esa-sceva/satcom-chunk-collection huggingface dataset:
# The Current State and Future Potential of Microbial Control of Scarab Pests
## 2 Non-Target Effects of Entomopathogens  
Scarab pests incur significant economic losses across horticultural sectors, with injury and control costs for the Japanese beetle alone estimated at $450 million per year [7]. Despite the availability of effective chemical controls, which include carbamates, organophosphates, pyrethroids, diamides, and neonicotinoids, concerns about human safety and negative impacts on bees, butterflies and other beneficial non-target organisms, are increasingly driving the market towards less toxic, more environmentally friendly insecticides. Central in this pursuit, is the use of microbial entomopathogens that can provide targeted control through classical or augmentative biocontrol strategies. Microbial pesticides currently make up only 1-2% of the market but their portion is increasing due to health and environmental safety concerns surrounding the use of synthetic insecticides and regulatory changes, particularly in European markets ([PERSON] et al., 2015). Due to this, new opportunities exist for novel microbial products and/or control programs to be developed and implemented.  
This review summarizes the microbial entomopathogens that have been discovered in scarab species, the formulations developed from them, and their efficacy. Lesser-known microbial species are also discussed as potential up-and-coming novel controls. In addition, we address the relative utility of different microbes to serve as biocontrol agents versus biopesticides as determined by their capability to produce natural epizootics. Finally, we explore the development of new techniques for improving efficacy, such as genetic engineering, synergistic interactions, auto-dissemination strategies, and improved formulations.
# X-ray Diffraction Data Analysis by Machine Learning Methods--A Review
## 2 Challenges in Traditional XRD Data Analysis
### Microstructural Characterization  
Extracting microstructural information, such as crystallite size, microstrain, and texture, from XRD data requires specialized techniques and complex mathematical models. Additionally, microstructural effects can lead to peak broadening and distortions, complicating the interpretation of diffraction patterns.  
Microstructural characterization can be accomplished through a Scherrer analysis [80], Williamson-Hall plot [81], and Warren-Averbach analysis [82], which relate peak broadening to crystallite size and microstrain. Texture analysis requires specialized mathematical models to deduce preferred crystallographic orientations in polycrystalline materials like March-Dollase model [83].
# Analysis of Coastal Zone Data of Northern Yantai Collected by Remote Sensing from 1990 to 2018
## 1 Introduction
Therefore, based on seven remote sensing images of Yantai from 1990 to 2018, this study uses a visual interpretation method to extract remote sensing information of the coastline as well as the 2 km coastal zone. Quantitative analysis of the Yantai coastal long time series of different spatio-temporal evolution characteristics and response relationship was conducted, and the correlation between coastline and coastal zone was also studied. The research results can provide basic data for coastal protection and coastal zone development and utilization and are of great significance for solving the health ecosystem problems of coastal zones.
## 3 Experimental Results
### Experimental Results for Wuhan
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Band** & **STARFM** & **FSDAF** & **Fit-FC** & **EDCSTFN** & **GAN-STFM** & **MLFF-GAN** & **STF-Trans** & **CTSTFM** & **Proposed** \\ \hline \multirow{4}{*}{RMSE} & 1 & 0.0583 & 0.0574 & 0.0636 & 0.0307 & 0.0257 & 0.0485 & 0.0224 & 0.0223 & **0.0195** \\  & 2 & 0.0495 & 0.0478 & 0.0522 & 0.0294 & 0.0341 & 0.0535 & 0.0294 & 0.0288 & **0.0246** \\  & 3 & 0.0459 & 0.0495 & 0.0437 & 0.0334 & 0.0279 & 0.0505 & 0.0363 & 0.0387 & **0.0274** \\  & 4 & 0.0583 & 0.0599 & 0.0515 & 0.0507 & 0.0635 & 0.0568 & 0.0537 & 0.0970 & **0.0413** \\  & Avg & 0.0530 & 0.0536 & 0.0528 & 0.0361 & 0.0378 & 0.0523 & 0.0354 & 0.0467 & **0.0282** \\ \hline \multirow{4}{*}{SSIM} & 1 & 0.6448 & 0.6396 & 0.6239 & 0.8070 & 0.8433 & 0.6907 & 0.8405 & 0.8634 & **0.8820** \\  & 2 & 0.6662 & 0.6437 & 0.6620 & 0.8128 & 0.8068 & 0.6644 & 0.7807 & 0.8093 & **0.8470** \\  & 3 & 0.6838 & 0.6171 & 0.6911 & 0.8029 & **0.8497** & 0.6513 & 0.7604 & 0.7933 & 0.8483 \\  & 4 & 0.5599 & 0.5214 & 0.5844 & 0.7172 & 0.7146 & 0.5634 & 0.6079 & 0.3777 & **0.7207** \\  & Avg & 0.6387 & 0.6054 & 0.6403 & 0.7850 & 0.8036 & 0.6424 & 0.7474 & 0.7109 & **0.8245** \\ \hline \multirow{4}{*}{UIQI} & 1 & 0.6375 & 0.5972 & 0.5536 & 0.7658 & 0.8159 & 0.6103 & 0.7599 & 0.7275 & **0.8579** \\  & 2 & 0.7041 & 0.6336 & 0.6407 & 0.8133 & 0.8183 & 0.6268 & 0.7692 & 0.7562 & **0.8643** \\  & 3 & 0.7969 & 0.7120 & 0.7690 & 0.8464 & 0.8965 & 0.7019 & 0.7994 & 0.7819 & **0.9106** \\  & 4 & 0.7769 & 0.7405 & 0.7800 & 0.8600 & 0.8460 & 0.7586 & 0.8081 & 0.1907 & **0.8802** \\  & Avg & 0.7289 & 0.6708 & 0.6858 & 0.8214 & 0.8442 & 0.6744 & 0.7842 & 0.6141 & **0.8782** \\ \hline \multirow{4}{*}{CC} & 1 & 0.8106 & 0.7868 & 0.8519 & **0.9129** & 0.8945 & 0.7761 & 0.8572 & 0.8264 & 0.9013 \\  & 2 & 0.7930 & 0.7599 & 0.8531 & **0.9139** & 0.8994 & 0.7645 & 0.8655 & 0.8611 & 0.9007 \\  & 3 & 0.8636 & 0.7884 & 0.8845 & **0.9388** & 0.9325 & 0.7874 & 0.8926 & 0.8780 & 0.9235 \\  & 4 & 0.8104 & 0.7810 & 0.8430 & **0.9077** & 0.9033 & 0.7863 & 0.7842 & 0.3400 & 0.8894 \\  & Avg & 0.8194 & 0.7790 & 0.8581 & **0.9183** & 0.9074 & 0.7786 & 0.8699 & 0.7264 & 0.9077 \\ \hline ERGAS & ALL & 3.7344 & 3.7053 & 3.9652 & 2.1718 & 2.0695 & 3.4732 & 1.9407 & 2.1609 & **1.5933** \\ \hline SAM & ALL & 17.7869 & 19.3630 & 18.8012 & 14.5189 & 13.4744 & 19.3714 & 15.2835 & 17.1857 & **12.4134** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative evaluation results with different methods on the Wuhan dataset. Figure 9: Fusion results on 18 October 2015, generated by different models on the Wuhan dataset. (**a**) Ground truth. (**b**) STARFM. (**c**) FSDAF. (**d**) Fit-FC. (**e**) EDCSTFN. (**f**) GAN-STFM. (**g**) MLF-GAN. (**h**) STF-Trans. (**i**) CTSTFM. (**j**) Proposed. proposed method attains the second-highest CC value and outperforms all other methods on the remaining metrics.
## 4 Discussion
### Parameter Impact Analysis
#### 4.2.3 The Size of Different Training Samples on Building Extraction  
The number of building training samples is significant for the training of CNN. Too few building training samples will lead to insufficient robustness of the model, resulting in a high rate of missed detection of the final building extraction results. Conversely, too many building training samples can lead to high training costs in model training, which is undesirable in the application. Therefore, we tested the ratio of the number of pixel points in the building training sample to the number of total building pixel points and gave the final training sample ratio through experiments.  
In this paper, we use the three different types of PolSAR sensor data. For different types of data, we select different proportions of samples as the input of CNN. By comparing the building extraction results at different scales, we obtain the sample ratio we need. The building extraction accuracy of the three data sets increases as the proportion of the training samples increases, as shown in Figure 13. After the selection ratio reaches 15%, the extraction accuracy of the building tends to be gentle with the increase of the sample proportion, and even a slight decrease. It shows that the increase in the number of building samples has improved the extraction effect of the building, but the data redundancy caused by the excessive number of training samples does not significantly help the extraction effect. Taking into account the real distribution factors of the building and the cost factor of the training model, we selected 15% of the building elements in the real building as training samples for all three types of data.
## 2 Materials and Methods
### Data Acquisition
#### 2.3.3 PlanetScope Dove-R  
The sensor onboard the PlanetScope Dove satellite constellation is named PS2, which is paired with a 2D frame detector; the detector has 6600 pixels across by 4400 lines down with 4 channels (blue, green, red, and NIR). PS2.SD bands are interoperable with those of Sentinel-2. This allowed for the creation of a simulated Sentinel-2 image. PlanetScope Dove-R 3 m\({}^{2}\) per pixel resolution surface reflectance images were exported using the Planet Explorer interface [39] and imported to ArcGIS for post-processing. PlanetScope (PS2.SD) analytic assets are multispectral imagery that come orthorectified, calibrated, and corrected for sensor artifacts and atmospheric interference. PlanetScope PS2.SD was able to capture clear surface reflectance images of the site, which provided the opportunity for Sentinel-2 simulation due to the spectral range similarities of both sensors, which have previously been used to improve each other's remote sensing applications [40]. Final images (including a top of atmosphere (TOA) reflectance version for the fractioned water pixel (FWP) analysis) were exported for the post-processing steps. Surface reflectance images were used for correlation testing.  
For the FWP analysis and Otsu thresholding, additional steps were taken for the TOA image, which included masking cloud interferences' cloud shadow, haze, and corrupted pixels using the useable data mask (UDM2 metadata file as per [41; 42]. TOA image PS2.SD images come in 16-bit with values as watts per steradian per square meter (W/m\({}^{2}\) sr \(\upmu\)m), and these were converted to reflectance using the reflectance coefficients provided in the metadata files. The UDM2 labelled each image pixel using a convolutional neural network [43].  
The PS2.SD image taken on 30 July 2020 was used to create a simulated surface reflectance Sentinel-2 image by matching the Sentinel-2 sensor's spectral and spatial resolution, as the Sentinel-2 image was missing due to cloud cover during satellite pass over. This was achieved by increasing the raster cell size to that of Sentinel-2 (10 \(\times\) 10 m\({}^{2}\)) using a resampling tool and shifting cells to the UAV master image via co-registration. The simulated Sentinel-2 RGB image and Fe(III) iron distribution map closely resembled a Sentinel 2 image and map taken on 7 August 2020. The simulated image underwent the same post-processing treatment as the PS2.SD images.  
In total, six suitable images were identified. The products were 32-bit floating-point single band images exported for post-processing via the Planet Explorer interface.
## 2 Materials and Methods
### Study Selection and Data Collection  
Data extraction process: Two reviewers independently extracted the data from each eligible study. A standardized data extraction form was used to collect the relevant study characteristics and quantitative outcome measures, such as mean gap sizes, sample sizes, and standard deviations, for fit accuracy in regard to removable partial denture (RPD) fabrication. Each reviewer conducted the data extraction independently to reduce the risk of bias and ensure accuracy. Following data extraction, the reviewers compared their findings. Any discrepancies between the reviewers were resolved through discussions. If disagreements persisted, a third reviewer was consulted to reach a consensus, ensuring data accuracy and consistency across the studies. In cases where key data points were missing or unclear, efforts were made to contact the original study investigators for clarification or to obtain additional information. This step was necessary to ensure that all the quantitative outcomes used in the meta-analysis were reliable and fully represented in the analysis. No automation tools were used in the data extraction or verification process. The reviewers conducted all the data collection manually, following the standardized protocol, to maintain control over the data quality.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 2 Literature Review
### Architecture
#### 2.2.2 The Mobile Edge Computing Layer  
To introduce network edges in a MEC layer, some of RSUs can be equipped with mobile edge computing (MEC) server units. MEC servers can also be deployed at temporal architecture components such as drones. In general, any entity that is equipped with a MEC unit would play the role of a network edge to offer various services including integration, localization, traffic data processing, traffic condition detection, connectivity and data access services with the aid of the cloud server layer. Vehicles can directly communicate with nearby network edges for service provision and data collection.
# Multiport Single Element Mimo Antenna Systems: A Review
## 3 Isolation Techniques
### Parasitic Elements  
This decoupling technique can be implemented to reduce the mutual coupling by placing the parasitic elements in between the antennas in MIMO configuration by cancelling a segment of the coupled fields through an opposite coupling field, which can reduce the total coupling on the selected antenna. These elements are not coupled to the antennas in any way. The parasitic element can be of any type like a resonator or shorted stubs [32]. Parasitic elements are also used to adjust the bandwidth, isolation range, and coupling quantity [33]. A parasitic decoupling structure of a T-shaped stub with a slot is employed on the backside of the substrate between the two square monopole elements [34]. The antenna matching is improved through the stub and the slot attached to stub reflects the radiation from the elements. Hence, the isolation is improved. Two orthogonal modes [35] used in the parasitic  
Figure 4: Neutralization line technique [29].  
elements creates a impedance bandwidth broader by coupling either in radiating patch or in the ground plane. Mutual coupling is minimized, and a good isolation is achieved by establishing new additional coupling path [36; 37]. Additionally, good isolation is achieved by the extra coupling path resisting the signal approaching from the other path.
# A Methodology to Optimize Natural By-Product Mixes for
## 3 Results and Discussion  
### Dry Density  
Table 6 shows the dry densities obtained for the corresponding combinations and specimens (S).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Combination** & & & **Dry Density (g/cm\({}^{3}\))** & & \\  & **S1** & **S2** & **S3** & **Mean** & **Dev. (s)** \\ \hline L01 & 2.105 & 2.098 & 2.125 & 2.109 & 0.014 \\ L02 & 2.160 & 2.161 & 2.167 & 2.163 & 0.004 \\ L03 & 2.150 & 2.161 & 2.153 & 2.155 & 0.006 \\ L04 & 2.054 & 2.070 & 2.069 & 2.064 & 0.009 \\ L05 & 2.053 & 2.057 & 2  
As depicted in Figures 5 and 6, the optimal values of the S/N ratio matched the optimal value of the means. While this is not always the case, it was convenient because it indicated that it was the most robust combination and would guarantee the highest increase in the dry density. Table 8 shows that the optimal levels of the modelled factors aimed to maximize the dry density were, in order of importance, MC Level 2, gravel Level 4, sand Level 3, sandy gravel Level 3, and fines Level 1. These levels expressed in mass units were 18.0 gravel, 32.0 sand, 26.6 sandy gravel, 24.0 fines and 8.0% MC of the total mass. The natural by-product mix combination that maximized the dry density, expressed as mass percentages, was 17.9% gravel, 26.4% sandy gravel, 31.8% sand, and 23.9% fines with 8.0% MC.  
MC was the parameter with the highest importance, as it is well known that dry density is highly dependent on the MC of the material. A direct relationship between the bulk density of the different granulometric fractions and their influence on the dry density was observed, in which the gravel had the greatest effect. The other three parameters (sandy gravel, sand, and fines) appeared to have a comparatively lower effect on the dry density of the samples, but it cannot be stated that they had no influence.
###### Abstract  
The mobility of a quadrotor UAV is significantly affected by its aerodynamics, especially when the closely spaced rotors are applied in the multi-rotor system. This paper addresses the aerodynamic modeling of non-planar quadrotor UAV with various rotor spacing (1 _d_-2 _d_) and disk plane angle (0-50 deg). The inter-rotor interference and the power models are also proposed in this paper. In order to validate the non-planar model, a series of CFD analyses and experiments were conducted. The obtained results demonstrate that the flow field of the non-planar quadrotor is extremely complicated when the unsteady flow is involved. The pulsation of partial angle of attack and pressure distribution is formed when the blade passes through the vortex. The thrust is increasing significantly along with the tilt angle, resulting from the stronger outflow of the non-planar rotors, which is also leading the power increment. However, the thrust increment is not that obvious when the spacing is larger than 1.4 \(d\). The experiments and the numerical simulation results provide consistent trends and demonstrate the effectiveness of the aerodynamic model of the non-planar quadrotor. The comparison with the traditional planar quadrotor validates that the proposed non-planar quadrotor has better aerodynamic and control performances with a larger power loading.  
Keywords:quadrotors; aerodynamic configuration; numerical simulation; hover; aerodynamic interference; non-planar model +
Footnote †: journal: Applied
# A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content
## 3 Materials and Methods: A Pipeline for Dynamic Range Expansion of Video Sequences
* Dequantization: This stage addresses false contours that might emerge in areas with smooth gradients following dynamic range expansion, effectively suppressing these artifacts. * Highlight boosting: By analyzing the luminance of the LDR image, overexposed/saturated regions are identified. This information guides the boosting of highlights in the expanded HDR luminance, increasing them to 6000 cd/m\({}^{2}\) for enhanced visual impact. * Saturation enhancement: The final HDR image is computed, and saturation is augmented at the pixel level without compromising the overall luminance. This adjustment compensates for the potential loss of colorfulness in the output. Each color channel of the resulting HDR image is stored in a 16-bit floating-point format to ensure accurate representation. * Temporal consistency: We integrate a simple temporal coherence technique into our pipeline to minimize temporal artifacts.
## 5 Conclusions
We express our thanks to [PERSON], [PERSON], [PERSON], and [PERSON] for their contributions to the field experiment. The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The following abbreviations are used in this manuscript:  
MPT Mud pulse telemetry  
FrFT Fractional Fourier transform  
FFT Fast Fourier transform  
MWD Measurement while drilling  
OFDM Orthogonal Frequency Division Multiplexing  
SSC Sweep-Spread carrier  
RFFI Radio frequency fingerprint identification  
ISI Inter-symbol interference  
DFE Decision feedback equalizers  
RLS Recursive least squares  
SC-FDE Single-carrier frequency-domain equalization  
LFM Linear frequency modulation  
ZF Zero forcing  
MMSE Minimum mean square error  
RMSE Root mean squared error  
AWGN Additive white Gaussian noise  
SNR Signal-to-noise ratio  
BER Bit error rate  
BFSK Binary frequency shift keying
# An Intelligent Real-Time Object Detection System on Drones
## 1 Introduction  
Drone technology has undergone rapid development in recent decades, which has resulted in a dramatic cost reduction for commercial drones, especially quadcopter drones. As a result, more drones are used in everyday life for different kinds of jobs, ranging from search and rescue to photography [1; 2; 3; 4]. In addition, by equipping various sensors, drones are able to provide information with different features, which makes it feasible to work as an intelligent system by merging outside information.  
Traditionally, drones are controlled and operated by drone pilots to achieve specified goals, such as searching certain targets, etc. Drone pilots play several roles throughout the flying process, including analyzing data in real time, and avoiding obstacles for safety requirements. However, several problems need to be addressed for drone pilots. On the one hand, drone pilots cannot continuously work without a rest and it is necessary to have a rest after working for a long time. On the other hand, even if drone pilots can help analyze data such as images, it is difficult for them to perform high-speed image processing, and some small targets may even not be spotted. Therefore, it is necessary to design an intelligent system for drones to address these problems.  
Various components and devices have been used to build intelligent systems, where the Field Programmable Gate Array (FPGA) is a well-known acceleration device for artificial intelligence (AI) tasks [5; 6; 7; 8]. In this research, we propose an intelligent real-time object detection system on drones. We first design an FPGA accelerator for real-time object classification and detection. The drone sends image data to the FPGA via the host, which areassembled into the data stream for access by multiple fetch and jump pointers. Activation values are fetched for each thread and then filtered. Processing elements tackle tasks in parallel by using multiple weights and threads, which significantly speeds up the running of deep-learning neural networks (NNs).  
Then, we build an intelligent system using the FPGA on drones, such that the drone can flexibly adjust flying routes to generate more pixels for objects which helps detect objects more accurately based on FPGA results. Experimental results reveal that our proposed FPGA design well utilizes FPGA computing resources, with 81.56% DSP and 72.80% LUT utilization rates, respectively. By using the Yolov3-tiny model for fast object detection, our system can detect objects at the speed of 8 frames per second (FPS) and achieve a much lower power consumption compared to state-of-the-art methods. More importantly, the intelligent object detection techniques provide more pixels for the target of interest and it can increase the detection confidence score from 0.74 to 0.90 and from 0.70 to 0.84 for persons and cars, respectively.  
The rest of the paper is organized as follows. Section 2 introduces related work, materials and methods are demonstrated in Section 3, results and discussion are presented in Section 4 and Section 5 concludes the paper.