esa-sceva/satcom-chunk-collection huggingface dataset:
# An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts
## 4 Experimental Study  
### Development of the BPNN Model  
The artificial neural network (ANN) with only one hidden layer can estimate almost all problems [61]. So, a single hidden layer BPNN was adopted in this paper. For the ANN architecture, NumHLNs is very important, and the determination of it is widely regarded as the most critical task. [PERSON] pointed out in his research that the upper limit of NumHLNs is 2 n + 1, where n denotes the number of input layer nodes [62]. So, 37 BPNN models with NumHLNs from 1 to 37 were developed according to n equals 18 in this paper. To avoid overfitting, the training set was randomly split into the training subset and the validation subset, which account for 75% and 25% of the training set, respectively. Then, the above 37 BPNN models were used to predict the training subset and the validation subset, and each was run 5 times (see Figure 7), which is also helpful to avoiding overfitting. Acctrain and Accvalidate in Figure 7 represent the average overall accuracy of each model predicting the training subset and the validation subset, respectively. The same is true in other figures.  
The phenomenon that the prediction performance of the training subset significantly exceeds that of the validation subset is usually considered as the overfitting phenomenon; otherwise, it is called the underfitting phenomenon. Both overfitting and underfitting indicate that the model is unreliable. The prediction performance in Figure 7 shows that the differences between Acctrain and Accvalidate in the 37 BPNN models are all less than 5%, and the values of Acctrain and Accvalidate do not change much after NumHLNs exceeds 3. To find the optimally performing BPNN model among these 37 models, a ranking method proposed by [PERSON] et al. [63] was adopted in this paper. Firstly, the Acctrain, Accvalidate and their difference abs \(|\)Acctrain\(-\)Accvalidate\(|\) of the 37 BPNN models were sorted from smallest to largest, respectively. Then, these three parameters were scored, and the model with the smallest Acctrain received 1 point, and the second smallest one received 2 points. In this order, the model with the largest Acctrain received 37 points. For Accvalidate, the models were also scored according to this rule. However, for abs \(|\)Acctrain\(-\)Accvalidate\(|\)  
Figure 7: Prediction results of the BPNN models with various HLNs.  
the opposite is true. The model with the smallest \(\mathrm{abs}\,|\,\mathrm{Acc_{train}}-\mathrm{Acc_{validate}}\,|\) received 37 points and the largest one received 1 point. Finally, the three scores of each model were summed, and the model with the highest total score is optimal. It is obvious in Figure 7 that the model with 25 HLNs achieves the optimal performance, whose average \(\mathrm{Acc_{train}}\) and average \(\mathrm{Acc_{validate}}\) are 0.9577 and 0.9388, respectively. So, 25 was chosen as NumHLNs.
## 6 Simulation Results and Analysis  
In order to investigate the synchronization performance of the system under different control strategies, a dual-motor speed synchronization cross-coupling control model is established on the MATLAB/Simulink platform. This model takes into account the mutual coupling relationship between the motors and the impact of friction disturbances on synchronization performance. By comparing the effects of different control strategies on the system synchronization accuracy, the advantages and disadvantages of each control method in practical applications can be thoroughly analyzed and evaluated. In the process of model establishment, typical motor parameters for the dual-motor system are used, and the specific motor parameters are provided in Table 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Sator resistance/\(\Omega\) & 1.4 \\ D-axis induction Ld/mH & 4.8 \\ Q-axis induction Ld/mH & 4.8 \\ Flux linkage \(\Psi_{\text{r}}/\text{wb}\) & 0.0593 \\ Pole pairs P & 4 \\ Inertia of motor 1 J1/kg\(\cdot\)m\({}^{2}\) & 1.56 \(\times\) 10\({}^{-4}\) \\ Inertia of motor 2 J2/kg\(\cdot\)m\({}^{2}\) & 1.56 \(\times\) 10\({}^{-4}\) \\ Rated power kw & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the dual-motor system.  
Figure 6: Block diagram of the dual-motor servo system with DOB.  
Figure 7: Block diagram of the improved DOB.
# Microalgal Growth in Aquaculture Effluent: Coupling Biomass Valorisation with Nutrients Removal
## 4 Conclusions  
In this study, the removal efficiency of nitrogen and phosphorus by _C. vulgaris_ in aquaculture wastewater was evaluated. Additionally, a biochemical composition analysis of the biomass was performed to further valorise the biomass. _C. vulgaris_ was able to grow in the aquaculture effluent with a growth rate of 0.260 \(\pm\) 0.014 d\({}^{-1}\), similar to that of the positive control, which was 0.276 \(\pm\) 0.004 d\({}^{-1}\). Additionally, it was found that the wastewater treatment was effective regarding nitrogen and phosphorus contents, as shown by the removal efficiencies of TN, NO\({}_{3}\)-N and PO\({}_{4}\)-P above 90%. It is important to note that in the AW assay only 3 d and 2 d were required to reduce TN and PO\({}_{4}\)-P to values below the EU legal limits, respectively. The biochemical analysis of the biomass revealed contents of lipids, carbohydrates and pigments typical and similar to values reported in other studies. However, the protein content was lower than expected, compared with the literature values.  
**Author Contributions:** Conceptualisation, A.F.E. and J.C.M.P.; methodology, A.F.E., E.M.S. and J.C.M.P.; formal analysis, A.F.E.; investigation, A.F.E. and S.M.S.; resources, R.A.R.B. and J.C.M.P.; writing--original draft preparation, A.F.E.; writing--review and editing, E.M.S., R.A.R.B. and J.C.M.P.; supervision, A.F.E., J.C.M.P. and R.A.R.B.; project administration, J.C.M.P.; funding acquisition, J.C.M.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was financially supported by: (i) LA/P/0045/2020 (ALiCE), UIDB/00511/2020-UIDP/00511/2020 (LEPABE) and UIDB/50020/2020-UIDP/50020/2020 (Associate Laboratory LSRE-LCM) funded by national funds through FCT/MCTES (PIDDAC); (ii) Project PIV4 Algae (Ref. PTDC/BTA-BTA/31736/2017; POCI-01-0145-FEDER-031736), funded by FEDER funds through COMPETE2020-Programa Operacional Competitividade e Internacionalizacao (POCI) and by national funds (PIDDAC) through FCT/MCTES; and (iii) Project PhotoBioValue (ref. PTDC/BTA-BTA/2902/2021), funded by FEDER funds through COMPETE2020-Programa Operacional Competitividade e Internacionalizacao (POCI) and by national funds (PIDDAC) through FCT/MCTES. [PERSON] and [PERSON] thank FCT for the financial support of their work through the FCT PhD Research Scholarships 2020.05477.BD and 2021.07412.BD, respectively.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors wish to thank the help of [PERSON] from ICBAS-UP in the collection of the wastewater samples.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Modeling Method of PV DC Arc Based on AR Model  
### Modeling Method Based on AR Model  
As mentioned in Section 2.2, we can use an appropriate times series model to estimate the PSD of the inverter noise because it follows a similar pattern. However, the arc current does not apply to the model, which can be criterion for arc fault detection. There are three commonly used time series models for a stationary stochastic process, including the Autoregressive model (AR), the Moving Average (MA) model, and the integrated Autoregressive Moving Average (ARMA) model. The AR model has the simplest structure, which requires the least computation and is most suitable for the DC current processing in the PV system by using an embedded microcontroller, as shown in Table 3.  
Then, the successive samples of inverter noise \(x(t)\) can be modeled by an AR-based model of order \(p\). which can be expressed by a difference equation as:  
\[x(t)=-\sum_{i=1}^{p}a_{p}(i)x(t-i)+\varepsilon(t) \tag{1}\]  
\(a_{p}(i)\)\(i=(1,2,\ldots,p)\) are the coefficients regarding the corresponding time series data. \(\varepsilon(t)\) is called the error terms, which is typically a white noise with zero-mean and a variance of \(\sigma_{t}\). In addition, \(\varepsilon(t)\) is uncorrelated with the sequence \(x(i)\)\((i<t)\). In Equation (1), the response variable \(x(t)\) is fitted by a linear combination of its previous values, which means the \(p\)-ordered AR process would be related to data \(p\) periods apart. Since the AR process can be expressed as the output of an all-pole filter, an estimated PSD of the same order \(p\) can be written as:  
\[\hat{P}_{X}(i)=\frac{\hat{\sigma}_{t}^{2}}{\left|1+\sum_{i=1}^{p}\hat{a}_{p}( i)e^{-j\omega t}\right|} \tag{2}\]  
Figure 3: The PSD of measured current under normal operation and arc fault conditions (TW means time window).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Spectral Distribution** & **Computation** & **Complexity** \\ \hline AR & Peak & Small & Easy \\ MA & Trough & Moderate & Moderate \\ ARMA & Peak, Trough & Large & Complex \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of commonly used models for the stochastic process.  
It can be seen from Equation (2) that the prediction accuracy of the PSD depends on how accurately the \(\hat{a}_{p}(i)\) and \(\hat{\sigma}_{t}^{2}\) can be estimated.
# Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
## 4 Controlling Weed in Paddy Fields at Different Growth of Stages
Automatic weed sampling provides a way to increase the amount of data obtained in the field (smaller sampling intervals) at lower overall costs of 7-13 USD/ha, and sensor technology is used exclusively for the application of herbicides, resulting in a reduction of herbicide usage of 30-70% [32]. Advanced weed management methods are required to manage weeds effectively. The process may include targeted and site-specific weed control, selection of weed seeds, different herbicide application (depending on weed distribution, spatial arrangement and soil properties), destruction of weed seeds over predation and microbial loss, nano herbicides, and optical spraying techniques. Advanced vision-guided robotics that can be adopted for site-specific weed management (SSWM) are transgenic herbicide-resistant crops, weed control and spraying robots, decision support systems, and pattern recognition modelling [33]. Implementing these technologies will help prevent unwanted species and improve existing weed management systems [34].
## 1 Introduction  
Extended reality (XR) technologies have evolved into ultra-high-definition, immersive displays due to breakthroughs in data acquisition, data transmission, and playback output devices for media content [1, 2]. XR technologies such as virtual reality (VR) can now provide more interactive and immersive experiences due to rapid technological advances such as state-of-the-art full-body motion tracking, gyroscopes, motion sensors, stereoscopic displays, and audio [3]. Although VR is primarily focused on entertainment, recent research studies highlight its implications in numerous other fields such as education [4], healthcare [5, 6], and training for disaster preparedness of natural hazards such as flooding [7].  
Recent research has shown that VR can administer a narrative-rich, high-fidelity environment that administers a fully immersive and interactive experience for serious games (SGs) such as disaster preparedness and evacuation training for hazardous situations [8]. Although VR SGs can be entertaining, their fundamental purpose is to provide training and education to its users. VR-based serious games offer a secure space to conduct training that would be risky and costly in real life [9]. Studies have been conducted to show that the skills learned during a well-designed VR SGs are long-lasting [10] and depend mainly on the efficiency and wayfinding capabilities of participants [11]. Wayfinding capabilities of the VR users can be enhanced by aiding the spatial knowledge through contextual cues [12]. These contextualized VR games can improve behavioral responses, learning outcomesand evacuation preparedness of users [13]. However, the role of contextual cues such as wayfinding in virtual reality environments (VRE) and its influences on user-perceived quality of experience is yet to be thoroughly researched [14].  
In the current study, we designed an experiment to investigate the impact of wayfinding cues on users' behavioral and physiological responses using Oculus Rift S head-mounted display (HMD). Thirty-nine participants were presented with VRE of a flooded city and asked to navigate the city to find a safe zone in a timed scenario. Participants were divided into three groups, i.e., a controlled group without any wayfinding cues, an experimental group with static wayfinding cues (directions only), and an experimental group with dynamic wayfinding cues i.e., assistive lights along with directions. It was hypothesized that wayfinding cues would significantly impact players' physiological and psychological behavior in VRE. Our findings suggest that integrating contextual wayfinding cues in VR training simulations can result in effective VR design and enhance users perceived experiences of these applications.  
The research article presents a detailed review of existing studies on VRE wayfinding in the next section. Section 3 explains the materials and methods used in the experimental design, followed by a detailed result section. Section 4 discusses the research findings, followed by a conclusion.
# Water Consumption, Quantity and Quality of Wastewater and Sewage Sludge from Polish Dairies
## 3 Results and Discussion
In most WWTPs, excess activated sludge was stabilized aerobically, directly in the activated sludge chambers or in separate stabilization chambers, followed by a solution combining aerobic and anaerobic sludge stabilization (aerobic in activated sludge chambers, anaerobic in separate digestion chambers). The sludge from two plants was transported to industrial biogas plants owned by another company. In one dairy, the thickened excess sludge is fed into an external biogas plant together with the post-flotation sludge. The sludge was usually dehydrated on filter presses. Agricultural application of dehydrated sewage sludge predominated (10/14 of the surveyed WWTPs). Only at one wastewater treatment plant did the sewage sludge not end up on agricultural land. No relevant data were collected for three plants (Table 3). Despite the discharge of domestic wastewater in WWTPs, the results of the sanitary examinations of the sludge showed that none of the samples contained dangerous pathogenic organisms that would prevent their agricultural application (Table 4). The dry matter of the sludges examined ranged from 6.14 to 23.65%, which means that the hydration of the sludges was between 76.35% and 93.86%. Therefore, the sludge has a sticky or earthy form. The organic matter content of the sludges ranged from 40.15 to 80.2%, which means that not all of them should be considered stabilized. According to [54], the technical threshold of stabilization is a reduction in the original organic matter in the sludge by 38-40% (Table 4). The fertilizing value of sewage sludge is determined not only by the organic matter content, but also by the nitrogen and phosphorus content as well as contents of other macronutrients. In the case of nitrogen, this value was between 3.86 and 8.29% of the dry matter (DM). For phosphorus, it was between 1.03 and 8.13% DM. These values are comparable to those determined in studies with sewage sludge from a dairy WWTP in Ireland [55] and to the data presented in the study by [PERSON] et al. [56] and by [PERSON] et al. [57]. Unfortunately, none of the reports from the studies on sewage sludge contained information on potassium content. The content of meso-elements (Mg and Ca) was in the range of 0.27-0.66% DM and 1.13-19.6% DM, respectively, and were higher than the values reported by [58] for biochemically treated activated sludge, but lower than those found for the lime-treated sludge from DAF.
## 3 Results
### Validation of Results
#### 3.3.3 Comparison of Results  
We examined the results of our computer simulation and compared them to a series of pictures from our actual experiments. When we evaluated them side by side, it was easy to see that the computer simulation matched the real experiment's results, confirming that our computer model can accurately predict the outcome.  
Figure 14 displays five images of the mixing process in the simulation and experiments. The image focuses on the mixing chamber, as it is the location where the process is most relevant. The images were captured using a digital USB microscope (Dino-Lite Digital Microscope AF4515 ZTL (AnMo Electronics Corporation, Taipei, Taiwan)). In the first stage, the blue fluid remains contained at the stop valve while the yellow fluid is introduced into the mixing tank. Progressing to the second stage, the yellow fluid reaches the stop valve, prompting its opening. Subsequently, the initiation of the mixing process is depicted as the blue fluid is released, causing both fluids to advance uniformly within the chamber. Moving to the fourth stage, an outlet duct is revealed, facilitating the controlled release of  
Figure 11: (**a**) Mesh size and convergence, (**b**) element quality.  
Figure 12: Mix index according to channel size variation.  
Figure 13: ANSYS image simulation result.  
the yellow fluid and ensuring thorough mixing within the chamber's final section. Finally, the mixing tank attains full capacity, indicating successful completion of the mixing process.  
In the experimental graphs and simulation charts, we can observe the similarity in the process. Figure 15 illustrates the details of this process resemblance. Through this analysis, we can validate that simulation, in this case, is a valid tool for the design of future devices that operate based on similar principles.
# Compact Model for L-Shaped Tunnel Field-Effect Transistor Including the 2D Region
###### Abstract  
The L-shaped tunneling field-effect transistor (LTFET) is the only line-tunneling type of TFET to be experimentally demonstrated. To date, there is no literature available on the compact model of LTFET. In this paper, a compact model of LTFET is presented. LTFET has both one-dimensional (1D) and 2D band-to-band tunneling (BTBT) components. The 2D BTBT part dominates in the subthreshold region, whereas the 1D BTBT dominates at higher gate-source biases. The model consists of 1D and 2D BTBT models. The 2D BTBT model is based on the assumption that the electric field originating from the gate and terminating at the source edge is perfectly circular. Tunneling path length is obtained by calculating the distance along an electric field arc that runs from gate to source. The 1D BTBT model is based on a simultaneous solution of the 1D Poisson equation in source and channel regions. Expressions for electric field and potential obtained from integrating the Poisson equation in source and channel regions are solved simultaneously to find the surface potential. Once the surface potential is known, all the other unknown variables, including junction potential and source depletion length, can be calculated. Using the potential profile, tunneling lengths were found for both the source-to-channel BTBT regime, and channel-to-channel BTBT regime. The tunneling lengths were used to calculate the BTBT tunneling rate, and finally, the drain-source current as a function of gate-source, and drain-source bias was calculated. The model results were compared against technology computer-aided design (TCAD) simulation results and were found to be in reasonable agreement for a compact model.  
Keywords:band-to-band tunneling; L-shaped tunnel field-effect-transistor; 2D BTBT; corner-effect +
Footnote †: journal: Applied Sciences
# Numerical Investigation of Thermal Efficiency of a Solar Cell
## 3 Results and Discussion
### Numerical Results and Discussion  
As seen in Figure 8, the trend line representing the points carries a slope of 1, which well-justifies the validation of the computation model and solver. The simulations are carried out at various heat inputs under different ambient conditions and different mass flow rates. For validation, simulations were carried out at two values of mass flow rate (0.025 kg/s and 0.05 kg/s). The different ambient conditions for which the simulations were carried out are shown in Table 11. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(Q_{net\,input}\) & \(Q_{total,\,cross\,section,\,PVcell}\) & \(Q_{total,\,PV\,cell,\,the\,back\,surface}\) & \(Q_{pipe,\,total}\) & \(Q_{gained\,by\,water}\) & \(Q_{total,\,loss}\) & \(Q_{uncertain}\) \\
**(W)** & **(W)** & **(W)** & **(W)** & **(W)** & **(W)** & **(W)** \\ \hline
197.16 & 8.42 & 7.65 & 103.67 & 73.15 & 192.90 & 4.26 \\
208.54 & 6.1 & 5.49 & 73.99 & 114.95 & 200.53 & 8.00 \\
167.44 & 4.39 & 3.88 & 52.67 & 104.5 & 165.44 & 2.00 \\
212.21 & 4.53 & 3.99 & 54.63 & 135.85 & 199.00 & 13.21 \\
118.44 & 2.46 & 2.08 & 28.40 & 62.7 & 95.64 & 22.80 \\
90.86 & 1.87 & 1.53 & 20.98 & 41.8 & 66.18 & 24.68 \\
59.95 & 2.21 & 1.84 & 25.42 & 20.9 & 50.38 & 9.56 \\
21.06 & 0.82 & 0.55 & 7.62 & 5.225 & 14.21 & 6.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Uncertain heat lost for different heat input. \begin{table}
\begin{tabular}{c c} \hline \hline \(Q_{net\,input}\) **(W)** & \(Q_{gained\,by\,water}\) **(W)** \\ \hline
197.16 & 73.15 \\
208.54 & 114.95 \\
167.44 & 104.5 \\
212.21 & 135.85 \\
118.44 & 62.7 \\
90.86 & 41.8 \\
59.95 & 20.9 \\
21.06 & 5.225 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Heat gained by water for different heat input. In order to get a semi-empirical relation representing thermal efficiency in terms of _Re_, graphs for thermal efficiency versus inlet _Re_ were plotted (Figure 9). This graph gives a better understanding of the variation in thermal efficiency due to inlet _Re_ because of the variation in velocity of water at inlet. The graph also proved to be vital in validating the empirical relation with previous research [5; 10; 13]. For the instances where hour by hour (or other short time based) performance calculation for the system was required, it may be necessary to start with daily data of global and diffuse radiation. Radiation data are the best source of information for estimating average solar radiation. The effect of the atmosphere in scattering and absorbing radiation was variable with time as atmospheric condition. Sometimes global radiation values are in the middle range between cloudless days and completely cloudy days, such as heavy clouds and continuous light clouds. Figure 7 shows the hourly relative solar radiation intensity data of total and diffuse radiation for clear skies, measured with a thermopile pyrometer. It can be seen from the graph that global radiation starts increasing from the morning and
## 5 Future Applications with Emphasis in Agriculture
### EO & Field Irrigation
The use of radar technologies to estimate LAI is well established (see ([PERSON], 2010; [PERSON], 2010)). These approaches are not without issues, the radar response can saturate at high LAI ([PERSON], 2010; [PERSON], 2010) and the results can be confounded by changes in soil moisture content. However, [PERSON] et al. (2010) showed that the issues with soil moisture confounding can be partially offset with limited input of independent estimates of ground soil moisture (e.g., TDR or cosmos stations) are included within the LAI estimation framework. It is clear though that in order to be able to use both optical and SAR in remote sensing applications still is required considerable research in order to reach the reliability (e.g., cloud obscuration) and system resilience to support real time precision irrigation application.