esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background: Deep Neural Networks
### DNN Models  
Since their origin, DNNs have been developed in a large number and a very diverse types of models in order to achieve high accuracy. The first DNN model to become famous was LeNet [28], a Convolutional Neural Network (CNN) that was used to recognize handwritten digits. The real boom for CNNs, which are the most widely used for object detection and recognition, came in 2012 when AlexNet [31] won the ILSVRC competition [32] by outperforming the earlier methods. Since then, also thanks to the increased availability of computational hardware and memory resources, the DNN models have become more and more complex and precise. Table 1 outlines a timeline of the models that have become more popular, describing the innovations introduced compared to previous models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Year** & **Contribution** & **\# Param** & **Depth** & \begin{tabular}{c} **Top-5 Acc** \\ **ImageNet (\%)** \\ \end{tabular} \\ \hline LeNet [28] & 1998 & First popular CNN & 60 k & 5 & - \\ \hline AlexNet [31] & 2012 & \begin{tabular}{c} - First CNN to win ILSVRC \\ - ReLUIntroduction \\ \end{tabular} & 60 M & 8 & 79.06 \\ \hline VGG16 [33] & 2014 & Smaller kernel sizes & 138 M & 16 & 90.37 \\ \hline GoogLeNet [34] & 2015 & Inception block & 4 M & 22 & 87.52 \\ \hline Inception & \begin{tabular}{c} v3 [35] \\ v4 [36] \\ \end{tabular} & 2015 & Factorized convolutions & 24 M & 48 & 93.59 \\ \hline ResNet [37] & \begin{tabular}{c} 50 \\ 152 \\ \end{tabular} & 2016 & \begin{tabular}{c} - Skip connections \\ - Residual learning \\ \end{tabular} & 60 M & 152 & 93.98 \\ \hline Xception [38] & 2017 & \begin{tabular}{c} Depthwise and pointwise \\ convolutions \\ \end{tabular} & 23 M & 38 & 94.50 \\ \hline \begin{tabular}{c} ResNetXt- \\ 101\_64x4d \\ \end{tabular} [39] & 2017 & Grouped convolution & 83 M & 101 & 94.70 \\ \hline DenseNet161 [40] & 2017 & \begin{tabular}{c} - Regular structure \\ - Information flow across layers \\ \end{tabular} & 28 M & 161 & 93.60 \\ \hline SeNet154 [41] & 2018 & \begin{tabular}{c} Exploit dependencies between \\ feature maps \\ \end{tabular} & 115 M & 154 & 95.53 \\ \hline NasNet-A [42] & 2018 & \begin{tabular}{c} - Neural Architecture Search \\ - Transfer learning \\ \end{tabular} & 89 M & 29 & 96.16 \\ \hline BERT-L [43] & 2019 & \
\begin{tabular}{c} Transformer network for Natural \\ Language Processing (NLP) \\ \end{tabular} & 332 M & 24 & - \\ \hline Megatron [44] & 2019 &
\begin{tabular}{c} Model parallel transformer \\ for NLP \\ \end{tabular} & 3.9 B & 48 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the most popular models in the history of DNNs.
## 5 Proposed Multi-EC Enabled Air-Ground VN Scenarios
### ML for Computation Offloading in Multi-Service Multi-EC Vehicular Environments
#### 5.3.2 Possible Challenges  
* **Resource Allocation problem:** In the vehicular environment, VUs often request multiple slices with stringent requirements. As described before, each network slice can be embedded as multiple VNFs embedded together. Each VNF of a network slice can have specific computation demands. Allocating a sufficient amount of resources for each slice, considering their particular requirements is a challenging problem over Muli-EC-enabled VN and needs to be addressed carefully for reducing service failures.
* **VNF Placement Problem:** Each service request can have specific demands in terms of latency, data rate, etc. To satisfy such requests performing a proper VNF placement over multi-EC enabled VN is can be beneficial. For example, to fulfill the stringent latency requirements of Ultra-Reliable Low Latency Communications (URLLC) based slices, placing several VNFs into proximity of an end-user (i.e., over RSU-EC or LAP-EC) can be beneficial with reduced transmission delays. However, in the case of enhanced Mobile Broadband (eMBB) slice, requiring high data rates, it is needed to explore higher layers of EC platforms, (i.e., BS-EC, HAP-EC, etc.) for having sufficient resources.  
Figure 11: Network Slicing based VNF placement.
# A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas
## 4 Experimental Evaluations
### Impact on Various Parameter Settings
#### 4.2.2 Spatial Parameter  
Next, we added the spatial parameter and referred to the forecasted PM2.5 value from the nearest \(n\) devices. Figure 13 shows the impact of a number of spatial references surrounding devices on the RMSE of PM2.5 value forecast. We varied the number of surrounding station references \(n\) from 0 to 70, where 0 means that we did not use any spatial references for air quality forecasting. The experimental results show that, as \(n\) increases, the RMSE first drops and then increases. When \(n\) is larger than 25, the RMSE may be even worse than the results without the reference to surrounding stations. The reason for this is that the reference range is too large, resulting in a large difference in air quality. To find the best parameter setting, we scaled the results of \(n\) up from 0 to 30. We could observe that every line had a minimum RMSE when \(n\) was about 5 to 7. While the spatial parameter has a low RMSE effect on the results, we set \(n\) as 6, based on the experimental results in the following experiments.
## 3 Experimental Results
### Islanding Detection Testing with Parallel Operation of Two Inverters
It was confirmed through this experiment that islanding detection of test inverters was impossible when the PV inverter was operating and the EESS inverter was charged using some of the energy that the PV inverter discharged. The measuring frequency was continuously changed within the reference range during islanding based on the frequency waveforms in Figure 10.  
Figure 8: Test result of case 4 (PV 20 kW, EESS +20 kW).
# Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information
## 2 Data Used and Study Regions  
The CYGNSS satellite constellation is an eight-satellite bi-static radar system launched by NASA in December 2016, collecting near-global (between 38\({}^{\circ}\)N and 38\({}^{\circ}\)S latitudes) daily reflected L-1 coarse acquisition (C/A) GPS signals in the form of delay-Doppler maps (DDMs), with four simultaneous point observations measured by a single satellite. The DDMs capture the behavior of the reflecting surface, characterized by the specular point of reflection and reflectivity of the surface itself, and strength of the reflected signal as indicated by the signal-to-noise ratio (SNR). For the model development, SNR data was obtained from the Level-1 bistatic radar cross-section (BRCS) of Earth surface data collection, hosted at the University of Michigan [29] (Supplementary Table S1). The globally available open-access Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation model (DEM) Version 3 (GDEM 003) dataset was also used in this study, which is provided as \(1^{\circ}\times 1^{\circ}\) tile at 30 m spatial resolution, and referenced to the WGS84/EGM96 geoid [30]. The proposed model is applied to two regions suffering from flood events: (a) the southern Indian state of Kerala with an area of 38,000 km\({}^{2}\) (August 2018); and (b) Bangladesh and parts of North and Northeast India, including the states of Uttar Pradesh, Bihar, West Bengal, Sikkim, Assam, and Arunachal Pradesh, totaling an area of 531,000 km\({}^{2}\), which witnessed widespread and heavy flooding during the monsoon season of 2017 [31]. Both study regions are having contrasting land use/land cover, with the state of Kerala characterized by uneven, hilly terrain, while parts of North India are among the flattest delta regions in the world. This makes it prone to regular flooding, while Kerala has witnessed sporadic periods of intensive flooding. North India is also home to the most fertile basins of Ganga and Brahmaputra, because of the sedimentation following its floods. The proposed model was evaluated for its robustness over those two regions featuring contrasting topographic and flooding behaviors. Sentinel-1 synthetic aperture radar (SAR) ground range detected (GRD) imagery [32] acquired in vertical (VV) polarization during the flood events of Kerala in August 2018 and over parts of North India across August 2017 (Supplementary Table S2) was used to derive the flood inundation extent that was used for validating against the inundation extent obtained from the proposed model.
## 1 Introduction  
In Spain, one of the most important olive oil producers worldwide [1], olive trees have traditionally been cultivated extensively under rainfed conditions, but in recent decades, there has been a shift towards super-intensive cultivation methods, increasing the number of trees per hectare, mainly due to the increase in consumer demand, promoted by the recommendations of the Food and Agriculture of the United Nations [2] based on its nutritional and sensory profiles [3]. This change in the irrigation system has drastically increased the water needs of olive orchards.  
In Spain, the region with the highest cultivation area of olive trees is Andalucia, which is a region with a high level of desertification risk, due to its low rainfall and high temperature. For these reasons, new agricultural strategies are being developedbased on reducing irrigation water but without reducing production or quality, such as the use of plant covers [4; 5; 6], precision irrigation [7] and/or the application of deficit irrigation [8]. The main two deficit irrigation strategies are: (i) regulated deficit irrigation (RDI), which reduces water during one or more phenological stages of fruit development, and (ii) sustained deficit irrigation (SDI), which reduces water throughout the whole fruit development process.  
In the case of olive trees, previous studies have shown that the application of deficient water during pit hardening (phase II) [9; 10] did not negatively affect production. In this sense, the Food Quality and Safety research group of the Miguel Hernandez University (UMH, Spain) developed a brand, hydroSOStainable products, to recognize farmers and producers that apply and control deficit irrigation techniques and fulfil the brand requirements [11; 12]. Thus, the aim of this study was to analyze the effects of two RDI treatments with different intensities and one SDI treatment (adjusted to the reality of the Spanish agriculture and water availability), on the quality of the hydroSOStainable olive oil produced, by analyzing the effects on: (i) the IOC (International Olive Council) quality parameters, (ii) the antioxidant activity (ABTS\({}^{\star\star}\) and DPPH\({}^{\star}\) methods), (iii) the total phenolic content (TPC) and (iv) the lipidic, volatile and sensory profiles, along two consecutive seasons (2018 and 2019).
# Money Neutrality, Monetary Aggregates and Machine Learning
###### Abstract  
The issue of whether or not money affects real economic activity (money neutrality) has attracted significant empirical attention over the last five decades. If money is neutral even in the short-run, then monetary policy is ineffective and its role limited. If money matters, it will be able to forecast real economic activity. In this study, we test the traditional simple sum monetary aggregates that are commonly used by central banks all over the world and also the theoretically correct Divisia monetary aggregates proposed by the Barnett Critique ([PERSON], 1994; [PERSON], 2014), both in three levels of aggregation: M1, M2, and M3. We use them to directionally forecast the Eurocoin index: A monthly index that measures the growth rate of the euro area GDP. The data span from January 2001 to June 2018. The forecasting methodology we employ is support vector machines (SVM) from the area of machine learning. The empirical results show that: (a) The Divisia monetary aggregates outperform the simple sum ones and (b) both monetary aggregates can directionally forecast the Eurocoin index reaching the highest accuracy of 82.05% providing evidence against money neutrality even in the short term.  
Eurocoin; simple sum; Divisia; SVM; machine learning; forecasting; money neutrality +
Footnote †: journal: _Algorithms_
## 4 Simulation Results
### Variable-Step GD Estimation
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{5}{c}{**Localization Results Based on 7 Relative Measurements**} \\ \hline
**Method** & **Convergence Rate** & **Localization Error (m)** & **Number of Iterations** & **Computation Time (ms)** \\ \hline Fixed-step GD/Equation (3) (w/o errors) & 98.9\% & 0.008 \(\pm\) 0.006 & \(27.1\pm 18.5\) & **0.076 \(\pm\)** 0.051 \\ \hline Variable-step GD/Equation (3) (w/o errors) & 95.5\% & 0.008 \(\pm\) 0.006 & \(232\pm 228\) & \(1.5\pm 1.4\) \\ \hline Fixed-step GD/Equation (4) (w/o errors) & **99.7\%** & 0.0001 \(\pm\) 0.0001 & \(1522\pm 1407\) & \(6.3\pm 5.9\) \\ \hline Variable-step GD/Equation (4) (w/o errors) & 90.0\% & **0.0000 \(\pm\)** 0.0001 & \(1032\pm 1404\) & \(3.9\pm 5.3\) \\ \hline Fixed-step GD / Problem (5) (w/errors) & **99.3\%** & **0.069**\(\pm\) 0.029 & \(28.0\pm 21.4\) & **0.115 \(\pm\)** 0.121 \\ \hline Variable-step GD/Equation (5) (w/errors) & 97.4\% & 0.078 \(\pm\) 0.034 & \(1466\pm 1306\) & \(2.8\pm 3.3\) \\ \hline Variable-step GD/Equation (6) (w/errors) & 88.0\% & 0.079 \(\pm\) 0.033 & \(982\pm 1337\) & \(8.6\pm 11.6\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative localization results using the different formulations of the estimation problem based on 7 relative measurements, with and without taking into account uncertainty models.
## 2 Laboratory Experiments  
### Experimental Setup  
The experiments were carried out in the Hydraulic Laboratory of Civil Engineering at Iskenderun Technical University, Iskenderun, Turkey, in a small-scale rectangular horizontal channel with the following dimensions: 1.00 m length, 0.50 m width, and 0.35 m height (Figure 1a). The channel bottom and walls were made of a 9-mm thick glass. The reservoir was set upstream, covering 0.25 m of the channel, closed by two symmetric 0.20-m wide impermeable walls and by a 4 mm thick and 0.10 m wide plate coated with aluminum and made of rigid plastic representing the dam. The partial hole of the dam was simulated by the fast complete plate opening (Figure 1b). First, a steel rope was connected to the plate top. Then, the rope was drawn over a pulley with a 15 kg weight hanging at the other end. By releasing the weight from 1.0 m above the floor, the plate was removed in about 0.04 s, an opening time shorter than the one recommended by [PERSON] and [PERSON] (1998) for a'sudden removal', equal to \(1.25(h_{0}/g)^{1/2}\) (where \(h_{0}\) is the reservoir depth, and \(g\) is the acceleration due to gravity) and corresponding to 0.15 s in the present tests.  
Figure 1: Schematic view of experimental setup and plate removal mechanism: (**a**) Plan and (**b**) A-A cross-section. Lengths are in [cm].
###### Abstract  
This study compares various F1-score variants--micro, macro, and weighted--to assess their performance in evaluating text-based emotion classification. Lexicon distillation is employed using the multilabel emotion-annotated datasets XED and GoEmotions. The aim of this paper is to understand when each F1-score variant is better suited for evaluating text-based multilabel emotion classification. Unigram lexicons were derived from the annotated GoEmotions and XED datasets through a binary classification approach. The distilled lexicons were then applied to the GoEmotions and XED annotated datasets to calculate their emotional content, and the results were compared. The findings highlight the behavior of each F1-score variant under different class distributions, emphasizing the importance of appropriate metric selection for reliable model performance evaluation in imbalanced multilabel datasets. Additionally, this study also investigates the effect of the aggregation of negative emotions into broader categories on said F1 metrics. The contribution of this study is to provide insights into how different F1-score variants could improve the reliability of multilabel emotion classifier evaluation, particularly in the context of class imbalance present in the case of phishing emails.  
emotion analysis; multilabel dataset; annotated datasets; F1-score; performance metrics Article
# Aerodynamics Analysis of Speed Skating Helmets: Investigation by CFD Simulations
## 1 Introduction  
In recent decades, high speed sports have put increasing attention in aerodynamics to strive for excellence. This is the case of speed skating, in which 90% of the total resistance comes from the aerodynamic drag when skating at 15 m/s [1]. In the last international championships, differences in standings were so small that a slight improvement in the aerodynamic performance could have led to a variation in the podium positions [2]. Speed skating has already been influenced by technological solutions targeting aerodynamic drag reduction. The so-called zig-zag strips and skin-suits helped improving the performance of the athletes by forcing the transition of the boundary layer from the laminar to the turbulent regime, thus postponing the flow separation about the athletes' limbs [1, 3, 4, 5]. At the Nagano 1998 Olympic winter games, the Dutch national team used the Delta-Flash suit developed with the experimental zig-zag strips [6], dominating the speed skating events with 11 medals in total and 3 world records [7]. After those Olympic winter games, manufacturers started to develop new suits to reduce drag force. One example is the Nike Swift Skin suit [8], which consisted of six selected fabrics of different roughness placed strategically on the body to minimize the aerodynamic drag. At the Salt Lake City 2002 Olympic winter games, this suit made skaters faster by up to 0.2-0.3 s per lap on a 400 m rink [6].  
According to the International Skating Union regulations and technical notes [9, 10], skaters should and may use a helmet in short and long track disciplines, respectively. Such regulation has attracted much attention towards helmet shape design. Although many cycling helmet aerodynamics assessments are reported in the scientific literature [11, 12, 13], no systematic study has been conducted on the aerodynamics of speed skating helmets. Racing velocity and posture differ between cycling and speed skating, leading to different criteria when conducting aerodynamic optimisations. Nevertheless, cycling gives good insight into the influence of a helmet. In cycling, it is reported that the use of a helmet can cause 19% of the total drag area of a cyclist in time-trial posture at 60 km/h or 16.6 m/s [13].  
The latter suggests that also in speed skating, the helmet can provide an aerodynamic advantage to the athlete.  
This paper performs an aerodynamics assessment of several helmet designs with the objective of understanding which helmet designs provides the minimum aerodynamic drag. A generic skater model is built using an open-source software, and eight configurations are investigated, including a bare-head configuration and a baseline configuration with the Omega helmet from Cadomotus.
# Assessing Ecosystem and Urban Services for Landscape Suitability Mapping
## 3 Results and Discussion
### Sensitivity Analysis
#### 3.2.2 Model's Behaviour under Changing Services Ranking  
The analysis of ratios between specific scenarios and the baseline support the understanding of the suitability indicator's sensitivity to the variation of the relative importance of services (Figure 7).  
Figure 7: Ratios between suitability indices and their baselines calculated for the different scenarios and for each landscape class (descriptions of landscape classes’ codes are reported in Table 1, scenario codes in Table 2). (**A,B**): economic perspective, (**C,D**): infrastructure perspective. (**A,C**): supply-based suitability, (**B,D**): demand-based suitability.  
Results from this analysis show that the relative importance of services strongly influences the final SI. When this factor varies, the suitability is reduced to 75% or increased up to 220% if compared with the baseline. When the importance of services needing accessibility is maximized (in INF and HOUS scenarios), suitability tends to decrease compared to the baseline. On the contrary, when the importance of services that do not require accessibility is maximized, suitability tends to increase compared to the baseline. This happens in NINF and FARM. These trends suggest that each landscape type offers high quantities of the services maximized in NINF and low quantities of the services maximized in INF and FARM. In the cases discussed above, suitability shows similar trends with supply-based scaling and demand-based scaling. However, in the COMM scenario, where many essential services are required, suitability is higher than the baseline in the demand-based suitability. Differently, it shows a different trend in supply-based suitability.
## 7 Integrated Localization and Communication  
This section focuses on the importance of localization for communication and autonomous navigation of unmanned vehicles and studies the joint localization and communication in future mmWave networks [176; 177].
# Recent Advances in Brillouin Optical Correlation-Domain Reflectometry
###### Abstract  
Distributed fiber-optic sensing based on Brillouin scattering has been extensively studied and many configurations have been developed so far. In this paper, we review the recent advances in Brillouin optical correlation-domain reflectometry (BOCDR), which is known as a unique technique with intrinsic single-end accessibility, high spatial resolution, and cost efficiency. We briefly discuss the advantages and disadvantages of BOCDR over other Brillouin-based distributed sensing techniques, and present the fundamental principle and properties of BOCDR with some special schemes for enhancing the performance. We also describe the recent development of a high-speed configuration of BOCDR (slope-assisted BOCDR), which offers a beyond-nominal-resolution detectability. The paper is summarized with some future prospects.  
Brillouin scattering; optical fiber sensing; optical correlation-domain reflectometry; distributed sensing; strain sensing; temperature sensing; nonlinear optics; structural health monitoring