esa-sceva/satcom-chunk-collection huggingface dataset:
# Feasibility of Non-Remanufactured Waste Bottle Glass as Supplementary Cementitious Material
## 1 Introduction
Previous research is also unanimous concerning improved durability when glass powder is used as a cement replacement, namely rising resistance against chloride ion penetration [21; 22; 24]. Increased GP content showed higher resistance to chloride ion penetration in mortar specimens [28; 34; 43]. Pore refinement by pore blocking and pore filling resulted in less permeable concrete containing GP, thus locking chloride ion penetration [44]. Compared with fly ash, GP improved chloride resistance but was less efficient than silica future [21; 22; 24; 29]. Higher carbonation depth on concrete/mortars were reported with the incorporation of GP [21; 22; 24] owing to the increased porosity at early ages and lower amount of Ca(OH)\({}_{2}\) in the cementitious matrix, which is typically observed when SCMs are used [30]. The increased GP dosage of up to 20% as a cement replacement in mortar reduced water absorption, which is attributed to the higher density of GP mortar mixes [30; 36]. The effect of GP on pore refinement and pozzolanic reactivity was well pronounced based on the depreciated water absorption values and voids ratio in GP-modified concrete [35]. A literature survey showed similar sorptivity results on reference and GP mortars, with 10% and 20% GP as cement replacements [21]. Other studies observed lower sorption in concrete incorporating GP due to the dense microstructure resulting from CSH formation and increased pozzolanic reaction [28; 45]. In addition, water penetration depth was lower in concrete with up to 60% glass powder than in the control mix [26; 34].
# Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks
## 4 Machine-Learning Models
### Boosting Algorithms  
Boosting is a subset of ensemble methods where a collection of models are trained sequentially to permit every model to improve and compensate for the weakness of its predecessor.  
Boosting algorithms differ in how they create and aggregate weak learners during the sequential stacking process. In our work, we used various boosting algorithms:  
Figure 2: Graphic representation of Random Forest algorithm for classification.  
* **AdaBoost**[40]: This was the first boosting algorithm developed for classification and regression tasks. It fits a sequence of weak learners on different weighted training data. It gives incorrect predictions more weight in sub-sequence iterations and less weight to correct predictions. In this way, it forces the algorithm to \"focus\" on observations that are harder to predict. The final prediction comes from weighing the majority vote or sum. The algorithm begins by forecasting the original dataset and giving the same weight to each observation. If the prediction is incorrect, using the first \"weak\" learner, the algorithm will give a higher weight value to that observation. This procedure is iterated until the model reaches a predefined value of accuracy. AdaBoost is typically easy to use because it does not need complex parameters during its tuning procedures and it shows low sensitivity to overfitting. Moreover, it is able to learn from a small set of features learning incrementally new information. However, Adaboost is sensitive to noisy data and abnormal values.
* **Gradient Boosting**[41]: This algorithm uses a set of weak predictive models, typically decision trees. Gradient Boosting trains many models sequentially that are then composed using the additive modeling property. In each training epoch, a new learner is added to increase the accuracy of the previous one. Each model gradually minimizes the whole system loss function using the Gradient Descent algorithm.
* **XGBoost** (Extreme Gradient Boosting) [42]: XGBoost is an optimized distributed Gradient Boosting library designed to be highly efficient, flexible, and portable. XGBoost minimizes a regularized (L1 and L2) objective function that combines a convex loss function based on the difference between the predicted and target outputs and a penalty term for model complexity. The training proceeds by adding new trees that predict the residuals or errors of prior trees that are then combined with previous trees to make the final prediction (4). \[F_{m}(X)=F_{m-1}(X)+\alpha_{m}h_{m}(X,r_{m-1}),\] (4) where \(\alpha_{i}\) and \(r_{i}\) are the regularization parameters and residuals computed with the \(i\)-th tree, respectively, and \(h_{i}\) is a function that is trained to predict residuals, \(r_{i}\) using \(X\) for the \(i\)-th tree. \(\alpha_{i}\) is computed using the residuals, \(r_{i}\) solving the following optimization problem: \[\arg\min_{\alpha}=\sum_{i=1}^{m}L(Y_{i},F_{i-1}(X_{i})+\alpha h_{i}(X_{i},r_{i -1}))\] (5) where \(L(Y,F(X))\) is a differentiable loss function.
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 7 Results  
Through the Monte Carlo simulation of Cherenkov events as seen by the ASTRI Mini-Array, we assessed and compared the performance of several supervised Machine Learning methods for gamma/hadron segregation and energy estimation. The methods investigated included the Random Forest method, Extra Trees method, and Extreme Gradient Boosting (XGB).  
For gamma/hadron segregation, our findings indicate that the most sensitive technique is \"GH SET 3-GH ENS 3\", a Stacking Ensemble Method composed of 42% Extra Trees, 28% Random Forest, and 30% XGB on the set of parameters \"GH SET 3\" (see Figure 2). Comparatively, this ensemble method delivered better performance than each of the individual methods. The optimal weightings for this ensemble were determined through a series of trials and cross-validation tests, ensuring maximum performance for gamma/hadron separation.  
Regarding energy estimation, our results show that the most effective technique is \"EN SET 1-EN ENS 4\", a Stacking Ensemble Method composed of 45% XGB, 27.5% Extra Trees, and 27.5% Random Forest on set of parameters \"EN SET 1\" (see Figure 2). Similar to the gamma/hadron segregation ensemble, the optimal weightings for the energy estimation ensemble were derived from extensive testing and fine-tuning, leading to improved energy estimation performance compared to any single method alone.  
To further evaluate the performance of the ensemble methods, we compared them to the more classical Random Forest method as implemented in A-SciSoft. Our results demonstrate that the Stacking Ensemble methods proposed in this study outperform the Random Forest method for the ASTRI Mini-Array event reconstruction both for the energy resolution and bias and for the gamma/hadron discrimination.  
In summary, our results indicate that the Stacking Ensemble methods identified in this study deliver superior performance for gamma/hadron segregation and energy estimation compared to individual and classical methods. These findings suggest the potential for these ensemble methods to enhance the analysis of data collected by the ASTRI Mini-Array, although further validation with real observational data is needed to confirm their effectiveness in practice.
# Hybrid Electrostatic-Atomic Accelerometer for Future Space Gravity Missions
### Hybrid Atomic-Electrostatic Accelerometer Concept
This link is materialized by the interferometer laser beam of the atomic instrument that could be retro-reflected directly on the electrostatic accelerometer proof-mass. In this configuration, the EA proof-mass becomes the reference for the atomic measurement. The output signal from the atomic sensor gives the differential acceleration between the free-falling atomic cloud and the EA proof-mass. It offers the possibility to rotate dynamically the CAI reference mirror during the measurement in-orbit and to compensate for the detrimental impact of satellite rotation on the atomic signal [13; 55; 56; 57; 58]. This method of operation is currently at a very exploratory stage to assess its potential for space applications. One has for instance to guarantee that, during rotation of the proof-mass, the EA is still measuring the three-axis accelerations with the same performance. As will be shown further on in the article, the use of the proof-mass as the reference mirror for the CAI and the need to rotate this mirror to counteract the satellite rotation lead to a degradation of the EA noise along one axis corresponding to the radial direction. A more conservative approach to atomic-electrostatic hybridization would be to operate two independent accelerometers whose output signals are merged through an adequate hybridization algorithm. In that case, the atom interferometer would dispose of its own mirror mounted on a piezo-driven tip-tilt stage [55; 58; 59; 60] to rotate the mirror and compensate the satellite rotation impact mainly due to the Coriolis effect. In this configuration, the EA performance could be similar for the three axes and its design simplified since there is no need to accommodate an optical access for the CAI laser. In this article, we only consider the more exploratory concept where the EA proof-mass is used as the atom interferometer mirror.
# Low-Cost Dual-Frequency GNSS Receivers and Antennas for Surveying in Urban Areas
## 3 Results and Discussion
### Results from Quality Control of GNSS Observations
#### 3.1.2 Multipath Analysis  
Multipath error is another important indicator that can influence the positioning solution, especially in urban areas. Low-cost antennas are more sensitive to multipath effects due to their quality and design, so the use of a ground plane is usually recommended. To better analyze the difference and the impact of the antenna, we used the same receiver in combination with the geodetic and the low-cost antenna, while a geodetic GNSS receiver was used as a reference for comparison.  
The code multipath error for the open sky is shown graphically in Figure 7 and for urban conditions in Figure 8. The RMSE values of the multipath for both scenarios are shown in Table 2. Results show that LC\(-1\) and LC\(-2\) have very similar performances both in open-sky and in urban conditions. The use of a geodetic antenna does not significantly improve the performance of low-cost GNSS receivers in any scenario. The RMSE for LC\(-1\) and LC\(-2\) is worse in urban conditions than in open-sky conditions, which has more influence than the type of antennas used.  
Figure 5: Sky plot of C/N\({}_{0}\) for LC\(-1\).  
Figure 6: C/N\({}_{0}\) for observations in F1 and F2 frequency in the urban areas: (**a**) F1 C/N\({}_{0}\) for LC\(-\)1, (**b**) F2 C/N\({}_{0}\) for LC\(-\)1, (**c**) F1 C/N\({}_{0}\) for LC\(-\)2, (**d**) F2 C/N\({}_{0}\) for LC\(-\)2, (**e**) F1 C/N\({}_{0}\) for GD\(-\)1, and (**f**) F2 C/N\({}_{0}\) for GD\(-\)1.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Scenarios** & **Frequency** & **LC\(-\)1** & **LC\(-\)2** & **GD\(-\)1** \\ \hline Open sky & F1 & 0.50 m & 0.51 m & 0.16 m \\  & F2 & 0.48 m & 0.57 m & 0.23 m \\ Urban area & F1 & 0.86 m & 0.90 m & 0.23 m \\  & F2 & 0.76 m & 0.70 m & 0.31 m \\ \hline \end{tabular}
\end{table}
Table 2: RMSE for multipath error in open-sky and urban conditions.  
For the geodetic GNSS devices used (GD\(-1\)), the RMSE in the open sky is two times better than for LC\(-1\) and LC\(-2\), while in urban areas with more multipath error, the RMSE for GD\(-1\) is 2-4 times better. The difference in RMSE for GD\(-1\) under both conditions is not as significant as for LC\(-1\) and LC\(-2\).  
Figure 7: Multipath for code observations in F1 and F2 frequency in the open sky: **(a)** F1 multipath for LC\(-1\), **(b)** F2 multipath for LC\(-1\), **(c)** F1 multipath for LC\(-2\), **(d)** F2 multipath for LC\(-2\), **(e)** F1 multipath for GD\(-1\), and **(f)** F2 multipath for GD\(-1\).
# The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
## 3 Materials and Methods
### Statistic Analysis  
The statistical analysis was performed on the normalized wavelengths. The 940 nm wavelength is used as a reference to present the results, enabling consistent comparisons among SQIs. The data were divided into five ranges based on normalized CF, ranging from 0 to 1 in increments of 0.2. The analysis also included the mean and SD to describe the centrality and dispersion of the data. Boxplots were created for each statistical and signal metric to visualize the results, organized by levels of normalized CF. Each plot features visuals indicating the median, IQR, and ends of the \"whiskers,\" which extend up to 1.5 times the IQR. In interpreting the boxplot elements, the analysis also considers the presence and implications of outliers.
Another advantage of dynamic navigation is that the implant is placed with a wide area of visibility and thus remains controllable. Moreover, the planned placement of the implant can be modified intraoperatively during the intervention according to the three-dimensional position [7; 8]. A recent systematic review demonstrated that dCAIS systems allow high accuracy during implant placements, accompanied by mean angle of deviation of less than 4\({}^{\circ}\) and superior accuracy with respect to freehand placement; the angle of deviations is slightly reduced with respect to the sCAIS system [9]. However, due to the complexity of the surgical procedure, sufficient training of the surgeon and team is required along with a significant procedure learning curve. The aim of this proof-of-concept study is to assess the clinical and radiographical performance and accuracy of the surgical workflow during maxillary immediate implant placement aided by DTX studio Implants planning software and X-Guide Navigations.
## 2 Materials and Methods
### Gridded Precipitation Products
#### 2.3.4 Imrg  
IMERG is available at [[https://giovanni.gsfc.nasa.gov/giovanni/](https://giovanni.gsfc.nasa.gov/giovanni/)]([https://giovanni.gsfc.nasa.gov/giovanni/](https://giovanni.gsfc.nasa.gov/giovanni/)) (accessed on 3 June 2021). The GPM project, a collaboration between the National Aeronautics and Space Administration (NASA) of the United States and the Japan Aerospace Exploration Agency (JAXA), began in 2014 to provide half-hourly global precipitation data with a spatial resolution of 0.1\({}^{\circ}\)[52]. The GPM satellite is equipped with two major sensors: the GPM Microwave Imager (GMI), which measures precipitation intensity, depth, and duration, and the Dual-frequency Precipitation Radar (DPR), which observes storm internal structure within and beneath clouds [53]. The GPM Constellation provides three levels of data processing (IMERG products), but the most commonly used are the gridded products that combine GMI and DPR rainfall averages or rainfall estimates combined from data of all active and passive microwave instruments in the GPM Constellation [52, 54]. There are three daily IMERG products: IMERG Early Run (near real-time with a latency of 4 h), IMERG Late Run (reprocessed near real-time with a latency of 14 h), and IMERG Final Run (gauged-adjusted with a 4-month latency) [54]. In this study, we selected the IMERG-v06 Final Run half-hourly products [53].
## 3 Results and Discussion  
### Experiment Result  
The fabricated structure of the antenna is shown in Figure 2c. The reflection and transmission coefficients are measured using a vector network analyzer (Anritsu MS 46122B, Anritsu Company, Morgan Hill, CA, USA). The measured and simulated results are plotted in Figure 5, which demonstrates that the measured reflection coefficients (\(\left|\,\mathrm{S_{11}}\,\right|,\mathrm{S_{22}}\,\right|\)) are under \(-10\) dB and transmission coefficients (\(\left|\,\mathrm{S_{12}}\,\right|,\mathrm{S_{21}}\,\right|\)) are under \(-15\) dB from 23.4 to 24 GHz, implying good impedance match and port-to-port isolation, respectively. The measured transmission coefficients show a slight discrepancy compared to the simulated data, with the measured values consistently lower by a few dB across the entire bandwidth. This difference can be attributed to various factors, including unaccounted dielectric losses, slight misalignment of the connectors, and the inherent sensitivity of the fabrication process at mm-wave frequencies. The radiation characteristics of the fabricated proposed antenna are measured in an anechoic chamber. To conduct the measurements, the proposed antenna is mounted on a plastic jig, as depicted in Figure 6. The radiation pattern in the azimuthal direction is measured by incrementally rotating the positioner at \(1^{\circ}\) intervals. Figure 7 shows the simulated and measured radiation patterns at 23.4, 23.7, and 24 GHz, which exhibit a generally similar trend, except that the variations in the measured radiation patterns of cross-polarization can be attributed to unwanted wave reflections and interferences from the jig or terminator/connector and other objects in the apparatus. The maximum gain is measured to be 11.34, 11.08, and 11.41 dBi at 23.4, 23.7, and 24 GHz, respectively. Figure 8 shows the simulated and measured axial ratio from 23.5 to 24 GHz.  
Figure 4: Simulated (**a**) total efficiency and axial ratio; (**b**) transmission coefficients depending on \(L_{c}\).  
Figure 5: Simulated and measured S-parameters. The shaded area is the operational bandwidth.  
Figure 6: Photograph of measurement setup in an anechoic chamber.  
Figure 7: Simulated and measured radiation patterns: (**a**) 23.4; (**b**) 23.7; (**c**) 24 GHz.
# ISO 50001 Data Driven Methods for Energy Efficiency Analysis of Thermal Power Plants
## 4 Results and Discussion
The 14-neuron single-layer ANN is trained on the whole dataset, but the single-layer 70-neuron ANN and the two-layer ANN are trained on a smaller dataset. Since the 14-neuron single-layer network is about 5 times smaller than the 70-neuron single-layer ANN, the 70 neurons and the two-layer ANN are trained on a dataset that is reduced accordingly with respect to the original whole dataset. Figure 8: Different neural networks’ test performances. Figure 7: Plant A acceptance bands (%) for energy consumption. The results in Figure 11 highlight how the 70-neuron ANN requires in any case the highest computational effort even in these conditions. The two-layer ANN is instead faster with respect to the 14-neuron single-layer ANN in the case of different training datasets. For the development of the cooling towers model, we chose the 14-neuron single-layer ANN. Figure 11: MSE error for different initializations of the 14-neuron single-layer ANN. Figure 9: Comparisons of different computational efforts with the same training dataset. Since the results of the network training are dependent also on the initialization values that are stochastically determined, the selected network has been trained multiple times and the results have been recorded. Figure 11 shows the results of the MSE for 30 different training sessions. The y-axis is set in a logarithmic scale to include the complete range of the errors. The convergence of the results is in all cases on the order of fractions of a second. The MSE value is always around \(10^{4}\), which corresponds to a percentage error of about 7-10%, as shown in the comparison reported in Table 2.  
Following the analysis described in the previous Section 3, to reduce the input data set complexity by two orders of magnitude, in a structured multi-level energy analysis, the ANN was designed with the aim of improving the predictive capability of power consumptions with respect to traditional models currently used in each plant. The optimal neural network, with 10 neurons in the input layer, 14 in the hidden one and 1 output (total current adsorption for the 16 fans, in Amps), selected after a comparative study on different structures, was trained and tested on the filtered dataset with the behavior reported in Figure 12; the resulting accuracy is reported in Table 2 in terms of the R coefficient, MAE (Mean Absolute Error), MSE and MAPE (Mean Absolute Percentage Error). Figure 12: Optimal neural network predictive performance. All the errors in training, validation and testing are centered on and contained in a tall and narrow bell-shaped form. This indicates that the result is almost symmetrical, and the network is well centered on the filtered plant input dataset. The neural network shows a good accuracy since the R coefficient is around 0.97 in the training and testing sessions.
# Regressive Machine Learning for Real-Time Monitoring of Bed-Based Patients
## 3 Results
### Cross-Validation  
The BR, our chosen model, was evaluated using a KFold cross-validation technique. This involved dividing the dataset into 'k' subsets. Each unique subset served as a test dataset, with the remaining subsets forming the training dataset. The model was fitted on  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **Accuracy** & **R\({}^{2}\) (Train)** & **R\({}^{2}\) (Test)** & **MSE (Train)** & **MSE (Test)** \\ \hline DTR & 0.8362 & 1.000 & 0.9035 & 0.000 & 0.3017 \\ GBR & 0.8621 & 0.9925 & 0.9702 & 0.0219 & 0.0932 \\ BR & 0.8621 & 0.9915 & 0.9592 & 0.0247 & 0.1276 \\ \hline \end{tabular}
\end{table}
Table 2: Performance comparison of Decision Tree Regressor, Gradient Boosting Regressor, and their ensemble, without SMOT.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Accuracy** & **R\({}^{2}\) (Train)** & **R\({}^{2}\) (Test)** & **MSE (Train)** & **MSE (Test)** \\ \hline DTR & 0.892 & 1.000 & 0.939 & 0.000 & 0.167 \\ GBR & 0.908 & 0.990 & 0.943 & 0.031 & 0.156 \\ BR & 0.950 & 0.996 & 0.959 & 0.012 & 0.112 \\ \hline \end{tabular}
\end{table}
Table 3: Performance comparison of Decision Tree Regressor, Gradient Boosting Regressor, and their ensemble, with SMOT.  
the training set and evaluated on the test set, a process repeated 'k' times. Therefore, the number of runs for the BR method equates to the number of folds in the cross-validation. The resulting mean and standard deviations are detailed in Table 4. The DTR and GBR were not subjected to these calculations before the decision to proceed with BR was made. The choice of BR over DTR and GBR was based on specific considerations, which are outlined and defended above. The mean and standard deviations of DTR and GBR, which were not used further, did not influence this decision.  
Table 4 further compares the performance of a model with and without the SMOT. The metrics used for comparison are the mean and standard deviation. The mean represents the average performance of the model, while the standard deviation measures the variability of the model's performance.  
It can be observed that the model performs better when SMOT is used, as indicated by the higher mean score. The standard deviation is also lower with SMOT, suggesting that the model's performance is more consistent when this technique is used.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 3 Resource Management Techniques in C-RAN
### Radio Resource Management Techniques  
Many techniques have been proposed by different researchers for radio resource management C-RAN by optimizing different metrics and following various constraints to enhance QoS, increase energy efficiency, and reduce cost. In this section, the state-of-the-art RRM techniques (categorized into power control schemes, joint optimization schemes, and sum-rate optimization) are reviewed considering the main objectives of the work, problem formulation, and techniques used in the work. Moreover, the evaluation techniques used in the reviewed works and the performance metrics considered for evaluating the schemes are discussed separately in Section 3.1.2.