esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Machine Learning and IoT in Smart Transportation
## 2 Machine Learning and IoT in Smart Transportation
### Machine Learning Algorithms
#### 2.1.2 Bayesian  
A Bayesian network is represented by a Directed Acyclic Graph (DAG), which implements a probability distribution for a given set of random variables. Given a training set as variables, the model can be estimated, which can be used later to assign labels to new data. A Bayesian network is a model that describes all the variables and their relationship. Thus, the observation of some variables in a Bayesian network can give information about the state of another set of variables in the network [42].  
In [18], the authors use a method called Bayesian Network Seasonal Autoregressive Integrated Moving Average-(BN-SARIMA), in order to achieve short-term traffic forecasting. The implemented method is a combination of a Bayesian Network and an ARIMA model as an estimator. An ARIMA model is a form of regression analysis, which is commonly used for time series analysis and forecasting. In an ARIMA model, one dependent variable regresses on the previous values of the same variable (AR). The predictions are based on the difference of the variables current and past values in a time series (I). Additionally, the Moving Average (MA) shows that the regression error is a linear combination of previously calculated errors. The authors compare the BN-SARIMA method with two deep learning algorithms. The data are location and speed information of vehicles, obtained from two large datasets. The methods were evaluated against the RMSE, MAE, and MAPE metrics. The results indicate that both deep learning algorithms and the Bayesian method offer similar results in short-term traffic forecasting.
# LDM-Ex-FDM: A Novel Multi-Service Transmission Scheme for the ATSC 3.0 System
## 2 Advanced Television System Committee (ATSC) 3.0 System Overview
### Puncturing Strategy  
At the transmitter end, the puncturing strategy based on BICM symbols of the LDM-Ex-FDM scheme is divided into two types, namely EISP and NEISP, as shown in Figures 5 and 6, respectively. The \(EL\) symbols used for fixed reception are modulated by BICM, and the modulated symbol puncturing accounts for 1/12 of the total. Symbol puncturing at equal intervals can be used for multi-service transmission in typical scenarios. Symbol puncturing at non-equal intervals can be used for multi-service transmission in scenarios where the \(LDM\) symbol constellation overlaps due to situations such as low power injection levels. The overlapping phenomenon of the constellation will reduce the service performance of mobile reception, resulting in the inability to completely eliminate the failure of the \(CL\) signal, thereby further reducing the service performance of the fixed reception. However, the overlap phenomenon of constellations usually exists when the power injection level of the \(LDM\) is small and/or the modulation order of \(EL\) symbols is high.  
The constellation comparison of the \(LDM\) symbols with and without NEISP in the LDM-Ex-FDM scheme is shown in Figure 7. When the power injection level of the \(LDM\) is 0 dB, and the symbols of the \(CL\) and \(EL\) are respectively modulated by QPSK and NU-64 QAM, the constellations of the \(LDM\) symbols overlap. Take the constellation marked  
Figure 4: The representation of core layer (\(CL\)), enhanced layer 1 (\(EL1\)), \(EL2\) in the multiplexer (Mux) output.  
Figure 3: The receiver structure with the successive interference cancellation (SIC) detector of the LDM-Ex-FDM scheme.  
Figure 2: The transmitter structure of the layered division multiplexing extension frequency-division multiplexing (LDM-Ex-FDM) scheme.  
by the blue upper triangle as an example. It is not known in advance which symbol of \(CL\) will be superimposed on the \(EL\) symbol, that is, it is uncertain in which quadrant it will appear. As a result, symbols of non-overlapping constellations will also be punctured when puncturing according to high-power symbols. Specifically, the selection of removed symbols in the NEISP algorithm is as described in Algorithm 1.  
Figure 5: The schematic diagram of the equal interval symbol puncturing (EISP) after bit-interleaved coded modulation (BICM) encoding in the LDM-Ex-FDM scheme.  
Figure 6: The schematic diagram of the non-equal interval symbol puncturing (NEISP) after BICM encoding in the LDM-Ex-FDM scheme.
## 3 Methodology  
Comparing with other superpixel segmentation algorithms, the advantages of 2D SLIC algorithm are as follows: (1) with equally sized compact superpixels; (2) with low computational cost and few input parameters; (3) with an optimal tradeoff between under-segmentation error and boundary recall. However, from the definition of distance measure, it's apparent that only the color and planar position of pixels are involved in, and the spatial geometric and some other attribute features which would have more of an effect on the segmentation results, are ignored. For geological objects, attitude is an important attribute to reflect the difference of the rock surface. Besides color and planar position, rock surface attitude is an essential component for distance measure to get a reliable segmentation result. Moreover, in some special circumstances, the spatial location may have more impact on geological objects segmentation. For example, pixels with similar color and proximal planar position but very far apart in the direction perpendicular to the image plane may be assigned into the same superpixel with 2D SLIC algorithm. Therefore, a new hybrid superpixel segmentation algorithm, MFF-SLIC, has been developed in this paper. The algorithm firstly describes a 1-N mapping relationship model for the TLS point cloud and digital images based on registration result. Instead of traditional distance measuring in 2D SLIC algorithm, a new multi-feature fusion distance measure model is defined, which combines color and position of pixels in digital images, local attitude of rock surface and spatial location of the TLS point cloud; Then an initial segmentation result consisting of a set of superpixels is obtained; the superpixels need to be further processed by hierarchical and optimized clustering in order to get the final result. The flowchart of MFF-SLIC algorithm is shown as Figure 2.  
In the process of initial segmentation, clustering centers are optimized to the position with the minimum gradient in a local neighborhood so that they are not edge pixels or noise pixels. The gradient calculation formula for \(3\times 3\) neighboring window is shown as Equation (4):  
\[G(x,y)=\|I(x+1,y)-I(x-1,y)\|^{2}+\|I(x,y+1)-I(x,y-1)\|^{2}, \tag{4}\]  
where \(I(x,y)\) means CIELAB color vector \((L,a,b)\) of pixel \((x,y)\); \(\|\cdot\|\) denotes the norm of the color vector.  
As the connectivity of pixels is not explicitly mandatory, some isolated pixels may exist in the end of the iteration for superpixel segmentation. A further connectivity processing is necessary to merge the isolated pixels into the nearest superpixels.  
Figure 2: The flowchart of MFF-SLIC hybrid superpixel segmentation algorithm.
## 2 RPC Model and Bundle Adjustment
### RPC Model  
With the commercialization of high-resolution satellite imagery, satellite companies have gradually replaced physical sensor models with RPC models in recent years for reasons of technical secrecy. The RPC model can directly map the relationship between image points and 3D points (including projection and localization). The projection in the RPC model can be represented as:  
\[(x,y)=P(X,Y,Z)=\left(\frac{a(X,Y,Z)}{b(X,Y,Z)^{*}}\frac{e(X,Y,Z)}{d(X,Y,Z)} \right), \tag{1}\]where \((x,y)\) and \((X,Y,Z)\) represent the coordinates of the image point and the 3D point, respectively, \(P(\cdot)\) denotes satellite camera projection. The coordinates \((x,y)\) and \((X,Y,Z)\) here are all normalized to the range of \([-1,\,1]\). There are usually 10 normalization parameters in the RPC model. In addition, \(\mathbf{a}(\cdot),\mathbf{b}(\cdot),\mathbf{c}(\cdot),\mathbf{d}(\cdot)\) represent the polynomials in the RPC model, which have the following form:  
\[\begin{array}{ll}\mathbf{a}(X,Y,Z)&=a_{0}+a_{1}Y+a_{2}X+a_{3}Z+a_{4}YX+a_{5}YZ+a_ {6}XZ+a_{7}Y^{2}+a_{8}X^{2}\\ &+a_{9}Z^{2}+a_{10}XYZ+a_{11}\ Y^{3}+a_{12}YX^{2}+a_{13}YZ^{2}+a_{14}Y^{2}X\\ &+a_{15}X^{3}+a_{16}XZ^{2}+a_{17}\ Y^{2}Z+a_{18}X^{2}Z+a_{19}Z^{3}.\end{array} \tag{2}\]  
The forms of \(\mathbf{b}(\cdot),\mathbf{c}(\cdot),\mathbf{d}(\cdot)\) are the same as \(\mathbf{a}(\cdot)\), which means the RPC model has 80 rational function coefficients.  
The RPC parameters are provided along with satellite images by image vendors. As an alternative to the physical sensor model, the generic RPC model describes satellite imaging in the fractional form of polynomials. This means that the RPC model is independent of the photographic platform and sensors. Nevertheless, the RPC model has limitations, such as the fact that the RPC parameters are physically uninterpretable. Therefore, the generic RPC model uses a mathematical model to describe the mapping relationship between image points and 3D points. Note that different satellite images have different parameters of the RPC model, which means that the RPC parameters have certain inaccuracies. These inaccuracies come from the errors caused by the sensors that record the attitude of the satellite. Besides, the inaccuracies in the satellite imagery can lead the image point to shift by tens of pixels [10].
## 3 Results and Discussion
### Irrigation Purposes
It is observed that, based on percent sodium, 87.5% and 12.5 % of the spring and well water samples belong to the good and moderate class, respectively [37]. %Na ranges between 21.10 mg\(\cdot\)L\({}^{-1}\) to 48.29 mg\(/\ell\) with a median value of 31.18 mg\(\cdot\)L\({}^{-1}\) (Tables 4 and 5). In most waters, calcium and magnesium are in balance, and a high level of Mg\({}^{2+}\) is attributable to the presence of exchangeable Na\({}^{+}\) in irrigated soils. Excess Mg\({}^{2+}\) alters soil quality making it alkaline ensuing in decreased crop yield and poor agricultural returns [61]. An Mg hazard (MH) value <50 is suggested for irrigation. MH in our study region ranges between 46 and 56 (Tables 4 and 5), with 94% of the spring and well water samples being classified as suitable for irrigation. Kelly's ratio (KR) signifies higher sodium content in water [39]. KR is as well one of the vital parameters in classifying irrigation water. KR index is an alkali hazard indicator. In case of high KR values, gypsum is advised to decrease the effects of Na\({}^{+}\) ions [41]. KR values of our spring and well water samples range from 0.2 to 0.8, with an average value of 0.36. Only water with KR < 1 is suitable for irrigation. Therefore, the spring and well waters studied in the El-Farafra Oasis are suitable for irrigation (Tables 4 and 5). Permeability Index (PI) is an essential variable for classifying permeability of the soils to irrigation water and is affected by prolonged application of irrigation water (higher salts), as it is influenced by Na\({}^{+}\), Ca\({}^{2+}\), Mg\({}^{2+}\), and HCO\({}_{3}\)\({}^{-}\) ions in soils. PI values of the study region ranged from 14.8 to 37.6, classifying the spring and well waters studied as belonging to the 'Good' category (Tables 4 and 5). Waters belonging to 'Good' and 'Moderate' classes are recommended for irrigation [42]. Potential salinity (PS) is applied for the categorization of water for agricultural purposes. PS < 3 mg\(\cdot\)L\({}^{-1}\) is good for agricultural applications [43]. The PS values in this area vary from 0.01 to 2.14 meq\(\cdot\)L\({}^{-1}\). Thus, all the spring and well waters are suitable for irrigation. As per the Soltan classification, all the waters studied belong to deep meteoric type (Tables 4 and 5). The spring and well waters data were plotted using the US Salinity Laboratory Staff diagram [35]. The results indicate that the groundwater of the El-Farafra area is suitable for irrigation of traditional crops. However, samples no. S3 and S4 can only be used to irrigate salt-tolerant crops in good drainage soils (Figure 4). In addition, the spring and well waters data were plotted using the Wilcox diagram [47]. The results indicate that the groundwater of the El-Farafra area is excellent-to-good for irrigation, except for samples no. S3 and S4 which are good to permissible (Figure 5). Figure 4: The US Salinity Lab. Staff diagram [35] for Irrigation water classification in the study area. Figure 5: Classification of water for irrigation use in the study area (after Wilcox [47]).
## 4 Results
### Field Testing and Validation  
The initial data used to validate our model originate from the interoperability information system between FEVs, whose charging system is optimized, and the smart grid, where the event-driven model predictive control is implemented. This system was tested in Ljubljana (Slovenia), as part of the final pilot results of the Mobincity project [50].  
The pilot test fields were divided into two main categories. The first, called partial validation, included software validation and the testing of specific functionalities, such as route recovery and updating, as well as the reservation module. These functionalities were tested as a preliminary step before moving on to the second phase. The second phase, system integration, focused on testing long-chain functionalities that involved almost all components and participants in the pilot.  
Key system features tested included trip planning for various detailed use cases and re-routing processes when needed for demand-side management. This phase considered constraints such as traffic conditions, user behavior, and battery status. The pilot tested the effectiveness of the predictive control model in lowering charging costs and enhancing energy system stability.  
Figure 7 shows the geographic area selected for field testing, encompassing all the necessary energy and transport infrastructure to ensure the system's proper functioning and validate its full range of functionalities.
# Requirements for Crafting Virtual Network Packet Captures
## 3 Requirements
### RFC Compatibility  
The process of generating network packets is mostly done for a specific purpose. As mentioned in the previous part, the correctness of the packets is a crucial aspect. In addition to this, the compatibility to the most recent Request-for-Comments (RFC) is important. These standards define relevant aspects such as protocol structures, valid and invalid entries, default values and recommendations for processing the information. If these standards or parts of them get ignored, different follow-up errors might arise. These errors might detract from the intended purpose of the packet capture. Hence, it is crucial that the generated network packets consider the valid specifications. This is a time-consuming part of the generation, because the different specifications and their expansions have to be analysed in detail. Some specifications depend on each other, expand older versions and add new functions. As an example, GRE as defined in RFC 2784 [43] does not provide the relevant header fields for NVGRE, but the extension of GRE defined in RFC 2890 [44] adds two optional header fields to the original header with a length of 32 bits each. NVGRE uses 24 bits to store the virtual subnet ID (VSID). The remaining 8 bits are used as a FlowID, which provides per-flow entropy for flows in the same VSID.
## 4 Benchmark Function Testing and Analysis
### Wilcoxon Rank Sum Test  
To further compare whether the ADGMPSO algorithm was significantly different from other algorithms, a Wilcoxon rank sum test was performed between the 20 iterations of ADGMPSO and the other algorithms at a significance level of \(p=5\%\). The null hypothesis (H0) was rejected if the value was less than \(p=5\%\) since there was a substantial difference between the two algorithms. If NaN was obtained, it indicated that the overall performance of the two algorithms was the same, and the significance could not be determined [34]. The findings of the R denoted by the symbols \"\(+\)\", \"\(-\)\" and \"\(=\)\" denoted that the performance of ADGMPSO was, respectively, superior to, inferior to, and equal to the compared algorithms. The results, shown in Table 6, indicate that ADGMPSO outperformed PSO and SSA significantly in all the tested functions. Eight test functions showed significantly better results than BOA and GWO, and two test functions showed approximately equal performance. Performance-wise, ADGMPSO generally outperformed the other algorithms by a wide margin.
# Power Consumption Comparison of GPU Linear Solvers for Cellular Potts Model Simulations
## 4 Results
This trend underscores the effectiveness in reducing power consumption of the custom CG solver, making it a preferable choice for energy-constrained environments. **Test 3: Power Consumption of the Overall Algorithm for the Gauss-Seidel and Custom CG Solvers. **  
Starting from the results achieved in Tables 1 and 2, it is evident that the Gauss-Seidel and custom CG solvers are the most efficient in terms of both time and energy consumption. Building on this observation, a comparative test evaluating the execution of complete GPU software implementations is introduced. The first implementation adopts the Gauss-Seidel solver, while the second utilizes the custom CG solver. The goal is to assess the overall power consumption of these full-scale GPU solutions. The results in Table 3 highlight that the GPU implementation using the custom CG solver (GPU\({}_{CG}\)) outperforms the one utilizing the Gauss-Seidel solver (GPU\({}_{GS}\)) in terms of power efficiency. For instance, at \(M=900\) GPU\({}_{CG}\) consumes 83.81 watts, which is lower than the 179.89 watts used by GPU\({}_{J}\). This trend is consistent across all the tested matrix sizes. \begin{table}
\begin{tabular}{c l c c c} \hline \hline
**M** & **Solver** & \(N_{\tau}\) **= 3000** & \(N_{\tau}\) **= 6000** & \(N_{\tau}\) **= 9000** \\ \hline
150 & [PERSON] & 2.71 & 3.94 & 5.03 \\  & LU & 2.43 & 3.04 & 3.79 \\  & Gauss–Seidel & 1.98 & 2.14 & 2.74 \\  & Custom CG & 1.39 & 1.54 & 2.22 \\ \hline
300 & [PERSON] & 3.73 & 6.04 & 5.93 \\  & LU & 2.73 & 4.64 & 4.39 \\  & Gauss–Seidel & 2.26 & 3.81 & 3.72 \\  & Custom CG & 1.75 & 2.69 & 2.54 \\ \hline
450 & [PERSON] & 6.18 & 9.62 & 10.58 \\  & LU & 4.48 & 7.67 & 9.21 \\  & Gauss–Seidel & 3.60 & 6.17 & 6.52 \\  & Custom CG & 3.01 & 5.14 & 5.81 \\ \hline
750 & [PERSON] & 16.01 & 16.48 & 17.08 \\  & LU & 11.79 & 14.29 & 15.41 \\  & Gauss–Seidel & 9.53 & 12.51 & 12.55 \\  & Custom CG & 7.06 & 10.59 & 10.90 \\ \hline
900 & [PERSON] & 18.02 & 22.99 & 25.81 \\  & LU & 14.60 & 20.08 & 22.51 \\  & Gauss–Seidel & 11.57 & 16.17 & 18.40 \\  & Custom CG & 8.39 & 13.51 & 14.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Power consumption (in watts) for different GPU solvers, Jacobi, LU, Gauss–Seidel, and custom CG, focusing on micro-computational kernels by varying time iteration \(N_{\tau}\) and number of Metropolis trials \(M\).
# Estimation of Thermal Diffusivity for Greenhouse Soil Temperature Simulation
## 2 Materials and Methods
### Data Collection  
Field experiments were conducted in a greenhouse at Wooster campus of The Ohio State University (latitude: 40.78 N, longitude: 81.93 W) with a wall height of 2.54 m, a width of 7.32 m, a length of 14.63 m, and an arch height of 2.08 m. The greenhouse has a pad and fan cooling system and a space heater heating system for temperature control. The greenhouse air temperature was measured in the middle of the greenhouse at the plants' level (1.2 m), while the soil surface temperature was measured immediately underneath a black ground cover with type K thermocouples. The soil temperature was measured in the middle of the greenhouse at depths of 0.1 m, 0.2 m, and 0.3 m using Decagon 5 TE soil moisture and temperature sensors (Decagon Devices, Inc., Pullman, WA, USA). Due to different sensor connectors, the greenhouse air and soil surface temperatures were recorded by one data logger (CR3000, Campbell Scientific Inc., Logan, UT, USA), while the soil temperatures were recorded by another data logger (EM50, Decagon Devices, Inc., Pullman, WA, USA). All the data were automatically recorded every 10 min. The outdoor weather was recorded by the Ohio Agricultural Research and Development Center (OARDC) weather station. The experiments were conducted from 1 September to 31 December 2016, and the data were separated into cooling and heating seasons for analysis.
###### Abstract  
This study proposes a comparison between two analytical techniques for edible oil classification, namely gas-chromatography equipped with a flame ionization detector (GC-FID), which is an acknowledged technique for fatty acid analysis, and Raman spectroscopy, as a real time noninvasive technique. Due to the complexity of the investigated matrix, we used both methods in connection with chemometrics processing for a quick and valuable evaluation of oils. In addition to this, the possible adulteration of investigated oil varieties (sesame, hemp, walnut, linseed, sea buckthorn) with sunflower oil was also tested. In order to extract the meaningful information from the experimental data set, a supervised chemometric technique, namely linear discriminant analysis (LDA), was applied. Moreover, for possible adulteration detection, an artificial neural network (ANN) was also employed. Based on the results provided by ANN, it was possible to detect the mixture between sea buckthorn and sunflower oil.  
oils; FAMEs; GC-FID; Raman spectroscopy; linear discriminant analysis (LDA); artificial neural network (ANN) +
Footnote †: journal: Applied Sciences
## 2 Field-Effect-Transistor Switch
In some specific cases, such as a field-effect-transistor, we may be able to derive a relation between the energy dissipation/energy-delay product and the error probability. Consider the conduction-band diagram in the channel of an \(n\)-channel field-effect-transistor along the direction of drain current flow as shown in Figure 1. In the OFF state, there is a potential barrier at the source-channel junction which prevents electrons in the source contact from entering the channel and turning the transistor ON. This barrier has to belowered by the applied gate potential \(\Delta V\) in order to allow electrons to enter the channel when the transistor has to be turned ON. Therefore, this barrier should be approximately equal to the quantity \(q\Delta V\). It is clear then that the transistor can spontaneously turn ON _while in the non-conducting state_ (causing a switching error that results in a bit error), if electrons can enter the channel from the source by thermionic emission over the barrier. The probability of entering the channel in this fashion, which is roughly \(e^{-(q\Delta V)/KT}\), is then the switching error probability \(p\). From Equation (2), we then get that the energy dissipation can be written as:  
\[E_{d}=\Delta Q\Delta V=C_{g}(\Delta V)^{2}=C_{g}[(kT/q)\ln(1/p)]^{2} \tag{5}\]  
and the energy-delay product can be written as:  
\[E_{d}\Delta t=(RC_{g})C_{g}(\Delta V)^{2}=\tau C_{g}[(kT/q)\ln(1/p)]^{2} \tag{6}\]  
where \(\tau=RC_{g}\) is the gate charging time. Equations (5) and (6) show the direct dependences of the energy dissipation and energy-delay product on the error-probability \(p\). These two equations clearly show that lower energy dissipation or lower energy-delay product are associated with higher switching error probability in a transistor switch. To provide an estimate of how much energy we must dissipate to maintain any semblance of reliability, we will assume that we can tolerate a maximum error probability of \(10^{-15}\)[5]. In modern day FINFETs, the gate capacitance may vary between 10 and 20 aF for a 0.5 \(\upmu\)m wide gate [6], and \(\tau\) is on the order of 100 ps. Using the values in Equations (5) and (6), we get that the minimum energy dissipation and the minimum energy-delay product that we can expect in this type of FET device, while maintaining minimum acceptable reliability, are \(\sim\)10 aJ and \(10^{-27}\)J-s, respectively.