esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Experimental Setup  
In our work, we used many network types, such as Resnet [40], VGG [26], and PyramidNet [46]. For a fair comparison with SIBDAs, we employed the same parameters as in [25]; this work [25] used 300 epochs, an initial learning rate of 0.1, continuous reduction by 10 at certain epochs (100, 150, 175, and 190), and a batch size set to 64. The probability of performing RSMDA was set to 0.5, similar to RE, and we also checked 10 different probabilities, as mentioned in Section 4.3.1. We re-performed the baseline and RE experiments for the fashionMNIST dataset as the original experiments [25] performed for the baseline and RE were on the old fashionMNIST dataset. The issue with the old fashionMNIST dataset is that a few test and training images overlapped with each other, as discussed in the GitHub repository ([[https://github.com/zhunzhong07/Random-Erasing/issues/9](https://github.com/zhunzhong07/Random-Erasing/issues/9)]([https://github.com/zhunzhong07/Random-Erasing/issues/9](https://github.com/zhunzhong07/Random-Erasing/issues/9)), accessed on 1 December 2022) of RE [25]. For a fair comparison with MIBDAs, we used the same setting as was mentioned in CutMix [36], where the epochs were 300, the batch size was 128, the initial learning rate was 0.1, the momentum was 0.9, and the learning rate decayed by 10 after every 30 epochs. We performed all of the experiments using a PyTorch module with 2 NVIDIA GetForce RTX 2080 Ti GPUs. Similar to the previous settings, each experiment was repeated at least three times unless otherwise mentioned.
# Theoretical and Experimental Analysis for Cleaning Ice Cores from EtisolTM 140 Drill Liquid
###### Abstract  
To reconstruct climate history of the past 1.5 Million years, the project: Beyond EPICA Oldest Ice (BEOI) will drill about 2700 m of ice core in East Antarctica (2021-2025). As drilling fluid, an aliphatic ester fluid, EtisolTM 140, will be used. Newly drilled ice cores will be retrieved from the drill soaked in fluid, and this fluid should be removed from the cores. Most of it will be vacuum-cleaned off in a Fluid Extraction Device and wiped off with paper towels. Based on our experiences in Greenland deep ice coring, most of the residual fluid can be removed by storing the cores openly on shelves in a ventilated room. After a week of \"drying\", the cores have a dry feel, handling them do not give \"wet\" gloves and they can easily be marked with lead pencils. This paper presents a theoretical investigation and some simple testing on the \"drying\" process. The rates of sublimation of ice and evaporation of fluid have been calculated at different temperatures. The calculations show that sublimation of the ice core should not occur, and that evaporation of fluid should be almost negligible. Our test results support these calculations, but also revealed significant fluid run-off and dripping, resulting in the removal of most of the fluid in a couple of days, independent of temperature and ventilation conditions. Finally, we discuss crucial factors that ensure optimal long-term ice core preservation in storage, such as temperature stability, defrosting cycles of freezers and open core storage versus storage of cores in insulated crates.
# Coupled Vibration Analysis of Submerged Floating Tunnel System in Wave and Current
## 3 Case Study
### Analysis Results
#### 3.2.4 Structural Response under Single Wave Force  
The time history response of the SFT due to the single action of wave force is shown in Figure 6. The steady-state amplitude of the tube is less than 1 mm, and the amplitude of \(\Delta\sigma\) equals about 1.03 Mpa. The underwater installation depth of the SFT is generally more than 30 m in order to meet the navigation requirements on the water surface [1; 2]. Because water particles in the wave field attenuate rapidly with water depth, other than under extreme wave conditions, the wave forces have little impact on the SFT.  
Figure 5: VIV response of the cable without the tube-cable coupling effect: (**a**) displacement in the mid-section of cable; (**b**) cable stress variation (\(\Delta\sigma\)).  
Figure 6: Response of SFT components under the single effect of wave force: (**a**) displacement in the mid-span of tube; (**b**) cable stress variation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Items** & **With Coupling** & **Without Coupling** & \begin{tabular}{c} **Relative Difference (Based on** \\ **Results without Coupling)** \\ \end{tabular} \\ \hline \begin{tabular}{c} Cable amplitude/m \\ Tube amplitude/m \\ \end{tabular} & 0.402 & 0.668 & \(-\)39.82\% \\ \
\begin{tabular}{c} Cable stress variation/MPa \\ \end{tabular} & 0.0185–0.0206 & — & — \\
\begin{tabular}{c} Cable stress variation/MPa \\ \end{tabular} & 20.675–23.076 & 0.058 & —(very large) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons between results with/without tube-cable coupling.
## 3 Data Fusion and Multimodal Integration
### Data Fusion Techniques
#### 3.1.2 Methods for Integrating Diverse Data Sources  
The method used for fusing data depends on the nature of the data and the complexity of the task. The primary levels of data fusion include the following:  
* **Low-level fusion (sensor level):** This approach integrates raw sensor outputs before any feature extraction. It is the most direct form of fusion and often uses simple mathematical operations. A typical formulation is a weighted sum: \[\mathbf{D}_{\text{fusion}}=w_{1}\mathbf{D}_{1}+w_{2}\mathbf{D}_{2}+\dots+w_{m }\mathbf{D}_{m}\] (7) where \(w_{1},w_{2},\dots,w_{m}\) are weights assigned based on sensor reliability or data quality [35].
* **Mid-level fusion (feature level):** In this method, features are first extracted from each modality, then combined into a single set before classification. This is effective when different modalities provide complementary insights. For instance, combining thermal and vibration features enhances diagnostic coverage. Principal component analysis (PCA) is a common technique here: \[\mathbf{F}_{\text{fusion}}=\text{PCA}(\mathbf{F}_{1},\mathbf{F}_{2},\dots, \mathbf{F}_{m})\] (8) where \(\mathbf{F}_{i}\) represents features from the \(i\)-th modality [36].
* **High-level fusion (decision level):** This method combines outputs from separate models trained on individual modalities. Techniques such as weighted voting or ensemble averaging are used to generate the final decision: \[\mathbf{D}_{\text{final}}=\sum_{i=1}^{m}w_{i}\cdot\text{Decision}_{i}\] (9) where Decision\({}_{i}\) is the output from the \(i\)-th classifier, and \(w_{i}\) is its corresponding weight [37].
## 1 Introduction  
Maps of groundwater vulnerability to contamination, which are the main tool in spatial planning and prediction of the effects of aquifer pollution, are compiled using many methods and procedures. Parameters and data taken for the vulnerability assessment are represented by average values and therefore are characterized by an \"average\" groundwater vulnerability to contamination. In the assessment of natural groundwater vulnerability to contamination, DRASTIC is the rating method commonly used. It is dedicated to areas of porous aquifers [1]. In the DRASTIC method, like in other methods of groundwater vulnerability assessment, the depth to groundwater table (D) and the groundwater recharge by infiltration (R) depend on climate, weather, hydrological, and environmental changes and are the main parameters of vulnerability assessment, especially in shallow groundwater areas. The value of D is taken for the vulnerability assessment based on statistical data from long-term monitoring [2; 3; 4; 5; 6; 7; 8], and the value of R is based on averaged precipitation data, lithologies of the near-surface zone, or retention data [4; 6; 9; 10; 11; 12]. The pollution vulnerability can be defined as 'the sensitivity of groundwater quality to an imposed contaminant load' depending only on the 'proper' characteristic of the aquifer'. The vulnerability is relatively static, excluding some variation over time due, e.g., to the piezometric level changes [13; 14; 15; 16].  
The aim of the studies was the identification of characteristic groundwater levels in the Vistula River valley for the variant vulnerability assessment. During the studies, the variants of groundwater vulnerability to contamination were calculated by taking into account the average, maximum, and minimum values of D. These variants correspond to the average vulnerability, vulnerability in conditions of hydrogeological drought [17], and vulnerability in the conditions of floods in river valleys [18]. The groundwater vulnerability assessment was carried out for three variants of infiltration: for the average groundwater levels from the period 1999-2013, and for the lowest and highest (average annual) values of groundwater levels. Sensitivity analysis and map removal parameter for all the variants, interpretation of vulnerability variations due to groundwater and recharge changes, and vulnerability validation with hydrochemical data usage were carried out.
## 2 Modelling and Methods
### ANN Development Protocol
#### 2.5.4 Model Architecture and Structure Selection  
The feedforward multilayer perceptron (MLP), the ANN setup that was utilised for this study, is one of the most popular artificial neural network architectures [31]. The number of input and output nodes was defined by the number of input and output variables, respectively. The input layer features 8 nodes, reflecting the number of input variables. This incorporates the dummy variables introduced due to encoding the categorical features, as mentioned previously. The output layer includes a single node representing the dependent variable, which the ANN has to make predictions against (non-exposed wall-face temperature through time). To achieve a Deep Learning model, a minimum of two hidden layers had to be constructed. The number of nodes on each layer was based on previous experience of ANNs achieving satisfactory prediction results. Figure 6 represents the structure of the ANN used as part of this study.  
Although methods of optimising the architecture and hyperparameters of the model do exist, achieving the optimum model was beyond the scope of this study, which instead focused on the impact of the quality and quantity of input data. Given that an identical model architecture was utilised for all 4 different models, the final comparison was undertaken on a level plain. Since the activation function has an impact on the performance of the model [37], it is worth mentioning that the rectified linear unit activation function was used in both hidden layers of all models. For clarity, the other hyperparameters used for the development of the ANN are presented in Table 6.
# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
###### Abstract  
Cover crops are planted during the off-season to protect the soil and improve watershed management. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying conservation practice implementation, and enabling estimation of biomass accumulation during the active cover period. Remote sensing detection of end-of-season (termination) for cover crops has been limited by the lack of high spatial and temporal resolution observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New Micro Satellite (VEN\(\upmu\)S) imagery (5 m, 2 days revisit). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time-series and then refines the estimate to the two dates with the most rapid rate of decrease in NDVI during the senescent period. The WIST algorithm was assessed using farm operation records for experimental fields at the Beltsville Agricultural Research Center (BARC). The crop termination dates extracted from VEN\(\upmu\)S and Sentinel-2 time-series in 2019 and 2020 were compared to the recorded termination operation dates. The results show that the termination dates detected from the VEN\(\upmu\)S time-series (aggregated to 10 m) agree with the recorded harvest dates with a mean absolute difference of 2 days and uncertainty of 4 days. The operational Sentinel-2 time-series (10 m, 4-5 days revisit) also detected termination dates at BARC but had 7% missing and 10% false detections due to less frequent temporal observations. Near-real-time simulation using the VEN\(\upmu\)S time-series shows that the average lag times of termination detection are about 4 days for VEN\(\upmu\)S and 8 days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data.  
cover crop; crop termination; crop harvest; remote sensing phenology; time-series analysis; VEN\(\upmu\)S; Sentinel-2 Article
# A Testbed to Evaluate the FIWARE-Based IoT Platform in the Domain of Precision Agriculture
## 1 Introduction
FIWARE is a powerful and complex technology, but to the authors' knowledge, there are not works that show how to use this technology in precision agriculture and study its appropriateness, its scalability and its efficiency for this kind of applications. The aims of this paper are to provide such comprehensible use case, and to check whether FIWARE can be scaled to the same extent as agricultural applications. The answers are important because the selection of a particular platform implies a long-term commitment. We have created a testbed to achieve this goal. The rest of this paper is structured as follows: Section 2 presents the state of the art. Section 3 provides an overview of the FIWARE platform. The testbed design featured in Section 4 is derived from an IoT use case scenario that applies the FIWARE concepts and components discussed in Section 3. Section 4 also describes the test plan designed to assess the FIWARE platform. Section 5 shows the results obtained. Lastly, Section 6 contains the conclusions drawn from the results and future work related to this research.
## 2 Materials and Methods
### Microbiome Analysis  
To process raw paired-end sequences (1,809,106 reads from 20 samples), the QIIME 2 [39] software package ([[https://qime2.org](https://qime2.org)]([https://qime2.org](https://qime2.org)); version: 2018.8) was employed. Paired ends were merged, reducing the 1,809,106 reads to 1,533,836 reads, and reads that could not be merged were excluded from further analyses. For quality control of the sequences, the Deblur plugin in Qime 2 [40] was used to associate erroneous sequence reads with the true biological sequence from which they were derived, thus producing high-quality sequence variant data. First, 213,390 reads (13.91% of the data) were removed by applying an initial filtering process quality score (q = 20). Second, using Deblur, all reads were trimmed to 285 bp, based on the median quality score. In addition, chimeric sequences were detected and excluded from analyses. 16S rRNA Operational Taxonomic Units (OTUs) were picked from the Illumina reads using a closed-reference OTU picking protocol against the Greengenes database ([[https://docs.qime2.org/2018.8/data-resources](https://docs.qime2.org/2018.8/data-resources)]([https://docs.qime2.org/2018.8/data-resources](https://docs.qime2.org/2018.8/data-resources)); data files: 13_8) clustered at 99% identity and trimmed to span only the 16S rRNA V4 region flanked by sequencing primers 515F-806R. In the next step, taxonomy assignments were associated with OTUs based on the taxonomy associated with the Greengenes reference sequence defining each OTU. This step discarded most of reads which were not found in the Greengenes database and found only 228,667 reads which were assigned taxonomy. Out of the 228,667 Illumina reads from the V4 region of the bacterial 16S rRNA genes that passed the QIIME quality filters, 90.6% (207,197 reads) matched a reference sequence at a 99% nucleotide sequence identity. Next, OTU counts were binned into genus-level taxonomic groups for plot preparation.  
Sequencing data were exported as individual fastq files and have been deposited in the Sequence Read Archive (SRA) NCBI ([[https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/)]([https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/))) under the accession no. SRP226690. Samples can be found under accession numbers from SRX7043689 to SRX7043708.
## 5 Discussion
### Validation of the Results
#### 5.1.2 Validation of Natural Frequency Results  
The natural frequency analysis was performed on DFF and ACH to obtain the vibration of the structure and relative deflection for each mode. AS 3600 was used to compare the maximum deflection for each mode, with the deflection limit and EN 1991-1 to examine the frequency of the structure.  
Figure 25 shows the maximum vertical deflection that occurred by each mode against the suggested total limit specified by AS 3600.  
It was observed that the deflection of the structure complies with the standard, even at higher frequencies. Moreover, a minimal gap between the deflection and limit was observed caused by the dead load and wind load in the Z direction, influencing mode 6 \(0.9G+W(Z)\). This is important to ensure the stability of the structure during dynamic loadings, such as seismic loads.  
EN 1991-1-1 recommends a minimum natural frequency of 8.4 Hz for the structure. The minimum natural frequency obtained for ACH was 54.1 Hz, which satisfies the limit. However, DFF had a minimum natural frequency of 8.21 Hz, slightly below the suggested 8.4 Hz limit, by 2.3%. This is an acceptable tolerance and would not affect the performance of the structure [60]. However, the reason for this difference can be justified using the formula for natural frequency, as shown by Equation (5).  
\[f=\frac{17.8}{\sqrt{\delta}}\approx\frac{18}{\sqrt{\delta}} \tag{5}\]  
The maximum deflection (\(\delta\)) is dependent on the gross second moment of area, loading and mesh quality, which were influenced by the idealisations made in Section 3.5.1. This affected the maximum deflection and resulted in a slightly different natural frequency.
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 3 Results and Discussion
### Parametric Investigations
#### 3.1.4 FSS Loading for Frequency Tunability and Gain Enhancement
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Ground Length** & \multirow{2}{*}{\(\mathbf{f_{r}}\)} & \multirow{2}{*}{\(\mathbf{S_{11}}\)} & \(-\)**10 dB BW** & \multirow{2}{*}{**Gain (dBi)**} & \multirow{2}{*}{**Gain (dBi)**} \\  & **(mm)** & & **(GHz)** & & **(GHz)** & & **at 3.5 GHz** \\ \hline Antenna without SRR & \multirow{2}{*}{9.7} & 3.44 & \(-\)33.14 & 1.69 (2.91–4.60) & 13.50 & \multirow{2}{*}{13.38} \\ without FSS & & 8.48 & \(-\)31.03 & 4.02 (6.92–10.94) & 6.51 & \\ (35.94 \(\times\) 38.97 mm\({}^{2}\)) & & & & & & \\ \hline Antenna with SRR & \multirow{2}{*}{9.7} & 3.44 & \(-\)42.37 & 1.71 (2.90–4.61) & 9.52 & \multirow{2}{*}{9.37} \\ without FSS & & 7.70 & \(-\)13.96 & 1.59 (6.86–8.45) & 6.15 & \\ (35.94 \(\times\) 38.97 mm\({}^{2}\)) & & 10.16 & \(-\)13.21 & 1.33 (9.47–10.80) & 7.41 & \\ \hline Antenna with & \multirow{2}{*}{9.7} & 3.62 & \(-\)32.29 & 1.75 (2.87–4.62) & 11.37 & 14.02 \\ SRR + FSS & & & & & & \\ \(35.94\times 33.96\) mm\({}^{2}\) & & & & & & \\ Gap, H = 7.0 mm) & 10.58 & \(-\)10.90 & 0.70 (10.22–10.92) & 7.20 & Enhancement \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Frequency tunning using SRR loading. Figure 12: SRR loading effect on FSS miniaturized Koch fractal antenna.
## 2 The Proposed Method
### The Parameter Optimization of the SVM and Feature Selection Based on GA
#### 2.3.1 Chromosome Design  
In this study, in order to achieve a reliable model in detecting and classifying LL faults, all four kernels introduced in Section 2.2.1 are used to select the best kernel, and subsequently the parameters of each kernel are also optimized. However, it should be noted that we only need to tune one parameter in linear and polynomial kernels, namely parameter \(C\), whereas two parameters should be tuned in gaussian and sigmoid kernels i.e., \(C\) and \(\gamma\). Therefore, each chromosome is composed of three parts: parameters \(C\) and \(\gamma\), and also the feature subset. Figure 3 shows a binary string used in each chromosome.  
In Figure 3, \(b_{C}^{1}\ \sim\ b_{C}^{n_{c}}\) indicates the misclassifying penalty \(C\), \(b_{\gamma}^{1}\ \sim\ b_{\gamma}^{n_{\gamma}}\) represents parameter \(\gamma\) in the kernel function, and \(b_{f}^{1}\ \sim\ b_{f}^{n_{f}}\) shows the binary string of the feature subset. Moreover, \(n_{C}\) and \(n_{\gamma}\) represent the number of the parameter \(C\), and the parameter \(\gamma\), respectively. Note that \(n_{C}\) and \(n_{\gamma}\) are determined based on the computational accuracy. In the binary code of the feature subset, \(n_{f}\) specifies the number of features in the training dataset. Moreover, each binary bit in the feature subset can be \"0\" or \"1\", in which \"1\" expresses that the corresponding feature has been selected to construct the model and \"0\" demonstrates that the corresponding feature has not been chosen.
## 3 Methods
### Investigation of Missing MODIS FRP inside Equatorial Swath Gaps  
The missing MODIS FRP inside its swath gaps was estimated using VIIRS FRP. The width of a MODIS swath gap across the meridian is a function of latitude, as described in Appendix A. The gap width decreases from 388.1 km at the equator to zero at \(\sim\)30.7\({}^{\circ}\)N and \(\sim\)30.7\({}^{\circ}\)S (Figure 3). By taking VIIRS FRP as reference data, missing MODIS FRP were estimated in the following steps. First, daily MODIS swath gaps were built by intersecting boundaries of adjacent swaths based on MODIS geolocation product (MYD03). Second, the swath gap polygons were applied to clip daily VIIRS fire detections observed inside swath gaps. Third, FRP from the clipped VIIRS fire detections were aggregated in 1\({}^{\circ}\) grids to examine the spatial patterns and were summed up across Africa for the whole study period to infer the underestimation of MODIS FRP due to swath gaps. Fourth, FRP from VIIRS fire detections observed inside and outside MODIS swath gaps were also summed up at an interval of one month to explore the seasonal variation of missing MODIS FRP.