esa-sceva/satcom-chunk-collection huggingface dataset:
# Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms
## 2 Dataset and Methods
### Machine Learning Algorithms  
The field of machine learning (ML) encompasses the development of algorithms with the capacity to learn and make predictions from data without human intervention. Essentially, machine learning algorithms discover patterns and relationships by analyzing datasets and use this information to make predictions or decisions on new data. Among the most prevalent uses of machine learning is regression. Regression is a machine learning task that aims to predict numerical values. For example, predicting the cost of a house based on its characteristics (e.g., size, location, and age) is a regression problem. The algorithm learns the relationship between features and target values in the given dataset and uses this information to make predictions on new data. Regression is used in a variety of areas, from financial forecasting to risk assessments in the healthcare industry.  
The success of machine learning depends on selecting the right datasets, using effective algorithms, and constantly evaluating the performance of the model. This process is becoming increasingly important in more industries due to its capacity to understand and analyze the complexity and diversity of data [48; 49].  
Machine learning algorithms such as random forest, gradient boosting, and AdaBoost each have their own strengths and weaknesses. The random forest algorithm reduces the risk of overfitting by using multiple decision trees and generally produces more stable results. However, when the model contains too many trees, the training time can be longer, and its performance may slow down on large datasets. The gradient boosting algorithm can provide high accuracy by correcting errors at each step and is particularly effective on complex datasets. However, its downside is that training can be time-consuming, and it may be prone to overfitting. The AdaBoost algorithm focuses on boosting simple models; while it can be effective on low-complexity datasets, it is sensitive to noisy data and may amplify small errors in the dataset. The choice of these models depends on the structure of the dataset and the nature of the problem.
## 2 Materials and Methods
### Demonstration of Two-Stage Cultivation  
A two-stage cultivation process was demonstrated using the organic carbon, nitrogen, and phosphorous from biological waste. For two simultaneous first-stage mixotrophic cultures, ALP2 from maintenance culture was centrifuged, washed twice in PBS, and inoculated at an \(OD_{680}\) = 0.50 into two foam-capped 50 mL flasks. These two flask cultures were agitated at 150 rpm on orbital shakers under 75 \(\mu\)mole m\({}^{-2}\)s\({}^{-1}\) light intensity from overhead polychromatic fluorescent bulbs and contained 25-30 mL of 0.2 \(\mu\)m-filtered BG-11\({}_{0}\) adjusted to pH = 7.0 with 1 M NaOH, supplemented with 1.587 g L\({}^{-1}\) urea and either (1) torrefied wheat-straw hydrolysate diluted to 10 g L\({}^{-1}\) glucose or (2) torrefied-wheat-straw condensate diluted to 0.35 g L\({}^{-1}\) potassium acetate. Such dilutions of condensate and hydrolysate further ensured lower concentration of inhibitory compounds (i.e., phenolics, furuns (HMF and aldehydes)) for microalgal cultivation. (It contained 25 mL of modified BG-11 medium supplemented with 0.250 m L of purified condensate representing a dilution to approximately 0.35 g L\({}^{-1}\) potassium acetate). These were cultivated mixotrophically to the near-stationary phase. The cells from these two cultures were then washed once with PBS to remove any residual nitrogen, organic carbon, and phosphate. They were then inoculated at \(OD_{680}\) = 0.250 each for a combined total of \(OD_{680}\) = 0.500 corresponding to a cell density of \(2.5\times 10^{6}\) cells mL\({}^{-1}\) into a second-stage, foam-capped 250 mL Erlenmeyer flask. This second-stage flask culture was cultivated phototrophically at 22 \({}^{\circ}\)C under 75 \(\mu\)moles m\({}^{-2}\)s\({}^{-1}\) photon flux density from overhead polychromatic fluorescent light bulb, agitated at 150 rpm on orbital shaker, and contained 100 mL of BG-11\({}_{0}\) medium devoid of KH\({}_{2}\)PO\({}_{4}\) and supplemented with 16.8 g L\({}^{-1}\) NaHCO\({}_{3}\) and 2.5% (\(v/v\)) of 0.2 \(\mu\)m-filtered anaerobically digested food waste effluent, at a pH 10 that was daily adjusted initially with 0.5 M HCl acetic acid. An exponential fed-batch strategy [37; 38] for ammonia-containing food waste effluent was subsequently used and expressed as follows Equation (8):  
\[m_{t}=m_{0}e^{kt} \tag{8}\]
# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 3 Analysis of the State of the Art
### Research Methodology
#### 3.2.3 Creation and Use of Datasets  
In many works, the starting point for building ML models was literature data, measurement data, or data generated from simulations. In Ref. [19], 2234 data points from the literature were used to train the GA-ANN model for combustion velocity. In Ref. [75], SVM models were trained on data from 147 tests with seven types of biofuels. Another study [76] collected CFD data (576 cases) to train ANNs to optimize fuel injection strategies with varying reactivity in the DDFS engine. In Ref. [77], the gas engine test data was used to train an ML model to predict IMEP. In Ref. [78], operational data from a coal-fired power plant was used to train an ANN model for optimizing combustion while maintaining environmental requirements (reduction in NOx emissions and ammonia consumption). Also, in Ref. [79], data from the industrial LIBS spectroscopy system was automatically analyzed and processed by synergistic regression (SR) algorithms to assess coal quality. In Ref. [80], an ANN model was developed that predicts as many as eight parameters of the operation of a natural gas-powered engine. Inputs such as ignition angle, lambda, and rotationalspeed were used for training. Ref. [70] used data from mahua wood gasification and trained RF and AdaBoost models to predict hydrogen yield and lower calorific value. In Ref. [81], physicochemical data of gasoline components was used to build a random forest model for predicting the octane number. A similar approach was used in Refs. [31; 82], where experimental or simulation data supported the development of models for the prediction of combustion parameters and emissions. Ref. [83] concerns the multi-criteria optimization of the operating parameters of a Wankel engine with the addition of hydrogen using SVM and a genetic algorithm. The regression model predicted emissions, efficiency, and combustion with an error of \(<\)1%. In Ref. [84], CFD simulations were performed for the GCI engine, analyzing the effects of gasoline-ethanol and butanol mixtures on soot emissions and combustion phasing. It was found that soot emissions depend on both the chemical and the physical properties of the fuel, especially with earlier injection times.
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 4 Materials and Methods
### Ontology-Based Stock Selection
#### 4.3.6 Strategies for Robustness in Extreme Scenarios and Real-Time Applications  
The framework was created to manage market dynamics, particularly during times of high volatility or macroeconomic shocks. To assess the model's robustness in extreme scenarios, indices sensitive to sudden changes--such as the _VIX_, _ILLIQ_, and _GARCH_--were incorporated into its evaluation process.  
In the context of real-time applications, the following strategies are proposed to enhance robustness and efficiency:  
* Incremental Updates: Instead of reprocessing the entire pipeline, partial updates of indices and the Non-Stationary Markov Chain (NMC) can be performed, significantly reducing latency during execution.
* Modular Inference: The separation of the framework's modules allows BERT inferences and NMC probabilistic calculations to be executed in parallel, optimizing response time and scalability.
* Computational Optimization: The use of tools like _TensorRT_ and _ONNX Runtime_ for optimized model deployment improves computational efficiency by reducing latency and maximizing the utilization of available hardware resources. For instance, as demonstrated by [PERSON] (2023) [42], converting models to the _ONNX_ format and deploying them on dedicated hardware, such as the Jetson Nano, achieved significant performance gains, increasing the frame rate from 1-2 fps to 30-43 fps and GPU utilization up to 99%. Additionally, [PERSON] (2024) [43] showed that _TensorRT_ effectively optimizes inferences for complex models, reducing latency and optimizing memory consumption in embedded devices.
* Stress Testing Simulations: Adding simulations of extreme scenarios to the training and validation process, such as market crashes or abrupt changes in economic indices, allows for evaluating and strengthening the framework's resilience under adverse conditions.  
These strategies ensure that the framework is applicable not only in experimental environments but also in production contexts that demand high performance, such as real-time trading, where latency and robustness are critical for success.
# Improved Proactive Routing Protocol Considering Node Density Using Game Theory in Dense Networks
## 3 Materials and Methods
### OLSR Improvement Selection
#### 3.2.3 Proposed Example  
Let us consider a highly dense network where the source node wants to send packets to the destination node. According to our algorithm, all nodes will calculate the number of HELLO messages received from its neighbours. This is denoted as the node_density. If the node_density is above a threshold, then the node willingness is set to WILL_ALWAYS. However, if the node_density is below or equal to the threshold, the node willingness is set to WILL_NEVER. For example, as seen in Figure 4, if the threshold is 5, the node on the left willingness is increased to 7 while the one on the right is set to 0. Thus, only nodes that have more one-hop neighbours has its MPR willingness increased to 7 and those with lesser one-hop neighbours has its willingness set to a value of 0. This game is played every 5 seconds in each node. Every node has to update its willingness value based on the terminal count, which is the number of HELLO messages received from its one-hop neighbours
## 5 Security Analysis and Comparison
### Compression According to Different Singular Values  
The main advantage of using SVD for compression gives freedom to a user. Users may choose the appropriate compression level with encryption according to the different singular values, as displayed in Table 7. Figure 16 illustrates the PSNR of the cameraman image at various singular values. Figure 17 shows the SSIM of the cameraman image on various singular values, and Figure 18 shows the PSNR and SSIM of the various test images on 150 singular values.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Methodologies** & **Proposed** & [8,33] & [34] & [35] & [36] & [37] & [38] & [39] & [29] \\ \hline Keyspace & \(2^{498}\) & \(2^{300}\) & \(2^{276}\) & \(2^{232}\) & \(2^{398}\) & \(2^{363}\) & \(2^{397}\) & \(2^{426}\) & \(2^{478}\) \\ \hline \end{tabular}
\end{table}
Table 6: Keyspace comparison of the proposed scheme with different literature studies.  
\begin{table}
\begin{tabular}{c c c} \hline
**Test** & **Values** & **Results** \\ \hline Frequency & 0.0359 & Success \\ Block Frequency & 0.2714 & Success \\ Cumulative Sums Forward & 0.0120 & Success \\ Cumulative Sums Reverse & 0.5171 & Success \\ Runs & 0.8475 & Success \\ Longest Run & 0.4237 & Success \\ Rank & 0.2785 & Success \\ FFT & 0.5650 & Success \\ Overlapping Template & 0.3225 & Success \\ Approximate Entropy & 0.2464 & Success \\ Linear Complexity & 0.1145 & Success \\ Serial & 0.1454 & Success \\ \hline \end{tabular}
\end{table}
Table 5: The outcomes of NIST tests on the suggested approach.
# A Feasibility Study of a Novel Piezo MEMS Tweezer for Soft Materials Characterization
###### Abstract  
The opportunity to know the status of a soft tissue (ST) in situ can be very useful for microsurgery or early diagnosis. Since normal and diseased tissues have different mechanical characteristics, many systems have been developed to carry out such measurements locally. Among them, MEMS tweezers are very relevant for their efficiency and relative simplicity compared to the other systems. In this paper a novel piezoelectric MEMS tweezer for soft materials analysis and characterization is presented. A theoretical approach has developed in order to carry out the values of the stiffness, the equivalent Young's modulus, and the viscous damping coefficients of the analyzed samples. The method has been validated by using both Finite Element Analysis and data from the literature.  
Keywords:MEMS; tweezer; piezoelectric; soft tissue; microsurgery +
Footnote †: journal: Applied Sciences
# Spatial and Momentum Mapping Modes for Velocity Map Imaging Spectrometer
## 3 Simulation
### Spatial Mapping Mode  
The spatial mapping mode is implemented to magnify the image of the zero kinetic energy ions with different scale factors. The interaction region is defined as a \(3\times 9\) point array in a \(1\times 3\) mm\({}^{2}\) region, which is larger than the interaction region defined by the laser beam waist of 68 um and the gas jet diameter of 1.2 mm. For a scaled factor, several hundred thousand iterations are needed to reach the linearly zoomed image. So, the simplified source definition is preferred rather than the various complicated distributions predefined in SIMION. Multiple simulations are conducted to achieve magnification factors of 2.2, 2.8, 5.9, and 7.6. The distance of the electrodes to the reaction region, the thicknesses and the inner diameter of each electrode, and the values of electrode voltages are summarized in Table 1. For larger scale factors, ions are decelerated more between electrodes 9 and 10, which may result in a larger microlens effect [26]. Figure 2 shows the simulated ion flight trajectory at the magnification factor of 7.6.  
In order to check the linearity in the magnification, we plot the final position \(X_{f}\) against the initial position \(X_{i}\) in Figure 3 for all the magnification factors. Then, we fit each data set with a function:  
\[X_{f}=k_{1}X_{i}+k_{2}X_{i}^{3}. \tag{1}\]  
where \(k_{1}\) and \(k_{2}\) are the coefficients. The even-order terms are excluded due to the rotational symmetry. The fitting results are \((k_{1},k_{2})=(2.18,1.03\times 10^{-3})\), \((2.84,1.53\times 10^{-3})\), \((5.93,1.15\times 10^{-2})\) and \((7.58,1.54\times 10^{-2})\), respectively, which indicates excellent linearity in the magnification. The linear magnification in the spatial mapping mode guarantees low distortion in the acquired image.  
Figure 2: Simulation of the spatial mapping mode with a scale factor of 7.6. The source region is defined as \(1\times 3\) mm\({}^{2}\). The black lines represent the trajectories of the ion and the red lines represent the equipotential curves of the electric field.
# Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting
## 3 Methodologies
### Random Forest  
Random forest (RF) is _one_ of the most powerful machine learning models for predictive analysis, comprising of a range or group of simple trees. RF is an enhancement of the decision tree model based on the method of bagging (bootstrapping + aggregating). The bagging method may constitute the overfitting problem of computer models. In bagging, multiple stochastic error scenarios are performed by choosing training sets individually and randomly from the total historical observation set; aggregating the forecast of each individual trained model is accomplished by taking its arithmetic mean value. The decision tree model is chosen as the individual model of forecast in the RF-based model. In addition to forecasting, RF can also assess the significance of explaining variables to the forecast according to the simple rule, \"the more relevant the explanatory variable, the more important the effect on the forecast\" in order to use the RF-based model for the selection of variables. In bootstrap sampling, the total observations are divided into two groups for each individual model: in-bag subset (i.e., training subset) and out-of-bag subset. The out-of-bag subsets may be used to determine the significance of each explanatory variable: (1) randomizing the values for one chosen explanatory variable in the out-of-bag subset; (2) using the randomized out-of-bag subset and the original sample to make new predictions; (3) testing the significance of the chosen explanatory variable by increasing the mean square error of the new forecast. A detailed description of RF can be found in [86, 87, 88]. The current study employs the random forest package [88].
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
###### Abstract  
Doppler LiDARs have become flexible and versatile remote sensing devices for wind energy applications. The possibility to measure radial wind speed components contemporaneously at multiple distances is an advantage with respect to meteorological mass. However, these measurements must be filtered due to the measurement geometry, hard targets and atmospheric conditions. To ensure a maximum data availability while producing low measurement errors, we introduce a dynamic data filter approach that conditionally decouples the dependency of data availability with increasing range. The new filter approach is based on the assumption of self-similarity, that has not been used so far for LiDAR data filtering. We tested the accuracy of the dynamic data filter approach together with other commonly used filter approaches, from research and industry applications. This has been done with data from a long-range pulsed LiDAR installed at the offshore wind farm 'alpha ventus'. There, an ultrasonic anemometer located approximately 2.8 km from the LiDAR was used as reference. The analysis of around 1.5 weeks of data shows, that the error of mean radial velocity can be minimised for wake and free stream conditions.  
Keywords:data density; spatial normalisation; temporal normalisation; carrier-to-noise-ratio; line-of-sight velocity; radial velocity; threshold filter +
Footnote †: journal:
## 3 Underwater Routing Protocols
### Gedar  
The authors in [43] propose a geographic and opportunistic routing with depth adjustment-based topology control for communication recovery (GEDAR). The protocol forwards packets from the source to the destination in a greedy and opportunistic fashion. A source node selects the set of forwarding nodes based on packet advancement. The packet advancement is obtained by subtracting the distance between a neighbor node and the destination from the distance between the source node and destination. In the recovery mode, when a node finds itself in the void region (has no neighbors at all), it informs its two-hop neighbors. Based on the received location information of the two-hop neighbors, it decides the new depth location to avoid the void zone. The selection of the forwarder set can be done at the source, relay or receiver side. Furthermore, the coordination methods among the forwarder nodes are either timer based or control packet based. The latter, however, leads to high energy consumption as the exchange of control packets among the sensor nodes consumes energy. In addition, the opportunistic routing selects nodes close to the water surface, which leads to unbalanced energy consumption.
## 3 Antenna Characterization
### Antenna Scattering Parameters and Gain  
Because of the longitudinal symmetry, the matrix of the 2-port scattering parameters is expected to be bisymmetric, meaning that \(S_{11}=S_{22}\) and \(S_{12}=S_{21}\). Therefore, Figure 5 only shows the simulated and measured scattering parameters \(S_{11}\) and \(S_{21}\). For \(S_{21}\), a very good agreement between simulated and de-embedded measured scattering parameters was observed. Here, waveguide losses of on average 0.658 dB/cm were de-embedded from \(S_{21}\). These losses were experimentally determined using the TRL test structures with different lengths. A substantial difference from the low simulated SIW losses around 0.2 dB/cm [17], which are based on the data sheet values, was observed. However, it has been shown that the data sheet values, which are determined for 10 GHz, significantly underestimate the dissipation factor of the used laminate at 60 GHz [21]. This results in measured transmission line losses, which are larger than expected from the simulations. For example, for a similar laminate in [20], a microstrip line loss of about 1 dB/cm at 50 GHz was experimentally determined. For \(S_{11}\), some deviations were observed, which can at least in part be attributed to the screw-on EICs. During the measurements, it was observed that the precise screwing of the ELCs affect the position and height of the reflection peaks. Notably the fabricated SIW LWA achieved a low \(S_{11}\) around broadside, where resonant matching of the radiating elements and CL transition is designed.  
Figure 4: Photograph of the antenna assembly consisting of the fabricated SIW LWA PCB with coupled-line (CL) transition, brass bracket and coaxial end launch connector (ELC).  
Figure 3: Schematic diagram of the antenna measurement setup.  
In Figure 6, the simulated and measured spectral antenna gain is depicted between 50 and 67 GHz. As can be seen, the SIW LWA shows a maximum gain of 15.4 dBi at 62 GHz. The gain is relatively flat over the frequency range with a variation of only \(+/-1.5\) dB from the mean gain of 14.0 dBi. The measured values show no major gain degradation at broadside frequency which is traced back to unit cell asymmetry.
## 3 Data Analysis Approach
### Multivariable Regression Analysis  
Multiple regression analysis is used to determine the dependency of surface roughness on selected machining parameters and vibrations in the machining. Besides the main influences of these variables, the effects of their interactions were included in the analysis.  
In the response surface methodology, all independent variables are correlated to the response as  
\[y=\phi(x_{1},x_{2},\ldots\ldots x_{n})+\varepsilon \tag{1}\]  
where \(y\) is the desired model of surface roughness, \(\phi\) is the response surface function to be determined by the independent variables \(x_{i}\), and \(\varepsilon\) is the statistical error of the measurements. The response surface function can also be defined as a linear combination of several independent variables with their interactions and one dependent variable. An explicit mathematical quadratic form is given by  
\[y=b_{0}+\sum_{i=1}^{N}b_{i}x_{i}+\sum_{i=1}^{N}b_{i}x_{i}^{2}+\sum_{i}\sum_{j}b _{i}b_{j}x_{i}x_{j}+\varepsilon_{i} \tag{2}\]  
where \(b_{i}\), \(b_{i}\), and \(b_{ij}\), are coefficients of the linear, quadratic, and interaction terms, respectively; \(x_{i}\) and \(x_{j}\) are input variables; and \(N\) is the number of the independent variables.  
In this study, the relationship between \(R_{a}\) is correlated to the cutting parameters such as cutting depth (\(a_{p}\)), spindle speed (\(n\)), and feed rate (\(f\)) using a nonlinear quadratic model and power law model, respectively.  
\[R_{a}=\ \beta_{0}+\beta_{1}a_{p}+\beta_{2}n+\beta_{3}f+\beta_{4}\cdot a_{p} \cdot n+\beta_{5}\cdot n\cdot f+\beta_{6}\cdot a_{p}\cdot f+\beta_{7}\cdot a_{ p}\cdot f\cdot n \tag{3}\]  
\[R_{a}=\beta_{0}\cdot a_{p}\,^{\beta 1}\,\cdot n\,^{\beta 2}\cdot f\,^{\beta 3} \tag{4}\]  
In the above equation, the influences of individual parameters and the interactions between them on the surface quality are considered. The multiple linear regression analysis is performed to determine the values of the coefficients with the least squares method.  
Figure 5: Morphologies of the machined surfaces under different cutting parameters.