esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 4 Our Proposed Slice-Based QoS Manager for IoT
### VM-Application  
Since the VM is intended to represent the application layer, it has the main role of running applications with the purpose of dealing with the information coming from the bottom of the architecture stack (Data Plane). If it chooses to process information itself, then data are processed between the cloud and the source device, and therefore, fog computing is occurring. On the other hand, applications can choose to strictly redirect that information to the cloud, where it would later be computed and consequently fog computing takes place.  
The VM-Application has two physical interfaces from its perspective, which represent OvS bridges, which are in reality virtual ones. As a matter of convention, we call them _eth0_and _eth1_. Interface _eth0_ has the main role of providing applications connectivity to the controller and consequently any device available in the Data Plane. On the other hand, _eth1_ is the interface that connects to the cloud, being responsible for passing information if any application chooses to process information by means of cloud computing.  
All applications run inside separate Docker containers, which are automatically created when the order is given by WebGM. This guarantees information isolation and the ability to provide each application different connectivity environments if needed. One of the reasons Docker containers and services are so powerful is that you can connect them together or connect them to non-Docker workloads. The deployed applications do not require being aware that they are running on Docker, or whether their peers are also Docker workloads or not, managing them independently of the platform they choose to use [47].  
In order to facilitate information exchanging between containers and the VM-Application, SOFTWAY4 IoT uses OvS (Northbound Interface), allowing the use of multiple VLANs on a single bridge. Of course, this results in a more complex and virtual-oriented system, but also allows for an incredibly adaptable and scalable system. Hence, each new slice is represented by a VLAN in a newly  
Figure 4: SOFTWAY4 IoT implementation and its relation to the SDN architecture.  
created OvS switch, and both \"physical\" interfaces are bridged by _br-int_ (_eth0_) and _br-ext_ (_eth1_). We must remind ourselves that each slice can have more than one running application, whose connectivity is handled by its OvS bridge with a newly running port. Figure 5 depicts the VM-Application system and networking scheme.
# A Novel Spectral-Spatial Classification Method for Hyperspectral Image at Superpixel Level
## 3 Experimental Results  
To assess the performance of the suggested method, we have tested our proposal on three benchmark hyperspectral datasets which are extensively used to evaluate the effectiveness of the HSI classification approaches. In the proposed semi-supervised HSI classification frame, the labeled samples are obtained by randomly marking pixels in the original HSI. Particularly, in the experiments conducted in this work, the generation of training set is to randomly label 10% of pixels per class for Indian Pines dataset and University of Pavia, and 1% of pixels from each class for Salinas dataset, respectively. In order to obtain objective classification results and to reduce the impact of the random labeling on the classification results, all the experiments performed in this work are repeated ten times independently. The final result is reported with the averages and standard deviations. Like most existing literatures, three commonly implemented evaluation criteria indices, overall accuracy (OA), average accuracy (AA), and Kappa coefficient (\(\kappa\)) are employed to quantify the classification performance.  
In the test stage, the proposed SSC-SL method is compared with other five state-of-the-art HSI classification algorithms: Support vector machine (SVM) [8], edge preserving filtering-based classifier (EPF) [52], image fusion and recursive filtering (IFRF) [53], SuperPCA approach [35], and PCA-SLICmethod. Among them, the spectral features of pixels are only taken into account in SVM classifier; the methods of EPF, IFRF, and SuperPCA are three typical spectral-spatial classification methods for HSI. The PCA-SLIC method is the other version of the proposal, in which superpixels are generated by taking the first three components in PCA and using the original SLIC algorithm.
## 3 Approach
### Automated Feature Extraction  
Measurable attributes such as height, stem diameter, and crown diameter can be automatically calculated for trees with known positions derived from the LIDAR data (see Figure 9) collected during the road surveys in autumn 2021.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Attribute** & **Description** & **Comment** & **Priority** \\ \hline \multirow{2}{*}{species} & \multirow{2}{*}{genus; scientific name} & more precise, but more difficult to determine than _genus_ & \multirow{2}{*}{medium} \\  & & & than _genus_ & \\ \hline leaf\_type & deciduous tree or conifer & can be derived from _species_ & low \\ \hline leaf\_cycle & \multirow{2}{*}{describes the property of shedding the foliage} & \multirow{2}{*}{can be derived from _species_} & \multirow{2}{*}{low} \\ \hline est\_height & estimated height in meters & & if no exact \\  & & specification possible & low \\ \hline height & height in meters & relevant for shading & high \\ \hline diameter\_crown & crown diameter in meters & relevant for shading & medium \\ \hline circumference & \multirow{2}{*}{
\begin{tabular}{c} trunk circumference \\ in meters \\ \end{tabular} } & can be calculated from _diameter\_trunk_ & low \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline
**Attribute** & **Description** & **Comment** & **Priority** \\ \hline \multirow{2}{*}{diameter\_trunk} & \multirow{2}{*}{trunk diameter in metres} & can be calculated & \multirow{2}{*}{low} \\  & & from _circumference_ & \\ \hline \multirow{3}{*}{location} & \multirow{3}{*}{\begin{tabular}{c} surroundings of the tree \\ (tree disc, tree strip, \\ green area) \\ \end{tabular} } & Relevant for statement & \multirow{3}{*}{high} \\  & & about possible & \\  & & drought stress. & \\ \hline \multirow{3}{*}{state} & \multirow{3}{*}{health condition of the tree} & \multirow{3}{*}{\begin{tabular}{c} Cannot be calculated on a \\ large scale due to lack of \\ training data. \\ \end{tabular} } & \multirow{3}{*}{none} \\  & & & \\ \hline \multirow{3}{*}{security needs} & \multirow{3}{*}{\begin{tabular}{c} need to make \\ arrangements \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} Cannot be calculated on a \\ large scale due to lack of \\ training data; unclear legal \\ situation in the \\ case of application. \\ \end{tabular} } & \multirow{3}{*}{none} \\ old & age of the tree & \multirow{3}{*}{\begin{tabular}{c} Can be estimated by _genus_ \\ and _height_ if necessary \\ \end{tabular} } & \multirow{3}{*}{low} \\ age & \begin{tabular}{c} date of planting of the tree \\ at the current location \\ \end{tabular} & \
\begin{tabular}{c} additional data sources \\ are necessary \\ \end{tabular} & none \\ \hline \multirow{3}{*}{own} & privately owned or & \multirow{3}{*}{
\begin{tabular}{c} Intersection with the real \\ estate cadastre is \\ necessary; relevant for the \\ security obligation. \\ \end{tabular} } & none \\  & & \\ \hline \end{tabular}
\end{table}
Table 3: Overview of additional attributes of trees and their prioritization in the project context.
## 5 Experimental Design and Results Analysis  
This research assesses the efficacy and practicality of the suggested method through a combination of simulated trials and real-world validation. For the simulation, we utilized the TUM dataset from the Technical University of Munich, Germany, which comprises visual and inertial data from diverse contexts and is extensively employed for the performance assessment of visual SLAM and visual-inertial odometry (VIO) algorithms. Utilizing standard trajectories from the TUM dataset and comparing them with the ORB-SLAM3, we concentrated on evaluating the benefits of the proposed technique in terms of localization precision and dense mapping. Furthermore, comprehensive performance evaluations and three-dimensional mapping simulation tests were executed utilizing the RCBD trajectory data from the TUM dataset to validate the algorithm's enhancements further.
## 3 Results and Discussion  
### Characteristics of Raw OFMSW and DSS  
In Table 3, basic substrate characteristics are compiled, serving as a basis for all AD experiments and for stoichiometric biogas and CH\({}_{4}\) yield calculations. The FM of the OFMSW sample was dried in separate fractions causing high standard deviations (SD), which was not relevant for this study. DM, oDM, and C, H and N contents of OFMSW are in line with the ranges presented in [PERSON] and Gonzalez-Martinez [1]. However, the DM and oDM contents, in particular, depend on various factors such as season or settlement structure. This is also represented by [PERSON] and Gonzalez-Martinez [1] with oDM contents for OFMSW of 84.24 \(\pm\) 10.09% DM.  
The basic characteristics (FM and oDM) of the DSS sample used in series 1 and series 2 slightly differed from each other. This could be explained by different storage durations of the DSS at the treatment plant or seasonal variations. However, both DM and oDM contents were higher compared to [PERSON] et al. [75], in which a DM content of 2.40 \(\pm\) 0.50% FM and an oDM content of 49.00 \(\pm\) 2.00% DM were reported. It cannot be expected that the characteristics of DSS fluctuate as strong as the characteristics OFMSW, but wastewater treatment plants can operate with different treatment approaches influencing DSS characteristics. The contents for C, H, and N of DSS (series 2) are almost identical, compared to mean values in Maier and Scheffknecht [76].  
Based on Equation (1), the calculated stoichiometric biogas potential of the OFMSW was 990 L/kg\({}_{\text{oDM}}\) (771 L/kgDM, 257 L/kg\({}_{\text{FM}}\)), with a stoichiometric CH\({}_{4}\) potential of 506 L/kg\({}_{\text{oDM}}\) (394 L/kgDM, 131 L/kgFM). The DSS of series 2 reached a stoichiometric biogas potential of 1051 L/kg\({}_{\text{oDM}}\) (633 L/kgDM, 26 L/kgFM), leading to a stoichiometric CH\({}_{4}\) potential of 514 L/kg\({}_{\text{oDM}}\) (400 L/kgDM, 16 L/kgFM). In the literature, the calculated stoichiometric yields of DSS were between 919 and 1533 L\({}_{\text{biogas}}\)/kg\({}_{\text{oDM}}\) and between 462 and 772 L\({}_{\text{CH4}}\)/kg\({}_{\text{oDM}}\), while OFMSW reached 1087 L\({}_{\text{biogas}}\)/kg\({}_{\text{oDM}}\) and 596 L\({}_{\text{CH4}}\)/kg\({}_{\text{oDM}}\). These values are in line with the DSS and OFMSW samples used in this study [17; 75; 76]. However, stoichiometric biogas and CH\({}_{4}\) yields assume complete digestion, which is not achieved in practice.
## 5 Methods for Estimation of HFR Video Characteristics
### Multifractal Spectrum  
The Hurst index is suitable to describe the behavior of self-similar processes using a single value. Nevertheless, the asymptotical consideration of a regression slope in a log-log domain may not always be sufficient to fully understand the video traces. Thus, multifractal concept represents a generalization of a fractal one, where instead of one dimension or exponent, a spectrum of exponents is defined to describe dynamics of time series. There are different multifractal approaches for spectrum calculation [74; 75; 76; 77; 78].  
In general, multifractal formalism is based on dimensions defined for a set of exponents within a small range, where dimensions can be given as  
\[D_{h}=\inf_{q}(qh(q)-\tau(q)+k), \tag{6}\]where \(q\) describes singularity, \(\tau(q)\) is a nonlinear function and \(k\) is a constant. For \(q=1\), information dimension is obtained, while for \(q>1\), strong singular, and for \(q<1\) less singular, structures are described. The Legendre transformation for \(\alpha\) enables obtaining the multifractal spectrum \(f(\alpha)\) as  
\[\alpha=\frac{d\tau(q)}{dq},\,f(\alpha)=q\alpha-\tau(q) \tag{7}\]  
Multifractal or singularity spectrum denoted as \(f(\alpha)\) can be described as a distribution of the quantity or Holder exponent \(\alpha\). In practice, Legendre spectrum gives a smooth concave function of the exponent useful in understanding the behavior of different structures [39; 40; 41; 58]. Here, for the spectrum calculation, Fraclab software version 2.2 is used [78]. An example of multifractal spectrum is presented in Figure 4 as function \(f(\alpha)\).  
The main characteristics are shown in the same figure and are used as features in discrimination and classification models in different fields [75; 76; 77]. The structure of a spectrum is usually considered through its left and right sides or tails by dividing singularity of components into two parts. The features represent width \(w\), asymmetry \(a\), tail slopes and similar. Also, there are characteristic points of a spectrum that are foundation of these features, like minimum \(\alpha\) value (\(\alpha_{\text{min}}\)), maximum \(\alpha\) value (\(\alpha_{\text{max}}\)) and \(\alpha\) corresponding to the curve maximum (\(\alpha_{0}\)). Moreover, information dimension can be found asymptotically using \(f(\alpha)=\alpha\) dimension. This is applied here as well to make a difference between video traces using multifractals in a statistical manner. Besides the Hurst exponent, these multifractal properties are also considered.
# On the Performance of LDPC-Coded Large Intelligent Antenna System
## 3 System and Signal Model for the Receivers
### Linear Feedforward Non-Iterative FDE Receivers
#### 3.1.2 Minimum Mean Squared Error  
The IDFT (Inverse DFT) of the block \(\widetilde{S}_{k}\), with \(k\) = 0, 1,..., \(N-1\) can be used to determine the data symbols for a linear MMSE-based receiver [17]:  
\[\widetilde{S}_{k}=\left[S_{k(1)},...,S_{k(R)}\right]^{T}=\left(H_{k}H_{k}^{H}+ \beta I\right)^{-1}H_{k}^{H}Y_{k} \tag{5}\]  
where  
\[\beta=\frac{\sigma_{N}^{2}}{\sigma_{S}^{2}}=\frac{\frac{E\left[|N_{k}|^{2} \right]}{2}}{\frac{E\left[|S_{k}|^{2}\right]}{2}} \tag{6}\]  
with regard to (3,4):  
\[F_{k}=\left(H_{k}H_{k}^{H}+\beta I\right)^{-1}H_{k}^{H} \tag{7}\]  
where \(I\) stands for the identity matrix. For all the contents of transmitter and receiver, \(\beta\) is taken to be the same. However, in LIS systems, the sizes of these matrices can be enormous, which imposes great requirements to perform the inversion of the channel matrix for each channel frequency response component.
# GPU Adding-Doubling Algorithm for Analysis of Optical Spectral Images
###### Abstract  
The Adding-Doubling (AD) algorithm is a general analytical solution of the radiative transfer equation (RTE). AD offers a favorable balance between accuracy and computational efficiency, surpassing other RTE solutions, such as Monte Carlo (MC) simulations, in terms of speed while outperforming approximate solutions like the Diffusion Approximation method in accuracy. While AD algorithms have traditionally been implemented on central processing units (CPUs), this study focuses on leveraging the capabilities of graphics processing units (GPUs) to achieve enhanced computational speed. In terms of processing speed, the GPU AD algorithm showed an improvement by a factor of about 5000 to 40,000 compared to the GPU MC method. The optimal number of threads for this algorithm was found to be approximately 3000. To illustrate the utility of the GPU AD algorithm, the Levenberg-Marquardt inverse solution was used to extract object parameters from optical spectral data of human skin under various hemodynamic conditions. With regards to computational efficiency, it took approximately 5 min to process a \(220\times 100\times 61\) image (\(x\)-axis \(\times\)\(y\)-axis \(\times\) spectral-axis). The development of the GPU AD algorithm presents an advancement in determining tissue properties compared to other RTE solutions. Moreover, the GPU AD method itself holds the potential to expedite machine learning techniques in the analysis of spectral images.  
Keywords:adding-doubling method; GPU implementation; optical spectral images; tissue properties; computational speed-up and accuracy +
Footnote †: journal:
## 3 Research Approach and Integration Design
### Integration Design
#### 3.2.2 Solar Generation Monitoring System  
The university receives unique real-time insights into its energy dynamics. This system is distinguished by its integration with a cutting-edge online monitoring platform (Figure 2). This not only allows for more effective management, but also gives stakeholders the option to remotely monitor and alter energy consumption, responding quickly to demand variations or any unforeseen concerns. The web-based monitoring system serves as a transparent lighthouse, enabling administrators and facility managers to make informed decisions, enhance energy efficiency, and demonstrate a commitment to sustainable practices.  
Figure 2: _Cont._This holistic approach to energy management is consistent with the university's commitment to environmental responsibility and technical innovation. By seamlessly integrating renewable energy sources and providing a user-friendly interface for monitoring and control, the institution not only decreases its carbon footprint but also establishes a precedent for using innovative technology in pursuit of a greener and more efficient campus. The synergy between various energy sources and the web monitoring system presents a picture of a university at the vanguard of sustainable energy practices, producing a campus climate where cutting-edge technology meets environmental care.
## 3 Results
### Ultraviolet-Visible (UV-Vis) Spectroscopy  
The electronic structure of crystalline solids is well established whereby different crystalline forms exhibited different band gap values. In the case of crystalline TiO\({}_{2}\), rutile (the most ordered TiO\({}_{2}\) structure) presents a band gap of \(\approx\)3.0 eV while anatase exhibits a higher value of \(\approx\)3.23 eV [64; 65; 53; 66]. Although physical models and mathematical tools developed for crystalline solids are not always valid for amorphous materials, electronic gaps do occur on amorphous materials, and an experimental \(\approx\)3.18 eV value was obtained for a-TiO\({}_{2}\) NPs [53].  
The UV-Vis spectra for the synthesized a-TiO\({}_{2}\) NPs are shown in Figure 2a. The straight-line extrapolation method and the Tauc's plot (used in the determination of semiconductors bandgap) were used to estimate the light absorption edge and bandgap values (E\({}_{\text{g}}\)) for the synthesized NPs (Figure 2b,c).  
The absorption bands (observed at 369 nm for pristine a-TiO\({}_{2}\) NPs and at 357 nm for a-TiO\({}_{2}\)-NH\({}_{2}\) NPs in Figure 2a) are due to the semiconductor excitation and quantum size effects, as reported by [PERSON] et al. [66]. The slight blue shift observed on the absorption band (in comparison with those of anatase at \(\lambda\) = 385 nm [67]), is attributed to the NPs sizes and morphologies, since the number of photons reaching the NPs core depends on their size and the optical properties [67].  
Figure 1: Physical characterization of amorphous TiO\({}_{2}\) (a-TiO\({}_{2}\)) (lower panels) and a-TiO\({}_{2}\)-NH\({}_{2}\) nanoparticles (NPs) (upper panels): (**a**) TEM images, (**b**) X-ray diffractograms, and (**c**) FTIR spectra.  
Amorphous-TiO\({}_{2}\) NPs show an experimental E\({}_{\rm g}\) value of 3.3 eV, slightly higher than some of the authors' previously obtained values [53]. Unlike crystalline forms, amorphous networks have plenty of structural possibilities which explains the dispersity of experimental bandgap values.  
The a-TiO\({}_{2}\)-NH\({}_{2}\) NPs experimental E\({}_{\rm g}\) value was 3.4 eV (very similar to the pristine a-TiO\({}_{2}\) bandgap value) meaning that the functionalization with the propylamine does not influence the main a-TiO\({}_{2}\) NPs electronic structure.  
Moreover, all the obtained experimental bandgaps lie in the UV regime, predicting no daylight stimulation on the synthetized a-TiO\({}_{2}\)-based NPs.
## 2 Autoregressive Integrated Moving Average (ARIMA)  
The Autoregressive Integrated Moving Average (ARIMA) model integrates Auto Regressive and Moving Average calculations. A basic requirement for predicting time-series with ARIMA is that the time-series should be stationary or, at the very least, trend-stationary [3,7]. A stationary series is one that has no trend, and where variations around the mean have a constant amplitude, e.g., [1,13,14]. Although ARIMA expects a stationary stochastic process as input, very few datasets are natively in such format, thus the use of differencing to \"stationarise\" is in the model identification stage [1,7] (Figure 1).  
The ARIMA model has 3 key parameters (\(p,d,q\)), all non-negative integers: \(p\) is the order (the number of time lags) of the autoregressive model, \(d\) is the degree of differencing (the number of times the data have had past values subtracted if random walk is chosen), and \(q\) is the order of the moving-average model. The values for \(p\), \(d\), and \(q\) are defined by calculations and subsequent analysis and the process of fitting an ARIMA model, by calculating these parameters, is commonly known as the Box-Jenkins method [15].  
Figure 1: The Box-Jenkins/ARIMA iterative fitting process.  
The integration of the \(p\) autoregressive and \(q\) moving average terms, gives rise to the formula [15]:  
\[\hat{y}=c+\epsilon_{t}+\sum_{i=1}^{p}\phi_{i}y_{t-i}+\sum_{i=1}^{q}\theta_{i} \epsilon_{t-1}\]  
where \(c\) represents a constant variable, error terms \(\epsilon_{t}\) are generally assumed to be independent, uniformly distributed random variables, \(y_{t}\) are previous observed values of the time-series and \(\phi_{i}\) and \(\theta_{i}\) are coefficients determined as part of the process.  
Each indicator under analysis here has been through the three-stage ARIMA/Box-Jenkins iterative modelling approach (Figure 1):  
1. Model identification, to ensure that variables are stationary: if not, differencing methods such as random walk are applied, any seasonality is identified, and then AC (Auto Correlation) and PAC (Partial Auto Correlation) functions are plotted [16].
2. Parameter estimation: using methods such as Non-linear Least Squares Estimation or Maximum Likelihood Estimation together with the outcome of the AC and PAC functions [16].
3. Model checking, to test whether predictions now adhere to the requirements of a stationary univariate process: in particular, residuals must be independent of each other and within an acceptable range. Additionally, the mean and variance should be constant over the time. If the model is still inadequate, the iterative process returns to stage 1 and an alternative method for differencing is applied.
# Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)
## 3 Singular Value Decomposition (SVD)  
Here we introduce a useful concept, singular value decomposition, which is a method of decomposing a matrix, e.g., \(A\) into three other matrices. The SVD represents an expansion of the original data in a coordinate system where the covariance matrix is diagonal.