esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Simulation and Experimental Methods
### Experimental Setup for Infrared Thermal Imaging  
An experimental platform was constructed to validate the simulation and detection methods (Figure 3). Defective BGA samples were prepared on FR-4 PCBs using a 4 \(\times\) 4 solder ball array, with missing and bridged solder balls manually introduced. Thermal images were captured using a FLUKE TiS55 infrared imager (Everett, WA, USA) and a VECTECH V-1515 heater (Xiamen, China). Imaging parameters--including emissivity, ambient temperature, and transmittance--were calibrated using SmartView Classic 4.4. To ensure consistency with the simulation, the experimental setup adopted similar thermal boundary conditions: a uniform top-surface heating at 100 \({}^{\circ}\)C or 200 \({}^{\circ}\)C and ambient air maintained at \(\sim\)20 \({}^{\circ}\)C to match the convective cooling conditions. This alignment enables reliable comparison between experimental and simulated thermal results.
# Dual-Path Beat Tracking: Combining Temporal Convolutional Networks and Transformers in Parallel
## 4 Feature aggregation and classification block
On RTX 4090 GPU, it took 20 h on average for a single fold to converge when trained in an 8-fold cross-validation manner following [6; 10]. In our work, we train on whole sequences with varying lengths, allowing the model to capture the full temporal structure of the data. A training instance corresponds to spectrograms derived from a single music file. For cross-validation, each dataset is split into eight parts based on the number of files in the dataset. This ensures that each fold contains an approximately equal number of files, regardless of sequence length, while ensuring separation between the training and testing data in each iteration of the cross-validation. Since we use four different datasets (Ballroom [34], Hainsworth [35], Harmonix [36], and SMC [37]) for training, we jointly train on 4 \(\times\) 7 parts (all folds except one per dataset) and predict on the remaining four parts of the corresponding dataset. After eight runs, each part is used as the test set once, covering all files in the dataset. We then present the average metrics across all runs to provide a comprehensive evaluation. For the GTZAN dataset, which is used for testing only, we compute the final results by averaging the outcomes from each of the eight runs. Following feature learning, a Dynamic Bayesian Network (DBN) infers the beat's period and phase. In our work, the default settings of the Madmom library [41] were used, which is a tempo range of 55-215 beats per minute, and the transition-\(\lambda\), which aims to control the ability of the model to react to tempo changes at a value of 100. Most recent work follows a common pipeline: the audio files are transformed into some spectrogram-like representation, and then a deep neural network predicts frame-wise beat and downbeat probabilities, which are post-processed to obtain the final predictions. It is important to note that the PGM-based post-processing methods such as DBN or Particle Filtering are not mandatory; however, studies attempting to infer beats removing DBN have noted that removing the DBN hurts the CMLt and AMLt metrics or requires the model to be overconfident in their prediction, typically indicating overfitting, or the accuracy clearly falls behind those that use DBN [12]. It is also noteworthy to mention several music pieces are inherently bound to fail: pieces with time signature changes, pieces whose tempo falls outside of the tempo range (or that slow down/speed up outside the tempo range), and pieces whose number of beats per measure are not included in the list of supported values. However, due to the common practice of using DBN-based post-processing, we keep it following the convention followed by most papers in beat and downbeat tracking.
# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
## 4 Results
### Statistical Validation
#### 4.1.1 Average HCV Delay  
Reducing delay is a primary objective of the platoon-based signal-optimization algorithm. The analysis examined how the algorithm improves HCV delay compared to both actuated signal control and the individual vehicle-based approach across different connected vehicle (CV) penetration levels.  
The results as shown in Table 2 indicate that the platoon-based algorithm significantly reduces HCV delay under both under-saturated and over-saturated conditions. In under-saturated demand conditions, the algorithm achieved a 50% reduction in delay compared to actuated signal control, with statistical significance (\(p<0.001\)). When compared to the vehicle-based approach, the reduction was 30% (\(p<0.01\)). Under over-saturated demand conditions, the delay reduction was 30% relative to actuated signal control (\(p<0.05\)), while the improvement over the vehicle-based method was 18% (\(p<0.05\)). These reductions highlight the efficiency of the platoon-based approach in minimizing delay across varying traffic scenarios.
## 1 Introduction
By adopting the embedded Markov chain and focusing on instances when the symbol queue is empty, we simplify our derived Markov chain into a one-dimensional Markov chain, with the data queue length being the system state. We investigate the analytic expression of average power consumption per slot in the embedded Markov chain. To derive the hard-delay-constrained policy, two heuristic algorithms are proposed. We utilize gradient descent, based on the policies obtained from these heuristic algorithms, to achieve an enhanced hard-delay-constrained policy that effectively strikes a balance between delay constraint and power consumption. Through simulations, we have also verified that our presented policy outperforms the traditional on-demand transmission policy and CSIR policy. The rest of this paper is organized as follows: Section 2 illustrates the system model. The finite blocklength coding with a cross-layer approach is investigated in Section 3. In Section 4, a heuristic algorithm is proposed to obtain the hard delay constraint policy. Numerical results and concluding remarks are presented in Section 5 and Section 6, respectively. The research flow chart of this paper is shown in Figure 1.  
Figure 1: Research flow chart.
# Kalman Filter and Wavelet Cross-Correlation for VHF Broadband Interferometer Lightning Mapping
## 4 Conclusions  
Broadband interferometry for the two-dimensional mapping of lightning progression was used to examine the effects of different factors on the accuracy of the lightning maps. In this paper, various lightning signal proceeding approaches are compared, including different filtration methods and cross-correlation techniques, introducing interpolation to effectively estimate the fractional time of arrival delay realizing the location of VHF lightning radiation sources in two dimensions. Simulations were conducted to provide enough analysis when comparing the performance of implemented approaches for estimating lightning maps. Aggregated results show that the CCWD outperforms the other correlation methods in estimating the TDOAs regardless of the interpolations. This is because the wavelet transform not only can reduce the noise but also weakens the effects of the peak resolution of cross-correlation. The filtering methods also show comparable performance with wavelet denoising, which shows slightly better performance when using Symlet function. Several improvements of the current study could be achieved by involving other parameters, such as tuning the sliding window size, the windowing function, the amount of overlap.  
Conceptualization, A.A. and M.R.A.; methodology, A.A.; software, A.A. and F.N.; validation, A.A.A., M.R.A., Z.K. and M.R.M.E.; formal analysis, A.A.; investigation, M.H.M.S.; resources, M.H.M.S. and S.A.M.; data curation, M.H.M.S.; writing--original draft preparation, A.A.; writing--review and editing, A.S.A.-K.; visualization, V.A.; supervision, M.R.A. and A.A.A.; project administration, M.R.A.; funding acquisition, A.A.A. and M.R.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Universiti Tenaga National (UNITEN) under BOLD2025 fund, in part by the Ministry of Higher Education, Malaysia, under Fundamental Research Grant Scheme (FRGS) (FRGS/2018/FEKEKK-CETRI/F00361), and in part by Universiti Teknologi Malaysia (UTM) under Q04G19 and R4F966 research grants (FRGS).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Related Work
### Multicast Mobility Support  
The multicast mobility support generally consists of the mobile source, mobile receiver and mobile forwarder [29], and the root cause of mobile multicast problem is derived from the IP dual properties which means that IP presents the identifier and the location of host. In this case, once mobile multicast source or receiver changes its attachment point, its IP address will be changed. As a result, the multicast session based on IP and port will break, and the multicast service will be disrupted. To solve this problem, lots of research work are based on mobility support protocols such as MIPv6 and PMIPv6 to support mobile multicast [64; 65]. However, these mobility support protocols are designed for unicast communication, and direct adoption may introduce additional delivery cost and make mobile multicast inefficient. In fact, the essence of mobility support protocols such as MIPv6 is to separate the identifier and the location properties of IP by introducing the Home of Address (HoA) and Care-of-Address (CoA) to represent the identifier and location of host, respectively. However, to provide the mobility support, all of them are based on fixed anchor such as Home Agent (HA) or Local Mobility Anchor (LMA) to perform the signalling interaction and data forwarding, which may result in the serious single point failure problem. Therefore, the fundamental solution for multicast mobility support is to separate the IP dual properties, and separates mobility signalling management and multicast data forwarding.  
In our scheme, the access network and core network adopt the different name spaces which separates the IP dual properties from the network architecture. Therefore, it provides the endogenetic multicast mobility support.  
From the above investigations, we can find that multicast has an important potential for applications. However, there is still lack of an effective controlled and secure multicast mechanism. Our previous work have stated the advantages of multicast service in UIN [19], while in this paper, we consider both group management and multicast routing to enhance the mobility and security support.
## 4 Open Issues
### Deterministic Scheduling for Massive Computing Traffic  
Services such as emergency communications and the metaverse require not only computing resources that are available at any time, but also deterministic guarantees for latency, packet loss, etc. to satisfy the strict QoS and QoE requirements. For example, in emergency communications scenarios, the real-time transmission of emergency disaster remote sensing and the rescue instructions requires that data transmission has strict delay and jitter constraints to ensure accurate feedback of disaster situation and timely release of instructions. The core challenge is how to achieve efficient, low-latency resource allocation and path selection for massive computing traffic in a dynamic, heterogeneous network environment [32]. Therefore, developing a deterministic scheduling mechanism for massive computing traffic to support advanced computing services effectively is an urgent issue.
## 3 Analytical Investigations  
To evaluate the performance of the suggested control method (i.e., LIPPF-controller) in eliminating the non-linear oscillations of the studied rotor-magnetic bearings system, an analytical solution for Equations (21)-(26) is sought by applying the perturbation analysis as follows [39,40]:  
\[y_{1}(\tau,\varepsilon)=y_{10}(T_{0},T_{1})+\varepsilon y_{11}(T_{0},T_{1})+O( \varepsilon^{2}) \tag{27}\]  
\[y_{2}(\tau,\varepsilon)=y_{20}(T_{0},T_{1})+\varepsilon y_{21}(T_{0},T_{1})+O( \varepsilon^{2}) \tag{28}\]  
\[y_{3}(\tau,\varepsilon)=y_{30}(T_{0},T_{1})+\varepsilon y_{31}(T_{0},T_{1})+O( \varepsilon^{2}) \tag{29}\]  
\[y_{4}(\tau,\varepsilon)=y_{40}(T_{0},T_{1})+\varepsilon y_{41}(T_{0},T_{1})+O( \varepsilon^{2}) \tag{30}\]  
\[y_{5}(\tau,\varepsilon)=\varepsilon y_{50}(T_{0},T_{1})+\varepsilon^{2}y_{51}(T_{0 },T_{1})+O(\varepsilon^{2}) \tag{31}\]  
\[y_{6}(\tau,\varepsilon)=\varepsilon y_{60}(T_{0},T_{1})+\varepsilon^{2}y_{61}(T_{0 },T_{1})+O(\varepsilon^{2}) \tag{32}\]  
where \(T_{0}=\tau\), \(T_{1}=\varepsilon\tau\), and \(\varepsilon\) is called a perturbation parameter used as a book-keeping parameter only through this work [40]. Accordingly, the ordinary derivatives \(\frac{d}{d\tau}\) and \(\frac{d^{2}}{d\tau^{2}}\) can be expressed in terms of \(T_{0}\) and \(T_{1}\) as follows:  
\[\frac{d}{d\tau}=D_{0}+\varepsilon D_{1},\,\frac{d^{2}}{d\tau^{2}}=D_{0}^{2}+2 \varepsilon D_{0}D_{1},\,D_{j}=\frac{\partial}{\partial T_{j}},\,j=0,1 \tag{33}\]  
To perform the perturbation procedure, the parameters of Equations (21)-(26) should be scaled as follows:  
\[ \begin{array}{l}f=\varepsilon\widetilde{f},\,\mu=\varepsilon\widetilde{\mu},\, \mu_{1}=\varepsilon\widetilde{\mu}_{1},\,\mu_{2}=\varepsilon\widetilde{\mu}_{2},\, \alpha_{j}=\varepsilon\widetilde{\alpha}_{j},\,\,\beta_{j}=\epsilon\widetilde{ \beta}_{j},\,\,\gamma_{j}=\varepsilon\widetilde{\gamma}_{j},\,\,\eta_{k}=\varepsilon \widetilde{\eta}_{k},\\ j=1,2,\cdots,7;\,k=1,3,5,6,7,8.\end{array}
###### Abstract  
In the field of cognitive radio spectrum sensing, the adaptive silence period management mechanism (ASPM) has improved the problem of the low time-resource utilization rate of the traditional silence period management mechanism (TSPM). However, in the case of the low signal-to-noise ratio (SNR), the ASPM algorithm will increase the probability of missed detection for the primary user (PU). Focusing on this problem, this paper proposes an improved adaptive silence period management (IA-SPM) algorithm which can adaptively adjust the sensing parameters of the current period in combination with the feedback information from the data communication with the sensing results of the previous period. The feedback information in the channel is achieved with frequency resources rather than time resources in order to adapt to the parameter change in the time-varying channel. The Monte Carlo simulation results show that the detection probability of the IA-SPM is 10-15% higher than that of the ASPM under low SNR conditions.  
c pacs: 10.390, 610.20.-a +
Footnote †: journal: Future Internet
# Borehole Trajectory Optimization Design Based on the SAC Algorithm with a Self-Attention Mechanism
## 2 Materials and Methods
### SAC Algorithm with Self-Attention Mechanism  
In the wellbore trajectory design, the action is usually a sequence (i.e., the state data of recent steps). Let \(k\) be the window length, \(S_{t}=[S_{t-k+1},S_{t-k+2},\cdots,S_{t}]\), and Actor and Critic are added to the self-attention layer, respectively:  
The state sequence \(S_{t}\) is passed through the self-attention layer to obtain the feature a with attention weight \(\hat{S}_{t}\):  
\[\hat{S}_{t}=\text{Self-Attention}(S_{t}) \tag{11}\]  
The Actor network uses this feature to output the action distribution: \(a_{t}\sim\pi_{\phi}(\cdot|\hat{S}_{t})\).  
The Critic network is based on state characteristics and action estimation Q-value: \(Q_{\theta}(\hat{S}_{t},a_{t})\).
## 4 Results and Discussion
### CR-InSAR Deformation Monitoring Results
As shown in Figures 18 and 20, CR08 served as the reference point due to its relatively high signal-to-clutter ratios and more stable cumulative deformation, which were observed through the TS-InSAR analysis of Sentinel-1A and the dual-satellite constellation. Figures 21 and 22 display the triangulation network established using the nine CRs, with MCF phase unwrapping and least-squares adjustment revealing subsidence across all nine points, particularly at CR09, which exhibited the most significant deformation with a cumulative LOS rate of \(-\)65.27 mm/yr and total deformation of \(-\)4.29 mm, as detailed in Table 5.  
Figure 18: Time series of CR intensity. Blue triangles and squares represent the scattering-intensity contrast between CRs and the background. Figure 21: Cumulative deformation of CR (with CR08 as the reference point). Figure 19: (**a**) Interferometric phase with atmospheric phase removed. (**b**) Interferometric phase with atmospheric phase removed. Figure 20: The vertical cumulative deformation results averaged within a 50 m radius centered on the CR point: (**a**) the local PS-InSAR monitoring results for the CR point based on Sentinel-1A, (**b**) the local PS-InSAR monitoring results of the CR point obtained from the dual-satellite constellation. As shown in Figure 23, the PS-InSAR LOS deformation results were projected vertically using the incidence angle parameters of the \"Fucheng-1\" and \"Shenqi\" dual-star constellation, with CR08 as the reference. The results show reasonable consistency between the time-series results of the nine CR points and the CR-InSAR results. This demonstrates the effective time-series monitoring capability of \"Fucheng-1\" and \"Shenqi\". Figure 23: Comparison of vertical cumulative deformation results from time-series PS-InSAR technology using Sentinel-1A and CR-InSAR technology versus results from the dual-star PS-InSAR constellation of “Fucheng-1” and “Shenqi”. \begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Last Deformation (mm)** & **Rate (mm/yr)** \\ \hline CR01 & \(-\)0.92 & \(-\)14.04 \\ CR02 & \(-\)3.74 & \(-\)56.91 \\ CR03 & \(-\)0.62 & \(-\)9.44 \\ CR04 & \(-\)2.09 & \(-\)31.77 \\ CR05 & \(-\)2.68 & \(-\)40.82 \\ CR08 & 0.0 & 0.0 \\ CR09 & \(-\)4.29 & \(-\)65.27 \\ CR10 & \(-\)2.95 & \(-\)44.89 \\ CR11 & \(-\)1.25 & \(-\)19.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Deformation inversion results for CR (with CR08 as the reference point). Figure 22: CR deformation velocity map (with CR08 as the reference point).
# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 3 Methods for Identifying Power Losses
### Non-Technical Energy Losses
#### 3.2.2 Energy Losses Caused by Errors in Administration and Process  
Anomalies in energy databases may also occur due to measurement errors, especially in areas where readings are taken manually. [PERSON] and [PERSON] have developed a methodology for detecting such measurement errors, which are also classified as non-technical losses [38]. The authors addressed the problem of field measurements, as there are many areas where there are large discrepancies with the obtained data. To address this challenge, they proposed a synchronized approach for phase measurement, encompassing both voltage and current. Consequently, the researchers' approach relied on precise current measurements to ensure the accuracy of power measurements and to mitigate the impact of outliers. The researchers employed an intelligent state theory-based tool and a recursive type of algorithm to identify the phasors of currents and voltages in the nodes to realize sets for such measurements. The findings of this study are of paramount importance, as they contribute to enhancing the reliability of the overall measurement system and consequently reducing losses caused by inaccurate measurements.  
Non-technical energy losses resulting from anomalies and fraud represent a growing problem in the field of electrical engineering. In [39], the authors conducted a research study on the integration of a method for detecting losses in this category in public power supply networks. To this end, the researchers developed a methodology for loss identification and localization, underpinned by a comprehensive analysis of a dataset comprising 57,000 observations derived from field trials. The intelligent model was designed to thoroughly analyze customer consumption behavior. To this end, an ANN with supervised learning capabilities was investigated, which analyzes data including energy consumption, alarms, and electrical magnitudes derived from smart electricity meters. To ensure the efficacy of the model, various performance classifiers were integrated into the ANN structure.  
The approach undertaken by the authors in [40] exemplifies an energy loss identification methodology that analyzes consumption behavior over an extended period. For the same purposes, a deep-learning ANN was analyzed. This ANN was designed with the purpose of analyzing transformed images based on input features. The efficacy of this approach was substantiated through empirical evidence, as evidenced by the substantial enhancement observed in the performance metrics of the tested model when employing this method on a real data sample.
# Asymmetric Encryption of Invisible Structured Light 3D Imaging
## 2 Invisible Structured Light 3D Imaging System
### Reconstruction of the Three-Dimensional Image of the Measured Object  
According to the light intensity distribution of the four-step phase-shifted sinusoidal fringes at each different position, the actual phase value at this position is:  
\[\phi(x,y)=\arctan[\frac{I_{0}(x,y)-I_{\pi}(x,y)}{I_{\frac{\pi}{2}}(x,y)-I_{ \frac{3\pi}{2}}(x,y)}] \tag{5}\]  
The phase function \(\varphi(x,y)\) after decryption of the measured object is the three-dimensional shape of the object \(h(x,y)\):  
\[h(x,y)=\frac{LTA\phi}{2\pi b+T\Delta\phi} \tag{6}\]  
where \(L\) represents the distance between the target and the imaging camera, \(T\) represents the period of the projected fringe, \(\Delta\phi(x,y)\) represents the phase of the measured object minus  
Figure 3: Elliptic curve decryption flow chart.  
Figure 2: Elliptic curve encryption flow chart.  
the phase value of the target plane, and \(b\) represents the distance between the receiving camera and the transmitting center. In this way, the height information of the object can be obtained by extracting the phase of the deformed fringe. In the simulation experiment, the parameters \(L\), \(T\), and \(b\) are set to 660, \(51/\sqrt{2}\), and 155, respectively.