the range of 0 to 400 ms, the effect of time delay on the on-demand services (on-demand video and on-demand audio) is almost the same, but when the time delay continues to increase, the QoE deterioration of on-demand video is more pronounced. A reasonable explanation to this phenomenon is the time-out retransmission mechanism. The amount of data that the video service has to retransmit is larger than other services, and due to the limitation of the throughput rate, it is unable to meet a high-load data transmission.
## 1 Introduction
Selecting a proper subset of nodes firstly satisfies the Sink request for the coverage of a desired area, and secondly, it puts unselected nodes in sleep mode and saves their energy, because not setting all nodes in active mode will prolong the lifetime of the lower-tier. In subsection 4.2, an algorithm named Fair Camera Node Selection algorithm (FCNS) performed by upper-tier sensors is presented to provide a good coverage of field in a way that each node is selected an almost equal number of times in multiple requests from Sink. Uniform selection of camera sensor nodes that avoids the early failure of some of them makes good coverage available for a longer time. A comprehensive description of its construction method is presented in subsection 4.2.1. All in all, by using image stitching in WMSNs based on the contributions presented in this paper, high-resolution images with wide field of view are provided while WMSNs constraints are considered. Moreover, contributed methods reduce energy dissipation and let the network preserves its functionality for a longer period of time. However, presented methods have high performance when there are not a lot of changes in the network topology like camera sensors locations. Therefore, these methods fit WMSNs applications whose sensor nodes have no mobility and whose cameras are placed in a fixed location. Analysis and simulations yielded expected results. The rest of the paper is organized as follows. Section 2 presents the related work. The topology of Two-Tier Camera-Based WMSNs is presented in Section 3. The methodology of generating high-resolution images with minimum energy waste in camera-based WMSNs, including image stitching technique and camera sensor node selection algorithm, is introduced in Sections 4. Section 5 provides simulation and experimental results. Finally, Section 6 concludes the paper.
# A Novel Dataset for Multi-View Multi-Player Tracking in Soccer Scenarios
## 3 The System and The Datasets
### Annotations Process  
\(1+N\)**MVMOT annotation strategy.** Manually labeling a multi-view multi-player tracking dataset is a time-consuming task.  
Traditional labeling methods, as seen in other datasets listed in Table 1, employ an interval sampling approach for all camera views. This may lead to incomplete trajectory data during algorithm evaluation. However, labeling all views frame by frame would require a significant investment of time and resources. Our \(1+N\) MVMOT annotation strategy allows us to meet evaluation requirements while also saving on annotation costs. Annotating on the main camera view frame by frame (20 fps) is sufficient to obtain the complete trajectory of all players, as it provides full coverage of the field. At this point, training MVMOT algorithm models to perform tasks such as cross-view ReID matching and cross-view data association, only requires annotating the down-sampled frames of auxiliary camera views at 1 fps. Finally, an evaluation is conducted on the main view to ensure evaluation stability and reduce ambiguity.  
More specifically, we firstly used the public annotation tool DarkLabel [81] to annotate each target frame by frame in the main camera views. Next, the pre-trained detector performed preliminary player detection on the auxiliary views. After that, we used self-designed annotation software to load the annotation data of the main camera view and the detection predictions of the auxiliary views. Finally, we adjusted the detection predictions of the auxiliary views and matched them to the corresponding identities in the main view. It is worth noting that the software also loads the calibration parameters of all cameras, which enables quick positioning and bounding box connections across all camera views. Further details of the self-designed software are described in Figure 4. Since annotating the multi  
Figure 3: Visualization of the camera calibration precision. Details are illustrated in Section 3.2.  
view multi-player dataset requires annotators to mentally perform spatial transformations, these calibration parameters can significantly reduce annotation errors.  
Each player in the dataset was assigned a unique identity, regardless of the number of times they appear. The objective of multi-view multi-player tracking is to identify the complete 3D trajectory of a particular player across a camera network. To provide a rigorous evaluation of the trackers, it was decided that these unique IDs would be used as the benchmark, which requires greater precision compared to standard tracking tasks.
## 3 Materials and Testing
### Sample Preparation and Testing Results  
In this study, 150 asphalt concrete samples of 2.5 inches in height and 4.0 inches in diameter were prepared according to the standard Marshall method ASTM D6927 [42]. The samples were mixed for each blend at five trials of asphalt cement contents (ACs%) of 3.5%, 4.0%, 4.5%, 5.0%, and 5.5% by total mix weight. Six replicate samples were prepared at each AC% trial and compacted with 75 blows per side by a Marshall hammer. The volumetric measurements, including the air void content (Va%), were determined for each sample. The mixtures' samples at each trial AC% were divided into two groups of triplicate samples of unconditioned (control) and conditioned specimens. The control samples were submerged in water for 30 min at 60 \({}^{\circ}\)C before they were tested for Marshall stability. The conditioned samples were tested after being submerged in water for 24 h at 60 \({}^{\circ}\)C. The RSI value of each sample group was then determined using Equation (1). The average RSI results are summarized in Table 3.  
\[\text{RSI}=\frac{S_{1}}{S_{2}}\times 100\% \tag{1}\]  
where \(S_{1}\) is the stability of the conditioned samples after being submerged in water for 24 h and \(S_{2}\) is the stability of the control samples after being submerged for 30 min.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Property**} & \multicolumn{6}{c}{**Aggregate Blends**} & \multicolumn{2}{c}{**Criteria**} \\ \cline{2-7}  & **Mix A** & **Mix B** & **Mix C** & **Mix D** & **Mix E** & **(Jordan’s MPWH)** \\ \hline Bulk specific gravity of combined aggregate (dry) & 2.531 & 2.557 & 2.613 & 2.601 & 2.545 & na \\ \hline Apparent specific gravity of combined aggregate & 2.694 & 2.706 & 2.718 & 2.711 & 2.701 & na \\ \hline Moisture absorption of combined aggregate (\%) & 1.334 & 2.079 & 3.771 & 2.521 & 1.858 & na \\ \hline Filler proportions (\%) & 2.62 & 4.97 & 7.34 & 6.06 & 3.72 & 2–8\% \\ \hline Abrasion loss (500 revolutions) (\%) & 25.2\% & 28.3\% & 26.5\% & 27\% & 25.8\% & 35\% max. \\ \hline Ratio of wear loss (100/500) (\%) & 20\% & 22\% & 19\% & 23\% & 22\% & 25\% max. \\ \hline Sand equivalent (\%) & 62.7\% & 65.5\% & 61.8\% & 63.2\% & 62.5\% & 50\% min. \\ \hline Clay lumps (\%) & 0.7\% & 0.4\% & 0.5\% & 0.4\% & 0.6\% & 1\% max. \\ \hline Soundness by sodium sulfate (\%) & 5.1\% & 5.4\% & 4.1\% & 4.6\% & 4.2\% & 9\% max. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Aggregate mixes’ characteristics and physical properties.  
It should be mentioned that 75% of RSI is the minimum satisfactory limit for a durable mix of asphalt [14; 43; 44]. The RSI results indicated that some testing groups of samples were prone to moisture damage by means of average RSI values of less than 75%. This supports the fact that the moisture sensitivity of the mixes would be influenced by varying the mix properties of Ab%, F%, AC%, and Va%.
## 3 Experimental Results and Analysis
### Sensitivity Analysis of 21 Geometric Errors  
The geometric error measurement results can be expressed as polynomial functions related to the position coordinates of the machine tool. Considering the nonlinear distribution of the measurement results, a third-order polynomial was employed to fit the geometric error elements, with the exception of the squareness error which remained constant and did not require fitting. After fitting, the geometric errors were substituted into Equation (1) to obtain the volumetric errors of the TH5656, as depicted in Figure 3, wherein the intervals for the \(X\)-, \(Y\)-, and \(Z\)-axes were set to 80 mm, 28 mm, and 35 mm, respectively. A total of 216 points were selected, and different colors were utilized to represent the magnitude of the volumetric errors. It can be observed that the ranges of \(\Delta x\), \(\Delta y\), and \(\Delta z\) were \(-23.26\)\(\upmu\)m to \(11.27\)\(\upmu\)m, \(-15.12\)\(\upmu\)m to \(3.56\)\(\upmu\)m, and \(-5.31\)\(\upmu\)m to \(5.29\)\(\upmu\)m, respectively. Then, the distribution of the influence factors of each geometric error element on the volumetric errors of the TH5656 can be calculated by substituting the magnitude of the 21 geometric errors from Table 2 into Equation (5). The resulting distribution of impact factors is presented in Figure 4. It reveals that the volumetric error \(\Delta x\) is primarily influenced by \(S_{XY}\) and \(S_{\text{XZ}}\), with the sum of their impact factors being 0.66, as shown in Figure 4a. Similarly, the volumetric error \(\Delta y\) is primarily affected by \(\delta_{y(X)}\), \(\varepsilon_{\alpha(X)}\), and \(\delta_{y(Y)}\), with a cumulative impact factor of 0.87, as shown in Figure 4b. For the volumetric error \(\Delta z\), it is mainly influenced by \(\delta_{z(X)}\), \(\varepsilon_{x(X)}\), and \(\delta_{z(Z)}\), with a cumulative impact factor of 0.95, as shown in Figure 4c. These errors are the identified key error elements. Thus, we can prioritize the measurement and compensation of these errors to improve error compensation efficiency and ultimately improve the machining accuracy.  
Figure 3: Volumetric error distribution of TH5656: (**a**) volumetric error \(\Delta x\) in \(X\)-direction; (**b**) volumetric error \(\Delta y\) in \(Y\)-direction; (**c**) volumetric error \(\Delta z\) in \(Z\)-direction.  
Figure 2: Comparison results: (**a**) yaw of \(X\)-axis; (**b**) position error of \(Y\)-axis; and (**c**) straightness of \(Z\)-axis.
## 5 Future Work  
In future work, we plan to design and test a larger (more suitable for measurements) hexacopter unmanned aircraft, conduct experiments with measurement equipment, perform radiation measurements of mobile communication systems, and evaluate the shortcomings of radiation measurement equipment. Additionally, efforts will be made to develop, improve, and enhance the operation of the ground station.  
Here are the key points that support the future and current evaluation methods:  
* In addition to radiation characteristics, we could measure signal quality metrics, such as Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), and throughput. These metrics should provide a more holistic view of the base station's performance and its ability to maintain high-quality communication.
* Our experiment included real-time monitoring of the base station's performance, allowing us to detect and address issues promptly. This dynamic approach ensures that performance evaluations are not static and can adapt to changing conditions.
* Furthermore, it is possible, with the help of mobile operators, to evaluate the base station's performance under different user densities and traffic loads. This aspect is critical, as it reflects real-world usage and helps in assessing the base station's capacity to handle peak loads.
# Harnessing Spatial-Frequency Information for Enhanced Image Restoration
## 6 Conclusions  
In this paper, we propose FreANet, which selectively extracts features from multiple domains and captures long-range spatial dependencies for image restoration. Specifically, we introduce a multi-domain feature extractor (MDFE) composed of spatial and frequency branches. Each branch employs strip convolutions with large kernels in parallel. Furthermore, we propose a multi-scale pooling attention (MPA) mechanism that learns interactions among elements in the spatial embedding space, utilizing global average pooling for efficiency. Finally, our FreANet demonstrates outstanding performance on 11 benchmark datasets for various restoration tasks, including defocus deblurring, motion deblurring, dehazing, and low-light enhancement.  
Conceptualization, C.-H.P., H.-D.C. and M.-T.L.; methodology, C.-H.P., H.-D.C. and M.-T.L.; software, C.-H.P.; validation, H.-D.C. and M.-T.L.; formal analysis, C.-H.P. and H.-D.C.; investigation, H.-D.C. and M.-T.L.; resources, C.-H.P.; data curation, C.-H.P. and H.-D.C.; writing--original draft preparation, C.-H.P.; writing--review and editing, H.-D.C. and M.-T.L.; visualization, C.-H.P.; supervision, H.-D.C. and M.-T.L.; project administration, H.-D.C.; funding acquisition, all authors. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by Seoul National University of Science and Technology.  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Enhancing the SRAM PUF with an XOR Gate
## 4 Results
### SRAM Device Characteristics
#### 4.2.1 Temperature Effects  
Temperature testing is essential for the comprehensive electrical characterization of SRAMs. Given that temperature variations can significantly affect the performance and reliability of electronic devices, it is crucial to evaluate the behavior of PUFs under varying thermal conditions.  
To assess the robustness and reliability of the SRAM devices, we conducted thorough tests and characterizations at three distinct temperatures: 0 \({}^{\circ}\)C, 23 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C. To isolate the impact of temperature on the SRAM devices and minimize the influence of external measurement circuitry, we employed flat flex cables and adapters during the temperature tests. This approach enabled us to focus solely on the thermal effects on the SRAM, providing a clear understanding of its isolated thermal behavior.  
We utilized the Associated Environmental Systems SD-501 temperature chamber to create a wide range of temperature conditions, spanning from \(-37\)\({}^{\circ}\)C to 180 \({}^{\circ}\)C. By examining the behavior of the PUFs across this temperature range, we aimed to gain insights into the effects of temperature variations on their reliability. This knowledge will facilitate informed decisions.  
The temperature significantly affects the classification of SRAM cells, determining whether they are classified as strong/weak '1's, '0's, or 'flaky'. On average, there was a 10.09% difference in SRAM cell classification between enrollments conducted at 0 \({}^{\circ}\)C and enrollments conducted at 23 \({}^{\circ}\)C. Similarly, the difference in classification between enrollments conducted at 23 \({}^{\circ}\)C and enrollments conducted at 80 \({}^{\circ}\)C was 18.66%. Notably, the difference in classification between enrollments conducted at 0 \({}^{\circ}\)C and enrollments conducted at 80 \({}^{\circ}\)C was 34.10%. However, even stable cells exhibited a relatively small but still significant change in cell type (strong '0', '1', or super-flaky). On average, there was a 0.26% difference in SRAM cell type between enrollments conducted at 0 \({}^{\circ}\)C and enrollments conducted at 23 \({}^{\circ}\)C. Similarly, the difference in cell type between enrollments conducted at 23 \({}^{\circ}\)C and enrollments conducted at 80 \({}^{\circ}\)C was 2.91%. Notably, the difference between enrollments conducted at 0 \({}^{\circ}\)C and enrollments conducted at 80 \({}^{\circ}\)C was 13.52%. These results correlate with increased thermal noise, affecting the stability of each cell.  
A heatmap illustrating the change between temperatures in a subsection of an SRAM device's enrollment can be seen in Figure 9.  
\begin{table}
\begin{tabular}{c c c c} \hline
**1’s Count** & **0 \({}^{\circ}\)C** & **23 \({}^{\circ}\)C** & **80 \({}^{\circ}\)C** \\ \hline
0 & 37.015\% & 35.490\% & 34.164\% \\
1–480 & 11.553\% & 13.027\% & 14.985\% \\
481–520 & 0.405\% & 0.457\% & 0.530\% \\
521–999 & 12.551\% & 14.194\% & 16.159\% \\
# Optimal Configuration and Path Planning for UAV Swarms Using a Novel Localization Approach
## 6 Conclusions  
In this paper, we have provided an algorithm for UAV path planning based on TDOA localization. The receiver measurement model and distance-dependent noise were presented, and optimal geometry based on CRLB was investigated in both static and movable scenarios. A hybrid SDP method and EKF estimator were applied to locate the emitter and the online sensor management presented here was particularly useful for TDOA measurements. The knowledge of optimal sensor-emitter geometries provided useful tactical information and revealed important insights into the impact of sensor-emitter geometries on the performance of emitter localization and tracking. Simulation results showed that the UAV complied with distance and angle rules when looking for an optimal path. The optimized path was able to provide accurate and stable localization.  
For future work, we will consider the long-term optimization, i.e., multi-step optimization, which may bring burdens for the reference receiver. Future work will also include obstacle avoidance in real applications, which may affect the localization accuracy.  
Figure 7: **(a) Optimal paths for dynamic emitter location and tracking. (b) Comparison of localization performance: optimal deployment and fixed deployment.**  
**Acknowledgments:** This research was funded by the National Natural Science Foundation of China (Nos. 61472443, 61502522) and Shaanxi Province Lab of Meta-synthesis for Electronic & Information systems. The authors sincerely thank the anonymous reviewers for their valuable and constructive comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.