esa-sceva/satcom-chunk-collection huggingface dataset:
# Dark Fermentation of Fruit Juice Effluents: Effects of Substrate Concentration
## 2 Materials and Methods
### Modelling and Statistical Analysis  
To accurately determine the behavior of the fermentative system, the experimental results were analyzed using mathematical modeling.  
In the literature, the Monod equation is the most straightforward model used in biological systems to describe the relationship between bacterial biomass growth rate and substrate consumption. Equation (1) presents the Monod equation:  
\[r_{X}=\mu_{max}\cdot\frac{S}{K_{S}+S}\cdot X-b\cdot X \tag{1}\]  
where, \(r_{X}\) = bacterial biomass growth rate (C mol\(\cdot\)(h\(\cdot\)L)\({}^{-1}\)); \(\mu_{max}\) = maximum specific bacterial biomass growth rate (h\({}^{-1}\)); \(S\) = substrate concentration in the bioreactor C mol\(\cdot\)L\({}^{-1}\)); \(K_{S}\) = saturation constant for substrate (C mol\(\cdot\)L\({}^{-1}\)); \(b\) = bacterial biomass decay rate (h\({}^{-1}\)); and \(X\) = bacterial biomass concentration (C mol\(\cdot\)L\({}^{-1}\)).  
However, the Monod model is not suitable for fitting experimental data when inhibition events caused by the substrate occurs. Therefore, a mathematical model able to describe the inhibition caused by the substrates, the Haldane-Andrews model, was applied to experimental data. The Haldane model is one of the most often used in the literature [29; 30; 31]. The Haldane-Andrews model, shown in Equation (2), is an extension of the Monod model that can predict the effects of substrate inhibition on bacterial biomass by introducing an inhibition parameter [21; 32].  
\[r_{X}=\mu_{max}.\frac{S}{K_{S}+S+\frac{S^{2}}{K_{i}}}.X-b.X \tag{2}\]  
where, \(K_{i}\) = inhibition constant for substrate (C mol\(\cdot\)L\({}^{-1}\)).  
The Haldane-Andrews model was used to describe the substrate consumption, and the bacterial biomass growth experienced with all the substrate concentrations tested, offering accurate predictions. The substrate inhibition effects over bacterial biomass can be explained by the reduction in the growth rate due to osmotic stress on cells, increase of the medium viscous (affecting the mass transfer) or due to multiple substrate molecules binding to the enzyme, causing a decrease in the reaction rate [30; 33; 34].  
Statistical analyses of variance (ANOVA) of the different process parameters among different runs were conducted. A threshold \(p\)-value of significant difference was set at 0.05.
## 3 Conceptual View of the Present Speech Recognition System
### Algorithm for CNN Implementation  
This work used a CNN model with six-layer convolution to implement the speech recognition system. Initially, we provided the speech as input to the design, and then, using the PRAAT technique, a Mel spectrogram was generated. We used six-layer 2D convolutional and two fully connected layers to extract and learn from the features. In addition, flattened layers were added between the 2D convolutional layers and the dense layers for better efficacy. Then, the Softmax activation function was used to activate the neurons and classify the adequate text from the speech input. Finally, the output was obtained in the output layer. The overall CNN implementation for the speech recognition system is illustrated in Algorithm 1.  
```
Parameters: Alpha-0.333, bias = 0.1 & axis = \(-\)1 Input: sample audio.wav is provided Step 1: Spectrogram and waveform is generated using PRAAT program Step 2: Using LIBROSA python library, MFCC is extracted Step 3: Feature learning using CNN, 6-2D Conv. And 2 FC layers Step 4: Flatten layer is included between Conv2D and Dense Layer (256 Units) Step 5: Activation by Softmax Activation Function Step 6: Loss calculation by 'Categorical Cross Entropy' Step 7: Comparing input (li1) with Ground Truth (gt)-li2 by diff (li1, li2) Output: Recognized words and Accuracy Measurement
```  
**Algorithm 1** CNN implementation for the speech recognition system.
## 4 Design of Dynamic Torque-Coordinated Control That Considers Engine Starting Conditions
### Active Damping Feedback Compensation Control Considering System Resonance  
#### 4.3.1 Active Damping Feedback Compensation Torques  
The ADFC torques of the two motors (\(T_{MG1\_Dump}\) and \(T_{MG2\_Dump}\)) can be obtained by the equivalent lever method [40] in Figure 6, which compensates for the torque fluctuations transmitted to the wheels.  
\[\left\{\begin{array}{ll}T_{MG1\_Dump}&=\frac{i_{2}-1}{i_{1}-i_{2}}\bigg{(} T_{R\_ad}+I_{R}\frac{(1-i_{1})\ddot{\theta}_{MG2}-(1-i_{2})\ddot{\theta}_{MG1}}{i_{2} -i_{1}}\bigg{)}\\ &+\frac{i_{2}}{i_{1}-i_{2}}\dot{T}_{\text{c\_ges\_est}}f+(I_{s1}+I_{MG1}) \ddot{\theta}_{MG1}\\ T_{MG2\_Dump}&=\frac{i_{2}-1}{i_{1}-i_{2}}\bigg{(}T_{R\_ad}+I_{R}\frac{(1-i_{1}) \ddot{\theta}_{MG2}-(1-i_{2})\ddot{\theta}_{MG1}}{i_{2}-i_{1}}\bigg{)}\\ &+\frac{i_{1}}{i_{2}-i_{1}}\dot{T}_{\text{c\_ges\_est}}f+(I_{s2}+I_{MG2}) \ddot{\theta}_{MG2}\end{array}\right. \tag{22}\]  
where \(T_{R\_ad}\) is the feedback damping torque and \(I_{R}\) is the ring inertia. In addition, \(\dot{T}_{\text{c\_ges\_est}f}\) was obtained by Kalman filtering of the estimated carrier torque \(\dot{T}_{\text{c\_ges\_est}}\); \(\ddot{\theta}_{MG1}\) is the MG1 acceleration; and \(\ddot{\theta}_{MG2}\) is the MG2 acceleration.  
The formula of the estimated carrier torque \(\hat{T}_{\text{c-g\_est}}\) (engine output shaft torque) is obtained as follows:  
\[\hat{T}_{\text{c-g\_est}} =(i_{1}-1)T_{\text{MC1}}+(i_{2}-1)T_{\text{MG2}}-(i_{1}-1)(I_{\text {s}_{1}}+I_{\text{MG1}})\ddot{\theta}_{\text{MG1}} \tag{23}\] \[-(i_{2}-1)(I_{\text{s}_{2}}+I_{\text{MG2}})\ddot{\theta}_{\text{ MG2}}\]  
To reduce the resonance of the transmission system itself and the torque fluctuations transmitted to the wheel, the feedback damping torque \(T_{R\_ad}\) is expressed as  
\[T_{R\_ad}=\left(\hat{T}_{R\_g\_est\_f}-f_{d}\left(\hat{W}_{ob}i_{0}-\hat{\theta }_{R}\right)\right)-T_{f}/r_{v}-T_{PID}(\hat{W}_{ob}i_{0},\hat{\theta}_{R}) \tag{24}\]  
where \(\hat{T}_{R\_g\_est\_f}\) is obtained by Kalman filtering of the estimated ring torque \(\hat{T}_{R\_g\_est}\); \(f_{d}\) is the feedback gain damping coefficient; \(\hat{W}_{ob}\) is the estimated wheel speed; \(\hat{\theta}_{R}\) is the estimated speed at ring gear; and \(T_{PID}(\hat{W}_{ob}i_{0},\hat{\theta}_{R})\) is the adjustment of the speed error at the gear ring, which is from a PID controller whose inputs are \(\hat{W}_{ob}i_{0}\) and \(\hat{\theta}_{R}\).  
The estimated ring torque \(\hat{T}_{R\_g\_est}\) is determined as follows:  
\[\hat{T}_{R\_g\_est} =-i_{1}T_{\text{MG1}}-i_{2}T_{\text{MG2}}+i_{1}(I_{\text{s}_{1}}+ I_{\text{MG1}})\ddot{\theta}_{\text{MG1}} \tag{25}\] \[\quad+i_{2}(I_{\text{s}_{2}}+I_{\text{MG2}})\ddot{\theta}_{\text {MG2}}\]  
The estimated speed at ring gear, \(\hat{\theta}_{R}\), can be obtained as follows:  
\[\hat{\theta}_{R}=\frac{(1-i_{1})\dot{\theta}_{\text{MG2}}-(1-i_{2})\dot{ \theta}_{\text{MG1}}}{i_{2}-i_{1}} \tag{26}\]
## 2 Materials and Methods
### Monitoring Sites  
FVA-BW manages and maintains 13 monitoring plots at 6 different sites (Figure 3, Tables 2 and 3) Rotenfels (RO) Altensteig (AL), Conventwald (CO), Heidelberg (HD), Ochsenhausen (OC)) in the forests of Baden-Wurttemberg, Germany. Of these 13 plots, ten plots at 5 sites are also listed as ICP Level 2 Monitoring plots according the ICP Forest Intensive Monitoring Program [109].  
Figure 3: Map of Baden Wurttemberg (SW Germany) showing the sites with soil gas monitoring run by Forest Research Institute Baden Wuerttemberg (FVA-BW). Dark green indicates forest areas.  
Each site has a plot of Beech forest (_Fagus sylvatica_. L.) and a plot of Spruce forest (_Picea abies_. (L) H. [PERSON]). The sites Heidelberg and Ochsenhausen include an additional treatment plot where the soil has been lined with CaMg(CO3)2 in 1984 and 1994, respectively. An additional Spruce plot was installed in Rotenfels, which is not part of the regular ICP Level 2 monitoring program. The sites and plots were selected to represent the typical forest regions of Baden-Wurttemberg and the most frequent local broadleaved tree (beech) and conifer species (spruce). All plots have been established before 1995 and are managed according to the guidelines for the ICP Level 2 monitoring program Soil gas monitoring started in 1998 on 8 plots and 2010 on 5 plots.  
The plots are covered by homogenous forests stand of approximately 0.25 ha (Table 3). The spruce plot and the beech plot are located next to each other. The management routine includes a combination of in situ measurements by permanently installed sensors and data loggers and repeated sampling (automatic and manual) on a regular scheme and subsequent analysis in the laboratory. Infrastructure and routines for soil gas monitoring have been established in addition to the internationally harmonized monitoring routine of the ICP Forest monitoring network. For the presentation of exemplary results we used data of the spruce plot of the ES site from the years 2005-2020.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Forest Stand**} & \multicolumn{3}{c}{**Soil**} & \multicolumn{3}{c}{**Humus**} \\ \hline
## 1 Introduction  
Wireless power transfer (WPT) is a promising method that can effectively enhance user convenience by transmitting power without electrical contact [1]. Among various WPT methods, capacitive power transfer (CPT), which uses an alternating electric field generated by a metal plate coupler [2], and Inductive Power Transfer (IPT), which uses an alternating magnetic field generated by a coil-based coupler [3], are primarily considered. Generally, the IPT method is preferred over the CPT method in terms of transmission distance and power level, as it offers higher power density at the same air gap. However, high-permeability core media in IPT systems cause eddy current losses and require a high system configuration cost. Therefore, when considering alternative media with high permittivity, CPT can be more viable than IPT [4]. Water is readily available and has a high relative permittivity of approximately 81 at room temperature [5]. This suggests that a CPT system using a water medium can achieve greater capacitance between couplers and improve coupling components at a lower cost compared to IPT systems. However, most types of water, except for highly purified distilled water, are easily ionized by impurities, making them electrically conductive [6]. Electrical conductivity in a CPT system allows current flow between the transmitter and receiver, preventing the formation of a proper electric field [7]. The water medium can be considered unsuitable for improving the performance of CPT systems depending on its conductivity level even though it has higher permittivity. Therefore, it is necessary to simultaneously consider the permittivity and conductivity of the medium in order to select an appropriate medium to replace air in the CPT method. Some CPT studies have achieved high power transfer efficiencies of over 80% underwater, proving the efficiency of CPT using water as a medium [8]. However, most underwater CPT studies have used parallel CPT coupler structures, but underwater wireless power transfer using stacked CPT coupler structures has not been verified so far.  
This paper will provide the design of a stacked CPT coupler structure and analyzation of its underwater transmission characteristics. In addition, a theoretical analysis of how the permittivity and conductivity of the medium affects the electric field in the frequency range and propagation distance where the CPT method is mainly used. The results of this study can be helpful in selecting appropriate mediums, which can improve the performance of CPT systems.
## 4 Discussion
### Digital Terrain Model  
The study results demonstrate an exceptionally strong correlation (\(R^{2}=0.999\)) between CASAL-derived ground elevations and airborne light detection and ranging (LiDAR) DTM products. This high correlation confirms CASAL's reliability for ground elevation inversion, particularly in flat or low-slope terrain. However, CASAL-DTM accuracy shows greater sensitivity to slope than to vegetation cover or tree height. This phenomenon likely results from reduced laser pulse penetration on steep slopes, increasing terrain inversion errors. Thus, CASAL applications in complex terrain may require correction through integration with additional data sources or algorithms. The median DTM difference (0.57 m) aligns with findings from comparable studies. For instance, [PERSON] et al. [15] reported GEDI median DTM differences of \(-0.26\) m and 0.18 m in Thuringia, suggesting systematic sensor-specific biases in topographic elevation inversion. [PERSON] et al. [42] noted significant GEDI DTM variations across land cover and forest types, likely attributable to vegetation-specific laser pulse reflection and penetration characteristics. These findings corroborate previous GEDI topographic accuracy studies, further validating LiDAR data inversion performance variations across land cover types. CASAL-DTM exhibits minor topographic elevation overestimation compared to ALS-DTM, though within a smaller magnitude. Specifically, CASAL-DTM demonstrates lower error metrics than GEDI, with mean square error (RMSE) = 3.83 m, Bias = 0.70 m, median = 0.57 m, and median absolute deviation (MAD) = 2.96 m. These results indicate CASAL's high accuracy for topographic elevation inversion, particularly in forested regions. Compared to GEDI, CASAL demonstrates more stable elevation inversion performance, potentially due to higher point cloud density and optimized data processing algorithms. Furthermore, the strong agreement between CASAL and ALS-DTM elevation inversions underscores CASAL's potential for forest terrain monitoring applications.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Forest Type** & **Number** & \(\mathbf{R^{2}}\) & **Bias (m)** & **RMSE (m)** & **MAD (m)** \\ \hline Coniferous forest & 994 & 0.326 & \(-0.35\) & 2.69 & 2.1 \\ Broad-leaved forest & 2981 & 0.502 & \(-0.42\) & 2.71 & 2.12 \\ Mixed forest & 10,399 & 0.653 & 0.40 & 2.57 & 1.91 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Canopy height estimation accuracy across forest types.
## 2 Methodology
### Experimental Setup and Measurements
#### 2.2.2 Experimental Equipment  
As shown in the experimental equipment in Figure 1, an eddy current type dynamometer (DY-230 kW, Hwanwoong Mechatronics, Gyeongsangnam-do, Korea) equipped with a fuel supply system and a fuel pump was used. A multi-gas analyzer (MK2, Euroton, Italy) measured the levels of CO,  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Properties** & **Diesel** & **Palm Oil Biodiesel** & **Test Method** \\ \hline Density at 15 \({}^{\circ}\)C (kg/m\({}^{3}\)) & 836.8 & 877 & ASTM D941 \\ Viscosity at 40 \({}^{\circ}\)C (mm\({}^{2}\)/s) & 2.719 & 4.56 & ASTM D445 \\ Lower calorific value (MJ/kg) & 43.96 & 39.72 & ASTM D4809 \\ Calculated cetane index & 55.8 & 57.3 & ASTM D4737 \\ Flash point (\({}^{\circ}\)C) & 55 & 196.0 & ASTM D93 \\ Pour point (\({}^{\circ}\)C) & \(-21\) & 12.0 & ASTM D97 \\ Oxidation stability (h/110 \({}^{\circ}\)C) & 25 & 9.24 & EN 14112 \\ Ester content (\%) & - & 96.5 Min & EN 14103 \\ Oxygen content (wt.\%) & 0 & 11.26 & - \\ Sulfur content (wt.\%) & 0.11 & 0.004 & ASTM D5453 \\ Hydrogen content (wt.\%) & 13.06 & 12.35 & ASTM D5453 \\ Carbon content (wt.\%) & 85.73 & 79.03 & ASTM D5291 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of test fuels.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Engine Parameters** & **Unit** & **Specification** \\ \hline Engine Type & - & In-line 4 Cylinder, Turbocharged, EGR \\ Maximum Power & kW/ rpm & 83.5/4000 \\ Maximum Torque & Nm/ rpm & 255/2000 \\ Bore \(\times\) Stroke & mm \(\times\) mm & 83 \(\times\) 92 \\ Displacement & cc & 1991 \\ Compression Ratio & - & 17.7:1 \\ Number of Injector nozzle holes & - & 5 \\ Injector type & - & Solenoid \\ Injector hole diameter & mm & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Engine Specification.  
CO\({}_{2}\), and NO\({}_{\rm x}\). HC was measured using a portable exhaust analyzer (HPC501, Pantong Huapeng Electronics, Shaanxi, China) [19]. A partial flow collecting type soot analyzer (OPA-102, Qurotech, Bucheon-si, Korea) was used to measure the PM level. The combustion pressure was acquired using a piezo-electric type pressure sensor ([PERSON], 6056a, Winterthur, Switzerland) at the position of the glow plug and was recorded by a data acquisition (DAQ) board (PCI 6040e, National Instrument, Austin, TX, USA). The discharged soot was collected by a copper grid (FCF400-CU, Electron Microscopy Sciences, PA, USA) and was used to analyze the shape of the soot particles by transmission electron microscopy (TEM, H-7650, HITACHI, Fukuoka, Japan) and scanning electron microscopy (SEM, SUPRA 40 VP, Carl Zeiss, Germany) images.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 2 Processes
### Volatilisation  
Volatilisation is the transition from a liquid to a gaseous state and is crucial for the fate of organic compounds in the water-soil environment [224; 225]. This process dominates the removal of low molecular weight aliphatics and is the most significant change in petroleum, leading to an enrichment of the high molecular weight fraction of residual hydrocarbons [79].  
A comprehensive understanding of the factors governing the evaporative transport of organic compounds is crucial for estimating the quantity and composition of chemicals entering and exiting soils and water bodies. The transport of organic compounds from water to the atmosphere is influenced by several factors, including the chemical and physical properties of the pollutant (such as solubility, molecular weight, vapor pressure, Henry's law constant, and Raoult's law parameter), the presence of other pollutants and their physical properties, water body turbulence, and air-water interface conditions, as well as soil properties and environmental factors such as temperature [226; 227]. Hydrocarbons  
Figure 6: Four main types of isotherms after Giles et al. [214].  
volatilize from residues bound to soil grains and the free phase, resulting in a depletion of lighter fractions from the remaining pool of organic chemicals, making it less mobile. Consequently, the vapor phase can travel long distances along preferred flow paths [228]. Vapor phase constituents in the soil may undergo biodegradation by microorganisms or re-volatilization into the atmosphere due to lower atmospheric concentrations, a characteristic feature of PCBs [229]. The release of volatile compounds into the atmosphere or soil gases follows a diffusion process. Two methods can be employed to calculate diffusion fluxes: the Stefan-Maxwell equations, which merge gas fluxes and express concentration gradients of each component in terms of the fluxes of other components; and the less stringent but less accurate first Fick equation, which posits that the diffusion flux density of a gas is proportionate to its concentration gradient and independent of other gases [230; 231].
## 3 Results and Discussion
### Continuous Observation Experiment  
A three-day continuous observation experiment was conducted on vehicle-mounted wind Lidar from 3 August to 5 August 2021. To understand the role of wind fields in energy and material transport, concentrations of different groups of VOCs were detected using PTR-TOF-MS at three sampling sites simultaneously. The navigation vehicle was equipped with temperature and humidity sensors to extract the temperature and humidity information in real time. During the three days, 2719 wind data points were collected. Among these, 484 points were discarded due to vehicle movement, unsuitable wind direction relative to the vehicle's heading, or some technical problem associated with the data acquisition.
## 3 Modeling
### Bi-Level Collaborative Optimization Model for Order Splitting and Reorganization  
After completing the modeling of the order splitting level and the order reorganization level separately, mathematical processing methods are required to integrate both models into a unified framework, enabling a more comprehensive global optimization of the two-stage order splitting-reorganization process. Based on the characteristics of the order splitting and order reorganization models, commonly used mathematical processing methods include constraint transformation, hierarchical weighting, joint multi-objective optimization, and bi-level collaborative optimization [28; 29; 30]. Considering the advantages of these four approaches, this study ultimately selects the bi-level collaborative optimization method for mathematical modeling.  
Finally, after mathematical processing, the bi-level collaborative optimization model is formulated as shown in Equation (44). In the upper-level optimization, it must be ensured that for each generated splitting scheme \(x\), there exists a feasible solution \(y\) in the lower level, and \(y\) must be a Pareto-optimal solution for the lower-level bi-objective optimization.  
\[\begin{array}{l}\underset{x}{min}\left(F,C_{split}\right)\\ \text{s.t.\ upper layer constrains}(Equation(9)\sim(22))\\ \forall x,\exists y\in\underset{y}{argmin}\left(C_{produce},T_{delay}\right) \\ \text{s.t.\ lower layer constrains}(Equation(29)\sim(43))\end{array} \tag{44}\]
## 4 Experiment and Results
### Training Process  
The Rainbow DQN_AM algorithm uses the Adam optimizer in combination with gradient descent for training. In each iteration, the batch size is set to 64. The number of epochs is set to 2880 simulation steps per epoch, and the model is trained for 100 epochs using real traffic flow data. The main hyperparameters of the algorithm are listed in Table 6, with values referenced from [39]. The learning rate \(\eta\), discount factor \(\gamma\), and replay experience capacity \(M\) are determined through experimental tuning.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Value** \\ \hline learning rate \(\eta\) & 0.001 \\ discount factor \(\gamma\) & 0.99 \\ batch size \(B\) & 64 \\ initial exploration probability \(\varepsilon_{i}\) & 0.99 \\ final exploration probability \(\varepsilon_{f}\) & 0.01 \\ [minimum value \(Z_{min}\), maximum value \(Z_{max}\)] & \(-\)500,100 \\ number of intervals & 51 \\ experience replay capacity \(M\) & 200,000 \\ multi-step learning steps & 3 \\ noisy network standard deviation & 0.1 \\ target network update frequency \(\mu\) & 100 \\ smoothing coefficient \(\tau\) & 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Main hyperparameter of Rainbow DQN_AM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Control Method** & **Reinforcement Learning Method** & **Reward Function** \\ \hline Rainbow DQN\_AM-CO2 Reduction & \multirow{2}{*}{Rainbow DQN\_AM} & Reward–CO2 Reduction \\ Rainbow DQN\_AM-Time & & Reward–Wait Time \\ DQN-CO2 Reduction & DQN & Reward–CO2 Reduction \\ D3 QN-CO2 Reduction & D3 QN & Reward–CO2 Reduction \\ actual signal timing & / & / \\ Webster signal timing & / & / \\ actuated signal timing & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 5: Control group experimental design.  
The network parameters from the training cycle with the highest average reward value were selected for model validation. Specifically, the parameters from the 92 nd, 99 th, and 71 st generations were chosen for the Rainbow DQN_AM, D3 QN, and DQN algorithms. As shown in Figure 7, the average reward values for the models indicate that the Rainbow DQN_AM algorithm exhibits the highest learning efficiency and fastest convergence, stabilizing after 14 generations. In contrast, the average reward curves for the DQN and D3 QN algorithms show significant fluctuations. From the 5 th to the 20 th generation, the reward values drop sharply, and from the 20 th to the 70 th generation, the reward values fluctuate and rise but tend towards zero. Overall, the reward curves for these algorithms remain lower than that of the Rainbow DQN_AM algorithm.