esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 New Sources of Genetic Resistance to Nematodes of Small-Grain Cereals
filipjevi_ was revealed in winter wheat accessions from the International Wheat Improvement Program using the 90K SNP platform [57]. Resistance was based on counts of cysts and females in root and soil samples, and was associated with 11 QTL. The PNW adapted cultivar Madsen harbored resistance against _H. avenue_ and _H. filipjevi_ under both controlled environments and in naturally infested fields in China [5]. Reduced root penetration by juveniles and reduced white females on roots was observed for both species. While the action of these new sources of resistance might be conditioned by environment and genotype of the nematode, transfer of resistance to adapted germplasm will not require extensive pre-breeding. Resistance to _P. neglectus_ and _P. thornei_ was reported in wheat and barley cultivars in Australia and the Pacific Northwest [7; 14; 26; 28]. Wheat generally sustained more damage than barley, and _P. thornei_ was more frequent, and hence more destructive, than _P. neglectus_[25; 26]. Wheat and barley cultivars were rated for grain yield in the field, with and without application of the nematicide aldicarb; those for which yield was not substantially increased by aldicarb were considered to have native resistance [26]. This resistance was better described as tolerance, because enhanced grain yield was not due to reduced nematode reproduction. Tolerance was observed in 38 of 45 cultivars of spring wheat and barley and 22 of 121 winter wheat cultivars. Additional resistance to _P. neglectus_ was mapped in doubled haploid populations of winter barley using DArT, restriction fragment length polymorphisms, SSR markers, and SNP markers [58]. Resistance, based on nematode counts from inoculations in a controlled environment, was attributed to six QTL. A major QTL conferred resistance to both _P. neglectus_ and _P. penetrans_, also a pathogen of small-grain cereals. Spring wheat genotypes from the CIMMYT semi-arid wheat screening nursery screens produced seven and six new QTL and associated DArT markers for _P. neglectus_ and _P. thornei_ resistance, respectively [14]. Resistance to these two species also was reported in 32 Iranian wheat landraces [59]. In growth chamber assays, nematode counts in roots plus soil samples were reduced in resistant landraces compared to control lines. Association mapping using simple sequence repeat (SSR) markers resulted in three clusters of markers, indicating that at least three different resistance loci were present among the 32 landraces. In a subsequent study, landrace IWA8608077, with moderate but consistent resistance against _P. neglectus_ and _P. thornei_, was used as a parent in a cross with the susceptible PNW cultivar Louise [25]. A resulting recombinant inbred line population was screened for visible root browning and reduction in nematode counts. Six major QTL for resistance were identified, four of which mapped to chromosome (Chr) 5A.
# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 3 Data and Methods
### Surface Water Mapping  
The developed approach for mapping surface water extent (SWE) and dynamics can be described as an optical and SAR-based fusion model (Figure 1). The approach integrates all multi-temporal observations from Sentinel-1, Sentinel-2 and Landsat-8 from 2017 to 2020 to map surface water at monthly intervals across the time-series, and to derive insights into seasonal and inter-annual water dynamics. The full mapping workflow was implemented in Google Earth Engine (GEE)--a cloud-computing platform that provides the necessary combination of large-scale computing resources with instant access to planetary scale archives of satellite imagery [63]. All the data is acquired from GEE. The Sentinel-1 dataset on GEE has already been pre-processed, including standard processing routines for orthorectification, radiometric and atmospheric correction.
# Last-Mile Optimization Using Neural Networks
## 2 Related Work
### E-Commerce Domain Classification  
One article introduced a novel approach--Learning to Accept Classes (L2 AC), a meta-learning-based OWL approach--and an experimental demonstration of its effectiveness in e-commerce product classification. L2 AC outperforms existing methods by simultaneously addressing the rejection of unseen examples and incrementally incorporating new classes, providing an integrated solution for OWL [24].  
[PERSON] et al. [25] proposed a supervised learning method for automatic product categorization in e-commerce, employing morphological analysis of product titles and assigning importance scores to tokens. A dimensionality reduction technique was proposed to address scalability issues. Evaluation on a real-world dataset demonstrated over 95% classification accuracy. Future work includes testing the model on large-scale data and assessing its efficiency in practical scenarios.  
One article presented a large-scale e-commerce product categorization system using deep neural models. It addresses challenges such as sparse data distribution and diverse text lengths, proposing a solution with deep belief nets and deep autoencoders. The system achieved 81% accuracy in matching predictions with merchant-assigned categories. Future work was suggested to explore the impact of incorporating image data on system performance [26].  
[PERSON] et al. [27] proposed a two-level approach for product classification in e-commerce. It utilizes a set of classifiers for path-wise, node-wise, and depth-wise labels in the taxonomy hierarchy, aiming to enhance classification accuracy. By considering the hierarchical structure, the method captures the specificity of product placement. The ensemble technique demonstrated improved results compared with previous approaches, showcasing the effectiveness of this comprehensive two-level strategy in leveraging taxonomy information for enhanced product classification.
## 2 Materials and Methods  
### Data Acquisition  
There is a strawberry experimental field located in the Plant Science Research and Education Unit (PSREU) at the University of Florida in Citra, FL, USA (29.404265\({}^{\circ}\) N, 82.141893\({}^{\circ}\) W) for dataset collection. The strawberry experiment field was 67 m long and six meters wide with five rows of strawberry plants, each being 67 m long and 0.5 m wide. The five rows were the 'Florida Radiance' cultivar. The drone used for image acquisition is DJI PHANTOM 4 PRO, and its specifications are shown in Table 1. The flight path and parameters are set through the ground control station to perform image capture.  
The drone collected data every 14 days and captured images 6 times in total. The shooting took place between 10:30 am and 12:30 pm, and the weather conditions were sunny and cloudy. Images under various weather conditions are used for model training to enhance its ability to recognize strawberry field images under different environmental conditions. The drone's flight trajectory was parallel to the ridge and perpendicular to the top of the ridge, capturing images with a resolution of 4000 \(\times\) 3000 pixels. When the drone captures images, the frontal overlap rate is 70% and the lateral overlap rate is 60%. All the flights were performed automatically by the DJI GroundStationPro (DJI Technology Co., Ltd., Shenzhen, China) iPad application, which is designed to conduct automated flight missions and manage the flight data of DJI drones. During each image capture, there are two flights conducted, capturing images from heights of 2 m and 3 m, respectively. The image capture at 2 m height takes 35 min per session, while the image capture at 3 m height takes 23 min per session. The initial images were divided into four types, and the example image is shown in Figure 1. The composition of the overall dataset is shown in Table 2. LabelImg 1.8.6 software was used to preprocess the original image, and the mature strawberries, immature strawberries, and strawberry flower targets were labeled, respectively. The annotated data was divided into a training set, a validation set, and a testing set in proportion. The final dataset composition is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Specifications**} \\ \hline Camera sensor & 20 M \\ Photo & JPEG; DNG (RAW); JPEG + DNG \\ Weight (Battery and Propellers Included) & 1388 g \\ Maximum Flight Time & 30 min \\ Operating Temperature & 0–40 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Drone specifications table.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Order** & **Date** & **Weather** & **Height** & **Num** \\ \hline
1 & 2 March 2018 & Cloudy & 2 m & 181 \\
2 & 16 March 2018 & Sunny & 3 m & 145 \\
3 & 30 March 2018 & Sunny & 2 m & 177 \\
4 & 13 April 2018 & Cloudy & 2 m & 172 \\
5 & 27 April 2018 & Cloudy & 2 m & 192 \\
6 & 11 May 2018 & Sunny & 2 m & 169 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Image acquisition information table.
# Consumer's Attitude towards Display Google Ads
## Materials and Methods
### Data Collection Procedures and Sample Characteristics  
The current research aims to examine the variables that impact the consumer's attitude towards Display Google ads. Online questionnaires were developed based on the previous literature. Non-probability sampling has been used to actualize this study. Data were collected from 432 diverse individuals who use the internet. A total of 74 surveys were excluded as answers were they do not notice Display Google ads, resulting in 358 surveys being valid for statistical analysis. Finally, structured equation modeling via AMOS 22 was used to analyze reliability testing, model validation, and model convergent validity. Respondents' demographic characteristics, age, gender, and educational level, are shown in Table 1.  
\begin{table}
\begin{tabular}{|c|c c c c|} \hline
**Gender** & **Frequency** & **Percent** & **Valid Percent** & **Cumulative Percent** \\ \hline Male & 162 & 45.3 & 45.3 & 45.3 \\ \hline Female & 196 & 54.7 & 54.7 & 100.0 \\ \hline
**Total** & **432** & **100.0** & **100.0** & \\ \hline
**Age** & **Frequency** & **Percent** & **Valid Percent** & **Cumulative Percent** \\ \hline
16 years or under & 2 & 0.6 & 0.6 & 0.6 \\ \hline
17-24 & 280 & 78.2 & 78.2 & 78.8 \\ \hline
25-34 & 44 & 12.3 & 12.3 & 91.1 \\ \hline
35-44 & 6 & 1.7 & 1.7 & 92.7 \\ \hline
44-54 & 8 & 2.2 & 2.2 & 95.0 \\ \hline
55 years or above & 18 & 5.0 & 5.0 & 100.0 \\ \hline
**Total** & **432** & **100.0** & **100.0** & \\ \hline
**Educational Level** & **Frequency** & **Percent** & **Valid Percent** & **Cumulative Percent** \\ \hline High School Student & 34 & 9.5 & 9.5 & 9.5 \\ \hline High School Diploma & 32 & 8.9 & 8.9 & 18.4 \\ \hline Bachelor’s Degree & 264 & 73.7 & 73.7 & 92.2 \\ \hline Master’s Degree & 20 & 5.6 & 5.6 & 97.8 \\ \hline PhD & 8 & 2.2 & 2.2 & 100.0 \\ \hline
**Total** & **432** & **100.0** & **100.0** & \\ \hline \end{tabular}
\end{table}
Table 1: Sample Characteristics.  
The demographical data in Table 1 show that the amount of females are 196 and the amount of males are 162, this indicates that females participated more than males. The majority of respondents are aged between 17 and 24 years, with a percentage of 78.2%. As for educational level, the majority of the study sample were in their Bachelor's degree, and their number was 264.
## 4 Experiment and Discussion  
### Sample Data Preparation  
According to the health analysis of the shearer, there are many variables which can be used to evaluate the shearer health condition. However, not all variables of the shearer have significant correlation to its health condition, and too many input variables to the FNN will reduce the training efficiency. Therefore, in this study, four variables of the shearer: cutting motor current (CC), traction motor current (TC), cutting part temperature (CT), and traction speed (TS), are selected for training the FNN model. The shearer health grade is the output of the forecasting model and its quantitative data can be scored according to the experts. In the experiment, we divide the health grade into four levels, from 1 to 4, and the higher the grade, the worse the shearer health condition. The notations for all of the variables are shown in Table 1. Based on above analysis, we can select samples for the FNN from the original database in the 22210 coal face of Pingdingshan Coal Industry, Henan, China.  
Figure 2: Optimizing the parameters of the fuzzy neural network (FNN) model with improved particle swarm optimization (IPSO).  
In general, the selected samples may contain some system noise. In order to specify the noise level, we had researched the problem of system noise through wavelet analysis. Taking the cutting motor current as an example, the analysis results of system noise were illustrated in Figure 3. The results indicated that the system noise fluctuated in the interval of [\(-\)0.8, 0.8] and had little impact on the subsequent application of cutting motor current. In summary, the collected datasets could be directly used as the sample data for the training and testing of proposed predictor.
## 4 Conclusions  
In this context, our study represents the novelty of applying a non-invasive spectroscopic technique by means of an FT-IR sensor for the authentication of commercial poppy seed oil. The present study fills an important gap in the available knowledge concerning the application of FT-IR spectroscopy in the framework of commercial poppy seed oil authentication and offers a faster and more efficient way to perform quality control processes within the oil industry. This approach of coupling the portable FT-IR sensor with PLSR modeling, thus proved to be equally efficient, allowing the authentication and prediction of major fatty acid profiles with no special preparation of the samples. The successful detection of adulteration in all tested samples proved the reliability of our approach for authenticity testing using a simple spectroscopic technique and chemometric modeling, as opposed to conventional techniques such as gas chromatography (GC-FID). As such, our proposed method, incorporating unique spectral patterns from the FT-IR sensor coupled with SIMCA, will not only be more accurate but also drastically simplify the authentication process and, hence, cheaper, and less time-consuming for routine testing. The present research provides several useful inputs toward offering a practical and effective tool to the manufacturer of oils and quality control agencies in their work for efficient ways of carrying out their activities. Beyond analytical methodology, this study paves the way for future improvements, particularly toward the development of comprehensive databases of adulterants. Therefore, this technique may increase robustness and adaptability upon increasing the number of adulterants in FT-IR spectral libraries. It could also be used with different types of adulterations in several edible oils. Overall, this study provides a practical tool not only for oil manufacturers but also for quality control agencies. It offers a reliable, non-destructive, and cost-effective solution for the authentication of poppy seed oil. Its portability makes it suitable for on-site applications, while its adaptability to other edible oils enhances its utility in combating food fraud. In addition, this approach supports the authenticity of the product, regulatory compliance, and assurance of public health, strengthens consumer confidence, and becomes a valuable asset for producers and regulatory authorities.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The author would like to thank [PERSON] (The Ohio State University, Department of Food Science and Technology) and [PERSON] for their technical support in this study.  
**Conflicts of Interest:** The author declares no conflicts of interest.
# Precise Orbit Determination of BDS-2 and BDS-3 Using SLR
## 5 Discussion  
[PERSON], [PERSON] and [PERSON] (2019) analysed the accuracy dependency of the SLR-only orbit determination on the number of SLR observation quantities as well as the number of SLR sites. The dependencies of 3-day arc solutions for BDS-2 MEO and IGSO satellites were involved and discussed. In the same way, we explore these dependencies of BDS-2 and BDS-3 satellites, as well as their 3-, 5-, 7-, and 9-day arc solutions in comparison with WUM orbits in this section.
# Compressed Nonlinear Equalizers for 112-Gbps Optical Interconnects: Efficiency and Stability
## Principle of Pruned Nonlinear Equalizers
### Pruning Algorithm  
It has been proved that most of multiplication operations in the nonlinear equalizers are redundant [23] as either weight value is small or connections in network level contribute very little to final results. Therefore, there is possibility of best pruning algorithm that can realize a lightest equalizer without sacrificing transmission performance. The choice of strategy to prune the complex networks as shown in Figure 1 become very important to reduce computation complexity for each architecture while maintaining equalization performance. We proposed a threshold-based pruning and retaining approach for equalizers, where coefficients after initial training are intentionally discarded based on a threshold and damages caused by pruning process are recovered as much as possible through retraining [3; 12]. The pruning process can be easily expressed as Equation (6), where \(S(\cdot)\) represents weight setting and \(T\) is threshold. With weight value setting to zero, connections relating to this value is cut in these complex networks. Due to different features of two architectures, there also needs special design considerations for each equalizer. For VE, second- and third-order coefficients are pruned because we find linear terms only occupy a small amount of computation complexity while contribute most on the performance gain due to bandwidth limitation of the experimental system. For NNE, all weights both from input layer to hidden layer and hidden layer to output layer are run for pruning based on the threshold. An iterative pruning process is developed to achieve a more efficient network structure.  
\[S(W)=0,\quad when\quad S(\cdot)<T. \tag{6}\]
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Advanced Antenna Designs in the mmWave Frequencies
#### 3.5.4 Example Designs of mmWave and FR2 Antennas
Another antenna design fabricated on a soft substrate (GFPL-970 LF) of six layers is presented in ref. [115]. The overall dimensions of the structure are (7 \(\times\) 7 \(\times\) 0.36) mm; it uses stubs for matching and supports differential feeding, which is folded with an L-probe to achieve double polarization while increasing the bandwidth. One last example, operating at higher frequencies (110 to 170 GHz) and with a 4 \(\times\) 4 antenna array constructed via the AiP technique, is presented in ref. [116]. The element repeated in the array is two magnetoelectric dipoles fed by SIW. The substrate is BT-based (GHPL970 LF and CCL-HL972 LF), which is inexpensive, and it is recommended for antennas above 100 GHz. The final construction has dimensions of (15 \(\times\) 15) mm and achieves a gain of 16.8 dBi with a bandwidth of 20 GHz. This antenna is to be used in devices operating in the D-band.
## 5 Conclusions  
This study demonstrates that clinical utility-driven ML enhances retinal OCT classification by harmonizing technical performance with diagnostic priorities. Logistic Regression emerged as the optimal model, balancing high accuracy and discriminative power while reducing false-negative rates through SMOTE-optimized data stratification. The framework's emphasis on NPV addresses critical clinical needs in sight-threatening conditions like AMD and DME, where delayed diagnosis risks irreversible vision loss. Learning curves and ROC validation confirmed stable generalization, with SMOTE reducing the training-validation accuracy gap. By integrating anatomical feature preservation, automated model selection, and translational metrics, this approach standardizes OCT analysis. The results underscore the viability of ML-driven OCT diagnostics in reducing interpretative variability, accelerating pathological biomarker detection, and improving clinical decision-making, with the hope of deployable clinically relevant AI tools in ophthalmic practice. Future work should focus on multicenter validation and real-time integration with clinical workflows to maximize patient-centered impact.  
Cross-validation on pathologies absent from training validated the framework's adaptability to novel retinal conditions, supporting its clinical viability in detecting unforeseen pathologies through shared microstructural signatures. This mitigates a key adoption barrier in ophthalmology, where exhaustive training on rare diseases is impractical.  
Conceptualization, M.S., R.S., D.R., D.N. ([PERSON]), D.N. ([PERSON]) and M.T.; methodology, M.S., R.S., D.R., D.N. ([PERSON]), D.N. ([PERSON]) and M.T.; software, M.S., R.S., D.R., D.N. ([PERSON]), D.N. ([PERSON]) and M.T.; validation, M.S., R.S., D.R., D.N. ([PERSON]), D.N. ([PERSON]) and M.T.; formal analysis, M.S., R.S., D.R., D.N. ([PERSON]), D.N. ([PERSON]) and M.T.; investigation, M.S., R.S., D.R., D.N. ([PERSON]), D.N. ([PERSON]) and M.T.; resources, M.S., R.S., D.R., D.N. ([PERSON]), D.N. ([PERSON]) and M.T.; data curation, M.S., R.S., D.R., D.N. ([PERSON]), D.N. ([PERSON]) and M.T.; writing--original draft preparation, M.S., R.S., D.R., D.N. ([PERSON]), D.N. ([PERSON]) and M.T.; writing--review and editing, M.S., R.S., D.R., D.N. ([PERSON]), D.N. ([PERSON]) and M.T.; visualization, M.T.; supervision, M.T.; project administration, M.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article are available in public repositories.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AMD & Age-related macular degeneration \\ AUC & Area under the curve \\ CNV & Choroidal neovascularization \\ CNN & Convolutional neural network \\ CSR & Central serous retinopathy \\ DME & Diabetic macular edema \\ DR & Diabetic retinopathy \\ KNN & K Neighbors Classifier \\ LDA & Linear Discriminant Analysis \\ \end{tabular}
## 2 Materials and Methods  
### Materials  
The solvents utilized in the UHPLC-MS/MS method in the present research were of HPLC grade. The solid compounds, namely, DVB and encorafenib (EFB), were purchased in analytical research grade. The current investigation centers on the examination of two distinct analytes, specifically dovitinib (CHIR-258) and encorafenib (LGX818). The analytes were obtained from MedChemExpress, a well-regarded pharmaceutical company (Princeton, NJ, USA). The purity of DVB (Cat. No.: HY-50905) was found to be 99.94%, but EFB (Cat. No.: HY-15605) demonstrated a purity of 99.63%. Acetonitrile (ACN; Product No. 34851), ammonium formate (NH\({}_{4}\)COOH; Product No. 70221), HLMs, and formic acid (HCOOH; Product No. 5.33002) were derived from Sigma Aldrich Corporation, located in St. Louis, MO, USA. The HLMs (Product No. M0317) were transported using dry ice to guarantee the maintenance of their integrity. The HLMs (20 mg/mL) were stored in a refrigerated environment at \(-\)78 \({}^{\circ}\)C upon their arrival until they were considered appropriate for use.