esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Models and Data Sources
### Spatial Econometric Model  
Let us first introduce an ordinary least squares (OLS) model used as the reference model. It is as shown as:  
\[y_{it}=\alpha+X_{it}\beta+\mu_{i}+\varepsilon_{it} \tag{2}\]  
where \(y_{it}\) represents the dependent variable of province \(i\) in year \(t\). In the research, \(y_{it}\) is the GTFP score. \(X_{it}\) represents a set of independent variables; \(\beta\) are parameters to be estimated; \(\alpha\) is a constant term; \(\mu_{i}\) represents the province fixed effect and \(\varepsilon_{it}\) is a random error.  
We assume that observations are mutually independent in a classical econometric model. However, mapping the GTFP scores and population aging of Chinese provinces enables us to discover that the observations are not randomly distributed, but instead are inclined to present spatial clustering. In other words, the observations of proximate provinces tend to have similar values. Technically speaking, there is spatial autocorrelation. Spatial clustering has been ignored in classical models and neglecting it could result in a biased estimate for our study. To solve this problem, we introduce a spatial lag model incorporating the spatially lagged dependent variable (i.e., GTFP) based on the classical OLS model. Shown as follows, it is:  
\[y_{it}=\rho Wy_{it}+\alpha+X_{it}\beta+\mu_{i}+\varepsilon_{it} \tag{3}\]  
where \(Wy_{it}\) is the spatial lag term of the dependent variable used to capture spatial clustering. In our study, it is also referred to as spatial interaction effects. \(\rho\) is a coefficient to be estimated, known as the spatial autoregressive coefficient. \(W\) represents an \(n\)\(\times\)\(n\) spatial weight matrix for describing the spatial arrangement of Chinese provinces. We adopt a rook contiguity spatial weights matrix, whose elements are equal to 1 if two provinces have common boundaries, and 0 otherwise. All other variables are the same as in Equation (2).  
Following the idea of the above spatial lag model, an explanatory variable may also have spatial interaction effects; these would indicate the impacts of driving factors of neighboring provinces on the GTFP of a particular province. As per the idea of including the dependent variable, spatial lags of explanatory variables can also be incorporated based on Equation (3). From the econometric perspective, it is of great theoretical significance to these spatial lags because the omitted-variable issue may lead to biased conclusions. As a result, another spatial econometric model can be shaped, namely the spatial Durbin model, expressed as:  
\[y_{it}=\rho Wy_{it}+\alpha+X_{it}\beta+WX_{it}\theta+\mu_{i}+\varepsilon_{it} \tag{4}\]  
where \(WX\) represents the spatial lags of explanatory variables used to capture the spatial interaction effects of explanatory variables. \(\theta\) are parameters to be estimated. All other variables are the same as in Equation (3).
## 5 Convergence of ML and Blockchain in IoV
### Prospects of ML and BC Convergence for IoV System
#### 5.1.4 Cross-Domain Object Detection, Knowledge Sharing, and Training for Autonomous Driving  
A model-sharing approach was developed for training autonomous cars [155]. A YOLOv2 model was designed to enhance the performance of autonomous driving in cross-domain object detection. The authors applied DTL for training the car with a blockchain-enabled MEC technology. BC provides the reliability of model sharing by means of consensus algorithm while MEC enabled low delay and real-time computational strength. Simulations were carried out on a public dataset and the results indicate that the proposed model achieved better efficiency and reliability. Blockchain has also been integrated in the learning process of autonomous cars to enhance knowledge sharing [156]. In this study, the authors proposed the concept of collective learning, where the entire car produced in a batch could be trained simultaneously using RL simply by training only one car and subsequently using BC to share the knowledge with other cars. The authors concluded that integrating blockchain features to the ML training of autonomous vehicles enhances its efficiency and optimize the training process. Similar studies have considered different framework for efficient knowledge sharing applications.  
[PERSON] et al. [157] developed a hierarchical blockchain-enabled federated learning algorithm for effective knowledge sharing in intelligent vehicles. By using a hierarchical blockchain framework coupled with a hierarchical FL framework, the authors were able to reduce the computation consumption and improve the learning accuracy and overall efficiency of the system. [PERSON] et al. [158] combined the DAG-based blockchain together with asynchronous FL scheme to develop an effective data sharing framework for IoV. In this study, an asynchronous federated learning mechanism enabled by Deep Reinforcement Learning (DRL) was utilized for effective node selection, while the blockchain ensured the reliability of shared data by executing a two-stage verification process. The authors reported higher learning accuracy as well as faster convergence of the overall process. Similar studies include efficient traffic data sharing [153], blockchain-based collective learning (BCL) framework for autonomous lane-changing systems [141; 159], effective learning framework for autonomous cars for efficient heuristic service in the supply chain industry [160], and efficient adaptive sharding to enhance driving experience [161].
## 2 Research Methodology
### Improved Artificial Rabbits Optimisation Method
To address potential issues of local convergence and insufficient search efficiency when ARO handles complex multimodal optimisation problems, this study introduces Levy flight mechanisms and selective opposition learning strategies, constructing an improved ARO method (LARO) to enhance optimisation performance [30]. 1. The Levy flight method introduces dynamic characteristics to the ARO algorithm updates through heavy-tailed distribution random walk patterns, with step lengths following a Levy distribution: \[\left\{ \begin{array}{rl}Levy(l)\sim u=l^{-1-\gamma},0<\gamma\leq 2\\ l=\frac{u}{|\varphi|^{1/\gamma}}\end{array} \right.\] (4) where \(l\) represents step length, \(u\sim N(0,\sigma_{u}^{2})\), \(v\sim N(0,\sigma_{v}^{2})\), \(\sigma_{v}=1\), \(\sigma_{u}=\left(\frac{\Gamma(1+\beta)\cdot\sin(\pi\cdot\beta/2)}{\Gamma((1+ \beta)/2)\cdot\beta\cdot 2(\beta^{-1/2})}\right)^{1/\beta}\). During the random hiding phase, Levy flight replaces the \(r_{4}\) random number (with parameter \(\beta\) typically set to 1.5 and \(\alpha\) set to 0.1) to prevent the algorithm from falling into local optima: \[\overset{\rightarrow}{v}_{i}(t+1)=\overset{\rightarrow}{x}_{i}(t)+R\cdot( \alpha\cdot\text{\emph{levy}}(\beta)\cdot\overset{\rightarrow}{b}_{i,r}(t)- \overset{\rightarrow}{x}_{i}(t))\] (5) 2. Selective opposition (SO) learning enhances algorithm performance by modifying opposition-based learning states when rabbit colonies are distant from optimal solutions. This strategy influences rabbit colony deployment through linearly decreasing thresholds. When colonies are far from optimal positions, it calculates near and far rabbit positions and updates the corresponding difference distances. The SO core lies in determining search direction effectiveness through correlation analysis, triggering update strategies to help rabbits escape local optima when correlation is poor, calculated through the following formula: \[\left\{ \begin{array}{rl}dd_{i}=\left|\overset{\rightarrow}{x}_{ibest,j}- \overset{\rightarrow}{x}_{ij}\right|\\ \text{R}_{src}=1-\frac{6\cdot\sum_{j=1}dd_{i}^{2}}{dd_{i}\cdot(dd_{i}^{2}-1)} \end{array} \right.\] (6) where \(dd_{i}\) represents the difference distance between each rabbit and the optimal rabbit position across various dimensions, and \(R_{src}\) measures the correlation between current and optimal positions. By combining Levy flight's dynamic search mechanism with selective opposition learning's intelligent guidance strategy, LARO maintains ARO's simple and efficient characteristics while enhancing global search capability and convergence accuracy, providing robust technical support for this study's model optimisation. For specific implementation details of the algorithm, readers may refer to the relevant original literature [28] for more comprehensive technical background and theoretical foundations.
# Light Harvesting and Optical-Electronic Properties of Two Quercitin and Rutin Natural Dyes
## Results and Discussion
### Prediction for Photovoltaic Performance  
The prediction for the electric current and voltage of dye molecules can provide theoretical support for evaluating molecular photoelectric properties to be used in DSSCs [67; 68]. \(J_{sc}\) depends on LHE [69]\(\Phi_{inj}\) and \(\eta_{coll}\), which can be calculated with the following formula [70]:  
\[J_{sc}=\int_{\lambda}LHE(\lambda)\phi_{inj}\eta_{coll}d\lambda \tag{2}\]  
LHE is a major factor that influences the short-circuit current [71] and \(\eta_{coll}\) is the charge collection efficiency. \(\eta_{coll}\) can be considered the same, owing to the same photoanode (TiO\({}_{2}\)) for a given DSSCs. \(\Phi_{inj}\) is the electron injection efficiency related to \(\Delta G^{inject}\), and \(\Delta G^{inject}\) can be calculated in accordance with citations [72; 73]. According to [PERSON] et al. [74], when \(\Delta G_{inject}\) is above 0.2 eV, which means that the \(\Phi_{inj}\) tends to 1. It can be found from Table 6. \(\Delta G_{inject}\) of S-Q and S-R are 1.653 eV and 1.773 eV, respectively, this shows that the \(\Phi_{inj}\) of S-Q and S-R tend to 1. The LHE of S-Q (0.603) is higher than S-R (0.563). Therefore, from the theoretical simulation, it can predict that S-Q has a larger J\({}_{sc}\).  
\(V_{\alpha c}\) can be considered as the difference between the quasi-fermi level of electrons in the CB of TiO\({}_{2}\) and the potential of oxidation-reduction couples. \(V_{\alpha c}\) can be obtained from the following formula [75]:  
\[V_{OC}=\frac{E_{CB}+\Delta E_{CB}}{q}+\frac{KT}{q}\ln\left(\frac{n_{c}}{N_{CB} }\right)-\frac{E_{redox}}{q} \tag{3}\]  
where, \(E_{CB}\), q, K, T, \(n_{c}\), and \(N_{CB}\) stand for the CB of the semiconductor substrate, unit charge, Boltzmann constant, temperature, number of electrons in the CB, and active density states in the CB of TiO\({}_{2}\). \(\Delta E_{CB}\) is the movement of \(E_{CB}\). For DSSCs, V\({}_{\alpha c}\) is affected by \(\Delta E_{CB}\), and \(\Delta E_{CB}\) is calculated by the following equation [76].  
\[\Delta E_{CB}=\frac{-qu_{normal}\gamma}{\varepsilon\varepsilon^{\prime}} \tag{4}\]  
where, \(u_{normal}\) is a vertical dipole moment [77], which corresponds to the direction of molecules perpendicular to the TiO\({}_{2}\) surface (here u\({}_{normal}\) is selected as y-axis orientation); \(\gamma\) is the adsorbed surface concentration, \(\varepsilon^{\prime}\) is a vacuum permittivity, and \(\varepsilon\) is the permittivity of the organic monolayer. Table S4 shows the results of the dipole moment [78]. The trend of \(u_{normal}\) is S-Q (15.997 D) \(>\) S-R (2.001 D), S-Q has the larger \(u_{normal}\) than S-R. According to the formula, \(\Delta E_{CB}\) and \(u_{normal}\) have a proportional relationship. The \(V_{\alpha c}\) can be affected by \(u_{normal}\) and \(\Delta E_{CB}\), and larger \(u_{normal}\) and \(\Delta E_{CB}\) contribute to larger \(V_{\alpha c}\). S-Q has a larger \(u_{normal}\) than S-R, which is beneficial for S-Q, producing a larger V\({}_{\alpha c}\).
## 5 Performance Testing of Hydraulic Support FBG Inclinometers
### Instrument Repeatability Analysis  
After the repeatability experiment, according to the two repeated cycles of the test curve, it can be seen that the wavelength value of the fiber grating essentially returns to the original value, indicating that the hydraulic support FBG inclinometer has good reusability.
# Numerical Simulation-Based Investigation of the Limits of Different Quasistatic Models
## 2 Maxwell's System  
Let us consider \(\Omega\subset\mathbb{R}^{3}\), an open connected domain with boundary \(\Gamma=\Gamma_{\rm H}\cup\Gamma_{\rm B}=\partial\Omega\) and \(\Gamma_{\rm H}\cap\Gamma_{\rm B}=\emptyset\). \(\Omega_{\rm c}\subset\Omega\) presents the conductive sub-domain and \(\Omega_{\rm nc}\subset\Omega\) is the non-conductive sub-domain, as shown in Figure 1.  
Figure 1: The domain of study.  
The full Maxwell system, listed below, describes the magnetic and electric behaviors of the electromagnetic device  
\[\
abla\times\mathbf{E}=-\frac{\partial\mathbf{B}}{\partial t}, \tag{1}\] \[\
abla\times\mathbf{H}=\mathbf{J}+\frac{\partial\mathbf{D}}{ \partial t},\] (2) \[\
abla\cdot\mathbf{B}=0,\] (3) \[\
abla\cdot\mathbf{D}=0 \tag{4}\]  
associated with the following boundary conditions  
\[\Gamma_{\mathrm{E}}:\mathbf{E}\times\mathbf{n}=\mathbf{0}, \tag{5}\] \[\Gamma_{\mathrm{B}}:\mathbf{B}\cdot\mathbf{n}=0,\] (6) \[\Gamma_{\mathrm{H}}:\mathbf{H}\times\mathbf{n}=\mathbf{0},\] (7) \[\Gamma_{\mathrm{J}}:\mathbf{J}\cdot\mathbf{n}=0 \tag{8}\]  
followed by the behavior laws of materials  
\[\mathbf{B}=\mu\mathbf{H}, \tag{9}\] \[\mathbf{J}=\sigma\mathbf{E},\] (10) \[\mathbf{D}=\varepsilon\mathbf{E} \tag{11}\]  
where \(\mathbf{E}\) is the electric field (V/m), \(\mathbf{H}\) is the magnetic field (A/m), \(\mathbf{B}\) is the magnetic flux density (T), \(\mathbf{J}\) is the current density (A/m\({}^{2}\)), \(\mathbf{D}\) is the electric flux density (C/m\({}^{2}\)), \(\mu\) is the magnetic permeability, \(\sigma\) and \(\varepsilon\) are the electric conductivity and permittivity, respectively.
# Lightning-Ignited Wildfires beyond the Polar Circle
## 3 Results
### Fire Dynamics and Seasonality  
For Eurasia and the entire Arctic, annual fire occurrence has been increasing since 2007, whereas for North America and Scandinavia, trends are non-significant (Figure 2a). In the second decade (2012-2022 vs. 2001-2011), the mean fire number increased ca. 3.3 times in Eurasia, 3.7 times in East Siberia, and about doubled in the entire Arctic (Figure 2b).  
Figure 2: **(a)** Since ca. 2007, the annual fire occurrence has been increasing for Eurasia and the entire Arctic. Trends are significant at \(p<0.1\). **(b)** The mean fire number increased in 2012–2022 (vs. 2001–2011) about 3.3 times in Eurasia. A twofold increase (19,717 vs. 9860) was observed in the Arctic.  
The majority of fires (ca. 76%) occurred in Russia (including ca. 65% in Eastern Siberia). The highest fire density (ca. 0. 1 for km\({}^{2}\)) was also observed in Eastern Siberia, which is about four times exceeds that in North America (Figure 3).  
The typical seasonal pattern of fire occurrence distribution in the entire Arctic is unimodal. However, for the severe fire years (e.g., 2019), the seasonal distribution turned to a bimodal shape (Figure 4a). The fire season duration is increasing in West Siberia and decreasing in the Far East sector (Figure 4b). In the second decade (2012-2022 vs. 2001-2011), the fire season increased (ca. 43 days; \(p<0.1\)) in West Siberia, whereas in the Far East, it decreased by ca. 19 days (\(p<0.05\)). For the other Arctic sectors, changes were non-significant. Fire season in West Siberia positively correlated to summer air temperatures (_rho_ = 0.40 at \(p<0.1\)), while the decrease for the Far East was related to soil moisture (_rho_ = \(-0.38\) at \(p<0.1\)) and SPEI (_rho_ = \(-0.47\) at \(p<0.05\)).
# A Power-Gated 8-Transistor Physically Unclonable Function Accelerates Evaluation Speeds
### State of the Art and Related PUF Works  
Before starting a new silicon-based PUF design, the implementation method needs to be considered first. The silicon-based PUFs can be implemented in an ASIC or a Field-Programmable Gate Array (FPGA). Since FPGAs have ready-to-use resources and can be purchased off-the-shelf, many early PUF works are based on FPGAs [2; 3; 4]. However, ASIC-based PUFs can offer far more energy and have a cost-efficient design, which is crucial for lightweight IoT devices [8; 9; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]. However, FPGA-based PUFs [35; 36; 37; 38; 39; 40; 41] are indispensable parts for applications which are not area or energy sensitive but time sensitive instead. In addition, FPGA is a feasible platform for assessing PUF design methods and performance. There are several novel FPGA-based bistable PUF works [36; 37; 38; 39; 40; 41], which we intend to implement in our test chip with the power gating method to compare the metastable behaviour and PUF performance.  
There are several PUF applications [42; 43; 44; 45] that previously applied the power gating method, but their purposes and implementations are different to this approach. [PERSON] et al. [42] implemented power-gated PUF blocks for further investigation. [PERSON] et al. [43] utilised the random duration of multiple power gating to replace voltage control so as to induce failure patterns to determine the Data Retention Voltage (DRV). In comparison, our design diminishes data retention swiftly for high-speed evaluation. Although with a different purpose, this research and its predecessor [46] have clarified that \"a strong DRV fingerprint is correlated with power-up tendency\". This substantiates that the stability or instability degrees of PUF cells come from their innate physical mismatch. However, it can be seen that 40 us is not enough to eliminate retention data at 25 C. In 2020, a 2D power gating scheme to relieve an enhancement-enhancement (EE) SRAM PUF from short-circuit currents and also to protect PUF data from attacks was presented by [PERSON] et al. [44]. However, there was no consideration of the major power gating parameters, such as the _SLEEP_ transistor design [47], power distribution network [48], etc. This scheme also has a half-selected cell problem, which requires additional peripheral circuits to lower the extra energy consumption.
## 4 Discussion  
### Potential Advantage of the Use of CVFZ in Vegetation Analysis and Policymaking  
Furthermore, in addition to distinguishing vegetation functions, the screening of highly representative and high-quality vegetation pixels could be a potential advantage of CVFZ in vegetation analysis. As illustrated in Figure 9 with MCD-2011, after being filtered by CVFZ, 81.3% of pixels were retained, and the \(D_{2}\) of different zones decreased, except for the evergreen broadleaf forest. The average \(D_{2}\) value decreased from 0.2309 to 0.2247. Notably, the LAI average of shrublands decreased from 0.53 to 0.32 in 2011, which is closer to [PERSON] et al.'s research conclusions [79]. This demonstrates that the remaining pixels of MCD-2011 exhibit higher similarity, which indicates potentially higher classification accuracy. This is because the VCA-MLC process does not require any land cover products as prior knowledge, thereby avoiding issues such as the misclassification of vegetation pixels in land cover products to some extent.  
Furthermore, CVFZ can also be used in conjunction with other land cover maps to further analyze vegetation for different functions. In this study, MCD12Q1 was used as an example because the use of MODIS products in classification and cutting can better ensure the consistency of results [62]. Additionally, because vegetation functions are distinguished at pixel scale, CVFZ can be utilized to analyze the differences in vegetation in a specific area, and it has significance for scientific analysis in climatology, ecology, and vegetation.  
As a derivative product of the hyperspectral image classification algorithm in multi-temporal image classification, CVFZ has several areas that could be improved. For instance, the process of endmember extraction could incorporate more LAI time phases, and the screening of LAI endmembers could introduce other prior knowledge. Additionally, the MLC process could be combined with machine learning methods [80; 81; 82].  
Figure 9: The LAI mean and \(D_{2}\) (indicated by the error bar) in MCD-2011 and MCD-CVFZ in the same vegetation. The red curve represents the proportion of pixels remaining after MCD is cut.  
Ecosystem services bridge the gap between policymaking and actual benefits by linking ecosystem characteristics as intermediate services and to human well-being as the ultimate service [83]. Our research helps map ecosystem services and establish indicators of the monitored ecosystems and final services, thus effectively mitigating the negative causality between local ecological change and economic growth [84]. In addition, where aspects such as agriculture of crop, livestock, and silvicultural production and environmental legislation for sustainability conflict [85], CVFZ can serve as a direct basis for conflict resolution. For example, lower zoning areas focus on agricultural development while higher zoning areas focus on environmental protection. The variation in vegetation exposure reflected by zoning types also provides new perspectives for thinking about vegetation type, quantity, and different spatial scales of study [86].
# AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening
## 2 Breath Sound and Respiratory Diseases in Young Children
It can occur during exhalation (expiration) or inhalation (inspiration) and may or may not be accompanied by breathing difficulties. Figure 3 shows asthmatic breath sounds, which have a longer duration than those of pneumonia but are shorter than normal breath sounds. They exhibit two dominant frequencies, around 1800 Hz and 2700 Hz, which are significantly higher than normal breath sounds. The spectrogram appears relatively steady. These acoustic characteristics align with healthcare providers' perceptions of asthmatic wheezing as they often describe it as a continuous high-pitched sound with a distinct tonal quality. Croup, a viral infection affecting the upper airways, produces a distinctive barking cough and noticeable breathing difficulties. The associated breath sounds include stridor, a high-pitched squeaky noise during inhalation caused by swollen, narrowed airways; a barking cough, characterized by a loud, harsh, seal-like sound; and hoarseness, resulting from vocal cord inflammation. As shown in Figure 4, the inhalation phase has a shorter duration and significantly higher amplitude than exhalation, aligning with the obstruction of the upper airway. Croup-related breath sounds contain more high-frequency components, particularly in the 6000-8000 Hz range, which corresponds to the characteristic high-pitched noise. The overall breath cycle is longer than that of asthma and pneumonia but shorter than normal breath sounds. Figure 2: Pneumonia breath sound analysis. (**upper**) Time-domain waveform. (**middle**) Frequency spectrum. (**lower**) Spectrogram.