esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methods
### Deformable Large Kernel Attention Mechanism and C2f_DLKA Module  
Deformable Large Kernel Attention (DLKA) is an enhanced version of Large Kernel Attention (LKA). While LKA achieves the same receptive field as self-attention, it requires fewer parameters and is computationally efficient due to its use of depth-wise convolution (DW), depth-wise deformable convolution (DWD), and \(1\times 1\) convolution. To construct a K \(\times\) K convolution kernel with an input of dimensions H \(\times\) W and C channels, the kernel sizes for DW and DWD are defined as follows:  
\[DW=(2d-1)\times(2d-1) \tag{4}\]  
\[DWD=\left\lceil\frac{K}{d}\right\rceil\times\left\lceil\frac{K}{d}\right\rceil \tag{5}\]  
where K represents the size of kernel and d is the dilation rate.  
DLKA combines the strengths of Large Kernel Attention (LKA) and deformable convolutions, enabling it to adapt to the free deformation of the sampling grid. It employs a convolutional layer to learn deformations directly from the feature maps, automatically generating an offset field, which makes its convolution kernel self-adaptive. This self-adaptive kernel allows the DLKA module to more effectively distinguish object boundaries compared to standard convolution operations. Furthermore, the offsets calculated by the aforementioned convolutional layer, followed by a 3 \(\times\) 3 convolution, are subjected to  
Figure 4: The structure of the DynamicConv module.  
Figure 5: The structure of the C2f_DynamicConv module.  
bilinear interpolation. This process is illustrated in Figure 6, and the DLKA module can be mathematically defined as  
\[Attention=Conv_{1\times 1}\big{(}DDWDConv\big{(}DDWConv\big{(}F^{\prime}\big{)} \big{)}\big{)} \tag{6}\]  
\[Output=Conv_{1\times 1}\big{(}Attention\otimes F^{\prime}\big{)}+F \tag{7}\]  
where \(F\) represent the input feature, which belongs to \(\mathbb{R}^{C\times H\times W}\) and \(F^{\prime}=GeLU(Conv(F))\). The component named \(Attention\in\mathbb{R}^{C\times H\times W}\) acts as a map, where each key reflects the value of a relative object feature. The symbol \(\otimes\) represents the product operation.  
To enable the DLKA module to play a crucial role in the YOLOv8 network structure, we integrate DLKA with the C2f module to form the C2f_DLKA module, as shown in Figure 7. This module is designed with fewer parameters and enhanced capabilities for processing the detailed features of SAR images, surpassing the performance of the original C2f module. Consequently, it is well-suited for detecting tiny targets.
## 3 Results and Discussion
### Nutritional Value of the Gluten-Free Bread  
The impact of Cc addition to improve the nutritional and minerals profile of GFB was evaluated, and the results obtained are summarized in Table 6.  
The increments of protein by Cc incorporation to GFB was noticeable: comparing CB to CcB20% (higher level tested), the protein content varied from 5.34 to 9.40 g/100 g, respectively, representing an increase around 80%. Carbohydrates levels varied from 45.9 to 31.5 g/100 g, representing a considerable reduction of 31.4% for CcB20%, compared to CB. According to a recent work [32], in which the supplementation of wheat bread with curd cheese to reduce the glycemic response was performed, these results can suggest that the incorporation of Cc as baking ingredients in GFB formulations can be an alternative to obtain breads with reduced glycemic index.  
In terms of lipids content, a significant increase of 35% for upper levels of Cc tested, was obtained.  
A considerable increase in ash content was also observed, and this result is clearly reflected in terms of major minerals profile. As it can be observed from Table 6, a significant increase in phosphorus (50.0%), magnesium (6.0%), and calcium (360.3%) were registered for Cc bread, that can cover more than 15% of recommended daily value as stated in European community (CE) Regulation N\({}^{\circ}\) 1924/2006 based on nutrition and health claims made on foods [33], and in the Regulation (UE) N\({}^{\circ}\) 432/2012 that specifies a list of permitted health claims made on foods [34].  
In terms of trace elements (Fe, Cu, Mn, and Zn), since their prevalence in buckwheat flour is higher than in curd cheese, their proportions were diluted by successive curd cheese additions.  
Figure 5: Linear relationship between bread quality attributes (BF—bread firmness and SBV—specific bread volume) with dough rheology properties (DV—dough viscosity and G’—elastic modulus, at 1 Hz): (**A1**)—BF vs. DV; (**A2**)—BF vs. G’; (**B1**)—SBV vs. DV; (**B2**)—SBV vs. G’.  
A balanced gluten-free diet should provide adequate levels of macro- and micronutrients, since the diagnosis of celiac patients is often associated to deficiencies in protein levels and mineral components, caused by the atrophy and subsequent damage of small gut villus [35; 36; 37].  
These nutritional improvements obtained by the addition of Cc on GFB formulation can give an additional contribution to fulfill the nutritional daily diet requirements in terms of protein and major mineral profile of celiac patients and also gluten-sensitive individuals.
# Prophet-CEEMDAN-ARBiLSTM-Based Model for Short-Term Load Forecasting
## 3 Experiment Results and Analysis
### Data
#### 3.1.4 Model Settings  
This paper uses the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) for performance comparison.  
\[\text{MAE}=\frac{1}{N}\left|\sum_{i=1}^{N}y_{i}-\hat{y}_{i}\right|, \tag{19}\]  
\[\text{MAPE}=\frac{1}{N}\sum_{i=1}^{N}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}} \right|, \tag{20}\]  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}y_{i}-\hat{y}_{i}}, \tag{21}\]  
where \(\hat{y}_{i}\) is the forecasted load value, \(y_{i}\) is the actual load value, and \(N\) is the number of samples.
## 4 Results
### Alternative Scenario #2
Despite the reduced transport demand for all O-D pairs by 10% compared to scenario #1, the travel time through link (5,6) for the solution using the Nash approach increased slightly, from a level of 5.13 (min) to 5.17 (min), which translates to a relative difference of 0.73%. This is caused by an increase in link traffic volume by 16.68%. In turn, when it comes to the results obtained with the Stackelberg approach, a vehicle number increased within the link (5,6) by more than 92% resulting in the travel time being extended by only 0.13%. The increase in the volume of traffic within transport links, for relatively low levels of the absolute number of vehicles, leads to minor differences in the average travel times for these links. Table 8 shows the impact of the traffic flow distribution on the state of traffic congestion for scenario #2. The highest congestion rate was obtained for links (6,7) and (7,8), while in the case of the remaining segment of the road network this value did not exceed 2 for the results obtained regardless of the optimization approach adopted. It should be emphasized the Stackelberg approach allows a better balance of the value of the congestion rate by reducing the difference between its maximum and minimum values within the road network. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Link**} & \multicolumn{2}{c}{**Congestion**} & \multicolumn{2}{c}{**Travel Time Increase (\%)**} \\ \cline{2-5}  & **Nash** & **Stackelberg** & **Nash** & **Stackelberg** \\ \hline (1,4) & 1.10 & 1.10 & 10.08 & 10.08 \\ (2,5) & 1.15 & 1.15 & 14.96 & 14.96 \\ (3,6) & 1.20 & 1.20 & 19.69 & 19.69 \\ (4,7) & 1.02 & 1.09 & 2.05 & 9.18 \\ (4,8) & 1.82 & 1.78 & 82.43 & 77.63 \\ (5,4) & 1.07 & 1.13 & 6.54 & 12.66 \\ (5,6) & 1.03 & 1.00 & 3.35 & 0.24 \\ (6,7) & 3.18 & 2.52 & 217.62 & 152.40 \\ (7,8) & 2.52 & 2.58 & 151.98 & 157.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Traffic congestion on transport network links for scenario #2. Figure 8: Compared travel times through individual links to road network for scenario #2. (Nash and Stackelberg differences (%) represent the percentage increase in travel time through each link compared to the results of scenario #1).
# Improved Selective Deep-Learning-Based Clustering Ensemble
## 4 Experiments
### Datasets and Evaluation Measures  
We conducted experiments on three widely used UCI datasets ([[http://archive.ics.uci.edu/](http://archive.ics.uci.edu/)]([http://archive.ics.uci.edu/](http://archive.ics.uci.edu/)) (accessed on 3 November 2023)), two classical image datasets ([[https://github.com/zhoujielaoyu/2018-NC-DREC](https://github.com/zhoujielaoyu/2018-NC-DREC)]([https://github.com/zhoujielaoyu/2018-NC-DREC](https://github.com/zhoujielaoyu/2018-NC-DREC)) (accessed on 3 November 2023)), and two real-world biological datasets ([[https://github.com/BinPro/CONCOCT/tree/develop](https://github.com/BinPro/CONCOCT/tree/develop)]([https://github.com/BinPro/CONCOCT/tree/develop](https://github.com/BinPro/CONCOCT/tree/develop)) (accessed on 3 November 2023)), where a summary of the statistics of these datasets can be found in Table 1.  
* **Cars**: Cars dataset is the 1983 ASA Data Exposition dataset, which contains mpg, cylinders, displacement, etc. (8 variables), for 392 different cars.
* **Iris** : Iris dataset [42] contains 150 samples for 3 classes, where each class refers to a category of iris plant.
* **Wine** : Wine dataset [43] is the results of a chemical analysis of wines grown in the same region, which determined the quantities of 13 constituents found in each of the three types of wines.
* **MNIST** : MNIST dataset is a set of well-known image data of 70,000 handwritten digits (10 class labels) with 784 pixels. **Mnist5** is the subset of the MNIST [44].
* **Strain** : Strain dataset is a set of synthetic mock metagenome data [45]. This dataset was constructed to investigate the impact of strain-level variation on clustering.
* **Species** : Species dataset is also a set of synthetic mock metagenome data [45]. It was designed to resolve species-level variation in a complex community.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Name** & **Sample** & **Feature** & **Category** \\ \hline Cars & 392 & 8 & 3 \\ Iris & 150 & 4 & 3 \\ Wine & 178 & 13 & 3 \\ Mnist5 & 3495 & 784 & 10 \\ MNIST & 70,000 & 784 & 10 \\ Strain & 9401 & 200 & 20 \\ Species & 37,585 & 232 & 101 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the datasets.  
To evaluate the quality of the clustering result, we adopted normalized mutual information (NMI) and adjusted Rand index (ARI) as evaluation measures. Greater values of these metrics represent better clustering performance. Detailed introduction can be referred to in the following literature: [46].
## 2 NB-IoT System Detailed Design and Implementation
### NPDSCH Receiver Chain Implementation
#### 2.2.8. Symbol De-Mapper and Demodulation  
In the demodulation process, the received symbols are mapped to values of '0' or '1' based on the constellation tables. The techniques used in demodulation are hard decision and soft decision, which both use log likelihood ratio (LLR). The soft decision technique gives less bit error rate (BER) than that of the hard decision technique, but due to using QPSK modulation, which gives small BER compared to, for example, 16 QAM, the hard decision technique is used [9; 23].  
The symbol de-mapper block takes the most significant bit (MSB) of each bus \(I,Q\) that comes from the NRS removal block, then the symbol is de-mapped based on its sign. The inputs of this block are two buses; each sends 12 serial bits and it also works with the double rate of the NRS removal block [9].
## 4 Discussion
As indicated in Table 1, southward from sample sites SBY11 to SBY1, where SBY11 is located near Yangkou town in Rudong County and SBY1 is located close to the Yangtze River Estuary, the quantitative contributions of the Yangtze River (a\({}_{1}\)) are all higher than 50%, while those of the ancient Yellow River (a\({}_{2}\)) are lower than 50%. However, northward from SBY12 to SBY23, where SBY12 is located near Bencha town in Rudong County and SBY23 occurs near the ancient Yellow River Estuary, the quantitative contributions of the ancient Yellow River (a\({}_{2}\)) are all higher than 50%, while those of the Yangtze River (a\({}_{1}\)) are lower than 50%. In addition, it could be deduced from Table 1 that the mixed area of these two large rivers occurs between the SBY11 and SBY12. This deduction further confirms the correctness of the previous understanding obtained in this study. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{3}{c}{**Regression Coefficient**} & \multicolumn{3}{c}{**Test Indices of the Regression Model**} \\ \cline{2-7}  & \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & \(\varepsilon\) & **Final Loss** & **R** & \(\mathbf{R^{2}}\) & **Variance** \\ \hline  & 1.00 & 0.00 & 1.39 & 61.6 & 0.987 & 0.973 & 97.3 \\  & 0.72 & 0.23 & \(-\)0.03 & 0.01 & 1.00 & 1.00 & 100 \\  & 0.62 & 0.36 & \(-\)0.05 & 0.01 & 1.00 & 1.00 & 100 \\  & 0.60 & 0.29 & \(-\)0.04 & 0.03 & 1.00 & 1.00 & 100 \\  & 0.95 & 0.02 & 0.00 & 0.00 & 1.00 & 1.00 & 100 \\  & 0.90 & 0.10 & 1.53 & 14.7 & 0.995 & 0.991 & 99.1 \\  & 0.97 & 0.00 & 0.59 & 47.0 & 0.989 & 0.978 & 97.8 \\  & 0.97 & 0.00 & \(-\)0.62 & 3.30 & 0.999 & 0.998 & 99.8 \\  & 0.92 & 0.00 & \(-\)2.20 & 43.4 & 0.985 & 0.970 & 97.0 \\  & 0.93 & 0.33 & \(-\)0.05 & 0.04 & 1.00 & 1.00 & 100 \\  & 0.91 & 0.51 & \(-\)0.08 & 0.03 & 1.00 & 1.00 & 100 \\  & 0.95 & 0.02 & 0.00 & 0.00 & 1.00 & 1.00 & 100 \\  & 0.90 & 0.10 & 1.53 & 14.7 & 0.995 & 0.991 & 99.
# An Efficient Method to Learn Overcomplete Multi-Scale Dictionaries of ECG Signals
## 2 Problem Formulation
Hence, a better alternative in this case is explicitly enforcing sparsity in \(\mathbf{\beta}\) by applying the so-called LASSO approximation [3], which minimizes a cost function composed of the \(L_{2}\) norm of the reconstruction error and the \(L_{1}\) norm of the coefficient vector:  
\[\mathbf{\hat{\beta}}=\arg\min_{\mathbf{\beta}}\|\mathbf{x}-\mathbf{A}\mathbf{\beta}\|_{2} ^{2}+\lambda\|\mathbf{\beta}\|_{1}, \tag{5}\]  
where \(\|\cdot\|_{1}\) denotes the \(L_{1}\) norm, and \(\lambda\) is a parameter defining the trade-off between the sparsity of \(\mathbf{\beta}\) and the precision of the estimation: the larger is the value of \(\lambda\), the sparser is the solution obtained, but also the larger is the mean squared error. Regarding the first question, it is obvious that a good dictionary, tailored to the shapes of the relevant waveforms in the ECG, will lead to a sparser representation and thus a better temporal localization of those waveforms. In the particular case of ECG modeling, many families of waveforms have been proposed within the related fields of sparse inference and compressed sensing, as discussed in the Introduction. However, there is increasing evidence that the best dictionaries are those constructed using atoms directly extracted from the signals to be modeled [15; 17; 20]. In the following, we describe a novel approach to construct a single overcomplete and multi-scale dictionary by learning the most representative waveforms from multiple patients. The goal of this paper is then investigating whether the resulting dictionary is able to model the outputs from multiple patients with a single set of representative waveforms.
## 2 Data Analysis  
### VMF Tropospheric Zenith Delay Product  
VMF is a mapping function model established by Vienna University of Science and Technology, and it has released a series of tropospheric delay products in which VMF1 and VMF3 grid products are the most important. VMF1 (Vienna mapping functions) grid products divide the earth surface into 2.5\({}^{\circ}\)\(\times\) 2.0\({}^{\circ}\) cells according to geocentric longitude and latitude, and provide global tropospheric zenith delay grid products every 6 h [11]. The data include mapping function coefficients, zenith dry delay (ZHD) and zenith wet delay (ZWD). In addition to grid products, it also provides zenith delay data of global IGS sites, including mapping function coefficients, ZHD and ZWD, and site meteorological data, with the same time resolution as grid products. Through an interpolation calculation of the grid, the grid tropospheric zenith delay can obtain the zenith tropospheric delay of all stations. Therefore, it is more widely used than the site tropospheric delay products. VMF3 is an upgrade product of VMF1, which further improves the grid accuracy on the basis of VMF1 and divides the earth's surface into grids with a 1.0\({}^{\circ}\)\(\times\) 1.0\({}^{\circ}\) spacing [12].
## 4 Results
### Soils Stabilized with Bottom Ash and Lime
#### 4.1.4 Variation in Unconfined Compressive Strength (UCS)  
In the soils treated with ash and lime, the setting and curing times are much longer than for cement; therefore, the strengths obtained at 7 days are much lower than those for cement.  
For soil 1, there is a clear tendency toward values of 25% added ash, which is the optimum value for this soil, given that with more added ash the strength values are smaller due to the lower density.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**CBR**} & \\ \hline \multirow{2}{*}{**Soil**} & \multirow{2}{*}{**S1 (100\% Clay)**} & \multirow{2}{*}{**S2 (65\% Clay)**} & \multirow{2}{*}{**S3 (50\% Clay)**} & \multirow{2}{*}{**S3 (50\% Clay)**} & \multirow{2}{*}{**20\%**} & \multirow{2}{*}{**S3 (50\% Clay)**} & \multirow{2}{*}{**20\%**} \\ \cline{2-2} \cline{5-12}  & \% Bottom Ash & & & & & & & & & & \\ \hline
0\% & 0 & 2 & 2 & 4 & 0 & 12 & 22 & 11 & - & 34 & 79 \\
1\% & 11 & 12 & 14 & 22 & 50 & 50 & 75 & 63 & 71 & - & - & - \\
2\% & 16 & 31 & 33 & 42 & - & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: CBR (180 s).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Maximum Density (KN/m3)**} & \multirow{6}{*}{**Optimum Wet**} \\ \cline{3-10}  & & \multicolumn{6}{c}{**Lime Content**} & \multicolumn{6}{c}{**Lime Content**} \\ \hline
**Soil** & **Bottom Ash** & **0\%** & **1\%** & **2\%** & **3\%** & **Bottom Ash** & **0\%** & **1\%** & **2\%** & **3\%** \\ \hline \multirow{3}{*}{S2 (65\% clay)} & 0\% & 22.12 & 21.35 & 21.12 & 21.00 & 0\% & 6.90\% & 8.00\% & 8.80\% & 9.00\% \\  & 10\% & 20.70 & 20.24 & 20.18 & 20.16 & 10\% & 7.80\% & 8.00\% & 8.70\% & 8.70\% \\  & 15\% & 20.14 & 20.00 & 19.75 & 19.83 & 15\% & 8.20\% & 8.20\% & 8.60\% & 8.75\% \\  & 20\% & 19.74 & - & 19.27 & - & 20\% & 7.70\% & - & 9.00\% & - \\ \hline \multirow{3}{*}{S3 (50\% clay)} & 0\% & 22.10 & 21.62 & 21.50 & 21.31 & 0\% & 6.30\% & 7.30\% & 7.75\% & 8.00\% \\  & 10\% & - & - & 20.60 & 20.44 & 10\% & - & - & 7.50\% & 8.00\% \\ \cline{1-1}  & 15\% & 20.35 & - & 19.85 & 20.10 & 15\% & 7.90\% & - & 8.40\% & 8.30\% \\ \cline{1-1}  & 20\% & 20.00 & - & 19.65 & 19.38 & 20\% & 7.25\% & - & 9.40\% & 8.40\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._Moreover, the strength does not increase proportionally to the increasing percentage of added lime, but it can in fact decrease.  
Soils S2 and S3 both obtain the greatest strengths with combinations of 15% ash and 2% lime for dry densities close to 100% of the modified Proster dry density (Table 5).  
The incorporation of bottom ash from thermal power plants and lime results in a significant increase in the bearing capacity and UCS of natural soils
## 2 Materials and Methods
### Data
#### 2.2.3 Satellite Time Series
CropSAR was used to download preprocessed, ready-to-use daily time series for three different indices: the Normalized Difference Vegetation Index (NDVI), the fraction of Absorbed Photosynthetically Active Radiation (fAPAR), and the Fraction of green vegetation Cover (FCover) [43]. While the NDVI is an estimation of the greenness of the land surface, fAPAR and FCover are both biophysical variables  
Figure 4: Overview of (**A**) the classification hierarchy and (**B**) the label distribution on both the first (left) and second level (right) of the hierarchy. of vegetation and can also be measured on the land surface. VITO derived fAPAR and FCover from satellite observations by using a neural network to estimate these biophysical variables from Sentinel-2. OpenEO provides an API that can be used to acquire and process large Earth observation datasets from different providers in the cloud [44]. The API was used to access Terrascope Level 2A Sentinel-2 data and subsequently transform the raw series of individual images into an equidistant time series. Processing consisted of first masking clouds and cloud shadows with the Scene Classification Layer (SCL) through a kernel process provided by openEO (standard deviation 1.5, window size 11). Then, gaps in the time series were filled by linear interpolation: the time series were first divided into equidistant time intervals, using the median value over a specified period, and then interpolation was applied to fill gaps in the time series. To fill gaps at the start or end of the time series, an extra month was added at the start (July) and end (May). Where gaps remained, the value of the next valid period was duplicated, and if gaps remained, the value from the previous valid period was duplicated. Time series were downloaded with dekadal time intervals for bands B02-04 (VIS), B05-07 (vegetation red-edge), B08 (NIR), and B11-12 (SWIR) of Sentinel-2. Dekadal time intervals consist of three intervals per month, with the first two being 10 days each and the third comprising the remaining 8-11 days, depending on the length of the month. In addition, time series were also downloaded with monthly time intervals for the same bands of Sentinel-2.