esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Results
### Tracking Performance of the Particle Swarm Optimization (PSO)-Based GMPPT Method  
The PSO GMPPT process was also applied for shading patterns 1-9. As an indicative example, Figures 17 and 18 illustrate the duty cycle and PV array output power variations during the execution of the PSO-based GMPPT algorithm for shading patterns 1 and 9, respectively. It can be observed that the variability of the duty cycle and the PV array output power are not affected significantly by the  
Figure 16: Results during execution of the proposed Q-learning-based GMPPT process for shading pattern 9: (**a**) duty cycle versus time and (**b**) PV array output power versus time.  
Figure 14: Results during execution of the proposed Q-learning-based GMPPT process for shading pattern 7: (**a**) duty cycle versus time and (**b**) PV array output power versus time.  
Figure 15: Results during execution of the proposed Q-learning-based GMPPT process for shading pattern 8: (**a**) duty cycle versus time and (**b**) PV array output power versus time.  
shape of the shading pattern applied. In contrast, as illustrated in Figures 8-16, the learning capability of the agent incorporated in the proposed Q-learning-based GMPPT approach enabled the progressive reduction of the duty cycle and the PV array output power variability, when shading patterns 1-9 were applied. The PSO-based GMPPT method required a constant period of time (approximately 11.5 s) in order to detect the location of the GMPP. A similar behavior of the PSO-based algorithm was also observed for shading patterns 2-8.
# Heterogeneous Graph Purification Network: Purifying Noisy Heterogeneity without Metapaths
## 1 Introduction
In general, we summarize the main dilemma that existing heterogeneous GNN methods face: they cannot balance the ability of avoiding artificial design with resisting structural and informational noise. In this paper, we attempt to solve this dilemma by proposing a framework called the **H**eterogeneous **G**raph **P**urification **N**etwork (HGPN) with the aim to purify the noisy heterogeneity from both structure and feature. Specifically, HGPN avoids using any hand-engineered metapaths and we model heterogeneity by splitting the original graph into edge type-specific subgraphs and adopting node type-specific parameters by the inter-subgraph aggregation method. A parallel heterogeneous edge purification mechanism is designed during the heterogeneous message propagation process to filter irrelevant edge structures in a learning manner. Then, we present a heterogeneous neighborhood-related residual update method to discriminate and help nodes with low quality attributes automatically. Finally, a novel type-specific normalization and cluster loss function are proposed so that the representation of nodes with IL problems can achieve basic constraint optimization as well as preventing feature over-mixing. Extensive experiments are conducted on various common heterogeneous graph datasets. The results show that our approach outperforms all existing methods consistently among all the datasets. The core contributions of our work are listed as follows:  
1. We analyze and summarize the main dilemma on which present heterogeneous GNN research should focus. Thus, a novel HGPN framework is proposed to solved the dilemma, which can jointly handle edge and node heterogeneity with inter and intra subgraph aggregation methods without any artificial metapaths. 2. To solve the mentioned IC problem, HGPN uses a new designed parallel heterogeneous structure purification mechanism so that the model can learn to filter noisy edge structures based on semantic information and downstream tasks. 3. To solve the mentioned IL and information over-mixing problem, we present the neighborhood-related residual update method, a novel type-specific normalization and cluster loss function to help all types of nodes achieve high-quality representations. 4. HGPN achieves SOTA performance with the proof of extensive experiment and analysis on four real world heterogeneous graph datasets. Remarkably, the accuracy of HGPN on the large OGB-MAG dataset exceeds the current SOTA method by 2.07% and 2.22% under different training settings. The rest of this paper is organized as follows: Section 2 summarizes previous research related to the studied problem. Section 3 formalizes the studied problem. Section 4 presents the framework and introduces each component of our model. Section 5 evaluates the proposed model through experiments. Finally, Section 6 concludes the entire paper.
# Integrated Photonics for IoT, RoF, and Distributed Fog-Cloud Computing: A Comprehensive Review
###### Abstract  
Integrated photonics is a transformative technology for enhancing communication and computation in Cloud and Fog computing networks. Photonic integrated circuits (PICs) enable significant improvements in data-processing speed, energy-efficiency, scalability, and latency. In Cloud infrastructures, PIC support high-speed optical interconnects, energy-efficient switching, and compact wavelength division multiplexing (WDM), addressing growing data demands. Fog computing, with its edge-focused processing and analytics, benefits from the compactness and low latency of integrated photonics for real-time signal processing, sensing, and secure data transmission near IoT devices. PICs also facilitate the low-loss, high-speed modulation, transmission, and detection of RF signals in scalable Radio-over-Fiber (RoF) links, enabling seamless IoT integration with Cloud and Fog networks. This results in centralized processing, reduced latency, and efficient bandwidth use across distributed infrastructures. Overall, integrating photonic technologies into RoF, Fog and Cloud computing networks paves the way for ultra-efficient, flexible, and scalable next-generation network architectures capable of supporting diverse real-time and high-bandwidth applications. This paper provides a comprehensive review of the current state and emerging trends in integrated photonics for IoT sensors, RoF, Fog and Cloud computing systems. It also outlines open research opportunities in photonic devices and system-level integration, aimed at advancing performance, energy-efficiency, and scalability in next-generation distributed computing networks.  
fog networks; cloud networks; silicon integrated photonics 2025
## 8 Cramer-Rao Bound on Covariance Matrix of Localization Errors  
The CRB is a commonly used lower bound on the estimation error covariance of any unbiased estimator. In other words, the difference between the covariance and the CRB is a positive semi-definite matrix. Moreover, the CRB is expected to be a good predictor for the performance of the maximum likelihood estimator (MLE) at a moderate noise level. In this section, we derive the CRB for the estimate of the transmitter's position in two cases: (1) array model errors are absent and (2) array model errors are present. To this end, we first introduce the  
where  
\[\mu_{\rm a}=\left[\mathbf{p}^{\rm T}\ \mathbf{\beta}_{\rm r}^{\rm T}\ \left(\mathrm{Re}\{ \overline{\mathbf{s}}\}\right)^{\rm T}\ \left(\mathrm{Im}\{\overline{\mathbf{s}}\}\right)^{\rm T}\right]^{\rm T} \tag{75}\]  
with \(\mathbf{\beta}_{\rm r}=\left[\left(\mathrm{Re}\{\mathbf{\beta}\}\right)^{\rm T}\ \left( \mathrm{Im}\{\mathbf{\beta}\}\right)^{\rm T}\right]^{\rm T}\). To proceed, the data vector is defined as  
\[\overline{\mathbf{x}}=\left[\overline{\mathbf{x}}_{1}^{\rm H}\ \overline{\mathbf{x}}_{2}^{\rm H }\ \cdots\ \overline{\mathbf{x}}_{N}^{\rm H}\right]^{\rm H}=\left[\overline{\mathbf{x}}_{1,1}^{ \rm H}\ \overline{\mathbf{x}}_{1,2}^{\rm H}\ \cdots\ \overline{\mathbf{x}}_{1,K}^{\rm H}\ \overline{\mathbf{x}}_{2,1}^{\rm H}\ \overline{\mathbf{x}}_{2,2}^{\rm H}\ \cdots\ \overline{\mathbf{x}}_{2,K}^{\rm H}\ \cdots\ \overline{\mathbf{x}}_{N,1}^{\rm H}\ \overline{\mathbf{x}}_{N,2}^{\rm H}\ \cdots\ \overline{\mathbf{x}}_{N,K}^{\rm H}\right]^{\rm H}, \tag{76}\]  
whose mean vector is given by  
\[\overline{\mathbf{x}}_{0}=\mathrm{E}[\overline{\mathbf{x}}]=\left[\overline{\mathbf{x}}_{1,1,0}^{\rm H}\ \overline{\mathbf{x}}_{1,2,0}^{\rm H}\ \cdots\ \overline{\mathbf{x}}_{1,K,0}^{\rm H}\ \overline{\mathbf{x}}_{2,1,0}^{\rm H}\ \overline{\mathbf{x}}_{2,2,0}^{\rm H}\ \cdots\ \overline{\mathbf{x}}_{2,K,0}^{\rm H}\ \cdots\ \overline{\mathbf{x}}_{N,1,0}^{\rm H}\ \overline{\mathbf{x}}_{N,2,0}^{\rm H}\ \cdots\ \overline{\mathbf{x}}_{N,K,0}^{\rm H}\right]^{\rm H}. \tag{77}\]  
Then, applying the results in [66; 67], the CRB matrix for vector \(\mu_{\rm a}\) can be obtained by  
\[\mathbf{CRB}(\mathbf{\mu}_{\rm a})=\frac{\sigma_{\varepsilon}^{2}}{2}\cdot\left( \mathrm{Re}\{\mathbf{\Omega}_{\mu_{\rm a}}^{\rm H}\mathbf{\Omega}_{\mu_{\rm a}}\} \right)^{-1}, \tag{78}\]  
where  
\[\mathbf{\Omega}_{\mu_{\rm a}}=\frac{\partial\overline{\mathbf{x}}_{0}}{\partial\mathbf{ \mu}_{\rm a}^{\rm T}}=\left[\mathbf{\Omega}_{\mathbf{p}}\ \mathbf{\Omega}_{\rm Re\{\mathbf{\beta}\}}\ \mathbf{\Omega}_{\rm Im\{\mathbf{\beta}\}}\ \mathbf{\Omega}_{\rm Re\{\mathbf{\overline{\mathbf{s}}}\}}\ \mathbf{\Omega}_{\rm Im\{\mathbf{\overline{\mathbf{s}}}\}}\right]. \tag{79}\]  
Using (16) and (77) and performing algebraic manipulations, the sub-matrices in (79) are described as  
\[\left\{
# CoAR: Congestion-Aware Routing Protocol for Low Power and Lossy Networks for IoT Applications
## 7 Conclusions  
In this paper, we propose a congestion-aware routing mechanism whereby the congestion is alleviated by using the resource control method that suitably detects the congestion based on the present and past traffic trends. To solve the parent selection problem within congestion, we have defined the framework as the multi-criteria decision-making problem, which is solved by using the TOPSIS method. Different routing metrics are used as the parent selection criteria so that the CoAR can balance the workload of nodes in industrial monitoring network to achieve the desired QoS. The congestion detection mechanism ensures the affected nodes to avoid transient congestion situation, meantime it renders the best parent selection to create an alternate path towards the root in order to control persistent congestion situations so that maximize the packet reception rate. Additionally, along with ensuring steady flow under low traffic loads, the CoAR is able to maintain higher reliability even at bursty traffic durations. Overall, to satisfy the IoT application requirements, CoAR is aware of network situation under heavy traffic as well as the unique characteristic of LLNs and IEEE 802.15.4. Extensive simulation tests are conducted to verify the performance. The simulation results validate that CoAR improves the PRR, end-to-end delay, packet loss ratio, throughput, and energy consumption, as compared to the existing protocols.  
**Author Contributions:** [PERSON] conceived the proposed protocol, performed the simulation tests, and wrote the manuscript; [PERSON] contributed to the refinement and revised the manuscript; [PERSON] supervised the work.  
**Funding:** \"This research received no external funding\".  
**Acknowledgments:** This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2018-2015-0-00378) supervised by the Institute for Information & Communication Technology Promotion (IITP).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Heavy Metals Research
The results of the determination of the metal content in the soils of the Poleski National Park are shown in Figure 6. The highest concentrations for the sum of the analyzed metals were found at the sites of Artosciezka, Las Tafle, and Zereminska. On the other hand, the sum of metal concentrations in the soils was the lowest in the area of the Czachary, Czeremnik, Dab Dominik, and Wielki Lan sites. The soils with the strongest and the weakest metal loads were not nearby, which does not indicate an unambiguous source of metals. Such a diversity proves the heterogeneous origin of metals in the soil material. Attention is drawn to the fact that metals are present in increased concentrations at similar sites or there is a certain correlation between the concentrations of metals at the sites. The I\({}_{\rm geo}\) values presented in Figure 7 show that all sites can be classified as \"uncontaminated\" for the four investigated trace metals: copper Cu, nickel Ni, lead Pb, and chromium Cr. Only for cadmium Cd, the studied soils obtained the geoaccumulation index value from \(-\)0.3 to 1.2. This indicates that the soils are moderately to heavily contaminated with cadmium Cd. \begin{table}
\begin{tabular}{l l c c c c} \hline
**Site** & **Name of Point** & **Cr** & **Cd** & **Cu** & **Ni** & **Pb** \\ \hline
23 & Maximum & 12.8 \(\pm\) 2.0 & 1.9 \(\pm\) 0.5 & 20.7 \(\pm\) 5.0 & 56.2 \(\pm\) 5.0 & 20.4 \(\pm\) 5.0 \\ \hline
24 & Average & 3.6 \(\pm\) 0.5 & 1.3 \(\pm\) 0.3 & 5.7 \(\pm\) 0.5 & 5.2 \(\pm\) 0.6 & 11.9 \(\pm\) 2.0 \\ \hline
25 & Median & 2.9 \(\pm\) 0.4 & 1.3 \(\pm\) 0.3 & 3.6 \(\pm\) 0.3 & 1.8 \(\pm\) 0.1 & 11.5 \(\pm\) 2.0 \\ \hline
26 & St. dev. & 0.69 & 0.08 & 1.12 & 2.66 & 0.84 \\ \hline
29 & Number of results & 21 & 21 & 21 & 21 & 21 \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._Figure 5: The hyposometric map of concentrations: (**a**) copper Cu, (**b**) cadmium Cd, (**c**) chromium Cr, (**d**) nickel Ni, and (**e**) lead Pb in mgkg\({}^{-1}\) in soil samples of individual sampling points in the Poleski National Park.
# A Survey on Satellite Communication System Security
## 5 Defense in Satellite Communication Systems
### Solutions for Integrity  
Anti-Spoofing  
Anti-spoofing refers to the range of techniques and strategies used to protect GNSSs in SCSs from spoofing attacks. The main goal of spoofing detection is to distinguish between the spoofing signal and the actual satellite signal in the received signal. If the real signal and the spoofing signal are not successfully detected, as seen in Section 3, this can have serious implications, especially in critical SCS parts such as the GNSS.  
So, in this section, we categorize anti-spoofing methods into three main types: physical parameter analysis methods, hardware-dependent methods, and cryptographic methods. Under the physical parameter analysis method section, we will look at approaches like Doppler shifts, signal parameter statistics analysis, arrival time and time difference, and residual signal detection. The overall steps of this method are shown in Figure 16. In the hardware-dependent parameter analysis method section, we will explore antenna arrays, angles of arrival, subspace projection, and signal quality monitoring. And the overall steps of hardware-dependent method are shown in Figure 17. Finally, for the cryptographic methods, we will examine NMA, protocol-based NMA, spreading code authentication (SCA), and encryption.  
Figure 16: Anti-spoofing using physical parameter analysis.  
Figure 17: Anti-spoofing using hardware-dependent parameter analysis methods.
## 3 Results and Discussion
The 'Z' or zero weighting is simply a filter with a flat frequency response. The proposed CMARA method modifies the mixed method from ISO standard 14121-2 [28], where the measurement of noise exposure using A-weighing as well as Z-weighing is incorporated. Based on the assessment using the proposed method, it is possible to predict the non-auditory effects of noise during its exposure. The proposed CMARA methodology has shown that LFN can have a negative impact on employee health in terms of long-term effects, as well as an impact on workplace comfort. Measurements of noise exposure using the Z-filter in the WA2 (work activity) of machining and WA3 of spot welding indicate that a longer exposure time that can pose a health hazard in the work process. This was also confirmed by a risk assessment using the proposed methodology, where the values of the resulting risks shown in Table 8 in these two cases (WA2, WA3) are at the level of high risk, four times at the level of medium risk (WA1, WA4, WA5, WA6), and in one case (WA7), at a low-risk level. The severity factor was determined qualitatively based on interviews with employees and, with one exception, it was assessed as moderate. After interviewing the employees, everyone confirmed that they felt their hearing was impaired during and after work. As part of the further development of the methodology, the aim was to quantify the parameters related to the non-auditory effects of noise, e.g., by measuring blood pressure and other health parameters that would indicate the impacts of LFN on the employees. Low-frequency noise (LFN) is oftentimes within the limit values of the usable frequency range of noise of instrumentation; therefore, special care is needed to ensure reliable results [34; 35; 36]. Many researchers [37; 38; 39; 40; 41] point out the inaccuracies associated with measuring noise using dB(A), as this method involves over-reliance on the average hearing sensitivity curve and related volume functions as predictors of noise nuisances. Even in cases where LFN is found and identified as a potential source, problems can arise because in many cases, the sound pressure level can be low relative to the average hearing threshold. Due to the risks of exposure to excessive noise, the legislation prescribes regular measurements. The trend of complex noise measurement is to measure not only the frequency but also its energy. The application of the Z-weighting filter makes it possible to measure the acoustic energy acting on humans. The discomfort caused by LFN occurs only at slightly higher levels than the audibility threshold, which is different for every individual [42; 43]. The rate of increase in the perceived volume is faster at low frequencies; therefore, one sound may be loud to one per-son while not yet audible to another. Another factor is that sensitivity to LFN occurs over time; therefore, the short-term effects on an individual may not give an accurate impression of what it is like to perceive this type of noise on a regular and long-term basis [44; 45; 46; 47].
## 2 Materials and Methods  
### STIL System Structure  
Figure 1 illustrates the configuration of the developed STIL system, which is composed of four primary components: a control unit, a laser emitter, a laser receiver, and a TDC unit. During the detection process, the control unit is pivotal to the system's operation, orchestrating the generation of trigger signals that activate both the laser and the streak camera, thereby ensuring precise synchronization between the laser pulse emission and the streak camera's recording. Furthermore, the control unit oversees the streak camera's timing and image format, guaranteeing that streak images are captured accurately. It also maintains system synchronization and manages the storage of ranging data, which is critical for precise time-of-flight (ToF) measurements and target analysis.  
The laser emitter generates fan-shaped laser pulses and detects the optical trigger signal via a positive-intrinsic-negative (PIN) photodiode. The laser emitter utilizes an Nd-YAG laser and beta barium borate (BBO) crystal to emit a 532 nm laser with minimal divergence. These pulses, shaped by beam shaping optics (BSO) into a fan beam, are detected  
Figure 1: Structure of the STIL system. The red line represents the 1064 nm laser, and the green line represents the frequency-doubled 532 nm laser.  
by a high-response PIN photodiode. It detects the diffuse reflection of the laser signal near the laser emission path and generates an optical trigger signal. The optical trigger signal is subsequently fed into the TDC to accurately timestamp the laser pulse emission.  
The laser receiver captures the laser spot reflected from the target. The streak camera within the receiver then converts the temporal and spatial information of the return signal into a two-dimensional streak image. This streak image provides a detailed representation of the time-resolved intensity distribution of the reflected laser pulse, allowing for precise analysis of the distance of the target. The front end of the receiving unit employs a narrow-band filter and a spatial diaphragm to eliminate background and stray light, thereby enhancing the signal-to-noise ratio of the received signal. As the core component of the receiving unit, a streak camera equipped with an MCP image intensifier records the laser echoes with exceptional time and spatial resolution. The pulse synchronized with the trigger signal of the streak camera is input into the TDC to mark the time of laser pulse reception.  
An 8-channel TDC unit precisely measures the time delay between the optical trigger and streak camera trigger pulses via internal field programmable gate array (FPGA) routing delays, thereby determining the exact timing of the laser pulse. By accurately capturing this time delay, the TDC ensures precise synchronization between the laser emission and the streak camera imaging, allowing for accurate ToF measurements or high-speed laser imaging.
# Temperature Effects on Vibration-Based Damage Detection of a Reinforced Concrete Slab
## 4 Damage Identification in Test Structure
### Statistical Criterion  
The reasonable output confidence interval is determined to verify the damage identification performance of the ARX model. The finite element model of the slab is constructed to obtain some analytical natural frequencies of the intact and damaged structure. Theoretically, the analytical natural frequencies of the intact structure should be distributed within the interval. Otherwise the damage may occur in the structure. The output confidence interval, whose confidence level is \(1-\alpha\), can be defined as [42]:  
\[\begin{array}{c}[\hat{y}_{k}-t_{\alpha/2,\
u}\sqrt{P_{d_{k}}},\hat{y}_{k}+t_ {\alpha/2,\
u}\sqrt{P_{d_{k}}}]\\ d_{k}=y_{k}-\hat{y}_{k}\\ P_{d_{k}}=E[(d_{k})^{2}]\end{array} \tag{26}\]  
where \(\hat{y}_{k}\) is the output of the model, \(t_{\alpha/2,\
u}\) is found from a statistical table of the Student's t-distribution, \(y_{k}\) is the experimental value, \(d_{k}\) is an asymptotically Gaussian distributed with zero mean and \(P_{d_{k}}\) is a certain covariance.  
For the intact structure, the first natural frequency calculated based on finite element analysis is shown in Figure 13, which indicates that the analytical natural frequencies are consistent with the output of the ARX model. The results of finite element analysis fall in the confidence interval, and the correctness of the ARX model is verified. Subsequently, the 5% and 10% uniform damage are introduced to the finite element model through the reduction of the elastic modulus of the slab and the first natural frequency is extracted. In Figure 14, for 5% uniform damage, although some frequencies fall in the interval, the ARX model can assess the health condition of the structure. Damage may occur in the structure, but the change of natural frequencies may arise from the temperature variations, and if the temperature is monitored simultaneously, the reason for the change can be determined. For 10% damage cases, the frequency data exceed the lower bound of the interval, damage occurs in the structure absolutely, which shows that the ARX model can indicate abnormal variation and has a good damage warning capability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **1 st Mode** & **2 nd Mode** & **3 rd Mode** & **4 th Mode** \\ \hline ARX model & 0.0104 & 0.0882 & 0.561 & 1.812 \\ LR model & 0.0119 & 0.0969 & 0.568 & 2.1904 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of root mean squared error (RMSE) values between ARX model and linear regressive model.