esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Inoculum and Substrate  
The inoculum used in the current study originated from an anaerobic thermophilic upflow packed bed reactor, able to sufficiently treat OMW under short hydraulic retention time [7]. Prior to DF experiments, the inoculum was thermally pretreated at 100 \({}^{\circ}\)C for 20 min and subsequently added into the batch reactor in a ratio of 15% \(v/v\). Such inoculum pretreatment was chosen for the enrichment of the H\({}_{2}\)-producing bacteria through the methanogens and H\({}_{2}\)-consumers deactivation, and a ratio of 15% \(v/v\) was chosen according to previous research studies [24; 28]. The characteristics of the inoculum are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Run** & **A** & **B** & **C** & **D** & **E** & **F** & **G** \\ \hline pH & 4.5 & 5.0 & 6.0 & 7.0 & 7.5 & 6.0 & 6.0 \\ Average organic loading (g COD/L) & 40 & 40 & 40 & 40 & 40 & 125 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Presentation of the tested runs under several pH and organic loading values.  
Figure 1: Influent and experimental conditions, and reactor configuration for the thermophilic DF of OMW.  
The OMW, which was used for the batch tests, originated from a three-phase olive mill, located in Patras (Achaia, Greece). The substrate was promptly stored under \(-18\)\({}^{\circ}\)C in the freezer in order to avoid any significant change on its physicochemical characteristics. For runs A-E (Table 2), the OMW was diluted with tap water in a ratio of 1:2 (one part OMW and two parts tap water); for run G, the respective ratio was 1:0.6 (organic loading doubling); and for run F, no dilution was needed. The physicochemical characteristics of the substrate without any prior dilution is presented in Table 4. The amount of the substrate was set in each run as the 85% (\(v/v\)) of the reactor's working volume.
## 3 Experimental Setup
### Data Splitting and Augmentation  
The raw dataset used in this study was collected under six motor speed conditions, ranging from 1690 rpm to 1740 rpm in 10 rpm increments. For each speed segment, 8 images per second were extracted, resulting in 1440 images over a 30 s period. Due to some erroneous measurements, the final number of valid images used in the study was 1424. The dataset was evenly distributed among three fault conditions: Normal, ITSC (inter-turn short circuit), and 4 Turn short.  
To ensure fair comparison across all methods, the 1424 images were divided as follows: 270 images (approximately 19%) were used as the test set, while the remaining 1154 images were further split into 924 training images and 230 validation images.  
To enhance the generalization performance of the deep learning classifier, address data imbalance, and mitigate limitations due to the relatively small training set, a SpecAugment-based data augmentation technique was applied to the training data.  
SpecAugment is a data augmentation method specialized for time-frequency images. It applies Time Masking, where random regions along the time axis are masked, and Frequency Masking, where random frequency-axis segments are masked.  
Figure 7: Input image types: (**a**) 3 Phase-RGB, (**b**) 3 Phase-DWT, (**c**) 3 Phase-STFT, (**d**) Id-Linear, (**e**) Id-DWT, and (**f**) Id-STFT.  
In this study, each training image was augmented three times, resulting in a training set four times larger when including the original samples. The same augmentation method was consistently applied to both three-phase current-based images (RGB, DWT, STFT) and single-phase Id current-based images. Figure 8 shows examples of the six image types after applying SpecAugment.
## 4 Case Study
### IEEE RTS-24 Node Case Study System
#### 4.2.5 Impact of Transmission Line Capacity  
To further investigate the role of energy storage in alleviating transmission congestion and delaying the upgrade of transmission lines, this section extends from the multi-stage coordinated planning of energy storage and transmission networks to single transmission grid planning scenarios. Specifically, the planning considers reducing the capacity of transmission lines to 90% and 80% of the original system capacity. The results are shown in Table 4.  
Figure 5: Impact of energy storage cost on energy storage configuration results. Figure 6: Impact of energy storage cost on transmission construction and wind and solar curtailment. To further quantify the synergistic effects between transmission grid planning and optimized energy storage configuration, this section introduces a synergy index, computed as shown in Equation (34). \[I_{rep}=(f_{TEP}-f_{TEP-ES})/f_{TEP}\times 100\% \tag{34}\]  
where \(I_{rep}\) denotes the synergy index between transmission grid planning and optimized energy storage configuration, \(f_{TEP}\) represents the total cost of the multi-stage planning scheme for a single transmission grid, \(f_{TEP-ES}\) represents the total cost of the multi-stage coordinated planning scheme for transmission grid and energy storage. From Table 4, it is observed that when the transmission capacity is reduced to 90% of the original, the total number of new line expansions across the three stages decreases by only 1 line. In the first stage, even with the same number of new lines (5 lines), the coordinated planning of transmission and storage opts for shorter line expansions, resulting in a reduction of investment by 70 million yuan in the first stage. As the system's transmission capacity decreases, the demand for new transmission line expansions increases, reflected in an increase in the number of new lines across the three stages. When the transmission capacity is at the original standard capacity, the total investment for the multi-stage planning of a single transmission grid is 2.362 billion yuan, while for the multi-stage coordinated planning of transmission grid and energy storage, it is 2.179 billion yuan, representing a reduction of 183 million yuan, with a synergy index of 7.75%. When the transmission capacity is reduced to 90% of the original, the total investment decreases by 203 million yuan, resulting in a synergy index of 8.00%. When reduced to 80% of the original capacity, the total investment decreases by 280 million yuan, with a synergy index of 9.85%. Comparatively, as the transmission capacity becomes insufficient, the economic benefits of the multi-stage coordinated planning of transmission grid and
# An Overview of Non-Isolated Hybrid Switched-Capacitor Step-Up DC-DC Converters
## 3 Switched-Capacitor Circuits Controlled by Transistors
Then, in the following switching state (back to the first state), \(C_{2}\), in series with the input voltage, charges \(C_{3}\) with three times the input voltage. In the next state, \(C_{3}\), in series with the input voltage, charges \(C_{4}\) with four times the input voltage. Finally, \(C_{4}\), in series with the input voltage, charges \(C_{5}\) with five times the input voltage. The topology can be extended. In the case of Figures 15 and 16, we are depicting an \(5\times\) voltage multiplier. By analyzing the equivalent circuits or switching states, the MMCC converter work in some way as a transistors-based version of the converter in Figure 4, but each capacitor has a pair of transistors that work as its \"digital\" inputs (instead of separate pulsating signals). Following the comparison and discussion, the MMCC converter seems similar to a combination of the _Series-Parallel_ with the _Fibonacci_; it has the Fibonacci voltage gain, which means its voltage gain can be relatively large with few power stages. All capacitors and all switches have the same voltage stress equal to the input (or low side) voltage. Those advantages make it superior to previous topologies, which is why their development can be considered a significant contribution. Several articles have been dedicated to this converter, including their resonant charge interchange, as will be discussed in Section 5.4. Table 1 shows a comparison of the transistors-based pure switched capacitors based on the number of switches, the maximum voltage stress on switches, the number of capacitors, the maximum voltage on capacitors, and the output voltage that can be used to obtain a voltage gain.
# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
## 3 Method
### Point Cloud Encryption  
#### 3.3.1 Theoretical Basis  
Considering the point cloud \(\mathcal{P}\) and the dimensionally lifted line cloud \(\mathcal{L}\) as random variables, the posterior distribution \(P(\mathcal{P}\mid\mathcal{L})\) can be obtained according to the Bayesian rule expressed in Equation (1):  
\[P(\mathcal{P}\mid\mathcal{L})=\frac{P(\mathcal{L}\mid\mathcal{P})P(\mathcal{P} )}{P(\mathcal{L})}\propto P(\mathcal{L}\mid\mathcal{P})P(\mathcal{P}) \tag{1}\]  
Since all line directions are determined independently and randomly, \(P(\mathcal{L}\mid\mathcal{P})=\prod_{i=1}^{N}P(l_{i}\mid p_{i})\). The probability \(P(l_{i}\mid p_{i})\) that a line \(l_{i}\) corresponds to a given point \(p_{i}\) is zero if \(p_{i}\) is not on \(l_{i}\); otherwise, the probability is constant since all line directions are equally likely. Consequently, the likelihood function \(P(\mathcal{L}\mid\mathcal{P})\) is a piece-wise constant. As long as each point \(p_{i}\) lies on its corresponding line \(l_{i}\), the original point cloud \(\mathcal{P}\) and the restored point cloud \(\mathcal{P}^{\prime}\) share the same likelihood \(P(\mathcal{L}\mid\mathcal{P})=P(\mathcal{L}\mid\mathcal{P}^{\prime})\). To obtain the maximum a posteriori, the prior \(P(\mathcal{P})\) must be maximized, assuming that all points lie on their lines. Nevertheless, it is challenging to define or learn a general prior distribution \(P(\mathcal{P})\), which makes it difficult to reason about the original point cloud.
# Performance Analysis of an Improved Gravity Anchor Bold Expanded Foundation
## 3 Finite Element Model
### Finite Element Model in ABAQUS  
#### 3.3.1 Finite Element Contact and Boundary Conditions  
Figure 8 shows the details of the finite element 3D model. All the steel bars in ABAQUS are built into the foundation. The bottom surface of the high-strength grouting layer is bound to the top surface of the foundation. The bottom of the soil is completely fixed. The horizontal displacement of the soil side surface and the rotation of the \(z\)-axis are limited. The upper surface of the soil is completely free. To avoid the influence of the soil boundary effect [46], the soil model needs a large geometric size. In this paper, a geometric model of soil with a diameter of 120 m and a height of 17.6 m is established.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dilation Angle**} & \multirow{2}{*}{**Eccentricity**} & \multicolumn{2}{c}{\(f_{b0}/f_{c0}\)} & \multirow{2}{*}{**K**} & \multirow{2}{*}{**Viscosity Parameter**} \\  & & & & \\ \hline
30\({}^{\circ}\) & 0.1 & 1.16 & 0.667 & 0.004 \\ \hline \hline \end{tabular}
\end{table}
Table 4: CDP model parameters.  
Figure 8: Finite element model: (**a**) foundation and soil and (**b**) details of the foundation.  
The model is a multi-interface contact finite element model. The setting of contact parameters determines the accuracy and convergence speed of the calculation. The surface contact settings in the model take into account the normal behavior and tangential behavior. In the tangential behavior, the friction formula uses a penalty method. The interface element method can better simulate complex geometric shapes and material properties, but it requires more computing time and memory space. The face-to-face contact method is more simple and easier to use. The reason why hard contact is used as normal behavior and friction is used as tangential behavior is that we pay more attention to the detachment and slip behavior between surfaces, which is of great significance to the safety of the foundation. Different friction coefficients are used between different materials, and the friction coefficient between the concrete and steel plate is set to 0.70 [47]. The friction coefficient between the steel plates is set to 0.80 [48]. The friction coefficient between the concrete and soil is assumed to be 0.40 [49]. Table 5 shows the contact parameters in ABAQUS.
###### Abstract  
Mapping snow cover (SC) on glaciers at the end of the ablation period provides a possibility to rapidly obtain a proxy for their equilibrium line altitude (ELA) which in turn is a metric for the mass balance. Satellite determination of glacier snow cover, derived over large regions, can reveal its spatial variability and temporal trends. Accordingly, snow mapping on glaciers has been widely applied using several satellite sensors. However, as glacier ice originates from compressed snow, both have very similar spectral properties and standard methods to map snow struggle to distinguish snow on glaciers. Hence, most studies applied manual delineation of snow extent on glaciers. Here we present an automated tool, named 'ASMAG' (automated snow mapping on glaciers), to map SC on glaciers and derive the related snow line altitude (SLA) for individual glaciers using multi-temporal Landsat satellite imagery and a digital elevation model (DEM). The method has been developed using the example of the Otztal Alps, where an evaluation of the method is possible using field-based observations of the annual equilibrium line altitude (ELA) and the accumulation area ratio (AAR) measured for three glaciers for more than 30 years. The tool automatically selects a threshold to map snow on glaciers and robustly calculates the SLA based on the frequency distribution of elevation bins with more than 50% SC. The accuracy of the SC mapping was about 90% and the SLA was determined successfully in 80% of all cases with a mean uncertainty of \(\pm\)19 m. When cloud-free scenes close to the date of the highest snowline are available, a good to very good agreement of SC ratios (SCR)/SLA with field data of AAR/ELA are obtained, otherwise values are systematically higher/lower as useful images were often acquired too early in the summer season. However, glacier specific differences are still well captured. Snow mapping on glaciers is impeded by clouds and their shadows or when fresh snow is covering the glaciers, so that more frequent image acquisitions (as now provided by Sentinel-2) would improve results.  
Keywords:Landsat; snow mapping; glacier; snow-covered area; mass balance; snow line altitude; equilibrium line altitude; multi-temporal analysis; digital elevation model +
Footnote †: journal: Remote Sensing
# Predictive Modeling of VO2 max Based on 20 m Shuttle Run Test for Young Healthy People
## 3 Results  
All models were evaluated using the prediction error \(RMSE_{CV}\) calculated during cross-validation (Tables 3 and 4). Figure 1 presents validation errors in relation to the parameters of the models. For Ridge models, this parameter is the lambda parameter \(\lambda\in[0,20]\) with a step of 1, in the case of the Lasso regression, the parameter \(s\in[0,1]\) has a step of 0.1. Artificial neural networks (MLP and RBF) were evaluated for the number of neurons in the hidden layer \(m\in\{2,3,\ldots,11\}\).  
By analysing the results for the models identified based on all variables (Table 3), it may be observed that the most accurate model for women was obtained using the RBF type neural network with eight neurons in the hidden layer (\(RMSE_{CV}=4.31\) [mL\(\cdot\)kg\({}^{-1}\cdot\)min\({}^{-1}\)]). The best model making predictions among men were based on the whole collection of variables which generates an error of \(RMSE_{CV}=5.50\) [mL\(\cdot\)kg\({}^{-1}\cdot\)min\({}^{-1}\)]. This was calculated with Ridge regression for \(\lambda=20\). A similar observation may be made for the most accurate model for all data where the Ridge regression (\(\lambda=12\)) is also the one which generates the smallest error (\(RMSE_{CV}=4.89\) [mL\(\cdot\)kg\({}^{-1}\cdot\)min\({}^{-1}\)]). The following stage of the analysis involved variable selection methods to improve the predictive capacity of the presented models, determine optimal input collections, and consequently to identify the factors which determine the VO\({}_{2}\)max prediction in the analysed group. The models determined by means of Lasso and stepwise regression were evaluated using LOOCV (Table 4). The application of variable selection methods improved the predictive capacity of the OLS model. The error level for the female group was \(RMSE_{CV}=4.11\) [mL\(\cdot\)kg\({}^{-1}\cdot\)min\({}^{-1}\)] when the bidirectional method was used, while, for males, the model obtained with forward regression turned out to be more precise--\(RMSE_{CV}=5.35\) [mL\(\cdot\)kg\({}^{-1}\cdot\)min\({}^{-1}\)]. For all data, the forward and bidirectional methods determined the same input
## 2 Literature Review
### Key Systems Engineering Best Practices  
Systems engineering best practices often result from lessons learned in numerous projects. The knowledge gained through experience in past projects is critical to improving systems engineering capabilities [9; 10; 14; 32; 33]. After a literature review of numerous studies on the best practices and lessons learned in systems engineering, the following themes emerged:  
* Systems engineering infrastructure is critical to project success [9; 10; 14; 32; 33].
* Requirements should be unambiguous, up-to-date, and vetted with project stakeholders [9; 10; 14; 32; 33].
* An effective systems engineering plan should be implemented as early as possible [9; 10; 14; 32; 33].
* Failure to adhere to good systems engineering could lead to cost and schedule overruns [9; 10; 14; 32; 33].
* People are the primary resource for successful systems engineering [9; 10; 14; 32; 33].
* Communication is critical for successful systems engineering [9; 10; 14; 32; 33].  
The following systems engineering handbooks and standards were compared: INCOSE Systems Engineering Handbook [9], NASA Systems Engineering Handbook [10], MIL-STD-499C--Systems Engineering [34], IEEE-1220--Application and Management of the Systems Engineering Process [35], EIA-632--Process for Engineering a System [36], ISO/IEC 15288--Systems and software Engineering System Life Cycle Process [37], and Capability Maturity Model Integration for Development [38]. The themes regarding best practices were found in each of these systems engineering standards and guidelines.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Value**} & \multicolumn{2}{c}{**True Values**} & \multicolumn{2}{c}{**Interval Scan**} & \multicolumn{2}{c}{**Impression CBCT Scan**} & \multicolumn{2}{c}{**Cast CBCT Scan**} & \multicolumn{2}{c}{**Extraoral Scan**} \\  & & **Mean \(\pm\) SD** & **Dev (\%)** & **Mean \(\pm\) SD** & **Dev (\%)** & **Mean \(\pm\) SD** & **Dev (\%)** & **Mean \(\pm\) SD** & **Dev (\%)** \\ \hline X\({}_{\rm{PM1}}\) & 38.69 & 38.63 \(\pm\) 0.047 \({}^{\star}\) & \(-\)0.149 & 38.67 \(\pm\) 0.015 \({}^{\star}\) & \(-\)0.055 & 38.71 \(\pm\) 0.012 \({}^{\star}\) & 0.053 & 38.64 \(\pm\) 0.002 \({}^{\star}\) & \(-\)0.119 \\ X\({}_{\rm{PM2}}\) & 44.29 & 44.24 \(\pm\) 0.067 \({}^{\star}\) & \(-\)0.132 & 44.24 \(\pm\) 0.017 \({}^{\star}\) & \(-\)0.124 & 44.26 \(\pm\) 0.020 \({}^{\star}\) & \(-\)0.079 & 44.23 \(\pm\) 0.002 \({}^{\star}\) & \(-\)0.153 \\ X\({}_{\rm{M1}}\) & 49.60 & 49.54 \(\pm\) 0.094 \({}^{\star}\) & \(-\)0.110 & 49.50 \(\pm\) 0.021 \({}^{\star}\) & \(-\)0.207 & 49.55 \(\pm\) 0.036 \({}^{\star}\) & \(-\)0.095 & 49.54 \(\pm\) 0.002 \({}^{\star}\) & \(-\)0.117 \\ X\({}_{\rm{M2}}\) & 52.80 & 52.77 \(\pm\) 0.131 \({}^{\star}\) & \(-\)0.047 & 52.99 \(\pm\) 0.022 \({}^{\star}\) & \(-\)0.208 & 52.68 \(\pm\) 0.017 \({}^{\star}\) & \(-\)0.226 & 52.76 \(\pm\) 0.002 \({}^{\star}\) & \(-\)0.068 \\ Y\({}_{\rm{PM1}}\) & 16.92 & 16.89 \(\pm\) 0.034 \({}^{\star}\) & \(-\)0.149 & 16.89 \(\pm\) 0.008 \({}^{\star}\) & \(-\)0.169 & 16.96 \(\pm\) 0.008 \({}^{\star}\) & 0.242 & 16.91 \(\pm\) 0.006 \({}^{\star}\) & \(-\)0.005 \\ Y\({}_{\rm{PM2}}\) & 23.90 & 23.84 \(\pm\) 0.034 \({}^{\star}\) & \(-\)0.231 & 23.86 \(\pm\) 0.011 \({}^{\star}\) & \(-\)0.142 & 23.92 \(\pm\) 0.016 \({}^{\star}\) & 0.107 & 23.87 \(\pm\) 0.008 \({}^{\star}\) & \(-\)0.099 \\ Y\({}_{\rm{M1}}\) & 32.65 & 32.60 \(\pm\) 0.042 \({}^{\star}\) & \(-\)0.150 & 32.61 \(\pm\) 0.012 \({}^{\star}\) & \(-\)0.126 & 32.70 \(\pm\) 0.020 \({}^{\star}\) & 0.132 & 32.64 \(\pm\) 0.006 \({}^{\star}\) & \(-\)0.051 \\ Y\({}_{\rm{M2}}\) & 43.23 & 43.19 \(\pm\) 0.043 \({}^{\star}\) & \(-\)0.100 & 43.17 \(\pm\) 0.013 \({}^{\star}\) & \(-\)0.140 & 43.23 \(\pm\) 0.018 & 0.003 \({}^{\star}\) & \(432.20\) \({}^{\star}\) & \(-\)0.038 \\ Z\({}_{\rm{IR}}\) & 4.83 & 4.86 \(\pm\) 0.015 \({}^{\star}\) & 0.578 & 4.89 \(\pm\) 0.011 \({}^{\star}\) & 1.302 & 4.93 \(\pm\) 0.009 \({}^{\star}\) & 2.091 & 4.93 \(\pm\) 0.019 \({}^{\star}\) & 1.987 \\ Z\({}_{\rm{IL}}\) & 4.63 & 4.76 \(\pm\) 0.016 \({}^{\star}\) & 2.750 & 4.76 \(\pm\) 0.012 \({}^{\star}\) & 2.688 & 4.81 \(\pm\) 0.012 \({}^{\star}\) & 3.770 & 4.78 \(\pm\) 0.008 \({}^{\star}\) & 3.261 \\ Z\({}_{\rm{MR}}\) & 3.61 & 3.69 \(\pm\) 0.009 \({}^{\star}\) & 2.036 & 3.65 \(\pm\) 0.016 \({}^{\star}\) & 1.057 & 3.73 \(\pm\) 0.007 \({}^{\star}\) & 3.338 & 3.70 \(\pm\) 0.004 \({}^{\star}\) & 2.410 \\ Z\({}_{\rm{ML}}\) & 4.31 & 4.17 \(\pm\) 0.037 \({}^{\star}\) & \(-\)3.315 & 4.19 \(\pm\) 0.032 \({}^{\star}\) & \(-\)2.775 & 4.22 \(\pm\) 0.01
###### Abstract  
In this paper, we test and evaluate policy gradient reinforcement learning for automated blood glucose control in patients with Type 1 Diabetes Mellitus. Recent research has shown that reinforcement learning is a promising approach to accommodate the need for individualized blood glucose level control algorithms. The motivation for using policy gradient algorithms comes from the fact that adaptively administering insulin is an inherently continuous task. Policy gradient algorithms are known to be superior in continuous high-dimensional control tasks. Previously, most of the approaches for automated blood glucose control using reinforcement learning has used a finite set of actions. We use the Trust-Region Policy Optimization algorithm in this work. It represents the state of the art for deep policy gradient algorithms. The experiments are carried out in-silico using the Hovorka model, and stochastic behavior is modeled through simulated carbohydrate counting errors to illustrate the full potential of the framework. Furthermore, we use a model-free approach where no prior information about the patient is given to the algorithm. Our experiments show that the reinforcement learning agent is able to compete with and sometimes outperform state-of-the-art model predictive control in blood glucose regulation.  
Keywords:reinforcement learning; Type 1 Diabetes Mellitus; policy gradient; deep learning; artificial pancreas +
Footnote †: journal: Applied Sciences