esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Approach
### Multi-Level Feature Fusion  
As the number of layers of the CNN increases, the information we obtain gradually evolves from low-level visual features to high-level semantic information. Usually, we only use the semantic features of the last layer for image processing. However, the low-level visual features often retain more spatial structure and detail information. In this section, we have designed a new feature fusion method to ensure that both the low-level visual features and high-level semantic information are fully utilized. We will perform a simple convolution process on each module in the network and combine them with the feature maps on the main path to perform fine-grained image classification. In particular, for our proposed TA-model and GA-model, multi-level feature fusion can fuse information between different branches of each module. For example, we can aggregate the attentions of the two branches of the TA-block, or we can link the grouping attentions of the GC-block. Here, we did not choose to simply sum the high-level information and the low-level features, but use the SORT method to obtain high-order features to improve the non-linearity and robustness of the network. Finally, multi-level feature fusion not only achieves the fusion between low-level visual features and high-level semantic information, but also achieves the combination of local features and overall features. A schematic diagram of multi-level feature fusion is shown in Figure 3.  
As shown in Figure 3, the modules in the dashed box are TA-block, GA-block, and GC-block, from left to right. Among them, the TA-model is composed of multiple TA-blocks in series, and GA-model is constituted of GA-block and GC-block. The darker colored modules in the network are convolved by the TA-block and GC-block, respectively. The \(\oslash\) is a SORT operation, which represents high-order information fusion between different hierarchical features. The resulting \(X_{out}\) are feature maps for fine-grained image classification. Adding the global pooling module and the dense module to \(X_{out}\) is the structure diagram of the entire network implementation.  
Figure 3: The structure schematic of multi-level feature fusion.
# Multi-Scale Concurrent Topology Optimization Based on BESO, Implemented in MATLAB
###### Abstract  
In multi-scale topology optimization methods, the analysis encompasses two distinct scales: the macro-scale and the micro-scale. The macro-scale refers to the overall size and dimensions of the structural domain being studied, while the micro-scale pertains to the periodic unit cell that constitutes the macro-scale. This unit cell represents the entire structure or component targeted for optimization. The primary objective of this research is to present a simplified MATLAB code that addresses the multi-scale concurrent topology optimization challenge. This involves simultaneously optimizing both the macro-scale and micro-scale aspects, taking into account their interactions and interdependencies. To achieve this goal, the proposed approach leverages the Bi-directional Evolutionary Structural Optimization (BESO) method. The formulation introduced in this study accommodates both cellular and composite materials, dealing with both separate volume constraints and the utilization of a single volume constraint. By offering this simplified formulation and harnessing the capabilities of the multi-scale approach, the research aims to provide valuable insights into the concurrent optimization of macro- and micro-scales. This advancement contributes to the field of topology optimization and enhances its applications across various engineering disciplines.  
topology optimization; microstructure; homogenization; concurrent; BESO; 2D and 3D design domains +
Footnote †: journal: Applied Sciences
# Designing an Adaptive Underwater Visible Light Communication System +
## 6 Receiver Design
### Transmitting Acknowledgment  
Once the angle with the maximum signal strength is determined and stored, the receiver adjusts its position accordingly. MATLAB then generates the ACK signal, which is modulated and transmitted via loopback to the transmitter. The transmitter, in a waiting mode, awaits the acknowledgment before proceeding with the next steps.
## 2 Datasets
### PR-OBS-3A Precipitation Product  
PR-OBS-3A, a EUMETSAT Hydrological Satellite Application Facility (HSAF) precipitation product, is the benchmark to evaluate the data fusion technique here proposed. In fact, this product and the one yielded by our methodology have the same spatial and temporal resolutions which is a very important aspect for a reliable comparison. A complete description of the algorithm is provided in [28]. PR-OBS-3A provides instantaneous precipitation rain rate retrieved from blending information produced by PMW and IR observations using SSMIS, MHS and SEVIRI sensors (channel 9, wavelength 10.8 \(\upmu\)m) [29]. The product is designed to provide a better time continuity and even spatial coverage with respect to the relatively infrequent precipitation observation of LEO instruments, processing, and calibrating IR observation from GEO platforms. PR-OBS-3A is calibrated directly by means of a \"rapid update technique\" lookup table of precipitation based on time-space coincident GEO and LEO information.  
PR-OBS-03 performs relatively well for the highest precipitation class when compared either with the radar or with rain gauge observations. Its performance starts to decrease in the intermediate precipitation class, until to become very poor for the lower precipitation regime. The analysis of monthly statistical scores shows a seasonal trend: it performs better during winter time, but an underestimation of precipitation rate is always observed [30; 31].
# An Efficient Distributed Coverage Hole Detection Protocol for Wireless Sensor Networks
###### Abstract  
In wireless sensor networks (WSNs), certain areas of the monitoring region may have coverage holes and serious coverage overlapping due to the random deployment of sensors. The failure of electronic components, software bugs and destructive agents could lead to the random death of the nodes. Sensors may be dead due to exhaustion of battery power, which may cause the network to be uncovered and disconnected. Based on the deployment nature of the nodes in remote or hostile environments, such as a battlefield or desert, it is impossible to recharge or replace the battery. However, the data gathered by the sensors are highly essential for the analysis, and therefore, the collaborative detection of coverage holes has strategic importance in WSNs. In this paper, distributed coverage hole detection algorithms are designed, where nodes can collaborate to detect the coverage holes autonomously. The performance evaluation of our protocols suggests that our protocols outperform in terms of hole detection time, limited power consumption and control packet overhead to detect holes as compared to other similar protocols.  
wireless sensor networks; coverage; hole detection +
Footnote †: journal: Sensors
# Investigation of the Fatigue Life of Bottom-Up Cracking in Asphalt Concrete Pavements
## 5 Conclusions  
In this paper, three types of asphalt pavement structures that are commonly used in practical engineering were selected to investigate the fatigue life of the bottom-up cracking of asphalt pavement, and the main conclusions are as follows:  
1. The proportion of fatigue propagation life to the fatigue life of cracks at different temperatures varied significantly. Therefore, it is essential to consider the fatigue propagation life of cracks at different temperatures for accurately calculating the entire fatigue life of asphalt pavement structures. That is, the fatigue life calculation model of a pavement structure can be expressed as \(N=N_{i}+N_{p}\);
2. For the fatigue life of a pavement structure, the crack propagation life decreased sharply with the decrease in temperature. When the temperature dropped from 25 \({}^{\circ}\)C to 0 \({}^{\circ}\)C, the crack propagation life of the three types of surface layer structures decreased by more than 300 times. When the temperature dropped from 0 \({}^{\circ}\)C to \(-\)10 \({}^{\circ}\)C, the crack propagation life of the three types of surface layer structures decreased by more than 600 times;
3. The increase in asphalt surface thickness can effectively improve the fatigue crack life of a pavement structure. Therefore, it is an ideal choice to increase the thickness of an asphalt surface appropriately for improving the service life of pavements. Certainly, high AC thicknesses might make the pavement vulnerable to top-down cracking, and considering the cost, understanding a reasonable thickness is worth further study.  
**Author Contributions:** Conceptualization and methodology, G.C. and Y.Z.; formal analysis, G.C.; investigation, X.H.; resources, J.Y. and Y.Z.; data curation, J.Y. and G.C.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z.; project administration, J.L.; funding acquisition, J.Y. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors gratefully acknowledge the support provided by the National Natural Science Foundation of China (Nos. 52108304 and 52108117), Special Funding of Chongqing Postdoctoral Research Programs (No. 2021 XM3045), China Postdoctoral Science Foundation (No. 2022M710540) and Chongqing Natural Science Foundation (No. 2022 NSCQ-MSX3349).  
**Institutional Review Board Statement:** The study did not require ethical approval.  
**Informed Consent Statement:** The study did not involve humans.  
**Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Energy-Efficient LoRa Routing for Smart Grids
## 4 Results and Discussion
### Performance Analysis
For the proposed protocol Cum_LEACH implemented for 5000 runs, the runs at which the first and last node died are given in Table 6. The percentage of dead nodes vs. the total number of runs is given in Figure 11. From this figure, it can be noted that 50% of the nodes were dead at 1149 rounds, whereas for the LEACH, SEP, and DEEC protocols, the number of runs was 509, 715, and 1006, respectively. This clearly helped in the extension of the network lifetime and, hence, helped maintain the network for a longer duration. The effectiveness of QK_ABO in generating multiple optimal paths once the CHs were selected is presented using the fitness value plot in Figure 12. A comparative analysis of the convergence of the proposed QKE-ABO and African buffalo optimisation (ABO), pelican optimisation algorithm (POA), and ant lion optimisation (ALO) algorithms is presented with the fitness value as a function of the optimisation iterations. Figure 10: Number of dead nodes vs. number of rounds for various CH selections. Figure 9: Number of operating nodes vs. number of rounds for various CH selections. The optimisation outcomes obtained regarding the objective function's number of evaluations are exhibited in Figure 10. The convergence to the optimal solution is exhibited by the net minimal value. In comparison to the existing models, the proposed QKE-ABO algorithm converged rapidly toward the minimal value. Hence, the proposed QKE-ABO method is more efficient. The full convergence curves of the proposed SMAx and the existing Mexican Axolotells Optimisation (MAX) and Sparrow Search Algorithm (SSA) are exhibited in Figure 13. The iterative process's convergence was ensured by the parameter ranges. When compared to the existing models' search process, the proposed SMAx algorithm converged to an approximate result after fewer iterations, exhibiting the model's better performance. Figure 11: Number of rounds vs. percentage of dead nodes for various CH selections. Figure 12: Comparative analysis of the QKE-ABO algorithm with existing algorithms.
# Lightweight Detection Method of Obfuscated Landing Sites Based on the AST Structure and Tokens
## 4 Experiment
### Lightweight AST
#### 4.2.1 Analysis of the Average Reduction Rate of Lightweight ASTs  
Table 4 also shows the percentage of nodes produced while generating lightweight ASTs for samples of 23 different obfuscation types. On average, we can reduce the number of nodes by 47.26% during the generation process. To generally validate the average reduction rate, we performed a \(t\)-test using a statistical significance of 0.05 under hypothesis H\({}_{0}\), which states that the average reduction rate during JavaScript obfuscation using a lightweight AST is 47% as follows:  
H\({}_{0}\) (null hypothesis): \(\mu\) = 47, H\({}_{1}\) (alternative hypothesis): \(\mu\) \(\
eq\) 47, where \(\mu\) is the population mean. We compute the sample mean (\(\overline{x}\)) = 47.26 and the sample standard deviation \(s\) = 2.47.  
The \(t\)-statistic is computed as \(t=\frac{\overline{x}-\mu_{0}}{s/\sqrt{\mu}}=\frac{47.26-47}{2.47/\sqrt{23}}=0.504\).  
According to the statistical significance of 0.05, t-statistic to reject null hypothesis must be higher than 1.960, but it is 0.504. Thus, we cannot reject the null hypothesis H\({}_{0}\): \(\mu\) = 47. Therefore, we can conclude that the average node reduction rate is 47%.  
The experimental average reduction rate of 47.26% for the samples of 23 different obfuscation types is greater than the theoretical average reduction rate of 9.59%, as shown in Table 2. This is because the representative minimum node reduction rate formula is established based on the expression and statement, which minimize the number of AST nonstructural nodes. In the real JavaScript environment, the expression and statement are used such that the AST nonstructural node is not minimal. Therefore, when generating lightweight ASTs, the rate of reduced nodes increases.
# Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry
## 5 Results
### Long-Term ASL Trend
Figure 7a,b show that the \(DT_{SA@TG}\) and \(DT_{TG@SA\ cycle}\) pairs display slightly improved agreement, with an RMSE of 1.2 mm/yr, compared to that of the \(DT_{TG}\) and \(DT_{SA@TG}\) pairs, which yield an RMSE of 1.5 mm/yr. Also, the overall average ASL trend of the 13 stations is 3.9 mm/yr using favourably hourly TG data, whereas utilizing instantaneous  
Figure 8: Sea-level trend estimates using hourly TG records (dark blue) and TG data at the time of SA cycles (light blue) for the period of 1995–2022 and the corresponding amount (%) of missing data in each station. The RMSE between the two estimated trends is 1.08 mm/yr. The whiskers represent the estimated trend uncertainties within 95% CI of each dataset. In general, the stations with missing data show larger discrepancies between the \(DT_{TG}\) and \(DT_{TG@SA\ cycle}\)-based trend estimates. (but time-wise irregular) SA data yields a trend estimate of 4.8 mm/y in the Baltic Sea. This experiment demonstrates that the temporal frequency of the instantaneous SA data could be sufficient for sea-level trend estimates, yielding an agreement of \(\pm\)1 mm/yr with the hourly TG data series (cf. Figure 8). Figure 7b,c suggest that SA data stand out as a dependable method to accurately determine ASL trends in proximity to the coast, with an accuracy of 1 to 1.5 mm/yr. This is of particular significance given the challenges associated with obtaining precise sea-level estimates in these coastal areas, especially within the unique characteristics of the Baltic Sea [33]. Moreover, it highlights the constraints of TG data for estimating RSL trends, especially for stations with missing data. While, historically, TG data records have been extensively utilized for sea-level trend determinations, the presence of data gaps and outliers can lead to distorted trend estimates, particularly on a regional scale. Therefore, SA data emerge as a reliable resource for determining sea-level trends on a regional scale, particularly over such areas where the quality or presence of TG stations may be insufficient.
# IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey
## 3 IoT Devices and Cloud Processing
### Data Transmission Technologies
#### 3.2.1 LoRaWAN  
LoRaWAN is a wireless communication protocol based on the patented LoRa technology. The patent was granted in 2014 to Semtec, and the first LoRaWAN protocol specification was released in 2015. The standards are now managed by the LoRa Alliance. Transceivers have a low sensitivity to noise and thus a greater range thanks to forward error correction (FEC), together with a proprietary spread spectrum modulation technique. The operating radio frequency varies according to the region of deployment and its local regulations, but it always belongs to a license-free band. A LoRaWAN network typically includes the following network elements. The end device is application-dependent, provides control or measurement functions (sensor, actuator) and is wirelessly connected to the LoRaWAN network via a gateway. The gateway receives and forwards messages (usually via the internet) to a network server. The network server provides network management functions such as data processing, message deduplication, configuration and scheduling, bandwidth regulation and message routing. The application server remotely collects data from sensors and transmits commands to device actuators [83; 84].  
The technology has a range of 5 km (urban) to 20 km (rural) [85]. Depending on the configuration, the payload can contain up to 242 bytes of data [86]. LoRa defines three device classes:  
* Class A--End devices can start transmission (uplink) at any time, while the base station transmits data only for a short time once the uplink is complete. Each uplink time slot is followed by two downlink slots, after which the receiver switches off. This class has the lowest energy demand.
* Class B--Devices have the same uplink and downlink slots as Class A, plus one extra downlink slot. The end-device receiver is switched on for this extra slot according to a predetermined schedule. The base station is programmed to follow this schedule.
* Class C--Receivers can listen continuously, except in transmit mode. This class of device is ideal for applications that require many downlink transmissions because the base station broadcasts at any time. Its disadvantage is higher power consumption [87].
## 1 Introduction
The main contribution of the proposed model is the direct formulation of the uncertainties related to the determination of the current state as well as the impact of the maintenance operation. It presents a novel approach to formulate the uncertainty associated with observing the current pipeline states as a continuous probability function. It explicitly expressed inspection errors (which can be extended to other types of errors or constraints) in the model, which may have been implicitly contained in the MDP state definition of prior studies. With this approach, the impact of inspection methods can be measured directly from the model. To the best of our knowledge, it is the first model to implement CSPOMDP along with the respective solvers for modeling a pipeline maintenance optimization. The model provides a springboard for developing decision-support systems that are more in tune with situations on the ground. The remainder of the paper is organized as follows: Section 2 presents a brief background on the corrosion process, which is followed by a literature review on Markov processes (MP) maintenance modeling in the oil and gas industry. Section 4 discusses the basic concepts of the POMDP method. It also describes the proposed CSPOMDP model for pipeline maintenance optimization. The application of the proposed model is demonstrated using consolidated numerical data from the literature in Section 5. Finally, Section 6 gives some concluding remarks and future research extension directions for the proposed model.
# Development and Opportunities of Clean Energy in China
## 4 Clean Energy in China
### Development and Implementation of Clean Energy in China
#### 4.3.1 Hydroelectric Power  
In western China, especially the five provinces (autonomous regions and municipalities directly under the Central Government) of Yunnan, Guizhou, Sichuan, Chongqing, and Tibet in the southwest region account for 2/3 of the national total. Hydropower resources are enriched in the Jinsha River, Yalong River, Dadu River, Lancang River, Wujiang River, Upper Yangtze River, Nanpan River, Hengshui River, Upper Yellow River, Xiangxi, Fujian, Zhejiang, Jiangxi, Northeast, Yellow River, Yuliu and Nujiang River and other hydropower sources [28]. These rivers have concentrated hydropower resources, which is conducive to the realization of cascaded and rolling development of the river basin, the construction of large-scale hydropower energy bases, and the implementation of \"West-East Power Transmission\" by giving full play to the scale benefits of hydropower resources. The developable installed capacity of China's hydropower resources is about 660 million kW, and the annual power generation is about 3 trillion kWh. From 2015 to 2020, China's hydropower generation has grown steadily (see Figure 4). In 2020, China's conventional hydropower installed capacity has reached 350 million kW, with an annual power generation of 1214.03 billion kWh. By 2050, China's remaining hydropower resources will still be 360 million kW, and the annual power generation will be 190 million kWh [29].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Nation**} & \multicolumn{3}{c}{**Emission Standard Limits (mg/m\({}^{3}\))**} \\ \cline{2-4}  & \(\mathbf{SO_{2}}\) & \(\mathbf{NO_{2}}\) & \(\mathbf{SMOKE}\) \\ \hline China & 42 & 100 & 20 \\ USA & 458 & 317.5 & 40.8 \\ Japan & 50 & 100 & 20 \\ EU & 155 & 190 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Emission standard limits of major air pollutants for thermal power plants in major countries in the world [27].  
Figure 4: China’s hydropower generation and growth rate from 2015 to 2021.