esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Correlating MYD, GOSIF, and SIF757 with GPP\({}_{\text{EC}}\) at a Single Tower  
Table 4 illustrates the R\({}^{2}\) of MYD-GPP\({}_{\text{EC}}\), GOSIF-GPP\({}_{\text{EC}}\), and SIF757- GPP\({}_{\text{EC}}\) models. On the whole, GOSIF (R\({}^{2}\) = 0.15-0.88, \(p<0.001\)) and SIF757 (R\({}^{2}\) = 0.08-0.99, \(p<0.001\)) were more strongly correlated with GPP\({}_{\text{EC}}\) than MYD (R\({}^{2}\) = 0.15-0.79, \(p<0.001\)). GOSIF demonstrates the strongest ability to estimate GPP. It can be concluded that SIF was better at estimating GPP than the LUE model at an ecological scale for a single flux tower. The low correlation of the MYD-GPP\({}_{\text{EC}}\) model is attributed to the fact that MODIS uses the LUE model to calculate GPP. LUE\({}_{\text{P}}\) is the critical input parameter of the LUE model, which is susceptible to the plant growth status and ecological environments. Therefore, theoretically, the light use efficiency varies with the environments and growth stages of vegetation. However, LUE\({}_{\text{P}}\) is generally determined as a fixed parameter in the LUE model, according to specific vegetation types, resulting in large uncertainties of estimated GPP [20]. R\({}^{2}\) is missing in sites US-ALQ, US-GLE, and US-Tw3 because there is insufficient available MODIS GPP data to build linear relationships. Poor correlations of SIF757-GPP\({}_{\text{EC}}\) at US-Tw1 and US-Tw4 are due to small amounts of SIF757 data. The footprints of OCO-2 could not match the flux towers well, even if the sampling range was extended to 25 km, resulting in a lack of high-quality data to establish a linear model at the US-Tw1 and US-Tw4 sites. Compared with OCO-2 SIF, the GOSIF provides a dataset covering the globe, thus avoiding the above problems.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviations and Acronyms** & **Explanation** \\ \hline GPP & Gross primary production \\ SIF & Sun-induced chlorophyll fluorescence \\ SIF757 & OCO-2 SIF at 757 nm retrieval band \\ SIF771 & OCO-2 SIF at 771 nm retrieval band \\ MYD & MYD17A2H/MODIS GPP \\ GPP\({}_{\text{EC}}\) & GPP of EC tower \\ GPP\({}_{\text{GOPF}_{\text{GOSIF}}}\) & GPP estimated using GOSIF-GPP\({}_{\text{EC}}\) model \\ GPP\({}_{\text{SFF}}\) & GPP estimated using SIF757-GPP\({}_{\text{EC}}\) model \\ GPP\({}_{\text{MYD}}\) & GPP estimated using MYD-GPP\({}_{\text{EC}}\) model \\ Re & Total ecosystem respiration \\ NEE & Net ecosystem exchange \\ EVI & Enhanced vegetation index \\ PAR & The photosynthetically active radiation received by the plant canopy \\ fPAR & The proportion of PAR absorbed by plant canopies \\ LUE\({}_{\text{P}}\) & Light use efficiency \\ CRO & Croplands \\ CSH & Closed shrublands \\ DBF & Deciduous broadleaf forests \\ ENF & Evergreen needleleaf forests \\ GRA & Grasslands \\ MF & Mixed forests \\ OSH & Open shrublands \\ NDWI & Normalized difference water index \\ WET & Permeland wetlands \\ \hline \hline \end{tabular}
\end{table}
Table 3: Explanation of Abbreviations and Acronyms.
# Fractional Frequency Reuse Optimal SINR Threshold Selection Using NIR and ISODATA
## 5 Discussion  
The ECDF curve shows the probability of achieving a particular throughput. Consider Figure 7 where the case of 10 UEs per cell. For a throughput value of 0.5 Mbps, the probability of achieving that throughput was 0.25 for NIR or 25%, meaning 75% of UEs achieve throughputs higher than 0.5 Mbps. The performance of ISODATA at that point was  
Figure 14: FFR configurations fairness chart for different number of user equipment per cell.  
Figure 13: Average edge throughput for a number of user equipment per cell.  
0.55 or 55%, meaning only 45% of UEs achieved throughputs higher than 0.5 Mbps. Whereas for static FFR, all the UEs fell below 0.5 vMBps, meaning the NIR method outperformed ISODATA thresholding and the static FFR approach. In some cases, such as in Figure 8, in the case of 20 UEs per cell, ISODATA outperformed NIR up to throughputs of 0.325; beyond that threshold, NIR performed better. These ECDF curves, therefore, came in handy in determining the probability of UEs achieving a certain minimum throughput level or service level. The graph in Figure 12 showed the highest individual edge UE throughput achieved by the various approaches. Figure 13 shows the graph of the average edge throughput for all the UEs for the various approaches under different cell-load conditions. Figure 14 shows the fairness graph. The higher the fairness index, the better since UEs with poor SINR were also catered for. NIR technique achieved the highest peak throughput values from 10 UEs per cell to 50 UEs per cell. For 10 UEs, 0.61 Mbps was recorded, and ISODATA recorded 0.59 Mbps while static FFR is 0.49 Mbps. This shows that NIR outperformed ISODATA by 3.3% and outperformed static FFR by 19.7%. For 50 UEs per cell NIR reported 0.17 Mbps, while ISODATA was 0.16 Mbps and static FFR was at 0.12 Mbps. NIR outperformed ISODATA by 5.9%, while it outperformed static FFR by 29.4%. Average throughput would be a more desirable measure of performance. All the edge UE figures are considered instead of a single peak value. NIR at 10 UEs per cell reported 0.54 Mbps, while ISODATA reported 0.52 Mbps and static FFR is 0.39 Mbps. NIR performed 3.7% and 27.8% better than static FFR. For 50 UEs, NIR and ISODATA performed the same at 0.14 Mbps but did better than static FFR at 0.09 Mbps. In only one instance of 20 UEs per cell, ISODATA marginally outperformed NIR by 6.5%.
## 2 Materials and Methods
### Four Lines Microstrip Arrangement  
The proposed arrangement of the quadruple frequency microstrip consists of a current conductor surface that Move into four lines, with a coplanar interleaved dielectric material between the conductor line and the ground plane (Figure 5). The total size of the arrangement is 80 mm in width and 225 mm long, with a dielectric height of 5 mm. Two types of dielectric materials were used, each with a width of 20 mm and 225 mm length. The strip conductor lines were placed at the centers of each. Each of the four strip lines were fed by a single current source connected to a perpendicular line. The base feeding line had a width of 80 mm and a length of 2 mm. The four strip lines had a width and length of 5 and 222.5 mm, respectively. Each line was separated by 20 mm. A loading capacitor was added at the end of the microstrip to tune it to the right frequency.  
Each line was set to operate at a different frequency of either 300, 280, 121, or 78 MHz. We opted to interleave the lines between high and low frequency, so that the frequencies were positioned in the order 300, 121, 280, and finally 78 MHz. This configuration was expected to reduce the coupling between the lines. In addition, we designed the arrangement so that the lines operating at high frequencies were placed over dielectric materials with lower relative permittivity, and the lines operating at low frequencies were placed over dielectric materials with higher relative permittivity.  
Figure 4: The S21 parameters reflecting the coupling between two microstrips for different configurations.
# Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization
## 4 Modulation and Demodulation Model Based on a Vector Quantization LSTM Autoencoder  
Modulation converts the signal into a form suitable for propagation in the channel by mapping it onto a carrier signal; demodulation converts the modulated signal into the original information format suitable for data processing. In the conversion process, modulation and demodulation accomplish the functions of compression and decompression to a certain extent. For example, in the traditional modulation method of MQAM, each constellation point represents one symbol, and the number of bits represented by each symbol is \(log_{2}(M)\) bits. From this perspective, we believe that the essence of modulation is to process the original information into compressed signals that need to be distinguished from each other. The design concept of the autoencoder network aligns with this by performing dimensionality reduction and data re-representation. Existing technologies in this area primarily focus on enhancing system effectiveness, often overlooking the resource con  
Figure 1: System model.  
sumption of the system itself. In certain scenarios, there is a direct conflict between resource usage and performance. Consequently, it is imperative to explore the use of lightweight models to minimize resource consumption. This paper introduces the integration of vector quantization technology and an LSTM-Autoencoder (LSTM-AE) into the modulation and demodulation processes.
## 1 Introduction
When the excitation amplitude is large enough, the system can cross potential energy barriers and jump between different potential energy wells, increasing the response amplitude of the collector's dynamic structure [28; 29]. However, when the input energy of external excitation is relatively low compared to the potential energy barrier, the system is trapped in a single potential energy well for small vibrations [30]. Bistable technology has inspired many interesting designs, such as the mechanically-guided three-dimensional assembly structure proposed by [PERSON] et al. [31], which is strategically designed to construct a soft cruciform-encapsulated piezoelectric energy harvester. Drawing inspiration from wings, [PERSON] et al. [32] presented a wings-inspired design that implements a parametric coupling mechanism, thus widening the frequency bandwidth of the energy harvester. [PERSON] et al. [33] proposed the electromagnetic kick method to enhance the output power of a monostable energy harvester through orbit jumps. [PERSON] et al. [34] presented a simple, portable, and bistable energy harvester that can be used to scavenge low-frequency vibrations. Inspired by the rapid shape transition of the Venus flytrap, [PERSON] et al. [35] proposed a novel, low-cost, bistable piezoelectric energy harvester for the purpose of broadband energy harvesting. Therefore, nonlinear technology can really improve the performance of energy conversion [36; 37]. In conclusion, it is a feasible scheme to improve energy capture efficiency based on nonlinear technology. In this work, a newly designed bistable S-type generator beam is proposed, and for two typical energy harvesting scenarios in low-frequency excitation environment (the up-and-down vibration excitation and the wind-induced vibration excitation), two kinds of energy harvesting devices designed using this S-type generator beam are tested respectively, and their energy harvesting characteristics and efficiency are analyzed and discussed.
## 6 Applications of RF Energy Harvesting
### Wireless Sensor Networks (WSN) and Smart Agriculture  
The agricultural sector has greatly benefited from RFEH, particularly in the deployment of wireless sensor networks (WSNs) for smart farming. Sensors distributed across vast farmlands monitor soil moisture, nutrient levels, weather conditions, and pest activity, allowing farmers to make precise decisions that optimize crop yield and resource utilization. RF energy harvesters power these sensors continuously, eliminating the need for costly battery replacements and reducing the ecological footprint [101]. For instance, one study demonstrated the use of aerial drones equipped with RF energy-harvesting sensor tags for autonomous agricultural monitoring. These battery-less sensors harvested energy from drone-emitted RF signals to measure environmental parameters like temperature and humidity, enabling efficient data collection over large farmlands [102]. Recent research demonstrated that RF-powered agricultural sensors enabled a 30% improvement in irrigation efficiency by providing real-time soil moisture data, contributing to water conservation and sustainable farming practices. Additionally, they proposed a hierarchical clustering-based dynamic data fusion algorithm for WSNs in smart agriculture. This approach enhanced energy efficiency and event detection precision, ensuring the reliable operation of RF-powered sensors in monitoring various environmental parameters [103]. Hence, integrating RFEH with machine learning models allows for dynamic adaptation to environmental changes, ensuring the long-term reliability and operational resilience of agricultural WSNs.
# Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy
## 3 Methodology  
### Basic Idea  
The severe data bias issues encountered by supervised denoising models can be addressed by leveraging the flexibility of unsupervised denoising models such as DIP. These models generate neural network parameters tailored to the specific noisy image for  
Figure 1: The U-shaped network architecture employed by the DIP method.  
denoising. Thus, supervised and unsupervised models complement each other effectively. Our observations indicate that the quality of the target image significantly influences the noise reduction effect during unsupervised fine-tuning (see Section 4.2 for details). To further enhance the denoising performance of supervised models, we propose a novel approach in this paper. As shown in Figure 2, our approach is divided into two stages. This approach utilizes an unsupervised training method similar to DIP for fine-tuning. The key to our approach lies in the quality of the preprocessed images. Therefore, we introduce a novel supervised noise reduction model, i.e., U-Transformer. This model replaces convolutional layers with Transformer modules in the classical UNet framework to preprocess the given noisy image. It generates a higher-quality image as input (target image) for the unsupervised fine-tuning network. As the gap between the preprocessed image and the final denoised image is already minimal, complex neural networks for non-linear mapping between them are unnecessary. Consequently, there is no need for complex multi-scale UNet architectures. A simplified network with only one Transformer block is sufficient. This results in a fine-tuning task that can be accomplished with minimal iterative update cost. In summary, we seamlessly integrate supervised and unsupervised methods while prioritizing execution efficiency. The proposed approach is expected to enhance the performance of supervised denoising models, including those with potentially higher denoising performance in the future. This improvement comes with increased robustness and scalability, achieved with minimal computational cost.
# Fast Screening Algorithm for Satellites Based on Multi-Constellation System
## 5 Conclusions and Prospect  
This paper designs a new satellite screening algorithm based on the traditional minimum GDOP algorithm and maximum volume algorithm, which ensures that the selected satellites have a better distribution state, and at the same time, the number of satellites involved in the solution is kept at more than 70%. This ensures a significant reduction in computational load with only a small loss in positioning accuracy.  
At a cut-off elevation angle of 5\({}^{\circ}\) and a satellite selection rate of 70%, the average GDOP value of the algorithm is 1.7303, and the three axial position errors are basically controlled within 2.5 m in the ECEF coordinates.  
Conceptualization, W.Z. and Z.W.; methodology, W.Z.; software, W.Z.; validation, W.Z.; formal analysis, W.Z.; investigation, W.Z.; resources, Z.W.; data curation, W.Z.; writing--original draft preparation, W.Z.; writing--review and editing, Z.W.; supervision, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 2 Silicon Photonics Micro-Transceiver with Multimode Interface
### Low-Form Factor Optical Coupling Interface  
Commercial vertical multimode fiber array units, as shown in Figure 16a, can be used to couple to the optical interface; however, such units take up an additional height of at least 3.8 mm today and would therefore be unsuitable for many space-constrained applications such as co-packaged optics in a 5G+ environment or data centers.  
The EOB instead uses a fiber with a cleaved angled end facet (Figure 16c) allowing light to be coupled directly from the optical pin to a horizontally arranged fiber, thus enabling very low profile interfaces. The 1 dB misalignment tolerance of the cleaved fiber along the axis of the horizontal fiber (denoted as offset y-direction) is about \(\pm\)10 \(\upmu\)m (Figure 16d) at 25 \({}^{\circ}\)C and is slightly tighter at around \(\pm\)8 \(\upmu\)m at 105 \({}^{\circ}\)C.  
Figure 16: (**a**) Commercial vertical fiber array unit (courtesy of Senko); (**b**) electro-optically pluggable micro-transceiver module “EOB”; (**c**) cross-sectional schematic showing low-form factor coupling based on cleaved fiber; (**d**) misalignment tolerance of cleaved fiber at 25 to 105 \({}^{\circ}\)C.
## 3 Rrt UAV Path Planning
### Spatial Path Planning Algorithm  
The \(RRT\) algorithm must be extended from the two-dimensional plane to three-dimensional space and be able to detect collisions in three-dimensional space in order to be used for UAV cluster trajectory planning. This section of the algorithm uses discrete path sampling first, followed by the use of sampling points on the map to detect collisions. This effectively reduces the algorithm's computing power requirements and accelerates operation; however, if the sampling frequency is too low, the path may pass through a barrier without a collision being detected, which is known as the mode phenomenon. In order to keep the experimental findings readable, invisible barriers were placed at the map's edge to limit the pathways to a specific visual range, and for the purposes of the later UAV path planning requirements, the paths could not be lower than the height of the ground. Additionally, the possible locations for the flight path corner are constrained to the selected range of random points.  
The random search tree algorithm in 3D terrain first selects a random location \(x\) in the location space reachable per unit interval time of the defined UAV model, finds the node \(v^{*}\) closest to the point, plans the new node \(v^{+}\), no collision can be generated in the path \((x^{*},x^{+})\), add the new path to the path graph, and when there is no obstacle in the path between the new node and the target node \(x_{e}\), adds the target node to the path, and ends the algorithm. The 3D \(RRT\) algorithm can handle the complicated static environment and can obtain a workable solution for the UAV flight path. The algorithm operates as illustrated in Figure 8, where the red path in the picture is the UAV path predicted by the algorithm.  
Figure 8: Planning 3D \(RRT\) algorithm paths.  
Figure 7: (**a**) Path optimization process illustration diagram. (**b**) Path optimization results.  
The starling clustering algorithm lacks path planning for the entire goal, reacts immediately to the environment, and so is less effective at solving the resulting path. In order to account for the global aspect of the clustering technique and the path flight efficiency, this part uses the fast random search tree algorithm. The rapid random search tree algorithm is still in a better position even though it can plan a workable solution that can handle complex paths. The following section evaluates the shortest path for the UAV based on this section, modifying the attitude throughout the flight and arriving at the intended position and attitude in the shortest amount of time in order to better adapt the path planning to the UAV flying state.
###### Abstract
Despite the benefits of flat dilatometer tests, such as minimal soil disturbance, consistent data, and reliable prediction of in situ soil mechanical properties, challenges arise from factors like pressurization rate, leading to potential errors in test results [12]. Additionally, it is important to note that direct measurement of pore water pressure through flat dilatometer tests is not possible. Comparisons have been made between the results obtained from the flat dilatometer test and other testing methods, including the standard dilatometer, self-boring pressure-ter, seismic cone, and seismic dilatometer. An example of the implementation of new instrumented DMTs is the instrumented flat dilatometer (IDMT) that provides precise and cost-effective measurements of the unload-reload modulus. Furthermore, the comprehensive pressure-displacement curve obtained from the IDMT holds promise for developing improved correlations for strength and initial in situ stresses [13]. The utilization of a multi-parameter/multi-test approach in situ investigation, incorporating tests like CPT, CPTu, and DMT, has gained significant attention as well. Ongoing advancements and research continue to expand the applications of the Marchetti flat dilatometer, while instrumented DMTs offer further possibilities. Although challenges exist in evaluating and correcting errors in flat dilatometer tests, these tests remain valuable in providing crucial insights into soil properties, foundation settlement, and various geotechnical considerations. Exploring these applications further through research allows us to unlock new possibilities and expand the boundaries of geotechnical engineering. By pushing the boundaries of knowledge, we can identify innovative and sustainable solutions to tackle complex geotechnical problems worldwide. In addition, the knowledge of various geomechanical parameters, such as shear strength, stiffness, and deformation characteristics will enhance. This knowledge is crucial for accurate and precise geotechnical analysis and ensures the safety and performance of infrastructure projects.
# Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera
###### Abstract  
In the last few decades, photovoltaic (PV) power station installations have surged across the globe. The output efficiency of these stations deteriorates with the passage of time due to multiple factors such as hotspots, shaded cell or module, short-circuited bypass diodes, etc. Traditionally, technicians inspect each solar panel in a PV power station using infrared thermography to ensure consistent output efficiency. With the advancement of drone technology, researchers have proposed to use drones equipped with thermal cameras for PV power station monitoring. However, most of these drone-based approaches require technicians to manually control the drone which in itself is a cumbersome task in the case of large PV power stations. To tackle this issue, this study presents an autonomous drone-based solution. The drone is mounted with both RGB (Red, Green, Blue) and thermal cameras. The proposed system can automatically detect and estimate the exact location of faulty PV modules among hundreds or thousands of PV modules in the power station. In addition, we propose an automatic drone flight path planning algorithm which eliminates the requirement of manual drone control. The system also utilizes an image processing algorithm to process RGB and thermal images for fault detection. The system was evaluated on a 1-MW solar power plant located in Suncheon, South Korea. The experimental results demonstrate the effectiveness of our solution.  
photovoltaic power station; fault detection; autonomous drone; thermal image analysis Article