esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Estimation Retrieval of Aerosol Fine-Mode Fraction from Ground-Based Sky Light Measurements
## 6 Conclusions  
In this paper, we proposed an optimal estimation inversion algorithm for the retrieval of the aerosol fine-mode fraction parameter from single-viewing multi-spectral measurements of sky light. The retrieved AOD and FMF from synthetic data produced a correlation coefficient greater than 0.99, representing good self-consistency of the inversion algorithm. Validation against the AERONET products showed that the correlation coefficients for aerosol volume fine-mode fraction (FMF\({}_{\text{v}}\)) and optical fine-mode fraction (FMF\({}_{\text{o}}\)) were 0.94 and 0.95, respectively. The mean residual error of all the retrieval data was 4.95%. These results strongly validate the feasibility of the inversion framework. In addition, by introducing data from the 1610 nm wavelength measurements, the correlation coefficients showed a significant improvement for FMF\({}_{\text{o}}\) (from 0.89-0.94). The comparison confirms the expected high value of NIR data to the retrieval of the aerosol fine-mode fraction.  
[PERSON] and [PERSON] conceived of and designed the experiments. [PERSON] and [PERSON] performed the field experiments; [PERSON] and [PERSON] assisted with the data analyzing and preprocessing. [PERSON] guided the optimal estimation method and revised the paper; [PERSON] conducted the algorithm operation and wrote the paper; [PERSON] and [PERSON] provided technical guidance and useful advice regarding the calibration of raw data.  
This study is support by the National Key R&D Program of China (grant number 2016 YFE0201400), the Open Fund of the State Key Laboratory of Remote Sensing Science (grant number OFSLRSS201710), and the National Natural Science Foundation of China (grant numbers 41871269, 41671367, 41701407, 41505022).  
We are very grateful to [PERSON] and [PERSON] for the UNL-VRTM code. The data and Fortran codes used for the forward simulations are available at the link in the website ([[http://www.unl-vrtm.org](http://www.unl-vrtm.org)]([http://www.unl-vrtm.org](http://www.unl-vrtm.org))). We also acknowledge the L-BFGS-B software package developed by [PERSON], [PERSON], and [PERSON].  
The authors declare no conflict of interest.
## 4 Discussion
At the same time, marine environmental variables, such as SST, SSS, SSH and Chl-a, are also im-portant for forecasting albacore fishing grounds with high accuracy, consistent with the findings of [PERSON] et al. (2005) and [PERSON] (2004) [31,60]. From the analysis of the im-portance of fishing ground variables (Figure 8), it can be seen that there are differences in the ranking of the importance of the contribution of marine environmental factors to the fishing ground forecasts of albacore in different months, with Lat ranking first in all months from February to December. This study found that latitude is a fundamental factor influencing climate, and that oceanic climate (SST) influences the distribution and variability of fishing grounds by affecting the growth, foraging and migration of albacore. There was also a significant effect of Chl-a on the results of the albacore fishing ground forecasts (Figure 8). It was found that changes in plankton abundance affect the abundance of fish resources as well as the distribution of fishing grounds, and that the waters around New Zealand have different trophic gradients influenced by important land as well as sub-tropical convergence zones, thus creating favorable conditions for high productivity and the development of plankton biota, similar to the findings of [PERSON] et al. (1991) [61]. Meanwhile, the El Nino phenomenon and wind speed in the Pacific Ocean may also affect the distribution of albacore fishing grounds. In a follow-up study, we will further explore the influence of different marine environmental factors on the forecast accuracy of albacore fishing grounds, so as to obtain more accurate fishing ground forecast information and provide technical and data support for the South Pacific fishery.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 3 AI and Ophthalmology: View of the Ensemble
### AI and Posterior Segment Diseases
#### 3.3.5 AI and Glaucoma  
Even if there is no gold standard for glaucoma detection, AI can help significantly improve glaucoma screening, diagnosis, and classification, as AI enables the automated processing of large datasets and the identification of specific and new disease patterns [51].  
First, fundus photographs were analyzed by ML to detect optic nerve impairment due to glaucoma [52]. Afterwards, larger datasets were analyzed using DL [53]. [PERSON] et al. processed a large dataset of 125,189 fundus images and obtained a sensitivity of 96.4% and a specificity of 87.2% [54].  
In addition, AI achievements have been released using digital visual field images as well as OCT, and patients have been evaluated using fusing data from both imaging modalities.
# Performance of Cooperative Relay NOMA with Large Antenna Transmitters
## 3 Achievable Rates, Outage Probabilities and Bit-Error-Rates Analysis
### Achievable Rates Analysis  
The maximum rate in the channel where the BER tends to zero [110] describes the achievable rate of the system. When the CSI is unknown at the transmitter, if separate equipowered transmissions are assumed, the achievable rates of the UEs can be determined using SINR values. From (19)-(21), the achievable rates of the UEs using AF coding scheme are respectively given by  
\[R_{UE_{1}}^{\rm AF}=Blog_{2}(1+\gamma_{R,1}^{\rm AF})\ \ \text{and}\ \ R_{UE_{2}}^{\rm AF}=Blog_{2}(1+\gamma_{R,2}^{\rm AF}) \tag{28}\]  
For DF coding scheme, the achievable rates at the destination nodes from (25)-(27) are  
\[\begin{split} R_{UE_{1}}^{\rm DF}&=\min[R_{x_{1}}^{ \rm RS},Blog_{2}(1+\gamma_{R,1}^{\rm DF})]\ \ \ \ \text{and}\\ R_{UE_{2}}^{\rm DF}&=\min[R_{x_{2}}^{\rm RS},Blog_{2} (1+\gamma_{R,2}^{\rm DF})]\end{split} \tag{29}\]  
The relay transmits the decoded signals to all UES in the second time slot.
## 5 Result
### Analysis of the Unsaturated Parameters of Different Soil Layers
Through grey relational analysis, we can discern the sensitivity of unsaturated soil parameters under diverse seepage scenarios, as well as identify spatially specific sensitive zones for these parameters. This approach provides valuable insights into which parameters are most critical in influencing the safety factor FS under specific conditions, allowing for more targeted and effective engineering interventions or design optimizations. As illustrated in Table 2, variations in flow conditions significantly impact the outcomes to varying degrees. Notably, under evaporation conditions, the sorting of grey relational degrees for identical unsaturated parameters within the same soil layer surpasses that under infiltration conditions. This disparity fluctuates depending on the type of unsaturated parameter being considered. Notably, the alteration in \(n\) yields the most pronounced difference, whereas \(q/k_{s}\) results in the least variation. Furthermore, the grey relational degree tends to be generally higher in the upper soil layers for all unsaturated parameters, owing to the more pronounced unsaturated effects in these layers. In the context of infiltration, the enhancement in the stability coefficient of grey analysis is more evident under evaporation conditions. It can be concluded that, under the action of unsaturated seepage, the soil layer in the inclined soil layer that is more sensitive to changes in unsaturated parameters appears in the upper layer. Under evaporation conditions, the correlation between changes in unsaturated parameters and the results of the supporting force is greater, and the difference in correlation between the upper soil layer and the lower soil layer will also be correspondingly larger. At the same time, in the given range of unsaturated parameters, the unsaturated parameter \(n\) is the most relevant to the final supporting force result. Figure 13: The safety factor FS versus \(n\) varying in different layers of soil.
# Can Biochar Improve the Sustainability of Animal Production?
## 3 Animal Feed
### Emissions from [PERSON] et al. [82] evaluated the use of biochar in feed versus zeolite and bentonite, on nitrogen retention, carbon content, and moisture content in poultry litter. The biochar additive was included at 1%, 2%, and 4% \(w/w\) to the feed. Litter was sampled over a 46-day period, and monitored for an additional 35 days for decomposition. The 2% and 4% treatments reduced the total nitrogen content in the excreta by 17% and 27%, respectively, vs. the control.  
However, for these same treatments, NH\({}_{3}\) emissions increased over the incubation and decomposition period by 47% and 43%, respectively. The carbon content for all treatments increased due to the addition of a stable form of carbon to the diet. This would lead to increased soil organic carbon when manure is land applied. The lowered nitrogen content was attributed to a higher nitrogen utilization by the birds, while the increased NH\({}_{3}\) volatilization was attributed to the higher manure pH, which shifted the NH\({}_{3}\)/NH\({}_{4}\)+ equilibrium towards NH\({}_{3}\)[82].  
In a similar study, [PERSON] et al. [83] tested the effect of biochar addition to feed at 2% and 4% \(w/w\) feed on 750 laying hens in terms of NH\({}_{3}\) and odor emissions from manure as well as production parameters, including animal weight gain and feed conversion ratio. Ammonia concentrations were reduced by 15% and 14% for the 2% and 4% biochar feed addition rates, respectively, compared to the control. Odor concentrations from the litter headspace were 32% lower for the 4% biochar feed litter than the control. The 2% addition rate was not tested due to experiment limitations.  
Adsorption of NH\({}_{3}\) to the biochar surface, as well as altered gut microbial activity, resulted in lower nitrogen excreted in the manure and therefore lower NH\({}_{3}\) and odorous VOCs [83]. Differences in bird performance were not statistically significant. Average daily weight gain decreased by 5 and 2% in relation to the control for the 2 and 4% treatments, respectively. Feed conversion ratio increased by 2 and 4% for the two treatments, respectively, indicating a lower quality feed [83]. [PERSON] et al. [81] also reported that biochar feed additives can have negative impacts on broiler production parameters.  
Biochar additive treatments resulted in a 2% decrease in live weight gain, 11% increase in feed conversion ratio, and 8% increase in feed intake. The current literature in this area focuses on poultry, specifically broiler and layer litter. More information on emission effects from biochar supplemented feed needs to be explored for other animals, including swine and cattle. While biochar addition can have significant impacts on poultry production, the potential emission benefits must be balanced with improved animal performance as well. However, a better understanding and characterization of biochar manufacturing as a feed additive may be beneficial in producing a biochar product that both reduces emissions and improves animal performance.
## 4 Denoising Methods
### Low-Pass Filter MATRIX Construction  
Digital filters are mainly divided into finite impulse response (FIR) filters and infinite impulse response (IIR) filters. For a FIR filter, the impulse response decays to zero in a finite time, and its output depends only on the current and past input signal values. Finite length unit impulse response filter, also known as non-recursive filter, is the most basic element in digital signal processing systems [44]. It can guarantee arbitrary amplitude-frequency characteristics while having strict linear phase-frequency characteristics. Meanwhile, its unit sampling response is finite length, so the filter is a stable system. Therefore, FIR filters are widely applied to communication, image processing, pattern recognition, and other fields.  
Our goal is to extract a clean signal \(x\) from the noisy signal \(y\). In order to achieve this, the common solution is to use a traditional linear low-pass filtering system. We use a zero-phase filter because it can reduce the noise in the signal while keeping the QRS waveform from shifting after noise reduction. The transfer function of the zero-phase filter is:  
Figure 2: Basis pursuit denoising (BPDN) reconstruction of the noise reduction process. (**a**) Original ECG signal, (**b**) basis pursuit (BP) reconstruction, (**c**) reconstruction error, (**d**) BPDN effect.  
\[H(Z)=\frac{Y(Z)}{X(Z)}=\frac{\sum\limits_{i=0}^{N}a_{i}Z^{-i}}{1-\sum\limits_{j=1 }^{N}b_{j-1}Z^{-j}} \tag{13}\]  
The effect of a linear time invariant (LTI) system on the input signal is to change the complex amplitude of each frequency component in the signal. The nature of this effect can be understood in detail by using the modulus phase representation to look at this effect. Take the discrete time situation as an example:  
\[\left|Y\left(e^{iw}\right)\right|=\left|H\left(e^{iw}\right)\right|\bullet \left|X\left(e^{iw}\right)\right| \tag{14}\]  
where \(\left|H\left(e^{iw}\right)\right|\) is called the gain of the system. The effect of an LTI system on the input Fourier transform model is to multiply it by the modulus of the system frequency response. The phase shift of the system can change the relative phase relationship among the components in the input signal, so that even if the gain of the system is constant for all frequencies, it is possible to produce great changes in the time-domain characteristics of the input. The vectorized output after filtering the input noise-containing data \(y\) is: \(y=Hx\)[45].
## 6 Simulation Verification  
In order to verify the feasibility of the control strategy and the correctness of the theoretical analysis, a simulation model of the PMSG-based wind turbine system with the auxiliary energy storage battery was built on the PSCAD/EMTDC platform. The parameters of the system are shown in Table 1 below.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbols** & **Parameters** & **Value/Unit** \\ \hline \(S_{\text{N}}\) & Rated power value & 2 MW \\ \(U_{\text{N}}\) & Rated value of AC phase voltage & 0.563 kV \\ \(u_{\text{den}}\) & Rated value of DC-link voltage & 1.1 kV \\ \(\omega_{\text{Den}}\) & Rated value of stator angular frequency & 84.6 rad/s \\ \(\omega_{\text{Rg}}\) & Rated value of grid angular frequency & 314 rad/s \\ \(f_{\text{N}}\) & Rated frequency of PMSG & 13.47 Hz \\ \(p\) & Pole pairs of PMSG & 42 \\ \(\psi_{\text{r}}\) & Magnetic flux linkage of rotor & 0.896 p.u. \\ \(L_{\text{s}}\) & Synchronous inductance of PMSG & 0.5495 p.u. \\ \(R_{\text{s}}\) & Stator resistance of PMSG & 0.00387 p.u. \\ \(H_{\text{WT}}\) & Inertia constant of wind turbine and PMSG & 4 s \\ \(H_{\text{C}}\) & Inertia constant of DC-link capacitor & 3.6 ms \\ \(u_{\text{Eden}}\) & Rated value of battery voltage & 0.78 kV \\ \(f_{\text{s}}\) & Switching frequency of GSC and MSC & 2 kHz \\ \(f_{\text{Es}}\) & Switching frequency of ESC & 10 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the wind turbine system.
## 2 Material and Methods
### Biogenic Amines (BAs) Determination  
Biogenic amines (BAs) were determined in dry fermented sausages samples after extraction using 10% trichloroacetic acid (TCA). The analysis of BAs was performed using an AAA 500 amino acid analyzer (Ingos, Praha, Czech Republic), equipped with an Ostion LG AAA8 ion-exchange column (3.6 \(\times\) 100 \(\upmu\)m, 8 \(\upmu\)m). Standards of BAs with a purity of 99% were obtained from Sigma-Aldrich (St. Louis, MO, USA). Solutions of BAs were prepared with a dilution buffer composed of 1.5 mM NaN3, 197 mM NaCl, and 73 mM citric acid in Milli-Q water. The system consisted of a filling chromatographic column and steel pre-column, two chromatographic pumps for transport of elution buffers and derivatization reagent, a cooled carousel for Eppendorf tubes, a dosing valve, a heat reactor, a Vis detector, and a cooled chamber for derivatization reagent. The volume of the injected sample was 100 \(\upmu\)L. The reactor temperature was set to 120 \({}^{\circ}\)C. The content of the BA (tyramine, putrescine, cadaverine, spermidine, agmatine, and spermine) was determined with a reference to the amine standards, which were supplied by Ingos, Czech Republic. The BA concentrations were reported as mg g\({}^{-1}\) of product.
## 3 Results
### Indices for Phytoplankton Bloom Characterization
#### 3.2.2 CSA Index  
The CSA Index inputs include the chl-a and turbidity derived from satellite data. CSA Index values of 1 correspond to negligible to no-bloom events, whereas a value of 0 indicates extensive growth. The most extensive blooms according to the CSA Index (corresponding to the lowest CSA Index values) were in the Hara and Kolga bays, Kihelkonna Bay, and Moonsund Sea, where the index was 0 (Figure 9). The Hara and Kolga and Kihelkonna bays are very open coastal areas and are highly influenced by the rest of the Baltic Sea, whereas Moonsund Sea is a shallow coastal area between the large western islands and the Estonian west coast. Conversely, three coastal areas had years when there were no blooms (corresponding to a CSA Index of 1). These were Eru-Kasmu Bay, Kassari-Ounaku Bay, and Matsalu Bay. For CSA, considerable variation in the results was observed between different years, and extreme values were calculated for the bays in different years. Looking at the whole period (2016-2022), the median CSA Index was highest in Kassari-Ounaku Bay (CSA = 0.76), Moonsund Sea (0.71), and the Hara and Kolga bays (0.65), whereas the lowest values were calculated for Parnu Bay (0.27), Narva-Kunda Bay (0.28), and the central Gulf of Riga (0.34) (Figure 9). The adjacent Kassari-Ounaku Bay and Moonsund Sea form an area between the large western islands and the mainland which could explain the similarities of the results. The Narva-Kunda and Parnu bays and the central Gulf of Riga are all highly productive and nutrient-rich areas, and the three areas are all heavily influenced by river inflows. The two other parts of the Gulf of Riga (NW and NE) also showed low index values indicating high levels of water exchange between them.  
Figure 7: N\({}_{2}\)-fixing cyanobacteria biomass (mg m\({}^{-3}\)) in (**A**) Haapsalu Bay in 2021 and (**B**) Muuga-Tallinna-Kakumäe Bay in 2022 from June to September estimated from OLCI (polygon average, black dots and line), from in situ samples (average of the sampling station measurements, red dots) and satellite-derived values from station locations (red stars).  
Figure 8: In situ (station average) and satellite-derived (polygon average) N\({}_{2}\)-fixing cyanobacterial biomass (mg m\({}^{-3}\)) in the years 2016–2022 in Estonian coastal areas. Boxplots represent 2016–2021 values (boxes represent IQR, middle line represents median, whiskers represent maximum and minimum values, and black dots represent outliers), and orange-red diamonds represent 2022 values. Note the scale differences in the \(y\)-axis.
# Distributed Localization with Complemented RSS and AOA Measurements: Theory and Methods
## 1 Introduction
The identified solutions are analyzed from both computational complexity and localization accuracy perspectives, with a theoretical lower bound included in the latter comparison in order to assess the margin available for further improvement. Lastly, we recognize some limitations of the existing solutions and identify possible directions for future research. Throughout the survey, upper-case bold type, lower-case bold type and regular type are used for matrices, vectors, and scalars, respectively. \(\mathbb{R}^{n}\) is used to denote the \(n\)-dimensional real Euclidean space and the transpose operator is denoted by \(()^{T}\). The normal (Gaussian) distribution with mean \(\mu\) and variance \(\sigma^{2}\) is denoted by \(\mathcal{N}(\mu,\sigma^{2})\), the von Mises distribution with mean \(\mu\) and concentration parameter \(\kappa\) is denoted by \(VM(\mu,\kappa)\), while the uniform distribution on the interval \([a,b]\) is denoted by \(\mathcal{U}[a,b]\). For any matrix \(\mathbf{M}\), its trace is denoted by \(\mathsf{tr}\left(\mathbf{M}\right)\). The designation \(\mathrm{diag}(\mathbf{v})\) denotes a square diagonal matrix in which the elements of vector \(\mathbf{v}\) form the main diagonal of the matrix, and the elements outside the main diagonal are zero. The \(N\)-dimensional identity matrix is denoted by \(\mathbf{I}_{N}\) and the \(R\times C\) matrix of all zeros by \(\mathbf{0}_{R\times C}\) (if no ambiguity can occur, subscripts are omitted). \(\|\mathbf{v}\|\) denotes the vector norm defined by \(\|\mathbf{v}\|=\sqrt{\mathbf{v}^{T}\mathbf{v}}\), where \(\mathbf{v}\in\mathbb{R}^{n}\) is a column vector. For a matrix \(\mathbf{A}_{r,c}\) denotes the \((r,c)\)-th entry of \(\mathbf{A}\). The cardinality (the number of elements) of a set is denoted by \(|\bullet|\) and the statistical expectation of the argument as \(\mathbb{E}(\bullet)\). The remainder of this survey is organized as follows. In Section 2, we introduce the problem of distributed target localization in a generic framework, and we provide details about the main performance metrics often used in the literature. We also summarize the main sources of errors for the considered radio measurements. Section 3 presents a set of existing statistical-based estimators that are available to solve the considered problem. This section summarizes the main ideas behind each of the existing solutions, without a rigorous formal analysis. In Section 4, we offer a set of results in order to give an intuition to the reader about the performance of each of the existing solution from both computational complexity and localization accuracy points of view. Lastly, Section 5 concludes the survey and identifies possible future research directions regarding the problem of interest.
# Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm
## 4 Experiment and Analysis
### Dataset  
The dataset for this experiment, a specialized pest image collection, was curated from data provided by the 10 th \"Teddy Cup\" Data Mining Challenge in 2022, after careful reselection and cleansing. The dataset can be accessed online at the [[https://bdrace.tipdm.com/#/competition/1481159137780998144/question](https://bdrace.tipdm.com/#/competition/1481159137780998144/question)]([https://bdrace.tipdm.com/#/competition/1481159137780998144/question](https://bdrace.tipdm.com/#/competition/1481159137780998144/question)) (accessed on 20 June 2024). All images are in JPEG format with a resolution of 5472 \(\times\) 3648 pixels. The dataset comprises seven pest types: rice stem borer (pest number: 7), white-backed planthopper (pest number: 9), brown planthopper (pest number: 10), yellow-legged predatory bug (pest number: 148), eight-spotted forester moth (pest number: 156), cricket (pest number: 256), and oleander hawk-moth (pest number: 280), with a total of 491 images. Each image features various types and quantities of pests, as detailed in Table 2.  
After the image preprocessing steps detailed in Sections 4.1.1-4.1.4, 340 representative images were selected for model development, divided into 238 images for the training set and 68 for the testing set. To expand the dataset and enhance training efficiency, each image was segmented into four smaller images prior to inclusion in the training or testing process. Consequently, the training set effectively comprises 952 smaller images. During the testing phase, each test set image was similarly segmented into four smaller images for analysis. After testing, these smaller images were reassembled into a single image, matching the original's resolution, to evaluate the results. All pest annotation tasks were conducted using the LabellImg tool, with pest identification numbers corresponding to those listed, and specific pest quantities (based on counts from uncropped images) detailed in Table 3.