esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Action-Rule Discovery Based on Attribute Correlation and Vertical Data Partitioning
## 4 Materials and Methods
### Experimental Setup
#### 4.3.2 Data Preprocessing  
Due to semantical inconsistencies in datasets representing 38 companies (resulting in low confidence in the extracted rules), we opted to select datasets from three companies for mining, labelling them as datasets A, B, and C. First, we followed several data pre-processing steps: (1) we removed columns based on a sparsity threshold, (2) we checked for correlations and removed redundant columns, (3) we handled null values, and (4) we categorized benchmarks into bins.  
We first examined the sparsity of the benchmark columns and removed any columns that had 75% or more null values.  
Next, we checked for correlations among all of the features using the pairwise correlation between numerical columns. Specifically, we used the Pearson correlation coefficient to measure the linear relationship between variables. If we identified any pairs of features with a one-to-one relationship, we removed one of the columns to eliminate redundancies.  
We then handled null values based on the column. For any rows where the PromoterScore was null, we removed them since this attribute is our decision attribute, and its availability is necessary. For the benchmark features, we treated nulls as a separate category and created a new category called \"No Response\".  
This leads us to the next step of categorizing benchmark features into bins. In addition to treating null values as \"No Response\", we assigned values between 0 and 4 (inclusive) to the category \"Low\", values between 5 and 6 (inclusive) to \"Medium\", values 7 and 8 to \"High\", and values 9 and 10 to \"Very High\".
## 4 Results and Discussion
### Results
#### 4.1.1 Results in Dijon
To analyze the benefit of feature representation, the confusion matrix (normalized with range [0, 10,000]) retrieved from the XGB-FR classification is presented in Figure 6.  
_'Winter rape'_, _'grape'_, and _'grass'_ achieved precision scores greater than 0.90, followed by _'winter wheat'_ and _'winter barley'_ with precision scores greater than 0.80. With precision scores less than 0.60, _'winter triticale'_, _'corn'_, and _'soy'_ obtained the worst results. This relates to the a priori distribution of crops in the study area. To improve overall accuracy, statistical classifiers tend to classify samples into larger categories when these categories are unbalanced. In addition, _'grass'_ and _'grape'_ achieved the highest precision and recall scores. When examining the confusion matrix in detail, it was found that it could be divided into a number of sub-regions (as depicted in different colors in Figure 6), including winter crops _('winter wheat'_, _'winter barley'_, _'winter rape'_, and _'winter triticale'_, indicated by light orange color), summer crops _('corn'_, _'soy'_, and _'sunflower'_, indicated by light green color), spring crop _('spring barley'_ indicated by light yellow color), and other crops (indicated by light blue color). On the one hand, heterogeneity between subregions is relatively high. For example, the planting, growth, and harvesting schedules for autumn crops are entirely distinct from those of summer crops. Thus, they are less likely to be misclassified relative to one another. By contrast, there are numerous misclassifications within this subregion due to their similar phenology (such as pairs of _'corn'_ and _'soy'_, _'wheat'_ and _'barley'_, _'grass'_ and _'alfalfa'_). In addition, it was found that _'winter rapeseed'_ has a higher precision score in the winter-crop sub-region. Its unique yellow flowers contribute to this.
## 2 Materials and Methods
### Thermal Regeneration Experiments  
In this study, a vertical fixed-bed furnace was used to perform the thermal regeneration experiments with two different methods. The first method involved simple pyrolysis under N\({}_{2}\) flow (500 mL/min), where the samples were heated at a rate of 10 \({}^{\circ}\)C/min up to a specified temperature (i.e., 800, 850, and 900 \({}^{\circ}\)C) and held for varying holding times (i.e., 0, 30, and 60 min). Approximately 5 g of the dried GAC was used for each experiment. After completion, the furnace was cooled to below 150 \({}^{\circ}\)C before removing the regenerated sample. The regenerated GAC samples were labeled as KN-temperature-time. For example, KN-900-30 referred to GAC regenerated at 900 \({}^{\circ}\)C and a holding time of 30 min.  
The second regeneration method was to reactivate the spent GAC samples by using a CO\({}_{2}\) atmosphere at the end of the first stage. In the pyrolysis-activation experiments, the first stage was to increase the system temperature to 500 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min. In the second stage, the gas atmosphere was switched to CO\({}_{2}\) flowing at 100 mL/min while stopping N\({}_{2}\) flow and further increasing the furnace to the target temperature. Samples regenerated under these conditions were labeled as KNC-temperature-time. For example, KNC-900-30 referred to a GAC sample thermally activated at 900 \({}^{\circ}\)C under CO\({}_{2}\) for 30 min.
# Characterization of a Quantum Random Number Generator Based on Vacuum Fluctuations
## 5 Conclusions  
In conclusion, a low-cost and high-speed QRNG based on homodyne measurements of vacuum fluctuations has been demonstrated and characterized. Detailed analysis of the main limiting factors to the implementation performance was made, and, in particular, a method for estimating the entropy contribution from the excess LO noise was presented. Results show that, when the total impinging power is constant, the entropy from classical contributions does not depend on the detection unbalancing, and, thus, the extractable randomness is constant. Through the proposed method, for an asymmetry of 50.75%, we find that only approximately 33.7% of the measured entropy can reliably be attributed to quantum fluctuations. Assuming that an eavesdropper has complete knowledge of electronic noise, an extraction rate of 0.5 true random bits per sample was achieved, which allows a theoretical throughput up to 19.53 Mbit/s. A randomness extraction operation is used to eliminate security loopholes introduced by electronic noise. This yields 192 kbit/s by applying a Toeplitz-hashing extractor, and 1.68 Mbit/s with an SHA-512 extractor. The resultant true RNs pass a proportion of 98.1% of the applied statistical tests, including the standard Dieharder and TestU01's _SmallCrush_ tests as well as most of NIST suite, corroborating the presence of high-quality randomness.  
Conceptualization, M.J.F., N.J.M., N.A.S. and A.N.P.; methodology, M.J.F., N.J.M. and N.A.S.; software, M.J.F.; validation, M.J.F.; formal analysis, M.J.F., N.J.M. and N.A.S.; resources, N.J.M. and N.A.S.; writing--original draft preparation, M.J.F.; writing--review and editing, M.J.F., N.J.M., N.A.S. and A.N.P.; visualization, M.J.F.; supervision, N.J.M. and N.A.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by Fundacao para a Ciencia e a Tecnologia (FCT) through national funds, by the European Regional Development Fund (FEDER), through the Competitiveness and Internationalization Operational Programme (COMPETE 2020) of the Portugal 2020 framework, under the projects DSPMetronNet (POCI-01-0145-FEDER-029405), QuantumPrime (PTDC/EEI-TEL/8017/2020), Q.DOT (POCI-01-0247-FEDER-039728), and UIDB/50008/2020-UIDP / 50008/202 (actions DigCORE and QuRunner). The authors also acknowledge support from the Programme New Talents in Quantum Technologies of the Gulbenkian Foundation (Portugal).  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
Figure 10: Correlation coefficients for the Raw, Toeplitz-hashed, and SHA-512 hashed outputs for 10\({}^{7}\) bit.
## 2 Methods and Materials
### Gaussian Process Regression Models  
The flow chart of the proposed GPR based core vector regression method is shown in Figure 1.  
GPR is a modelling technique that is non-parametric named after [PERSON] because it is adopted from the Gaussian distribution. It uses the Bayesian approach to perform the estimation and inference of the parameters ([PERSON] (2000)).  
Let the function representing the global horizontal irradiance (GHI) be \(f(X)\), where \(X\) is a finite subset \(x_{1},x_{2},\ldots,x_{n}\) of the independent variables such as temperature, barometric pressure, wind speed, among others. The proposed method assumes that GHI follows a Gaussian process (GP). It is described by a mean function, \(m(X)\) and covariance function, \(k(X,X^{1})\) and the data are stochastic over given periods. The GPR model is given in Equation (1).  
\[f(x_{i})\sim\text{GP}(m(X),k(X,X^{1})), \tag{1}\]  
where \(m(X)\) and \(k(X,X^{1})\) are the mean and covariance functions, respectively, and \(X\) is the input function of the weather variables evaluated against another input function \(X^{1}\).  
A GPR model is typically formulated by defining the parameters of Equation (1), which is defined by the mean and covariance function calculated as follows  
\[m(X)=f(X)^{T}\beta, \tag{2}\]  
\[k(X,X^{1})=\sigma^{2}r(X,X^{1}), \tag{3}\]  
where \(\beta\) is a vector of coefficients, \(\sigma\) is the covariance and \(r\) is the basic function. An example of the basic function is the radial basis function, whose kernel is given in Equation (4)  
\[k(x,x^{1})=\sigma^{2}\text{exp}(-\frac{1}{2\ell^{2}}\ ||\ x-x^{1}\ ||^{2}), \tag{4}\]  
where \(\ell>0\) is a length scale parameter which determines the length of the 'wiggles' in the function. Therefore, in this case the basic \(r\) function is  
\[r(x,x^{1})=\text{exp}(-\frac{1}{2\ell^{2}}\ ||\ x-x^{1}\ ||^{2}). \tag{5}\]  
Figure 1: Flowchart of Gaussian process core vector regression model.
## 2 Navigation and Localization
### Geophysical Navigation
#### 2.3.3 Bathymetric Navigation  
One simple use of bathymetric maps for AUV navigation is the use of isobaths. An isobath is an imaginary curve that connects all points having the same depth below the surface. A controller [63] can be designed for an AUV to follow an isobath whit only low-level localization equipment--such as echo sounder--and ensures that it never leaves a pre-defined area. Terrain-Referenced Navigation (TRN), Terrain-Aided Navigation (TAN), and Terrain-Based Navigation (TBN) are all similar approaches for GN [64]. These systems estimate the errors in both a main navigation system--such as an INS--and the terrain database to provide a highly accurate position estimate relative to the digital terrain database. TBN operates by correlating the actual terrain profile overflow with the terrain information stored in the terrain database. A basic measurement equation [55] for TBN is given by:  
\[\mathrm{y}\ =\ h(\mathrm{x})\ +\ \mathrm{e}, \tag{8}\]  
where \(h(\cdot)\) is the terrain elevation function, \(x\) is the vehicle location, \(y\) is the measured terrain height, and \(e\) is the measurement noise. An example of terrain correlation in one dimension for a single altimeter measurement is represented in Figure 10.  
Figure 9: Geomagnetic map features.  
[PERSON] et al. [65] worked on a TAN algorithm that combined TERCOM and PF. Experiments were performed to compare the proposed algorithm with the BITAN II algorithm. Results showed that the North and East position error remained below 100 m for the new algorithm, and the mean error was less than half of the mean error for the BITAN-II algorithm. Based on those results, authors concluded than their system was more reliable, possessed a higher positioning precision and a better stability than the one used for comparison.  
[PERSON] et al. [66] proposed a low-complexity PF-based TAN algorithm for a long-range, long-endurance deep-rated AUV. The potential of the algorithm was investigated by testing its performance using field data from three deep (up to 3700 m) and long-range (up to 195 km in 77 h) missions performed in the Southern Ocean. Authors compared TAN results to position estimates through DR and USBL measurements. Results showed that TAN holds a potential to extend underwater missions to hundreds of kilometers without the need for surfacing to re-initialize the estimation process. For some of the missions analyzed, the RMSE of the TAN algorithm was up to 7 times lower when compared with the DR measurements and the absolute water-depth difference was reduced up to 66% when compared with USBL measurements. [PERSON] et al. [67] proposed a TRN system for vehicles with low-grade navigation sensors, with the aim of improving navigation capabilities of simple DR systems. The algorithm uses an 8-dimensional particle filter for estimating critical motion sensor errors observed in the vehicle. Field trials were performed on an AUV with DR navigational accuracy of 5-25% of Distance Traveled (DT). The ability of TRN to provide 5-10 m navigational precision and an online return-to-site capability was demonstrated.
# Digital Twin Technology-Based Networking Solution in Low Earth Orbit Satellite Constellations
## 4 System Model
### Networking Routing Protocol  
#### 4.2.1 Low-Orbit Constellation Network Routing Protocol  
The routing protocols in the low Earth orbit (LEO) satellite network [16; 17] include the inter-satellite routing protocol, feeder satellite-to-ground routing protocol, and user satellite-to-ground routing protocol. The inter-satellite routing protocol is deployed among the LEO satellite nodes to establish inter-satellite forwarding paths. This protocol takes into account the periodic and dynamic changes in the topology of the LEO inter-satellite network. The feeder satellite-to-ground routing protocol is deployed between the LEO satellites and gateway stations to address the feeder selection problem among multiple gateway stations and within a single gateway station. When designing this protocol, the frequent switching of feeder links caused by the high-speed movement of the LEO  
Figure 1: Schematic diagram of constellation network at low orbit.  
satellite nodes is considered, and the routing selection incorporates ephemeris calculation and real-time feeder detection. The user satellite-to-ground routing protocol is deployed among satellite terminals to enable addressing and forwarding between terminals. In the design of this protocol, the frequent switching of user links caused by the high-speed movement of LEO satellite nodes during user communication is taken into account.
## 2 Materials and Methods  
### MODIS Reflectances and Related Vegetation Parameters  
The MODerate-resolution Imaging Spectroradiometer (MODIS) flies on the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Terra (morning overpass) and Aqua (early afternoon overpass) polar-orbiting satellites. MODIS bandwidths for Channels 1-7 used here are 620-670 (red), 841-876 (near infrared 1, NIR\({}_{1}\)), 459-479 (blue), 545-565 (green), 1230-1250 (NIR\({}_{2}\)), 1628-1652 (short-wave infrared 1, SWIR\({}_{1}\)) and 2105-2155 nm (SWIR\({}_{2}\)), respectively. We primarily use the collection 6 MCD43D Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) on a high resolution grid of 0.00833\({}^{\circ}\) latitude \(\times\) 0.00833\({}^{\circ}\) longitude. MCD43D uses data acquired over a 16-day period at multiple angles for clear skies. Daily gridded reflectances are based on the 16-day periods and weight the data as a function of quality, observation coverage and temporal distance from the day of interest. These data are derived using a kernel-driven semi-empirical BRDF model [59; 60; 61]. Other MODIS data products examined here, such as different reflectance datasets, composited NDVI, FAPAR (or FPAR) and GPP estimates, are described in Appendix A.
## 3 Results and Discussion
### Characteristics of MFP-Processed Samples
#### 3.2.1 Grain Alignments and Microstructures
\begin{table}
\begin{tabular}{c c c c c} \hline  & \(x\) = 0.0 & \(x\) = 0.15 & \(x\) = 0.2 & \(x\) = 0.3 \\ \hline \(d_{avg}\) (\(\upmu\)m) & 1.30 \(\pm\) 0.35 & 1.34 \(\pm\) 0.33 & 1.37 \(\pm\) 0.27 & 1.38 \(\pm\) 0.20 \\ \(t_{avg}\) (\(\upmu\)m) & 1.18 \(\pm\) 0.21 & 0.92 \(\pm\) 0.09 & 0.82 \(\pm\) 0.08 & 0.77 \(\pm\) 0.05 \\ Aspect ratio & 1.10\(\pm\)0.31 & 1.46\(\pm\)0.20 & 1.67\(\pm\)0.22 & 1.79\(\pm\)0.12 \\ V\({}_{avg}\)(\(\upmu\)m\({}^{3}\)) & 1.30 & 1.07 & 0.99 & 0.95 \\ D(g/cm\({}^{3}\)) & 4.92 & 4.93 & 4.93 & 4.92 \\ D/D\({}_{\rm th}\) & 96.2 & 96.5 & 96.5 & 96.2 \\ \hline \end{tabular}
\end{table} 
Table 3: The average grain sizes corresponding to the length of the hexagonal plate (\(d_{avg}\)) and the thickness of the hexagonal plate (\(t_{avg}\)), its standard deviation (SD), average aspect ratio, average volume (V\({}_{avg}\)), sintered density (D) and relative density (D/D\({}_{\rm th}\)) for the Sr\({}_{1-x}\)La\({}_{x}\)Fe\({}_{12-x}\)Co\({}_{x}\)O\({}_{19}\) (\(x\) = 0.0, 0.15, 0.2, and 0.3) MFP samples sintered 1230 \({}^{\circ}\)C for 2 h in air. M-type grains have been reported to have a hexagonal plate shape, with the basal plane perpendicular to the c-axis [1], which can also be observed in the planar microstructure in Figure 8. To grow into a hexagonal plate shape, the \(a\)-axis growth rate must be faster than the \(c\)-axis growth rate. Grain growth occurs via the diffusion of components through the liquid phase formed at the grain boundary during the sintering stage. Through the MFP process, the \(c\)-axis of the plate-shaped M-type grains can be dominantly aligned parallel to the applied field. Since the basal plane orientation is in the same direction as other grains during sintering, growth may be hindered, resulting in a slight increase in \(x\). The \(d_{avg}\), \(t_{avg}\), and aspect ratios listed in Tables 1 and 3 are represented as a function of \(x\) for non-MFP1 and MFP samples, respectively, in Figure 9. Interestingly, as shown in Figure 9a,b, with the increasing \(x\), while the \(d_{avg}\) values continuously increase and the \(t_{avg}\) values slightly decreases for non-MFP1 samples, the \(d_{avg}\) values remain almost constant, and \(t_{avg}\) values continuously decrease for the MFP samples. In addition, except for \(x=0.0\), both the \(d_{avg}\) and \(t_{avg}\) values of the MFP samples become smaller than those of the non-MFP1 samples with the increasing \(x\). These differences result in an opposite variation in the average grain volumes with the increasing \(x\), as shown in Figure 9c, while the aspect ratios of MFP samples are relatively lower than those of non-MPF1 samples; however, both increase with the increasing \(x\), as shown in Figure 9c.
# An Optimal Resource Allocation Scheme with Carrier Aggregation in 5G Network under Unlicensed Band
## 3 System Model  
In this paper, we construct a heterogeneous 5G network under an unlicensed band with a CA feature, which can be represented by a graph \(\mathbf{G}(\{\mathbf{M},\mathbf{S},\mathbf{W}\},\mathbf{U})\). In \(\mathbf{G}\), \(\mathbf{M}=\{m_{1},\ldots,m_{M}\}\) is the set of \(M\) macrocell base stations (MBSs), \(\mathbf{S}=\{s_{1},\ldots,s_{S}\}\) is the set of \(S\) small cell access points (APs) and \(\mathbf{W}=\{w_{1},\ldots,w_{W}\}\) is the set of \(W\) Wi-Fi APs. \(\mathbf{U}=\{u_{1},\ldots,u_{U}\}\) is the set of \(U\) UE. The nodes from M, S, W and U are arranged according to Poisson Point Processes (PPPs) with intensities \(\delta_{m}\), \(\delta_{s}\), \(\delta_{w}\) and \(\delta_{u}\) in the Euclidean plane, respectively.  
In a 5G network with a CA feature, RBs from different CCs are aggregated and utilized by access points (APs) based on carrier quality and QoS type. Moreover, data services are classified into delay-sensitive service and rate-sensitive service with corresponding QoS requirements, where \(D_{t}\) is the delay threshold for delay-sensitive service and \(C_{t}\) is the rate threshold for rate-sensitive service. The UE in delay-sensitive service is associated with small-cell APs in 5G and 5G-U with bias \(\psi_{d}\), while the UE in rate-sensitive service is associated with Wi-Fi APs in unlicensed band with bias \(\psi_{r}\), which means UE bias \(\psi_{u}=\{\psi_{d}\cup\psi_{r}\}\). To protect the performance of Wi-Fi UE from the 5G-U UE, the minimum unlicensed spectrum resource is reserved based on the QoS requirement of rate-sensitive UE, as shown in Figure 1.  
In this paper, we adopt the [PERSON] fading channel model, which results in a random channel power gain in exponential distribution with unit mean [27]. Thus, the received power \(p_{iu}^{r}\) at UE \(u\) from AP \(i\) is shown as  
\[p_{iu}^{r}=p_{iu}^{t}g_{iu}d_{iu}^{-\alpha},\qquad\forall i\in \mathbf{S}\cup\mathbf{W},u\in\mathbf{U}, \tag{1}\]  
where \(p_{iu}^{t}\) is the transmit power from node \(i\) to node \(u\). The random variable \(g_{iu}\) accounting for multipath fading follows an exponential distribution with mean \(\frac{1}{\mu}\). \(d_{iu}=\sqrt{|x_{i}-x_{u}|^{2}+|y_{i}-y_{u}|^{2}}\) is the distance between node \(i\) and node \(u\), where \((x_{i},y_{i})\) and \((x_{u},y_{u})\) denote locations of node \(i\) and node \(u\) in the Voronoi tessellation. Path loss exponent is \(\alpha\).
# Comparing End-to-End Machine Learning Methods for Spectra Classification
## 2 Related Work
### Recurrent-Based Approach  
For time series data processing, two variants of the RNN models, Long Short-Term Memory (LSTM) [48], Gated Recurrent Unit (GRU) [49], in particular, can effectively capture temporal dependencies, thus can work efficiently on various complex time series processing tasks, such as prediction, recognition, and classification [18; 24; 25; 50; 51; 52]. For example, [PERSON] et al. [25] showed the ability of LSTMs to recognize patterns in multivariate time series of clinical measurements. In the meantime, RNN-based architecture is also used in combination with the CNN-based module to construct a hybrid convolutional-recurrent neural architecture that can automatically extract features and capture their short-term and long-term dependencies at the same time [17; 18; 21]. [PERSON] et al. [18] developed a Long- and Short-term Time-series network (LSTNet) framework for multivariate time series forecasting. The method combines the strengths of CNN and RNN, and can effectively extract short-term local dependency patterns and long-term patterns in data at the same time. In addition, they exploited an attention mechanism to alleviate the issue of nonseasonal time series prediction. [PERSON] et al. [17] proposed LSTM FCN and ALSTM-FCN deep learning models for end-to-end time series classification, which are enhancements of a Fully Convolutional Network (FCN) with LSTM sub-module or attention LSTM sub-module. Although the enhanced models can significantly improve classification performance, the limitation is that it is designed for univariate time series. In 2019, the authors of [45] subsequently introduced squeeze-and-excitation block to augment the FCN block in the existing LSTM-FCN and ALSTM-FCN models, which can capture the contexture information and channel-wise dependencies, so that the model can be used for multivariate time series classification. [PERSON] et al. [52] proposed an end-to-end DuPLO deep learning architecture for the analysis of Satellite Image Time Series data. It is also a combination of both CNNs and GRUs models, which can exploit feature information from different points of view, and then produce more diverse and complete information representations for classification tasks [52].  
RNN-based models have also been applied to spectra classification in spectroscopy. For example, in [53], [PERSON] et al. detected the pseudo-brain tumors via stacked LSTM neural network with Magnetic resonance spectroscopy (MRS) data, achieving high-precision binary classification of brain tumors and normal brain tissue. In [54], the RNN models were built to analyze Raman spectra to classify blood species. However, the application of RNNs to time-resolved neutron or x-ray scattering experiments is still scarce [31].