esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Datasets and Methods  
### Data Preparation  
In the study, the categorization of drought levels during the pivotal growth stages of wheat was based on the Chinese agricultural standard \"Field Survey and Grading Technical Specifications for Winter Wheat Disaster\", specifically the section on winter wheat drought disaster (NY/T 2283-2012) [19]. These stages include the jointing (RJ), the heading and flowering (HF), and the flowering to maturity (FM) phases. Drought conditions were classified into five categories: Optimum Moisture (OM), Light Drought (LD), Moderate Drought (MD), Severe Drought (SD), and Extreme Drought (ED) [20], as detailed in Table 1. Considering the uneven soil moisture distribution and the challenges in precise irrigation management, soil moisture sensors were strategically placed using a greedy ant colony algorithm for node deployment, ensuring an accuracy of \(\pm\)1%. With the deployment of these sensors, data regarding soil moisture levels were collected. Concurrently, monitoring equipment was utilized to photograph wheat under the various drought conditions, creating a dataset that correlates images of wheat experiencing different levels of drought stress with the soil moisture monitoring data.
## 2 Materials and Methods
### Data Used
#### 2.2.2 Gross Primary Productivity (GPP) and Vegetation Transpiration (VT) Data  
The Penman-Monteith-Leuning version 2 (PML-V2) model is a water-carbon coupled diagnostic biophysical model [46; 47]. PML_V2 products offer evapotranspiration (ET), its three components (i.e., vegetation transpiration (hereafter, VT), soil evaporation, and an interception from vegetation canopy), and gross primary productivity (GPP) data at 500 m and 8-day resolution. The key benefits of the PML_V2 products are that it estimates the transpiration and GPP via canopy conductance [47; 48]. On the other hand, it separates evapotranspiration (ET) into three components (i.e., transpiration from vegetation, direct evaporation from the soil, and vaporization of intercepted rainfall from vegetation) [46]. The PML_V2-based products have exhibited more robust competency with the flux site observations globally than the MODIS-based GPP [46; 47]. Nevertheless, PML_V2 products are similar to or noticeably better than major state-of-the-art ET and GPP products widely used by water and ecology science communities [47]. The details on PML_V2-based GPP and VT datasets can be found in [PERSON] et al. [47].  
Gross primary production (GPP) is an essential component of carbon balance and indicates the total carbon intake (carbon sequestration) through photosynthesis per unit of time and per unit of area. It is crucial to analyze variations in long-term estimates of the CO2, including the atmosphere fluxes, to determine the global or regional carbon balance, especially in forest ecosystems, which are the primary sinks of atmospheric carbon in the biosphere. To better understand ecosystem carbon dynamics, agricultural productivity, and climate change, accurate GPP estimations are decisive [49; 50; 51]. Thus, the present study intended to deploy the annual GPP product to analyze the impacts of mining activity on the rate of atmospheric carbon dioxide uptake by the vegetation during photosynthesis.  
VT is a vital process in the water cycle and is responsible for most of the water vapor present in the atmosphere. VT is a process in which water is mainly evaporated through the stomata of plant leaves and released into the atmosphere. This process is called evaporative cooling, which helps to regulate the foliage's temperature and the surrounding air. Hence, the VT has a significant role in the local and global water vapor budget. Thus, the present study used a PML_V2-based VT dataset for analyzing the losses in VT caused by the mines-induced vegetation cover loss in the study area during 2001-2021.
# Intrusion Detection Method Based on Preprocessing of Highly Correlated and Imbalanced Data
###### Abstract  
This paper examines traditional machine learning algorithms, neural networks, and the benefits of utilizing ensemble models. Data preprocessing methods for improving the quality of classification models are considered. To balance the classes, Undersampling, Oversampling, and their combination (Over + Undersampling) algorithms are explored. A procedure for reducing feature correlation is proposed. Classification models based on meta-algorithms such as SVM, KNN Naive Bayes, Perceptron, Bagging, Random Forest, AdaBoost, and Gradient Boosting have been thoroughly investigated. The settings of the base classifiers and meta-algorithm parameters have been optimized. The best result was obtained by using an ensemble classifier based on the Random Forest algorithm. Thus, an intrusion detection method based on the preprocessing of highly correlated and imbalanced data has been proposed. The scientific novelty of the obtained results lies in the integrated use of the developed procedure for reducing feature correlation, the application of the SMOTEENN data balancing method, the selection of an appropriate classifier, and the fine tuning of its parameters. The integration of these procedures and methods resulted in a higher F1 score, reduced training time, and faster recognition speed for the model. This allows us to recommend this method for practical use to improve the quality of network intrusion detection.  
computer systems; network; machine learning; data preprocessing; SMOTEENN; ensemble classifier; random forest; gradient boosting
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 2 Materials and Methods  
In recent years, smart agriculture has flourished in several areas. Furthermore, deep learning applications in smart agriculture have spread widely and yielded advanced, satisfactory results. This paper intends to survey and analyze deep learning techniques and their applications in agriculture to be a modern and comprehensive reference for researchers.  
This survey work is conducted in three steps. The first step involved collecting relevant research by searching keywords (agriculture, deep learning, convolutional neural networks, recurrent neural networks, crop monitoring, disease detection, and irrigation systems). Keyword-based searches were performed for journal and conference papers from the IEEE Xplore and ScienceDirect scientific databases and the Web of Science and Google Scholar scientific indexing services. A total of 70 articles were collected that were later reduced to 60, which presented some deep learning models. We studied these papers and further narrowed the numbers to 40 papers that conducted deep learning experiments and provided some results. In the second step, the selected scientific papers were analyzed and compared in terms of:  
* The areas of smart agriculture that were focused on.
* The problems that they tried to solve.
* The deep learning techniques and models used.
* The dataset used.
* The data preprocessing and data augmentation methods that were used.
* The results in terms of accuracy or precision.  
In the third and last stage, we found that some weaknesses and shortcomings existed in surveyed papers, and we tried to suggest some improvements to overcome these issues.  
As an output of this survey, we proposed a hybrid deep learning model consisting of CNN and SVM that is expected to improve existing models' performance and working range.
# Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets
## 3 Models
### Training  
The training was done with the same parameters for all the networks. The optimizer used was from Adam [34], with a learning rate of \(10^{-3}\) and default parameters of Keras (\(\beta_{1}=0.9\), \(\beta_{2}=0.999\), the exponential decay rates for the moment estimates). The gradient norm was clipped to one to avoid the exploding gradient problems [35]. The batch size was one (i.e., one slice) for every network  
Figure 4: Illustration of the KIKI-net from [10]. The KCNN and ICNN are convolutional neural networks composed of a number of convolutional blocks varying between 5 and 25 (we implemented 25 blocks for both KCNN and ICNN), each followed by a ReLU non-linearity and featuring between 8 and 64 filters (we implemented 32 filters). For both the varying numbers, the supplementary material of [10] shows that the higher the better. The ICNN also features a residual connection.  
Figure 5: Illustration of the PD-net from [9]. Here, \(\mathcal{T}\) denotes the measurement operator, which in our case is the under-sampled Fourier transform, \(\mathcal{T}^{*}\) its adjoint, \(g\) is the measurements, which in our case are the undersampled k-space measurements, and \(f_{0}\) and \(h_{0}\) are the initial guesses for the direct and measurement spaces (the image and k-space in our case). The initial guesses are zero tensors. Because we transform complex-valued data into 2-channel real-valued data, the number of channels at the input and the output of the convolutional subnetworks are multiplied by 2 in our implementation.  
except the U-Net, where the whole volume was used for each step. For all networks, to maximize the efficiency of the training, the slices were selected in the eight innermost slices of the volumes, because the outer slices do not have much signal. No early stopping or learning rate schedule was used (except for KIKI-net to allow for a stable training where we used the learning rate schedule proposed by the authors in the supporting information of [10]). The number of epochs used was 300 for all networks trained end-to-end. For the iterative training of the KIKI-net, the total number of epochs was 200 (50 per sub-training). Batch normalization was not used; however, in order to have the network learn more efficiently, a scaling of the input data was done. Both the k-space and the image were multiplied by \(10^{6}\) for fastMRI and by \(10^{2}\) for OASIS because the k-space measurements had values of mean \(10^{-7}\) (looking separately at the real and imaginary parts) for fastMRI and of mean \(10^{-3}\) for OASIS. Without this scaling operation, the training proved to be impossible with bias in the convolutions and very inefficient without bias in the convolutions.
## 6 Discussion
### Practical Implications  
This study offers several actionable insights for governments, policymakers, and cloud service providers aiming to mitigate the challenges associated with cloud service adoption. The validated measurement scale can be adopted by policymakers in conducting studies that will enhance governance frameworks to address the challenges of cloud adoption. The high reliability of the measurement items could suggest possible areas that the government can prioritise during decision making regarding cloud investments based on the most critical influencing factors by measuring changes in the identified factors over time. Stakeholders can assess the effectiveness of interventions aimed at facilitating cloud adoption for government development purposes. This practical application could enable more evidence-based decision making and the continuous improvement of technology interventions in development contexts.  
The government and policymakers are expected to apply the scale to prioritise areas of concern, such as information security and privacy challenges, and strategically measure and balance the benefits of cloud adoption while mitigating data protection and sovereignty concerns. This suggests that cloud implementations that address citizen concerns about data protection are more likely to gain government acceptance, ultimately enabling the realisation of development benefits. Moreover, equipping government and cloud service providers with the necessary tools to measure adoption barriers ensures efficient and secure integration in the public sector.  
Additionally, this study provides a reliable model for future research and policymaking in government at the intersection of cloud computing adoption and development objectives in government contexts. This contributes to the growing body of knowledge on how IT implementation, particularly cloud computing, can serve as a catalyst for sustainable development through improved governance, service delivery, and digital inclusion.
## 4 Discussion
### Clinical Implications
#### 4.6.5 Addressing Challenges and Building on Current Innovations  
Despite the promising advancements, the successful clinical implementation of GTR membranes in immunocompromised populations requires addressing several challenges. The lack of human studies and limited understanding of long-term outcomes necessitate extensive clinical trials. Additionally, the heterogeneity of systemic conditions in immunocompromised patients requires stratified research that accounts for variations in disease severity and treatment regimens. Developing standardized protocols for membrane selection, application techniques, and monitoring could enhance consistency and effectiveness in clinical settings.
# An Efficient Approach to Manage Natural Noises in Recommender Systems
## 4 Experimental Analyses  
### Evaluation Metrics  
The quality of recommendation algorithms is generally evaluated based on the predictive accuracy and the recommendation accuracy [27]. The former computes the errors between predicted ratings and actual ratings. Hence, the smaller the values are, the higher the accuracy is. The classical metrics are the Mean Absolute Error (\(MAE\)) and Root Mean Square Error (\(RMSE\)), which are given by  
\[MAE=\frac{\sum_{(u,i)\in T}|r_{ui}-p_{ui}|}{|T|}, \tag{18}\]  
\[RMSE=\sqrt{\frac{\sum_{(u,i)\in T}(r_{ui}-p_{ui})^{2}}{|T|}}, \tag{19}\]  
where \(T\) is the set of predicted ratings and \(p_{ui}\) represents the predicted rating of user \(u\) on item \(i\).  
For the recommendation accuracy, _Precision_, _Recall_, and \(F1\) value are three typical metrics. _Precision_ refers to the proportion of successful recommended items in all recommended items. _Recall_ is the proportion of successful recommended items in all items that users really like. \(F1\) value is the comprehensive value of _Precision_ and _Recall_.
# SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge
The main idea behind the development of the proposed physical layer is to provide developers and researchers with a starting point so that they can mainly focus their attention on developing novel intelligence and reasoning algorithms to combat spectrum scarcity. The presented physical layer can be employed in prototyping and experimentally testing intelligent algorithms for dynamic optimization of spectrum usage. Its source code can be found on its GitHub project page [7]. * Two concurrent bursty filtered OFDM based PHY layers that operate independently of each other in full or half-duplex modes. * A novel frame structure with a focus on higher throughput (with 32 Modulation Code Scheme values (MCS), achieving more than 84 megabits per second (Mbps) for a configured PHY bandwidth (BW) of 9 MHz and MCS 31) and with control signals being sent by the robust and more reliable M-sequences instead of LTE-like control channels. * FPGA based FIR filters that improve/speed up the PHY processing performance while mitigating Out-Of-Band Emissions (OOBE). * Proposing a two-stage synchronization sequence detection algorithm, which uses a Cell-Average Constant False Alarm Rate (CA-CFAR) algorithm in the second stage in order to improve the detection performance when compared to an approach that only employs plain correlation to detect the presence of the synchronization sequence. Our results show that the proposed two-stage approach improves subframe detection and synchronization in low SNR scenarios. * Cyclic Prefix (CP) based Carrier Frequency Offset (CFO) estimation implemented with Single Instruction, Multiple Data (SIMD) functions and a more accurate and fine-grained complex exponential correction signal, which generates correction signals with frequencies closer to the estimated ones. * Simple receive (Rx) combining scheme where the detected and synchronized signals coming from the two independent PHYs are combined for improved performance in low SNR scenarios. * The effectiveness of the presented physical layer is verified through a comprehensive set of experiments and their respective results. We present and discuss the results of experiments including measurements of Central Processing Units (CPU) and memory utilization, throughput, the effectiveness of a filtered form of the Orthogonal Frequency-Division Multiplexing (OFDM) waveform, etc. The remainder of the paper is organized as follows: Section 2 presents some related pieces of work that apply artificial intelligence algorithms and methods to the spectrum scarcity/sharing problem. Section 3 describes the design of SCATTER PHY in detail and specifies its main features. Section 4 showcases the experimental verification of the achieved benefits. Section 5 concludes the paper.
# A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments
## 4 Results
### Comparison between Range Errors and Pre-Fit Residuals  
In smartphone GNSS data processing, quality control is vital and more challenging compared to geodetic data. Pre-fit outlier rejection is one effective quality control method. Therefore, in this subsection, the correlation between ranger errors and pre-fit residuals is assessed. As shown in Figure 9, different colors represent different satellites. It can be concluded that the range errors and pre-fit residuals for P1 have a higher correlation than P5, while the range errors and pre-fit residuals for P5 are at a lower level compared with P1. The reason for a higher correlation in P1 could be that the smartphone antenna is designed for the first frequency, and may not be optimized for other frequencies. To further evaluate the correlation satellite-by-satellite, Figure 10 illustrates the relationship among correlation coefficient, elevation angle, and SNR, where each dot represents one satellite and different colors denote the corresponding SNR values. With a lower elevation angle, the correlation coefficients tend to be smaller. Usually, GLONASS satellites have the highest correlation coefficients despite larger range errors. One possible reason is that, for other constellations, due to the high noise level itself for smartphones, the correlation between range errors and pre-fit residuals can be easily hidden within the noise; while there is a larger range error for GLONASS satellites, so the correlation can be more conspicuous. The results indicate that pre-fit residual rejection/de-weighting is worth further investigation and may provide potential solution improvement in future research, as it reflects the range errors with a high confidence level.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**P1**} & \multicolumn{4}{c}{**P5**} \\ \cline{2-9}  & **Mean** & **STD** & **95 th per** & **Number** & **Mean** & **STD** & **95 th per** & **Number** \\ \hline Open-sky & 0.8 & 7.3 & 14.5 & 32267 & 0.9 & 2.4 & 5.1 & 14198 \\ \hline Sub-urban & 2.2 & 9.4 & 18.2 & 1443 & 1.2 & 3.8 & 5.9 & 658 \\ \hline Vegetation & 1.5 & 7.5 & 17.1 & 354 & 1.4 & 1.9 & 3.9 & 176 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics for satellite absolute range errors under different environments (unit: meter).  
Figure 9: Correlation between range errors and pre-fit residuals.
# Mobility Prediction of Mobile Wireless Nodes
## 6 Results and Evaluation
### Evaluation of the Gauss-Markov Mobility Model  
Gauss-Markov is the third mobility model used in this study. Figure 6 represents the model's accuracy in all scenarios. The accuracy of LR was significantly affected by an increase in the number of APs, decreasing from 84% to 49%. The other classifiers had good results in all scenarios, with the average prediction accuracy reaching 89% with LDA, 88% with GNB and DT, 71% with SVM, and 66% with k-NN.
# Compact Quad-Port MIMO Antenna with Ultra-Wideband and High Isolation
## 4 Result and Discussion
### Diversity Properties of MIMO Antennas
#### 4.3.3 Mean Effective Gain  
In a multipath fading environment, the Mean Effective Gain (MEG) can be used to measure the total efficiency of the MIMO system, the total gain, and the effect produced by the propagation environment [25]. It is generally required that the MEG between the units within the MIMO system is less than 3 dB, and the MEG can be estimated from the S-parameters of each port [26].  
\[MEG_{i}=0.5\times\left(1-\sum_{j=1}^{4}S_{ij}\right)(i=1,2,3,4) \tag{6}\]  
\[MEG_{ij}=MEG_{i}-MEG_{j}(i\
eq j) \tag{7}\]  
The \(MEG_{1}\), \(MEG_{2}\), \(MEG_{3}\) and \(MEG_{4}\) at the four ports calculated by formula (6) are all less than 6 dB, as shown in Figure 17a-c. Similarly, \(MEG_{12}\), \(MEG_{13}\) and \(MEG_{14}\) calculated by Formula (7) are all between \(-0.2\) dB and \(0.2\) dB, and the data details are shown in Figure 17d; it can be seen that the average effective gain of the proposed MIMO antenna performs well over the whole operating band.  
Figure 16: Diversity Gain curve of proposed antenna at: (**a**) port 1; (**b**) port 2.
## 2 Materials and Methods
### Statistical Analyses  
The analysis was performed using IBM(r) SPSS(r) Statistics 28 (IBM Corporation, Chicago, IL, USA). The Shapiro-Wilk test was used to evaluate data normality. The independent \(t\)-test analysis was used to evaluate the differences in the anthropometric variables and performance variables of \(V\)O\({}_{2}\) max, RE, and maximum running time between youth and pro athletes. [PERSON]'s test indicated no significant difference; thus, equal variances were assumed. A two-way analysis of variance (ANOVA) was used to evaluate the effects of playing position and playing standards (pro vs. youth) on the various LT methods and their interactions. A mixed-design ANOVA was used to examine the main effects of group (playing standard) on lactate accumulation, the main effect of time (lactate accumulation at each stage of the incremental testing), and the interaction between group and time. Athletes who surpassed the 18 km/h stage or terminated their performance earlier were excluded from this part of the analysis. Thus, only 36 pro and 25 youth athletes completed the 18 km/h stage and were subsequently included in the analysis.  
_Bland-Altman analysis._ The limits of agreement between the different measurement methods of LT for youth and pro athletes were quantified using the Bland-Altman analysis (\(M\pm\) 1.96 \(SD\)). Scatter plots were prepared by plotting the difference between each of the two LT methods on the \(y\)-axis and the average of the measurements on the \(x\)-axis [34]. The three horizontal lines represent the mean difference (bias) and the upper and lower limits of agreement (LOA), which are the mean bias \(\pm\) (1.96 * SD). Similarly to [PERSON] et al. (2016) [25], and for the same reasons [35], we used less than \(\pm\)1 km/h as the criterion for agreement between the LT methods. The agreement was also analyzed using Intraclass Correlation Coefficients (ICCs) with upper and lower 95% confidence intervals. Finally, we performed regression analyses to determine the variation observed in running performance, which was explained by the variation in LT determination methods. For all statistical analyses, significance was accepted at \(p<0.05\).