esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal and Acoustic Design of a Shelter for High-Voltage Electrical Equipment
## 2 Theoretical Background  
### Heat Transfer  
The goal of this project is to maximise heat dissipation inside the shelter where electrical equipment, such as switchgears (Figure 1), is installed.  
A restriction from the switchgear supplier is to ensure that the maximum average temperature of the air inside the shelter stays below 40 \({}^{\circ}\)C. When dealing with airflow, the major mode of heat transfer is convection. If properly designed, the difference in the air temperature produces air motion inside the shelter domain due to a different air density inside the volume. As shown by [PERSON] [17], it is possible to merge the basic Navier-Stokes equation, and the heat transfer equation coupled with fluid velocities. The governing equation of motion for heat transfer applicable in this study is:  
\[\left(\kappa\
abla^{2}\ T\right)+\mu\Phi+q^{\prime\prime\prime}=\rho c_{p}\ u( \
abla T) \tag{1}\]  
where \(\kappa\) is the thermal conductivity, \(T\) is the temperature, \(\mu\Phi\) denotes the viscous dissipation, \(q^{\prime\prime\prime}\) is the generated heat per unit volume, \(\rho\) is the density of air, \(c_{p}\) is the specific heat capacity at constant pressure and \(u\) is the velocity of the fluid. In the following, for simplicity, the viscous dissipation \(\mu\Phi\) is omitted from Equation (1) and thus it degenerates into the following equation:  
\[\left(\kappa\
abla^{2}\ T\right)+q^{\prime\prime\prime}=\rho c_{p}\ u(\
abla T) \tag{2}\]  
The aim of the study is to verify if natural convection is effective enough for heat dissipation or if forced convection is necessary. The amount of heat power removed by convection is computed as:  
\[W=hA(T-T_{0}) \tag{3}\]  
where \(h\) is the convective heat transfer coefficient, \(W\) is the heat power removed, \(A\) is the area of the solid surface on which convection occurs, \(T\) is the temperature of the solid surface and \(T_{0}\) is the temperature of the air flowing around the solid surface, with \(T>T_{0}\). These equations are used by COMSOL Multiphysics(r) for a computational fluid dynamics (CFD) simulation of a shelter model to verify its thermal compliance.
## 3 Results and Discussion
### Modified Proctor  
Figure 8 shows the modified Proctor compaction curves obtained in this study and others for allophanic soils reported in the literature. The maximum dry density (\(\gamma_{d_{max}}\)) determined in this investigation for the undisturbed samples was 9.76 kN/m\({}^{3}\) with an optimum moisture content (\(w_{opt}\)) of 64%. Therefore, it must be noted that the maximum dry density achieves a value slightly lower than the water unit weight (\(\gamma_{w}=9.81\) kN/m\({}^{3}\)). On the other hand, for oven-dried samples, a value of 10.95 kN/m\({}^{3}\) for the maximum dry density is achieved, with an optimum moisture content of 46%.  
The maximum dry density and the optimum moisture content obtained on undisturbed and oven-dried allophanic soils samples are significantly different. Similar results were observed by other researchers [9; 13]. The optimum moisture obtained in oven-dried samples ranges from 35 to 60%, while in the undisturbed samples it is between 70 and 140% (Figure 8). In contrast, the maximum dry density is higher in oven-dried (\(\gamma_{d}\): 9 to 12 kN/m\({}^{3}\)) than in undisturbed samples (\(\gamma_{d}\): 5 to 9 kN/m\({}^{3}\)). Some Proctor curves found in the literature cannot be used to determine the compaction characteristics as they do not have a clear maximum or they do not even show a relationship between dry unit weight after compaction and soil water content. For this reason, in this study, these curves were not analyzed, and thus their results are not included in this investigation. One of these curves can be seen in Figure 8, corresponding with oven-dried material from Indonesia [9]. The author suggests using a different compaction control method for those materials, based mainly in strength rather than in compaction itself.  
The zero-air-void curve is a function of the dry density, moisture content, and the specific gravity of the soils. Figure 8 shows two zero-void-curves for allophanic soils for different specific gravity values: (I) a mean specific gravity of soils obtained in this work (\(G_{S}=2.44\)), and (II) a value determined by [PERSON] [12] (\(G_{S}=2.7\)). In general, the wet side of the Proctor curve in undisturbed allophanic samples is very close to the zero-air-void curve.  
Figure 8: Compaction curves of undisturbed and oven-dried soils, zero-air-void curves with \(G_{S}=2.7\) and \(G_{S}=2.44\).
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 3 Methodology
### \(\sigma^{\circ}\) SAR Backscattering at Grid Scale
In this case, if \(\Delta VV_{P}\) is less than 0.5 dB then irrigation did not take place (\(\sigma^{0}_{G}\) at 10 km bare soil plots have slightly increased more than that at the plot scale). On the other hand, if the value of \(\Delta VV_{P}\) is greater than 0.5 dB, then we calculate the difference (\(\Delta\)) between \(\Delta VV_{P}\) and \(\Delta VV_{G}\) (\(\Delta=\Delta VV_{P}-\Delta VV_{G}\)). If the value of \(\Delta\) is greater than 1 dB, then irrigation has more chance than rainfall and the point is assumed corresponding to an irrigation event. Inversely, if \(\Delta\) is less than 1 dB then no irrigation occurs. The indicator \(\Delta\) was also used to help confirm whether a point is an irrigation event or not in the case of slight change in \(\Delta VV_{P}\) (\(-0.5\) dB \(\leq\Delta VV_{P}<0.5\) dB). When the value of \(\Delta VV_{P}\) is between 0 and 0.5, the value of \(\Delta\) must be greater than or equal to 1.5 dB in order toconsider the point as an irrigation event. Similarly, \(\Delta\) should be greater than or equal to 2 dB if the value of \(\Delta VV_{P}\) is between \(-0.5\) and \(0\). Finally, we consider that if the value of \(\Delta VV_{G}\) is less than 0.5 dB, then irrigation could be detected in case the value of \(\Delta VV_{P}\) respects certain criteria which will be detailed in Section 3.7.
## 2 Materials and Methods
### Specific ANAMOX Activity (SAA)  
The specific anammox activity (\(SAA\)) was analyzed using a respirometer (BRS-100, EETech Co., Chuncheon, Korea). N\({}_{2}\) gas production was calculated using the actual gas (N\({}_{2}\)) concentration measured using a respirometer according to the user's manual (Equation (1)).  
\[\mathrm{N}_{2}(\mathrm{molN}_{2}/\mathrm{min})=\frac{P\cdot\Delta N}{R\cdot T \cdot\Delta t} \tag{1}\]  
where \(\Delta N\) is the net increase in N\({}_{2}\) gas volume (L) measured from a respirometer over the reaction time; \(\Delta t\) is reaction time (min); \(R\) is the ideal gas coefficient (0.082 atm\(\cdot\)L/mol\(\cdot\)K); \(T\) is the temperature (K); \(P\) is the pressure (1 atm).  
The \(SAA\) was calculated from the N\({}_{2}\) gas production rate divided by the biomass concentrations (gVSS/L) in the vial (Equation (2) [12] and the relative \(SAA\) (\(rSAA\), %) was calculated by Equation (3).  
\[SAA\ (\mathrm{gN}_{2}/\mathrm{gVSS}/\mathrm{day})=\frac{\mathrm{N}_{2}\cdot M \cdot f}{\mathrm{XV}_{\mathrm{L}}} \tag{2}\]  
\[rSAA(\%)=\left(1-\frac{SAA_{c}}{SAA_{0}}\right)\times 100 \tag{3}\]  
where \(X\) is the biomass concentration (gVSS/L); \(M\) is the molecular weight of nitrogen gas (28 g/mol); \(f\) is the time modifying factor (1440 min/day); \(\mathrm{V}_{\mathrm{L}}\) is the volume of liquid (0.1 L); \(SAA_{0}\) is the \(SAA\) when the salt concentration is zero; \(SAA_{c}\) is the \(SAA\) at each concentration. All experiments were conducted in duplicate.
## 5 Improved DSR Using Phonological Features in Vocabulary-
### Results
#### 5.4.3 Cross-Lingual Setting  
We also demonstrate the effectiveness of PFs in phone recognition in the cross-lingual setting where the model is trained with **D_extgt** data and tested with the target language  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Intelligibility Level**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Training Data Amount**} \\ \cline{3-5}  & & **10\%** & **30\%** & **90\%** \\ \hline
**High** & **PHN** & 0.633 \(\ddagger\) & 0.404 \(\ddagger\) & 0.116 \\  & **PFs** & **0.359 \(\ast\)** & **0.241 \(\ast\)** & 0.108 \\  & **Combi.** & 0.387 \(\ast\)** & **0.242 \(\ast\)** & **0.102 \(\ast\)\(\ddagger\)** \\ \hline
**Medium** & **PHN** & 0.622 \(\ddagger\) & 0.447\(\dagger\) & 0.191 \\  & **PFs** & **0.378 \(\ast\)** & **0.298 \(\ast\)** & 0.183 \\  & **Combi.** & 0.406 \(\ddagger\)** & **0.300 \(\ast\)** & **0.174 \(\ast\)\(\ddagger\)** \\ \hline
**Low** & **PHN** & 0.764 \(\dagger\) & 0.631 \(\dagger\) & 0.485 \\  & **PFs** & **0.642 \(\ast\)** & **0.570 \(\ast\)** & 0.483 \\  & **Combi.** & **0.648 \(\ast\)** & **0.565 \(\ast\)** & **0.469 \(\ast\)\(\ddagger\)** \\ \hline \hline \end{tabular}  
* **Bold numbers:** Best PER(s) within statistical significance level in each case; \(\ast\): \(p\)-value \(\leq\) 0.05 compared to corresponding **PHN** method; \(\ddagger\): \(p\)-value \(\leq\) 0.05 compared to corresponding **PFs** method.  
\end{table}
Table 8: Averaged PER across speakers from each intelligibility level of different recognition methods using training data selected by **Ordered** method and for different data amounts.  
dataset(s). In Table 9, we present the PER results averaged across all datasets and across speakers at each intelligibility level.  
The **PFs** layer demonstrates a clear advantage in the cross-lingual scenario across all speaker intelligibility levels and in the average results across all datasets when compared to the **PHN** layer. However, combining the two layers diminishes the benefits provided by PF detection, resulting in the highest recognition error. The superior recognition results of the **PFs** layer underscore its effectiveness in recognizing unseen phones and mitigating vocabulary fitting, particularly when contrasted with the phone target. This advantage is consistent across different speaker groups. Nonetheless, despite these improvements, the recognition method still fails to provide practical phone transcriptions due to the relatively high-PER results.
###### Abstract  
Against the background of the gradual deepening of China Railway's market-oriented reform, and in order to improve the revenue and competitiveness for high-speed railway (HSR) passenger transport, this paper studies the joint optimization problem of the high-speed railway ticket pricing and allocation considering the dynamic demand characteristics of passengers during the pre-sale period. Firstly, we use the compound non-homogeneous Poisson process to describe the passengers' ticket-purchasing process and use the sparse method to simulate the passengers' ticket demand during the pre-sale period. Secondly, taking the ticket pricing and allocation as the decision variables and considering the full utilization of the train seat capacity, a stochastic nonlinear-programming mathematical model is established with the goal of maximizing the train revenue. A particle swarm algorithm is designed to solve the model. Finally, this study takes the G19 train running on the Beijing-Shanghai HSR in China as a case study to verify the effectiveness of the model and algorithm. The results show that the joint optimization scheme of ticket pricing and allocation considering dynamic demand yields a revenue of CNY 601,881, which increases the revenue by 1.01% with a small adjustment of the price compared with the fixed ticket price and pre-allocation scheme. This study provides scientific support for the decisions made by railway transportation enterprises, which is conducive to further increasing the potential ticket revenue and promoting sustainable development.  
high-speed railway; dynamic pricing; ticket allocation; passenger ticket pre-sale period; revenue management
## 6 Test and Analysis

Table 4: Test data in basic arrays A, B and C with the same target. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & \(\mathbf{P}(\mathbf{x},\mathbf{y},\mathbf{z})\) & \(\mathbf{P_{T}}(\mathbf{x_{T}},\mathbf{y_{T}},\mathbf{z_{T}})\) & \(\mathbf{\Delta P}(\mathbf{\Delta x},\Delta\mathbf{y},\mathbf{\Delta z})\) \\ \hline
1 & 31.64, \(-\)0.23, 14.89 & 30.84, 0.7, 14.18 & 0.8, 0.93, 0.71 \\
2 & 30.38, \(-\)4.01, 18.61 & 29.73, \(-\)4.79, 18.04 & 0.65, 0.78, 0.57 \\
3 & 32.11, \(-\)0.12, 15.48 & 31.63, \(-\)0.41, 14.66 & 0.48, 0.29, 0.82 \\
4 & 30.81, \(-\)2.17, 14.38 & 30.46, \(-\)3.06, 13.97 & 0.35, 0.89, 0.41 \\
5 & 31.34, \(-\)1.23, 13.93 & 32.18, \(-\)1.95, 13.11 & \(-\)0.84, 0.72, 0.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The test data of the proposed method in this paper and the double high-speed camera method. effectively circumvent the impact of environmental factors on acoustic sensors, reducing errors. This is the core innovation of this paper. Through observation of the comparative experimental results, it is evident that the proposed method in this paper approaches the test accuracy level of the high-speed camera method.
# Staining-Independent Malaria Parasite Detection and Life Stage Classification in Blood Smear Images
###### Abstract  
Malaria is a leading cause of morbidity and mortality in tropical and sub-tropical regions. This research proposed a malaria diagnosis system based on the you only look once algorithm for malaria parasite detection and the convolutional neural network algorithm for malaria parasite life stage classification. Two public datasets are utilized: MBB and MP-IDB. The MBB dataset includes human blood smears infected with _Plasmodium vivax_ (_P. vivax_). While the MP-IDB dataset comprises 4 species of malaria parasites: _P. vivax_, _P. ovale_, _P. malariae_, and _P. falciparum_. Four distinct stages of life exist in every species, including ring, trophozoite, schizont, and gametocyte. For the MBB dataset, detection and classification accuracies of 0.92 and 0.93, respectively, were achieved. For the MP-IDB dataset, the proposed algorithms yielded the accuracies for detection and classification as follows: 0.84 and 0.94 for _P. vivax_; 0.82 and 0.93 for _P. ovale_; 0.79 and 0.93 for _P. malariae_; and 0.92 and 0.96 for _P. falciparum_. The detection results showed the models trained by _P. vivax_ alone provide good detection capabilities also for other species of malaria parasites. The classification performance showed the proposed algorithms yielded good malaria parasite life stage classification performance. The future directions include collecting more data and exploring more sophisticated algorithms.  
malaria parasite detection; malaria parasite stage of life; plasmodium parasites; deep learning; you only look once (YOLO); convolutional neural network (CNN) +
Footnote †: journal: Applied Sciences
# Bearing Estimation for Indoor Localization Systems Using Planar Circular Photodiode Arrays
## 4 Measurements and Evaluation
### Bearing Estimation  
The next stage of the processing chain is the bearing estimation phase. In this section, the effect of the reflections and the tilt of the PCPA will be examined to the angle estimation. In the evaluation, both the LS and the FD estimation methods will be utilized and the results will be compared.
# Optimization of PV and Battery Energy Storage Size in Grid-Connected Microgrid
## 3 Proposed Algorithm
### Objective Function  
In this study, the energy cost is chosen as an objective function. The goal is to obtain minimum total energy cost for the MG without compromising defined constraints; thus, the optimum PV and BESS size can be found.  
The energy cost (\(EC\)) is calculated as:  
\[EC=(PV_{Total,energy}\times PV_{Cost})+(BESS_{Total,energy}\times BESS_{Cost}) \tag{6}\]  
Here, \(PV_{Total,energy}\) and \(BESS_{Total,energy}\) are total output energy of PV and BESS, respectively. They are generated from the energy management algorithm in a defined time span. \(PV_{Cost}\) and \(BESS_{Cost}\) are the cost of PV and BESS, which include the capital, replacement, operation and maintenance costs.
# Underwater Drone Architecture for Marine Digital Twin: Lessons Learned from SUSHI DROP Project +
## 2 Materials and Methods
### IUIV Payload  
All subsystems within the UUV have specific operational capabilities dedicated to piloting, positioning or surveying. This paper is not focused on the piloting part, but is instead intended to deepen the challenges related to positioning and surveying techniques for UUV [42], explaining the tools and methods used and their specific implementation in Blucy.  
A first precaution necessary for the proper functioning of all subsystems is a precise synchronization of the clocks of the different sensors: fundamental for the correct alignment of the acquired data and to guarantee a high quality of the following processing. All data are synchronized using UTC timestamps and are then georeferenced thanks to the coordinates obtained by the positioning instruments.  
A second necessary precaution is the choice of subsystems. The approach followed had to consider the small size of the sensors to allow installation in the limited space available within the UUV. At the same time, among the sensors available for use on the UUV platform, a further parameter of choice was the analysis of power consumption that must necessarily be contained to ensure greater autonomy. Finally, the price-performance ratio was also considered with the aim of keeping costs low.
## 2 Methods and Performance Figures
### Coupling between FC and RL  
Two aspects motivate the coupling of the Q-learning algorithm with a fuzzy logic controller. The first one is the easiness with which complex controls can be designed and modified using fuzzy logic. The second one is their intrinsic ability to approximate and discretize functions [69]. Essentially, the FC is designed as a function of a limited input quantity (solar irradiation) \(x\) to a limited output quantity (circulator command) \(y\). The input quantity is transformed into a linguistic variable by using the linguistic values \"low\", \"medium\", and \"high\". A similar transformation is used for the output quantity. The rule base embedded in the inference engine represents the identity and is not subject to change. The RL is coupled to this FC by changing the definitions (in terms of membership functions) of the input linguistic values, i.e., the actions made by the Q-learning agent are performed directly on the FC fuzzifier (Figure 2).  
Two ways to perform the coupling were considered: changing the position of the peak of the \"high\" linguistic value and changing the position of the \"medium\" linguistic value. Figure 2(a),b clarifies how the membership functions were modified by a change of the controlled parameters in the two cases. Figure 3(a),b shows the resulting input-output mappings under seven different values of the control parameter.  
Figure 3: Deformations of the input membership function by changing the position of the maximum value, generating the family of the high radiation peak fuzzy controllers, (H)-FC (**a**) and the position of the medium value, generating the family of the medium radiation peak fuzzy controllers, (M)-FC (**b**).  
Figure 2: The combination of the Q-learning algorithm and the fuzzy logic controller.  
The action performed by the RL is the selection of the trans-characteristic to be in the next period. In the first case, this is like changing the control gain used in the circulator control.  
This arrangement allowed us to come up with a controller with a relatively small state-space (693 states) and a behaviour not too far from those used in experimental practice (control proportional to the solar irradiation).
# Model Predictive Control for Six-Phase Induction Machines with Insight into Past Current Errors
## 4 Compensation of Accumulated Current Errors
### RAE-MPC Conceptual Behavior  
Furthermore, a qualitative example of the internal workings of the algorithm is displayed in Figure 7. The left graph presents the evolution of the errors for a scheme governed by the cost function of a standard FCS-MPC, as depicted in (7). It can be observed how the error in the \(x\) component grows notably over time in a similar fashion to Figure 4. In the present cycle, this fact is explained by the selection of \(V_{1}\) as the optimal control action. Even though its error in \(x\) is high, the error in the other components would be low enough to minimize \(J\). In contrast, the right graph shows the effect that a simplification of the proposed control method would have in the studied component. More concretely, it has been considered that the cost function only takes into account the accumulated current error for the sake of clarity. As it can be observed, the recorded errors in \(x\) for two (\(e_{\text{xs}}^{\prime\prime}\)) and one (\(e_{\text{xs}}^{\prime}\)) cycles ago are 1 A and 3 A, respectively. Therefore, the accumulated error in the memory stage (\(e_{\text{xs}}^{\text{ms}}\)) is their sum, 4 A. According to (10), the optimal switching state in terms of the \(x\) component would be the one that obtains the error closest to \(-e_{\text{xs}}^{\text{ms}}\), depicted as a white dot in the graph. This variation is responsible for the change in chosen control action, which is now \(V_{3}\) instead of \(V_{1}\). As a consequence, the accumulation of the error in the \(x\) component is avoided.  
It is worth noting that the selection performed by the proposed method is more complex than the one depicted in Figure 7, since the optimization of the accumulated errors is carried out in the four currents. Furthermore, the cost function is actually the sum of the two shown in the example, as described in (11). For that reason, a compromise between obtaining a small sum of errors and minimizing accumulated individual errors is expected.  
Figure 6: RAE-MPC switching state selection process.
## 2 Experimental Setup
### 3D NRF-CT Image Measurement  
We used a high-flux LCS gamma-ray beam to measure the 3D NRF-CT image. Since the total gamma-ray flux was proportional to the average power of the laser with a proportional constant of 3.5 \(\times\) 10\({}^{6}\) photons/s/W, we used the Tm-fiber laser system with a typical average power of 36 W to generate the LCS gamma-ray beam. When the laser system was operated at 36 W, a total flux higher than 10\({}^{8}\) photons/s with a 100% energy bandwidth could be generated. We used a collimator with a hole diameter of 2 mm, which is twice as wide as that of the collimator used for 3D gamma-CT image measurement. The LCS gamma-ray beam flux passing through the collimator was numerically estimated using the EGS5 Monte Carlo simulation code [19], and thus we found that the flux density was 10 photons/s/eV and that the maximum energy was 5528 MeV with 2.9% energy bandwidth at the FWHM, which was able to excite the \(J^{\pi}\) = 1\({}^{-}\) NRF level at 5512 MeV in \({}^{208}\)Pb. Although the NRF-CT imaging technique is time-consuming, we were able to shorten the image acquisition time for each measurement point, which had been measured in our previous research [13] by adjusting the CT sample scanning pattern and increasing the LCS gamma-ray beam's intensity flux. The overall time needed to obtain a 3D NRF-CT image was approximately 48 h. Reference [14] gives more details on the experimental setup, the CT sample scanning plan, and a schematic diagram of the 3D NRF-CT image.