esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method and Data
### Econometric Model  
Before investigating the co-evolutionary mechanism between regional green technological innovation (RGTI) and carbon reduction in the construction industry (CRCI), the first task was to verify the potential relationship between RGTI and CRCI. This is particularly important for uncovering the intrinsic mechanism from the perspective of NQP. Accordingly, this study developed a bidirectional fixed-effects model and a mediation effect model to systematically quantify the pathways through which RGTI influences carbon emissions intensity of the construction industry (CEICI). Additionally, a mediation effect test was employed to thoroughly analyze the moderating role of NQP, clarifying the impact of new quality productivity on the process by which green technological innovation facilitates carbon reduction in the construction industry. The specific formulas are presented in Equations (6) and (7).  
\[CEICI_{it}=\gamma_{0}+\gamma_{1}RGIT_{it}+\gamma_{2}Control_{it}+\delta_{i}+ \lambda_{t}+\varepsilon_{it} \tag{6}\]  
\[CEICI_{it}=\gamma_{0}+\gamma_{1}RGIT_{it}+\gamma_{2}NQP_{it}+\gamma_{3}Control _{it}+\delta_{i}+\lambda_{t}+\varepsilon_{it} \tag{7}\]  
In Equations (6) and (7), \(i\) represents provinces, and \(t\) denotes years. The dependent variable, \(CEICI_{it}\), refers to the carbon intensity in the construction sector, calculated as the ratio of construction industry carbon emissions to construction industry value added. The independent variable, \(RDL_{it}\), represents regional green technological innovation levels. Control variables include energy structure (\(ES\)), degree of government intervention (\(DGI\)), industrial structure (\(IS\)), research and development investment (\(R\&D\)), level of openness to the outside world (\(LOOW\)), and regional economic level (\(LRE\)). Additionally, \(\delta_{i}\) and \(\lambda_{t}\) represent individual and time-fixed effects, respectively, while \(\varepsilon\) denotes the random disturbance term.  
To further explore the intrinsic mechanism of regional green technological innovation on carbon reduction in the construction sector from the perspective of new quality productivity (NQP), a mediation effect model was introduced. This approach aims to uncover the transmission pathway through which regional green technological innovation impacts carbon reduction via NQP as a mediating variable. The specific model formula is presented in Equation (8).  
\[NQP_{it}=\gamma_{0}+\gamma_{1}RGIT_{it}+\gamma_{2}Control_{it}+\delta_{i}+ \lambda_{t}+\varepsilon_{it} \tag{8}\]  
In Equation (8), \(NQP_{it}\) serves as the dependent variable, while \(RGIT_{it}\) acts as the independent variable. The meanings of the remaining symbols are identical to those in Equation (7).
# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 3 Methods
### Correlation Filter Tracking Model Based on the Deep-Pruned Feature Network
#### 3.4.4 Updating of Correlation Filter  
In the tracking process, the position of the target in frame t is predicted to be \((x_{t},y_{t})\) based on the feature map \(F_{CL,l}^{t-1}(S)(l=1,2,\ldots,KN_{CL})\) of the target in frame \(t-1\) using Equations (16)-(18). In frame \(t\), a search box \(S_{t}\) of the same size as the initial target box T is constructed centered on \((x_{t},y_{t})\), and the ideal gaussian response corresponding to this tracking box is \(G_{t}\in R^{M\times N}\). The features of the region where \(S_{t}\) is located are extracted using the deep-pruned feature network noted as \(F_{CL,l}^{t}(S_{t})\). The filter \(W_{t}^{l}\) is updated online by Equation (19) [30].  
\[W_{t}^{l}=\frac{\hat{F}_{CL,l}^{t}(S_{t})\odot\left(\hat{G}_{t}\right)^{\ast}} {\sum_{k=1}^{KN_{CL}}\hat{F}_{CL,k}^{l}(S_{t})\odot\left(\hat{F}_{CL,k}^{t}(S_ {t})\right)^{\ast}+\lambda}\ (l=1,2,\ldots KN_{CL}) \tag{19}\]  
Here, \(\hat{F}_{CL,l}^{t}(S_{t})\) represents the Fourier transform of the \(lth\) feature channel, and \(\left(\hat{G}_{t}\right)^{\ast}\) represents the complex conjugate of the Fourier transform of the ideal gaussian response \(G_{t}\).
# Source-Based Size-Resolved Optical Properties of Carbonaceous Aerosols
## 2 Materials and Methods
### Size-Resolved Aerosol Optical Properties  
Aerosol optical properties play important roles in regulating the Earth's radiation budget.  
The light scattering and absorption properties of atmospheric aerosols depend on the aerosol size distribution, chemical composition, and refractive index, which means that the contribution of aerosol optical properties differs from the mass contribution.  
The aerosol extinction coefficient (\(b_{ext}\)) can be calculated using the Mie theory based on the mass concentration, chemical composition, and size distribution [7; 14]. Figure 2 shows the composition-based extinction coefficients (\(b_{ext}\)) of carbonaceous aerosols with different geometric mean diameters (\(d_{g0}\)) and imaginary refractive indices (IRIs) of HULIS of 0.006 and 0.3. The refractive indices are 1.9-0.66i for EC, 1.595-IRIi (i.e., 1.595-0.006i and 1.595-0.3i) for HULIS and 1.53-0.006i for WSOC and WSOC (water-insoluble organic carbon) [14]. It should be noted that there uncertainty exists in calculating aerosol optical properties by the Mie theory. The Mie theory assumes aerosols are spherical particles, and so does not consider the morphology of non-spherical aerosols, or their aggregation. The model of homogeneous compact particles results in a systematic underestimation of the scattering phase function for almost all angles, but most typically, in the backscatter zone [20]. The impact of material mixing on the absorption efficiency of carbonaceous particles increases with the particle size and for particles with high carbon content [21]. More rigorous studies should be conducted that consider the non-spherical shape and aggregation of particles in order to accurately estimate aerosol optical properties.
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 5 Conclusions  
The integration of AIoT in PV systems represents a game-changing approach to enhancing solar energy monitoring, fault detection, and optimization. This survey explores the core applications of AI and IoT in solar energy systems, highlighting their roles in improving efficiency, reliability, and sustainability. By utilizing AI techniques such as ML, DL, and GenAI, solar energy systems can achieve advanced fault diagnosis, predictive maintenance, and accurate energy forecasting. Meanwhile, IoT-enabled solutions enable real-time monitoring, smart tracking, and automated cleaning, ensuring peak system performance.  
Despite these advancements, several challenges remain, including issues related to data, model adaptability, deployment constraints, and environmental limitations. Overcoming these challenges will require further research into scalable AI models, resilient IoT architectures, and advanced data-processing techniques. Additionally, emerging technologies like Edge AI, Federated Learning, Blockchain, and Digital Twins hold significant promise for shaping the future of AIoT-driven solar systems. Standardization and cross-sector collaboration will be crucial to addressing interoperability barriers and accelerating the global adoption of AIoT solutions.  
Looking ahead, the continuous evolution of AIoT will pave the way for more intelligent, autonomous, and efficient solar energy management systems. By integrating advanced analytics, real-time optimization, and secure data-sharing mechanisms, AIoT has the potential to usher in a new era of sustainable energy solutions. This survey lays the groundwork for future research and innovation, offering valuable insights into the current landscape and future possibilities of AIoT in solar energy applications.  
[PERSON] and [PERSON] were involved in conceptualization, collection of data, writing the paper, and designing the figures. [PERSON] and [PERSON] supervised the content and structure of the paper and contributed to collection of data, editing and revisions. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the support of the Autonomous University of the State of Quintana Roo (UQROO) in this publication.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
AI Artificial Intelligence  
AIoT Artificial Internet of Things  
ANN Artificial Neural Network  
ANNBM Artificial Neural Network-Based Model  
ART Adaptive Resonance Theory  
CNN Convolutional Neural Network  
DA-DCGAN Domain Adaptation and Deep Convolutional Generative Adversarial Network  
DNN Deep Neural Network  
DT Decision Tree  
ETSG Electrical Time Series Graph  
FCM Fuzzy C-Mean  
FFBP Feedforward Backpropagation  
FFNN Feedforward Neural Network
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 3 Outdoor Measurements and Wireless Link Characterization
### Communication Range Outdoor Measurements  
In this section, the link quality characterization of a point-to-point communication link deployed in different outdoor environments (i.e., urban and suburban areas) is presented. Two different output power levels on the transmitter's side (i.e., -10 dBm and 0 dBm) and two different data rates on the transmitter's and receiver's sides (i.e., 50 Kbps and 500 Kbps) are used in order to analyze how link reliability, latency, and energy consumption can be traded-off against each other. The total packet size was set to 31 Bytes, as depicted in Figure 9, and for each measurement 400 packets are sent.
## 2 Description of the Heat Transport and Distribution Networks Based on Absorption Systems
### Absorption Heat Pumps (AHP) in Step-Down Thermal Substations (SDSs)  
The SDS consists of one or more absorption heat pumps driven by the hot water of the primary or heat transport circuit coupled with a water-to-water heat exchanger, as shown in Figure 1b, with the objective to supply hot and/or chilled water to a local distribution district thermal network. The type of absorption machine used for a given thermal station will depend mainly on (1) the required hot water temperature of the local district heating and (2) the heating and/or cooling demand profiles of the connected end users.  
For the SDS, three types of absorption systems working with the working pair H\({}_{2}\)O/LiBr are analyzed: Single-effect, double-lift, and double-effect absorption chillers. Figure 4 shows the Duhring scheme for these absorption chiller configurations. The single-effect configuration (see Figure 4a) is able to produce both chilled water at the evaporator and hot water at the condenser and absorber but at quite a low temperature.  
Another option to produce heating and cooling is a double-lift absorption heat pump (Figure 4b) when driven heat is available at the required temperature. This kind of cycle was already investigated in the past using organic-based working fluids [32] and it is also known in the literature as a heat-coupled half effect cycle [33] or double lift cycle II [34]. This cycle is able to produce hot water at a higher temperature than a single-effect cycle, but at the expense of a lower heating and cooling efficiencies (COP and EER, respectively). The double lift is a three-pressure cycle with two pairs of evaporator-absorbers as shown in Figure 4b. The main benefit of this cycle is the increase in the temperature difference between evaporator E2 and absorber A1. Thus, the output temperature from absorber A1 is higher than the usual temperature in an absorption system. The heat rejected in the absorber A2 is used as input heat for the higher-pressure evaporator, E1. A double-effect cycle (Figure 4c) is another option when the driving temperature available is high to provide cooling at high efficiency, but as in the case of the single effect, the provided heat is at quite a low temperature.
## 4 Performance Evaluation and Comparison
### Evaluation Metrics
#### 4.1.1 Accuracy  
Accuracy stands as a fundamental metric in the realm of classification models, offering a measure of the overall performance. It signifies the proportion of correctly classified instances, encompassing both true positives and true negatives, in relation to the total number of instances present in the dataset. Mathematically, the accuracy is computed as follows:  
\[Accuracy=(TP+TN)/(TP+TN+FP+FN)\]
# GPU-Accelerated Eclipse-Aware Routing for SpaceWire-Based OBC in Low-Earth-Orbit Satellite Networks
## 5 Conclusions  
This paper proposed a GPU-accelerated Eclipse-Aware Routing method that minimizes hop count while balancing energy consumption for real-time routing on an onboard computer to solve the problems of dynamic topology changes and energy constraints in LEO satellite networks. The proposed method finds the most efficient route based on not only hop counts but also satellite battery status and eclipse duration. Furthermore, to solve the real-time problem in the conventional method based on CPUs, the proposed method has been implemented on both a CPU and a GPU for parallel processing, which can reduce the computation time significantly. Experimental results showed that the GPU-based implementation operates up to 3.081 times faster than the CPU-based approach while maintaining routing accuracy, as confirmed by the consistency of results between the PC and OBC environments. Furthermore, an experiment using a SpaceWire-based communication  
Figure 11: SpaceWire Brick Mk4 internal structure and experimental scenarios.  
Figure 12: SpaceWire Device Configuration GUI screen viewed from the receiver’s laptop.  
system demonstrated that the proposed algorithm can dynamically update routing tables in onboard networks, validating its feasibility in realistic satellite communication scenarios. In future works, research on extending the proposed method to include not only LEO satellites but also MEO and GEO satellites for more efficient network routing.  
Conceptualization, H.L. and M.H.; methodology, H.K. and H.L.; software, H.K.; validation, H.K. and H.L.; formal analysis, H.L.; investigation, M.H.; writing--original draft preparation, H.K.; writing--review and editing, H.L. and M.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Agency for Defense Development, funded by the Korean government (Defense Acquisition Program Administration) (UI237032 TG). Data are contained within the article.  
The authors declare no conflicts of interest.
## 4 Results and Discussion
### Prediction of Suspended-Sediment Transport
#### 4.2.3 Studenska Brv, Maribor Measuring Site
The use of an Acoustic Doppler Current Profiler to determine the concentration and distribution of suspended sediment over a river profile has proven to be a good alternative to other methods when used in conjunction with appropriate software that allows the conversion of acoustic signals to the concentration of suspended sediments. This approach allows the presentation of a comprehensive picture of sediment transport through a selected cross-section of a water body.
# Feasibility of LoRa for Smart Home Indoor Localization
###### Abstract  
With the advancement of low-power and low-cost wireless technologies in the past few years, the Internet of Things (IoT) has been growing rapidly in numerous areas of Industry 4.0 and smart homes. With the development of many applications for the IoT, indoor localization, i.e., the capability to determine the physical location of people or devices, has become an important component of smart homes. Various wireless technologies have been used for indoor localization including WiFi, ultra-wideband (UWB), Bluetooth low energy (BLE), radio-frequency identification (RFID), and LoRa. The ability of low-cost long range (LoRa) radios for low-power and long-range communication has made this radio technology a suitable candidate for many indoor and outdoor IoT applications. Additionally, research studies have shown the feasibility of localization with LoRa radios. However, indoor localization with LoRa is not adequately explored at the home level, where the localization area is relatively smaller than offices and corporate buildings. In this study, we first explore the feasibility of ranging with LoRa. Then, we conduct experiments to demonstrate the capability of LoRa for accurate and precise indoor localization in a typical apartment setting. Our experimental results show that LoRa-based indoor localization has an accuracy better than 1.6 m in line-of-sight scenario and 3.2 m in extreme non-line-of-sight scenario with a precision better than 25 cm in all cases, without using any data filtering on the location estimates.  
LoRa; indoor localization accuracy; smart home; LOS; NLOS +
Footnote †: journal: Applied Sciences
###### Abstract  
A set of magnetotelluric (MT) data collected from 28 stations at the Kasane Hot Spring in northern Botswana was used to derive a 3-D model of the electrical resistivity distribution around deep geologic structures that we associated with geothermal resources. The dimensionality analysis results revealed that 3-D modeling should be used to determine the electrical structure. The resistivity models revealed a conductive layer with an average thickness of 200 m, representing the overlying sediments of the Proterozoic volcanic rocks. The thick high-resistivity zone (>100 \(\Omega\)m) below the conductive layer can be associated with Mesoproterozoic bedrock. The MT measurements in this area show a tube-shaped conductive anomaly that could serve as a fluid pathway feeding the hot spring. A fracture-controlled meteoric fluid circulation presumably determines the existence of the Kasane Hot Spring system.  
magnetotelluric; Ghanzi-Chobe Belt; 3-D MT inversion; resistivity; geothermal; Kasane; Botswana 2023
## 3 Derivation of the Governing Equations
Let \(\phi\) be an Airy stress function, satisfying Equations (1) and (2); then, we can obtain  
\[\sigma_{x}=\frac{\partial^{2}\phi}{\partial y^{2}},\sigma_{y}=\frac{\partial^ {2}\phi}{\partial x^{2}},\tau_{xy}=-\frac{\partial^{2}\phi}{\partial x\partial y} \tag{11}\]  
Substituting the shear force expression of Equation (10) into Equation (3), the following equation can be obtained:  
\[D_{x}\frac{\partial^{4}w}{\partial x^{4}}+D_{xy}\frac{\partial^{4}w}{\partial x ^{2}\partial y^{2}}+D_{y}\frac{\partial^{4}w}{\partial y^{4}}=t\sigma_{x}( \frac{1}{R}+\frac{\partial^{2}w}{\partial x^{2}})+t\sigma_{y}\frac{\partial^ {2}w}{\partial y^{2}}+2t\tau_{xy}\frac{\partial^{2}w}{\partial x\partial y} \tag{12}\]  
Applying the strain expression in Equation (7) to Equation (8), the following equation can be obtained:  
\[\frac{1}{E_{x}}\frac{\partial^{2}\sigma_{x}}{\partial y^{2}}+\frac{1}{E_{y}} \frac{\partial^{2}\sigma_{y}}{\partial x^{2}}-\frac{2}{E}\frac{\partial^{2} \tau_{xy}}{\partial x\partial y}-\frac{\
u}{E}(\frac{\partial^{2}\sigma_{y}} {\partial y^{2}}+2\frac{\partial^{2}\tau_{xy}}{\partial x\partial y}+\frac{ \partial^{2}\sigma_{x}}{\partial x^{2}})=(\frac{\partial^{2}w}{\partial x \partial y})^{2}-\frac{\partial^{2}w}{\partial x^{2}}\frac{\partial^{2}w}{ \partial y^{2}}-\frac{1}{R}\frac{\partial^{2}w}{\partial y^{2}} \tag{13}\]  
Using Equation (11), Equations (12) and (13) become  
\[D_{x}\frac{\partial^{4}w}{\partial x^{4}}+D_{xy}\frac{\partial^{4}w}{\partial x ^{2}\partial y^{2}}+D_{y}\frac{\partial^{4}w}{\partial y^{4}}=tL(w,\sigma)+ \frac{t}{R}\frac{\partial^{2}\phi}{\partial y^{2}} \tag{14}\]  
\[\frac{1}{E_{x}}\frac{\partial^{4}\phi}{\partial y^{4}}+\frac{1}{E_{y}}\frac{ \partial^{4}\phi}{\partial x^{4}}+\frac{2}{E}\frac{\partial^{4}\phi}{\partial x ^{2}\partial y^{2}}=S(w,w)-\frac{1}{R}\frac{\partial^{2}w}{\partial y^{2}} \tag{15}\]  
where \(L(w,\sigma)\) is a nonlinear term, and \(S(w,w)\) is a higher order term. \[L(w,\sigma)=\sigma_{x}\frac{\partial^{2}w}{\partial x^{2}}+\sigma_{y}\frac{ \partial^{2}w}{\partial y^{2}}+2\tau_{xy}\frac{\partial^{2}w}{\partial x \partial y},\ S(w,w)=(\frac{\partial^{2}w}{\partial x\partial y})^{2}-\frac{ \partial^{2}w}{\partial x^{2}}\frac{\partial^{2}w}{\partial y^{2}} \tag{16}\]  
Due to the external forces per unit thickness of the shell boundary causing the principal compressive stresses \(p_{x}\), \(p_{y}\) and \(p_{xy}\), the following equations can be obtained:  
\[p_{x}=-\sigma_{x},p_{y}=-\sigma_{y},p_{xy}=-\tau_{xy} \tag{17}\]
# Use of Real-Time GNSS-RF Data to Characterize the Swing Movements of Forestry Equipment
## 3 Results
### Swing Angle Analysis  
Figure 5 shows a graphical comparison between the observed angle interval for each trial and the angles derived from GNSS-RF data packets for each transmission interval at both locations and for both elements. It is evident in the figure that the 2.5 and 5.0 s intervals most accurately capture overall angle measures at the grapple location, and that the heel location introduces greater error at both intervals. The solid black line in the figures represents a 1:1 relationship between the observed and the predicted values derived from the GNSS-RF recorded data packets. A clear trend of under prediction of swing angle can be seen in the figure for all transmission intervals, transponder locations, and machine elements. The most severe under-predictions are associated with the 10.0 s transmission interval, however. Both 2.5 and 5.0 s transmission intervals appeared to follow similar trends of underestimation in the predicted angles. Figure 4: Bar chart representing the proportion of correct classifications of the Return cycle elements at the grapple and heel locations at 18 angle intervals. Figure 5: These figures represent the relationship between GNSS-RF derived angle measurements and observed angle intervals at the Grapple and heel locations for the (**a**) Swing Unload and (**b**) Return elements. Equivalence-based regression analysis showed that the only combination of GNSS-RF derived angles for which both the intercept and slope were statistically similar to the observed values was the transponder located at the grapple with a 2.5 s interval capturing the swing unload cycle element (Table 4). In all other instances the intercept was dissimilar for the predicted angle values when compared to the observed values. Only the 5.0 s interval swing unload element at the grapple and the 2.5 s interval at the grapple for the return element were found to be similar in regards to the slope of the regression line. The extent to which swing movements associated with each GNSS-RF transponder could be discerned visually varied with transmission interval (Figure 6). Visual interpretation of the tracks indicated that the 2.5 s transmission interval more accurately represented swing movements compared to 5.0 and 10.0 s transmission intervals. The data support this interpretation in that the angle totals best represented the actual swing angle totals for the 2.5 s interval. Higher frequency of data point collection creates a comparably smoother swing track than the 5.0 and 10.0 s transmission intervals. The movements of the cab during the experiment were also captured by an additional transponder located on the rear corner of the cab. The transponders located at the grapple and heel remained equidistance from the cab transponder throughout the duration of the experiment.