esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Applications in Optimization
### Self-Organizing Migrating Algorithm (SOMA)
#### 4.4.3 Mixed Fruit Fly Optimization  
The Fruit Fly Optimization Algorithm (FOA) is a meta-heuristic technique proposed by Pan [91] that is inspired by the behavior of fruit flies. It simulates the foraging behavior of fruit flies. In comparison with other swarm intelligence-based algorithms, FOA has a sample framework with few parameters, and is easily understood and implemented. It has been improved as the Mixed Fruit Fly Optimization Algorithm LGM-FOA (Logistic Mapping-FOA) on the basis of the logistic map of Mitic et al. [92]; however, the algorithm shows an ideal state with respect to convergence precision and stability in the optimization process, as there are three discontinuous points from the logistic map. To solve this problem, Luo et al. [93] proposed a new mixed fruit fly algorithm that uses the Lozi map instead of the logistic map to conduct a global search for the optimal parameter values. It uses the value as the center to perform tiny fluctuations in order to obtain a final optimal value of quadratic optimization, thereby improving the initial value selection method of LGMFOA. In support of the simulation between vector machine regression forecasting and the original Fruit Fly Algorithm, Particle Swarm Optimization (PSO), and LGM-FOA, this result testifies that the convergence accuracy of this new mixed fruit fly algorithm has obvious advantages.
# Survey for Soil Sensing with IOT and Traditional Systems
## 3 RF-Based Soil Sensing Systems in Internet of Things (IoT)
### LTE-Based Soil Sensing Systems  
The prior studies by [12,14] introduced RF-based methods for soil moisture sensing. While these approaches emphasize affordability, Ref. [15] focuses on reducing additional device requirements and enhancing energy efficiency. The systems presented in the earlier works necessitate power for the signal emitters (such as Wi-Fi or RFID readers) or both transceivers (like Wi-Fi AP and clients), which may hinder their large-scale outdoor deployment. In contrast, the research in [15] presents an economical LTE-based soil moisture sensor using readily available commercial hardware. Unlike Wi-Fi or RFID systems, the LTE-based setup does not require any additional hardware for signal transmission, capitalizing on the widespread presence of base stations. Figure 6 shows the hardware arrangement of their system on the LTE-receiving stage.  
Central to their method is the linkage they draw between the phase changes of the intercepted LTE signal, soil moisture, and dielectric permittivity. Initially, they utilize an empirical equation to delineate the connection between the dielectric constant \(\epsilon\) and volumetric water content (VWC) \(M_{oi}\), as presented in Equation (10) and as per [70].  
\[M_{oi}=0.1138\sqrt{\epsilon}-0.1758. \tag{10}\]  
Through the measurement of the RF wave propagation speed \(c_{s}\) in the intended soil, the dielectric constant can be deduced with the light speed in the air \(c_{o}\) as illustrated in Equation (11) [15].  
\[\sqrt{\epsilon}=\frac{c_{0}}{c_{s}}. \tag{11}\]  
Hence, the soil moisture \(M_{oi}\) can be inferred from the propagation speed \(c_{s}\) of the RF signal within the target soil. The propagation speed of the RF signal can be de  
Figure 5: RFID -based soil moisture sensing system.The blue dashed arrow indicates the sweeping of the reader’s TX power, while the red dashed arrow shows the process of DMRT readings  
Figure 6: LTE-based soil moisture sensing system.  
duced from the spacing between two receiving antennas \(d\) and the refraction angle \(\theta\). Additionally, \(\gamma\) represents the time difference of signal arrival between the two antennas, as expressed in Equation (12).  
\[c_{s}=\frac{d\sin\theta}{\gamma} \tag{12}\]  
They also introduce an auto-calibration mechanism for phase offset, addressing hardware limitations and minimizing the sensor's energy consumption. Their energy-efficient design allows the system to operate on battery power for up to 16 months. Not only is their system considerably more affordable ($55) compared to traditional devices ($850), but it also boasts a remarkable accuracy (3.15%), rivaling that of premium soil moisture sensors through extensive experiments. This research significantly advances the application of RF sensing in real-world smart agriculture scenarios.
## 4 Experiment
### Baseline Models  
To ensure a comprehensive evaluation of the proposed methodology, the results are compared with three widely recognized supervised 3D reconstruction algorithms that serve as baseline models:  
1. **3D-R2N2:** A deep residual network that uses convolutional layers to predict the 3D structure of an object from one or more 2D images. Developed by researchers at Stanford and Adobe, this model has become a benchmark in 3D reconstructions from 2D images.
2. **AtlasNet**: Proposed by Facebook AI, this model utilizes a collection of 2D patches, or atlases, to reconstruct the 3D geometry of objects. It leverages a PointNet encoder, demonstrating high proficiency in generating detailed 3D shapes.
3. **Occupancy Networks**: Occupancy Networks represent a novel approach to 3D.  
The rationale behind selecting these specific models as baselines is their prevalence in the domain of 3D reconstruction and their known efficacy in various scenarios. By juxtposing our results with these established models, we aim to provide a clear benchmark for the capabilities of our proposed approach.
# Compact MIMO System Performances in Metallic Enclosures
## 4 Experimental Results and Discussions
### Findings and Limitations  
The findings and the limitations of the detailed 2 \(\times\) 2 MIMO prototype as well as the constraints on the RF measurements are as follows:  
* BER reduces with the increase in the Tx gain of the USRP or an increase in the Rx gain of the USRP.
* EVM reduces with the increase in the Tx gain of the USRP or an increase in the Rx gain of the USRP, except for some Tx-Rx gain combinations where the Tx amplifier of the USRP is overdriven.
* The EVM of the digital communication link is time-varying in a time-varying environment. For the fixed Tx-Rx gain combination, the EVM is time-varying when the stirrer is moving.  
Figure 11: Measured MIMO EVM of SDR channel 1 and SDR channel 2 in the presence of the MIMO interference when the metal enclosure was loaded with ten pieces of RF absorber cones. The Tx gain was swept from 2 to 10 dB. The AGC gain was set to 50 dB.  
* For the fixed Tx-Rx gain combination, the EVM and BER degrade in the presence of the interference, and further degrade when the magnitude of interference increases. We have the following limitations:
* There is a software limitation in creating the feedback loop for a closed-loop MIMO system. This is extremely challenging in Simulink. It can be conducted by shifting the baseband signal processing blocks directly to the FPGA of the USRP.
* The testbed can be extended to a multi-user setting after conducting adjustments in the Simulink model and by using more X310 USRPs and an appropriate number of antennas.
* The testbed in its current form has the limitation of leveraging the maximum sampling rates of the USRPs. Increasing the sampling rates or increasing the number of USRPs shall increase the risk of a higher number of underflows and overflows in the system. We do not have consistent links in the presence of underflows and overflows. This is a limiting factor in understanding the link.
# A Review of Digital Twin Technology for Electric and Autonomous Vehicles
## 3 Digital Twin Technologies for Electromobility
### Digital Twin and Virtual Sensors  
EVs use many environmental sensors to perceive and act according to their perceptions. This section illustrates how logical entities, called virtual sensors (VSs), can be used in a DT framework as the virtual twins of physical sensors. VSs can support new services for smart transportation, addressing issues related to battery charging and route planning for EV drivers based on parameter estimation/prediction. In particular, VSs derive new data from existing information generated by physical sensors and utilize a data processing algorithm to process the data input and produce the required output [28].  
Figure 5: Key DT technologies for electromobility.  
For example, [PERSON] et al. [2] introduced and designed new virtual sensors to enhance the EV charging experience. The proposed model provides a smart charging service that allows drivers to find the best charging points for their vehicles. Another example is given by [PERSON] et al. [29], who proposed a methodology for estimating the state of charge in EVs. The method relies on VSs and other measurements available in the vehicle, such as speed, acceleration pedal position, and battery voltage. VSs also play an essential role in enhancing user experiences and optimizing EV services, which could support the growth of the EV market.  
In [30], [PERSON] et al. developed a new EV service to improve user experiences while preparing for a trip. They designed three VSs that help the driver predict the cost and required energy for the journey. The proposed VSs can estimate the energy demand based on historical data from past trips. Some relationships between the sensors can be determined over time by the virtualization platform, for example, by utilizing and exploiting ML technologies to improve the functioning of the sensors. Therefore, combining the technology of VSs with the EV simulation model can provide a way to solve complicated issues, such as battery management, vehicle energy management, and vehicle control.
# Theory and Design of Blink Jamming
## 3 Modelling Formation Jamming  
In order to validate the theoretical understanding of formation jamming the blink jamming engagement was modelled in MATLAB and Simulink, with the radar receiver implemented on an FPGA in a hardware co-simulation. This model, shown in Figure 6, created a 3-D engagement between two jamming sources travelling independently and an amplitude comparison monopulse receiver with an antenna tracking system. The scenario accurately replicates a common electronic warfare problem of deceiving a semi-active guided missile.  
The Field Programmable Gate Array (FPGA) sub-systems were implemented in System Generator, then run using hardware-in-the-loop mode on a Xilinx Kintex-7 KC705 FPGA development board. This method served to validate the proposed electronic attacks using an actual radar receiver, as opposed to a software model. The remainder of this section details the model, test-bed, and FPGA implementation. The radar was modelled at baseband with a sampling frequency of 50 MHz.
# Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems
## 7 Conclusions  
Using machine learning systems, this 4-year study in a pilot rice farm of about 110 hectares in Greece resulted in a predictive model for topdressing nitrogen demands solely from soil and satellite data. The required dataset was derived mainly from open-source satellite data (Sentinel-1 and Sentinel-2 imagery), limited field surveys (for soil sampling), and records from the farmer's machinery (yield maps); Sentinel-2 scenes could also be used for management zone delineation instead of RapidEye images if the latter were not available; the use of UAS imagery was only ancillary and could be skipped, too.  
Model building helped in better understanding fertility issues in the soils of the area, and finally, in providing thorough and timely suggestions to the grower. In parallel with its main goal, the study also achieved to produce an algorithm for mapping inundated rice fields and thus indicate seeding dates, which proved to be a critical variable in the prediction process. It is foreseen that by entering additional variables to the model, such as weather conditions, water regime within the field, weed infestations, and diseases, would further improve model performance.  
The developed model (an XGBoost model) can be considered as fully operational, repeatable, and possibly expandable to larger rice cultivation areas. Used as a paradigm, the model would allow reduction of fertilizer costs for growers and could have a significant positive effect in the protection of waters.  
With a steadily view to supporting growers' decision making, Ecodevelopment S.A. is planning to expand the application of the new predictive model in different study areas, thus empowering the generality of the proposed approach.  
Conceptualization, M.I. and C.K.; methodology, M.I., C.K. and I.P.; machine learning, M.I. and I.P.; validation M.I., C.K. and I.P.; resources, K.K.; data curation, C.K., I.R., I.P. and S.M. ([PERSON]); writing--original draft preparation, M.I., C.K., G.I., I.P., S.M.([PERSON]), and I.R.; writing--review and editing, G.I., C.K., V.A., K.K. and S.M. ([PERSON]); visualization, M.I. and C.K.; supervision, G.I. and S.M. ([PERSON]); All authors have read and agreed to the published version of the manuscript.  
No funding was provided for this research.  
Cordial thanks to [PERSON] (farmer) and [PERSON] (steward) for their pioneering spirit and dedicated support to the research requirements throughout the study; also, to [PERSON] for the conversion of the fertilization digital maps to the appropriate format for Kverneland Geospread/Tellus Pro VRT system. Finally, many thanks to the personnel of the Soil &Water Resource Institute, Hellenic Agricultural Organization DEMETER for conducting meticulously the soil analyses.  
No conflict of interest is declared.
## 2 Materials and Methods
### Model Improvement
#### 2.2.3. CBAM Attention  
This study introduces the CBAM to improve the model's feature representation capability. This module integrates both channel and spatial attention mechanisms, providing the model with comprehensive and effective feature extraction capabilities [32]. Figures 4 and 5 depict the core idea of the CBAM, which aims to refine the input feature map in two stages: first, by refining the dependencies among channels using the channel attention module (CAM), and then by enhancing features along the spatial dimension using the spatial attention module (SAM).  
The CAM assesses the significance of each channel, thereby weighting the channel dimension of the feature map to highlight the contributions of key feature channels. It uses  
Figure 4: CBAM architecture [32].  
Figure 5: Computational process of the attention sub-module [32].  
Figure 3: RepLKNet network architecture [30].  
global max pooling and global average pooling to capture local details and global information, respectively, from the feature map. The output of CAM is articulated as follows:  
\[M_{c}(F)=\sigma(MLP(AvgPool(F))+MLP(MaxPool(F))), \tag{1}\]  
where A and M stand for the global average pooling and max pooling procedures, respectively, and \(F\) is the input feature map. Additionally, the multi-layer perceptron is represented by M, and the sigmoid activation function is represented by \(\sigma\).  
The SAM further optimizes the spatial dimension of the feature map by generating a spatial attention map along the channel dimension using pooling operations. The significance of various geographical places in the feature map is reflected in this map. The model adaptively improves the feature responses of significant regions while reducing noisy or irrelevant parts, by multiplying the spatial attention map by the feature map. The output of the SAM is articulated as follows:  
\[M_{s}(F)=\sigma\Big{(}f^{7\times 7}([AvgPool(F);MaxPool(F)])\Big{)}, \tag{2}\]  
where [_AvgPool (F)_; _MaxPool (F)_] is the concatenation of the average pooling and the max pooling results along the channel axis, and \(f^{7\times 7}\) indicates a \(7\times 7\) convolution operation. This two-stage attention mechanism allows the CBAM to effectively capture essential information in the feature map's channel and spatial dimensions, thereby enhancing the model's ability to perceive key features.
# _remote sensing_
## 3 Related Data and Correction Models in Cal/Val of Altimeters
### Mass  
It is essential to define the local MSS/sea surface between the coastal ATGs and altimeter footprints in the Cal/Val campaigns [12; 15]. The MSS corrections in altimeter calibration can be maintained by self-measured sea surface and global/regional MSS models [18; 33]. The MSS is the sum of the marine geoid and mean dynamic topography (MDT), in which the geoid dominates. The MSS gradient is small over most of the oceans. However, in the vicinity of continental shelves, trenches, islands, sea mounts, and coastal zones with significant terrain changes, such gradients can exceed up to 10 cm/km [34; 35; 36; 33].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **K1** & **K2** & **M2** & **N2** & **S2** & **O1** & **P1** & **Q1** & **RSS** & **RSS* \\ \hline DTU 16 & 2.60 & 1.46 & 9.03 & 2.52 & 2.10 & 5.88 & 0.78 & 1.64 & 4.17 & 4.20 \\ NAOOjb & 2.94 & 1.02 & 9.51 & 2.63 & 2.07 & 5.63 & 1.04 & 1.61 & 4.28 & 4.38 \\ GOT4.10 & 2.80 & 0.70 & 8.54 & 2.77 & 2.43 & 5.72 & 0.81 & 1.61 & 4.04 & 3.83 \\ HAMTIDE12 & 2.63 & 0.62 & 8.60 & 2.59 & 1.97 & 5.53 & 0.78 & 1.58 & 3.96 & 3.95 \\ EOT20 & 11.18 & 0.75 & 9.32 & 2.61 & 2.37 & 5.57 & 2.07 & 1.49 & 5.73 & 4.96 \\ FES2014 & 1.98 & 0.96 & 9.21 & 2.58 & 2.07 & 5.88 & 0.96 & 1.67 & 4.17 & 4.31 \\ \hline \end{tabular}
\end{table}
Table 3: Comparisons between the in situ tide and tide model (Unit: cm). RSS include all the four tide gauges. RSS* represent the RSS without the B329 tide gauge (Figure 2).
# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 5 Discussion
### Analysis Based on the Method Used
#### 5.1.2 RNN  
In the section on the RNN-based model, the following conclusions could be drawn:  
1. According to the datasets used, there was 1 paper that used a combination of technical analysis, and sentiment and news analysis to predict the stock. The rest used technical analysis only.
2. For the variables, all the RNN-based models used a multivariable input, and open price, close price, highest price, and the lowest price were used in all models as an input.
3. It could be found that all the papers changed the traditional RNN model to pursue a higher performance in prediction. Two of the papers chose the C-RNN based model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Metrics** & **Reference No.** & **Corresponding Value** \\ \hline RMSE & [86] & 0.6866 \\ Accuracy & [87] & 66.32\% \\  & [89] & 47.8\% \\  & [92] & 79.7\% \\  & [84] & 4.49 \\  & [88] & 0.8 \\ Sharpe ratio & [90] & 0.6418 \\  & [91] & 6.68 on EURUSD currency \\  & [84] & 1.4228 \\  & [87] & 1.0531 (0.25\%) \\  & [88] & 1.118 \\ Return rate & [89] & 1.611 (0.3\%) \\  & [90] & 1.4316 \\  & [91] & 1.0968 on EURUSD \\  & & currency \\ MSE & [85] & 1.05 \\ MDAE & [85] & 0.71 \\ Correlation & [86] & 0.9564 \\ Precision & [87] & 72.1\% \\ Recall & [87] & 77.32\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: The metrics and results for the papers which used other deep learning methods.  
4. The metrics used in each paper were different; in total, there were 8 metrics used for measuring the performance of the RNN-based model.
5. RMSE and accuracy were selected by multiple articles. We note that the paper in [29] has a much lower RMSE than that of paper [27], and paper [28] had higher accuracy than that of paper [29].
## 4 Conclusions  
Eighty-nine compounds were screened by SPME/SAFE-GC-MS/O with chromatographic columns of DB-Wax and DB-5 in 15 brands of EVOOs. Eighty compounds were identified by SPME-GC-MS/O and 54 by SAFE-GC-MS/O method. Forty-four compounds were detected by both methods. Undecanol, (\(Z\))-4-decneal, (\(E\))-2-dodecenal, and 2-nonanone were found in EVOOs for the first time only by SAFE. 2-Nonanone was the key aroma compounds identified by AEDA. SPME had better extraction efficiency to aroma compounds in EVOOs, while SAFE could extract the most effective components (seven key aroma compounds). Hence, it is feasible and advantageous that SAFE is applied to the aroma extraction of EVOOs.  
Eight classes of flavor compounds were identified in the 15 brands of EVOOs, including 17 alcohols, 22 aldehydes, 9 ketones, 4 acids, 14 esters, 5 aromatics, 12 alkene, and 6 others. Eleven compounds were identified as the key aroma compounds in alternative brands of EVOOs by SAFE-AEDA. Key aroma compounds authenticated by AEDA had some differences from those by OAVs. The aroma compounds of Spain EVOOs were similar, and those of some of samples from Italy and Greece were alike.  
[PERSON] collected samples, drafted and revised the manuscript; [PERSON] carried out the laboratory work and performed statistical analysis; [PERSON] conceived, designed and coordinated the study; [PERSON] helped to edited and revised the manuscript. All authors gave final approval for publication.  
Financial support was provided by The National Key Research and Development Program of China (No. 2018 YFD040050204), National Natural Science Foundation of China (No. 31871838), and Project of High-level Teachers in Beijing Municipal Universities (No. IDHT20180506).  
The authors declare no conflict of interest.
# Part-Based Obstacle Detection Using a Multiple Output Neural Network
## 3 Solution Description  
The core of the solution is the multiple-head artificial neural network, based on an encoder-decoder structure that uses a color image as input, whereas the output consists of multiple prediction modules: an obstacle detection module, a semantic segmentation module, and a vanishing-point detection module. The input part, based on an encoder, will extract the proper and significant features from the input image, whereas the decoder, which is based on semantic segmentation solutions, will provide multiple predictions (outputs). We have trained each module independently and we have used the same loss function. The CNN architecture is presented in Figure 1.  
The detection module will identify individual objects or obstacles from the road scene, including vehicles, trucks, buses, cyclists, pedestrians, etc. This is based on semantic segmentation approaches; more specifically, on a modified U-Net CNN decoder module. The method of extracting the individual object instances is unique, meaning that we made use of the semantic segmentation CNN to label the image pixels with the corresponding object quarters (parts), instead of using it to label free space or specific obstacles. The labeled object parts were then grouped further into individual objects, using post-processing that took into account the proximity and position of the quarters as part of a full object. This approach simplifies the overall artificial network architecture by making use of the direct connections with the decoder's layers. Another advantage of a multiple output network is that the encoder part is shared between the modules and the model can easily be extended, to predict other information. The detection module output features four layers (channels) that encode the binary status of the image pixels as being part of an object quarter (top left, top right, bottom left, bottom right). Using these parts, the algorithm described in Section 3.4 is able to extract the object instances as bounding boxes and as the labeled regions of pixels.  
Figure 1: The multiple-output neural network architecture.