esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Microbiological Quality and pH of BWBs

Table 1: Total number of lactic acid bacteria (LAB) and pH values of fermented BWBs. The obtained results showed that the expected level of bacterial counts--\(10^{8}\) log CFU/mL--was achieved in most of the prepared fermented beverages after 8 h of fermentation, except for the beverage with the addition of ground flaxseed inoculated with a mixture of LAB cultures, where the LAB concentration was 7.50 log CFU/mL. It is worth emphasizing that in some variants, including BWBs with _L. plantarum_ and _L. paracasei_ as well as BWBs with flaxseed inoculated with _L. paracasei_, the level of \(10^{8}\) log CFU/mL was reached after 6 h. The highest number of LAB (at the level of \(10^{9}\) CFU/mL) was obtained after 24 h of fermentation in beverages fermented with _L. paracasei_ with and without the addition of ground flaxseed as well as after 48 h in beverages with the addition of flaxseed inoculated with _L. plantarum_, for which the results were 9.24, 9.53, and 9.63 log CFU/mL, respectively. It should be underlined that only the result for the BWB with flaxseed fermented by _L. plantarum_ after 48 h was significantly different from all obtained results. It is also worth noting that the BWBs inoculated with the LAB mixture achieved lower numbers, with the highest result of 8.95 in the BWB after 24 h and 8.93 in the BWB with flaxseed after 48 h of fermentation. No presence of _E. coli_ or fungi (both yeast and filamentous fungi) was detected in all prepared BWBs variants. During fermentation, as the number of bacteria increased, a significant decrease in the pH value of all beverages was observed. No significant differences in pH values were observed only in the case of BWBs fermented with LAB mixture (between 6 and 8 h of fermentation) as well as BWBs with and without flaxseed inoculated with _L. plantarum_ (between 8 and 12 h of fermentation). The lowest pH values were recorded in BWBs fermented by _L. paracasei_ and LAB mixture after 48 h of fermentation, with pH of 2.77 and 2.81, respectively. The beverages with the addition of ground flaxseed inoculated with _L. paracasei_ and LAB mixture after 48 h of fermentation were also characterized by pH values < 3.0; however, these values were significantly higher compared to BWBs without the addition of flaxseed. It is worth noting that the addition of ground flaxseed increased the pH value of the beverages regardless of the inoculant (pH values were significantly higher at each hour of fermentation compared to samples without ground flaxseed). Lower pH values were observed in beverages fermented by _L. paracasei_ (with and without the addition of ground flaxseed) and the mixture compared to the _L. plantarum_ strain.
## 4 Results and Discussion
### Level 4: Deep Learning  
To address RQ4.1, a Convolutional Neural Network (CNN) [41,42] has been employed in this study to estimate the total CO2 emissions and fuel consumption of vehicles from multiple inputs. CNN is a form of deep neural network that is often used to explore visual imagery [37,43]. The deep learning model has been built using Google Collab and results are presented in Figure 19 and Table 14.  
It can be seen from Table 14 that the CNN model always delivers stable and high coefficient of determination values in all scenarios. Compared with Table 13, while the CNN model is yet to reach the highest R squared score, in any case, the model is likely to attain it with stable predictions. Moreover, Figure 19 demonstrates that the CNN model could predict with high accuracy.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & & \multicolumn{3}{c}{**Multiple**} & \multicolumn{3}{c}{**Logarithmic**} & \multicolumn{3}{c}{**Univariate Polynomial Regression**} \\ \cline{3-10} \multicolumn{1}{c}{**Predictor**} & \multirow{2}{*}{**Target**} & \multicolumn{3}{c}{**Logarithmic Regression**} & \multicolumn{3}{c}{**Regression**} & \multicolumn{3}{c}{**Univariate Polynomial Regression**} \\ \cline{3-10} \multicolumn{1}{c}{} & & & & & & & & & & \\ \hline Model (Year) + Engine Size (L) + & Total & Fuel & 0.68184 & 0.61418 & \(-\)0.31802 & 0.66658 & 0.69331 & 0.69174 & **0.70389** & 0.67582 \\ Cylinders & (L/100 km) & & **0.71549** & 0.62154 & \(-\)0.31802 & 0.68728 & 0.69041 & 0.69018 & 0.70343 & 0.71083 \\ Fuel Consumption in City (L/100 km) + Fuel Consumption in Highway (L/100 km) & & 0.99968 & 0.55908 & \(-\)0.31802 & **0.99968** & 0.99968 & 0.99968 & 0.99968 & 0.99968 \\ \hline Model (Year) + Engine Size (L) + & CO2 Emissions (g/km) & **0.74119** & 0.49410 & \(-\)0.04007 & 0.71355 & 0.71902 & 0.72576 & 0.72994 & 0.70450 \\ Engine Size (L) + Cylinders & & **0.73955** & 0.42943 & \(-\)0.04007 & 0.71247 & 0.71506 & 0.72388 & 0.72922 & 0.73300 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Coefficient of determination (R squared) values of Multiple Linear Regression, Logarithmic Regression, Multivariate Polynomial Regression, Transformation of data, and Exponential Regression models.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Predictor** & **Target** & **Convolutional Neural Network** \\ \hline Model (Year) + Engine Size (L) + Cylinders & Total Fuel Consumption (L/100 km) & 0.70061 \\ Engine Size (L) + Cylinders & 0.69482 \\ Fuel Consumption in City (L/100 km) + Fuel & 0.99964 \\ Consumption in Highway (L/100 km) & & \\ \hline Model (Year) + Engine Size (L) + Cylinders & CO2 Emissions (g/km) & 0.68912 \\ Engine Size (L) + Cylinders & 0.71746 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Coefficient of determination (R squared) values of Convolutional Neural Network.
## 5 Conclusions  
This paper proposes a transmission protocol to realize the effective support of dynamic TDD in the HAPS-IMT heterogeneous network and the QoS guarantee of various services. Power control and time slot resource allocation in the proposed transmission protocol are combined through LP-ABS, which provides GBS with higher resource configuration flexibility while mitigating cross-link interference. Further, this paper determines the specific transmission protocol configuration scheme by maximizing the expected throughput of the system. In order to realize the resource allocation of various services under this transmission protocol, a utility function is introduced to describe the degree of satisfaction of the service. Each user's time slot resource allocation scheme is obtained by solving the problem of maximizing cell utility. The simulation results show that the PCDT algorithm proposed in this paper can improve the average throughput of the system and reduce the outage probability. In future work, we will consider further introducing heterogeneous networks on the ground, such as micro base stations and indoor base stations. Therefore, resource allocation strategies in more complex scenarios need to be studied.  
Conceptualization, C.W. and L.W.; software, L.W. and X.Z.; validation, L.W. and X.Z.; data curation, L.W., C.W. and X.Z.; writing--original draft preparation, L.W. and X.Z.; writing--review and editing, C.W. and W.W.; project administration and funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (NSFC) under the Grant No. 61801033.  
The authors declare no conflict of interest.
# Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
## 3 Sensors
### Physiological Measurement Devices  
Activity logging, eye tracking, and visual sensing mostly capture observed behaviours. However, certain sub-processes of engagement are internal, and internal state changes and brain activities require physiological sensors attached to human body to measure, such as EEG, ECG, electro-oculogram (EOG), GSR, etc. For instance, the EOG produces Slow Eye Movement (SEM) and Rapid Eye Movement (REM) features [37], which are significant for sleepiness and drowsiness detection but cannot be acquired by mobile eye trackers. [PERSON] et al. [2] comprehensively reviewed physiological cues insinuating driver drowsiness, including EEG, ECG, EOG, blinks, SEM, heart rate, blood pressure, respiration rate, Electromyogram (EMG), GSR, and skin temperature. Traditionally, these need to be measured in clinical settings. With the advent of smart wearable devices such as the Fitbit or Apple Watch, some indicators can be continuously monitored throughout daily  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Data Type** & **Scale** & **Scenario** & **Labels** \\ \hline HBCU [56] & Video & 25.5 h & E-learning & Engagement degree \\ \hline DAiSEE [57] & Video & 25 h & E-learning & Engagement class \\ \hline EngageWild [58] & Video & 16.5 h & E-learning & Engagement class \\ \hline EngageNet [59] & Video & 31 h & E-learning & Engagement degree \\ \hline AffectNet [62] & Image & 1 M samples & Emotion & Emotion category \\ \hline CK+ [63] & Video & 593 & Emotion & Emotion category \\ \hline FER2013 [64] & Image & 35k samples & Emotion & Emotion category \\ \hline HARMONIC [15] & Multimodal & 5 h & HRI & Eye state, EMG, and poses \\ \hline UE-HRI [78] & Multimodal & 54 clips & HRI & Onsets of engagement state \\ \hline TOGURO [14] & Multimodal & 5.83 h & HRI & Engagement level \\ \hline YawDD [68] & Video & 342 clips & Driving & Onsets of drowsiness \\ \hline NTHU-DDD [69] & Video & 450 clips & Driving & Onsets of drowsiness \\ \hline FatigueView [38] & Video & 1384 h & Driving & Eye state, FAU, and onsets \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vision-based datasets related to engagement estimation.  
life. The combination of physiological cues can be even more useful than a single measure in isolation.
# Commercially Biochar Applied for Tartrazine Removal from Aqueous Solutions
## 3 Results and Discussion
### Testing the Biochar Efficiency as Tartrazine Adsorbent from Synthetic Aqueous Solutions
#### 3.2.4 The Influence of the Tartrazine Initial Concentration on the Adsorption Process  
As expected, the initial concentration of the tartrazine solution should have an effect on the tartrazine removal degree. In order to test it, the removal efficiency was measured at 30 \({}^{\circ}\)C stirring for 20 min at 400 rpm a mixture of 5 mg biochar with 5 mL tartrazine solution  
Figure 5: The influence of the pH, temperature, adsorbent dose, contact time, and the initial concentrations of tartrazine adsorption onto commercial biochar.  
of different concentrations from 1 to 20 mg L\({}^{-1}\). From the results depicted in Figure 5, it can be concluded that the maximum removal degree was obtained at low tartrazine concentrations.
## 7 Analysis and Results
The communication protocols considered in the analysis are the message queuing telemetry transport protocol (MQTPP) [108] and the limited application protocol (LAP), for which the mean transfer duration of the central server was calculated as [109]:  
\[T_{duration}=\frac{ST_{n}-ST_{1}}{Complete\ packets} \tag{6}\]  
Figure 5: Average throughput for various implementation scenarios for separate nodes and multiple nodes. Separate node (I), in-band; separate node (II), standalone; multiple nodes (I), in-band; multiple nodes (II), standalone. Figure 6: Patients served per cell (\(X\)-axis) for both separate-node and in-band configurations. where \(ST_{n}\) and \(ST_{1}\) denote the time stamp of end packets and initial packets, respectively. The above communication protocol was considered to identify the efficiency of minimizing the time delay (average). Analysis of the central server data packets of different sizes is depicted in Table 6.  
The total packet loss was evaluated using the following equation [108]:  
\[\text{Rate of packet loss}(\%)=\left(1-\frac{\text{Successful transmission}}{\text{Compete packet}}\right) \tag{7}\]  
The packet loss with respect the packet size is shown in Figure 8 for MQTTP, which shows that with increasing packet size, the packet loss also increases. Therefore, it is necessary to continue tracking the size of packet while sending the information in the network to maintain the quality with minimal loss. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Packet Size** & \(\mathbf{\tau}\pm\mathbf{\rho}\) **(LAP)** & \(\mathbf{\tau}\pm\mathbf{\rho}\) **(MQTPP)** \\ \hline
30 & 2.428 \(\pm\) 0.001 & 0.091 \(\pm\) 0.004 \\
60 & 5.332 \(\pm\) 0.002 & 0.764 \(\pm\) 0.006 \\
600 & 6.712 \(\pm\) 0.006 & 2.128 \(\pm\) 0.016 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Central server data packet analysis. Figure 8: Packet size vs. packet loss (MQTTP). Figure 7: Average delay for single-node and multiple-node configurations. Keeping the above parameters fixed, the network was simulated by changing the sensor quantity per room and the packet length. The packet delivery ratio is shown in Figure 9, which is expressed as:  
\[Packet\ delivery\ ration=\frac{z}{\sum_{i=1}^{N}y_{i}} \tag{8}\]  
where \(y_{i}\) denotes the total packets, \(i\)' is the node and \(N\) is the total nodes in a network. The throughput of the network for MQTTP and LAP is depicted in Figure 10.
## 3 Hardware and System
### System Overview  
In this section, we present the network system based on our designed sensor nodes. The system is based on the IEEE 802.15.4 standard described in the previous section to allow low range communications and energy consumption between the different nodes. It includes a set of sensor nodes organized as a network. The coordinator is connected to a control system acting as the base station. An illustration of this system is shown in Figure 4. The software architecture of the network includes two units as follows.  
Figure 4: System overview.  
Figure 3: The sensor node prototype.
## 2 Materials and Methods
### Study Design and Instrumentation
#### 2.2.4. Experimental Procedures  
Participants from both groups underwent static and dynamic measurements using the previously described tools. All testing sessions were conducted with participants sitting on a chair with their backs straight and feet flat on the ground to minimize unwanted trunk movement.  
The first set of measurements involved static assessments of AHP. Head rotation was quantified using DG, with participants instructed to gaze at an object placed 5 m away. Following this, the head inclination was measured using the eyeTilt application on an iOS smartphone. The smartphone was held upright, with the operator following the on-screen guides to identify anatomical landmarks such as the eyes and/or nose to ensure accurate alignment with the subject's head. Lastly, a 5 s static measurement was performed with the MoCap to obtain quantitative data on both head rotation and inclination. The three-dimensional positions of the markers were used to calculate these angles, employing the same conventions applied during goniometer measurements. Specifically, angles of head rotation and inclination to the subject's right were considered positive, while angles to the left were assigned negative values. This convention was consistently applied across all measurements to ensure uniformity in data collection.  
To assess the cervical ROM in participants and compare the measurements with both static head rotation and inclination angles, as well as with the CROM from healthy subjects, participants were instructed to perform specific dynamic movements. Each movement was repeated twice for consistency. The following tasks were performed (Figure 3):  
Figure 2: Visual representation of marker placement on anatomical landmarks on a young participant.  
* Head flexion/extension (Figure 3a): Starting from their natural neutral position, participants were instructed to perform controlled head flexion by gradually lowering their head forward and bringing the chin toward the chest. After returning to the neutral position, participants then executed a controlled head extension, tilting the head backward toward the scapulae and lifting the chin toward the ceiling.
* Head lateral bending toward left/right (Figure 3b): From the neutral position, participants were asked to tilt their head laterally toward one shoulder, return to the neutral position, and then repeat the movement to the opposite side.
* Returning to the neutral position, the same movement was repeated toward the left.  
In addition, an orthodontic evaluation was conducted by means of intraoral and extraoral photographs for each patient using occlusal mirrors and cheek retractors. Only patients with no prior orthodontic treatment were assessed, as previous treatments may have already compensated for possible anomalies. Furthermore, the absence of permanent first molars, which are considered key teeth for occlusion analysis, is considered exclusion criteria. According to these considerations, only 9 patients were available, and thus, these data were not included in this study.
## 3 Study on Surface Integrity of Milled Workpiece Surface
### Microscopic Topography of Milled Workpiece Surface
#### 3.1.3 Effect of MQL on the Microscopic Topography of Milled Workpiece  
MQL is a green machining method that uses the high-pressure oil mist to cool and lubricate the machining area. The number and position settings of the high-pressure oil mist nozzles, air pressure, and lubrication amount used all affect the lubrication effect of MQL. Figure 13 shows the surface topography of the machined workpiece at different lubrication amounts. According to Figure 13, it can be seen that under MQL machining condition, when the lubrication amount is 40 mL/h, the feeding tool marks are more obvious compared to dry milling. Compared with the lubrication amount of 80 mL/h, there are more banding bulges on the bottom surface of slot milling, which is caused by plastic deformation. Thus, when the lubrication amount is 40 mL/h, it cannot improve the plastic deformation of the workpiece surface caused by the extrusion of cutting heat and tool flank. The cooling effect from the high-pressure oil mist makes feeding tool marks more obvious.  
The rational selection of MQL amount is of great significance. When the amount of lubricating oil is too small, not only can it not effectively improve the surface quality, but it can also make the surface quality deteriorate sharply. In MQL machining, there is a critical amount of lubricating oil. When the critical amount is reached, a stable lubricant film is formed between the workpiece and the milling cutter. After that, increasing the lubrication amount used does not improve the quality of the oil film, and the lubrication effect does not change significantly.
# Power Management Strategy for an Autonomous DC Microgrid
## 4 Experimental Tests
### Test 2  
The solar irradiance and the PV cell temperature are profiled in Figure 8a. It is clear that the solar irradiance fluctuates fiercely and frequently, causing strong PV power fluctuations as shown in Figure 8b.  
Figure 9a demonstrates that at noon, excess PV power goes beyond \(p_{B\_MIN}\), while PV power is limited afterwards. This is case 2. The SC is recharged several times while the electrochemical battery bank absorbs and discharges power dynamically.  
Figure 9b,c show that both \(SOC_{B}\) and \(SOC_{SC}\) have respected their dedicated restrictions. Since PV power fluctuates severely and frequently, DC bus voltage remains within a \(\pm\)2% fluctuation amplitude  
Figure 8: (**a**) Solar irradiance and PV cell temperature and (**b**) Load power demand and PV MPPT power.  
Figure 7: Experimental results of test 1 with DG load-following mode: (**a**) DC microgrid power evolutions, (**b**) SOC evolution of battery bank, (**c**) SOC evolution of the SC, and (**d**) DC bus voltage.  
of the rated DC bus voltage, as shown in Figure 9d. The DG is not used because PV power is quite sufficient.
## 4 Conclusions  
Based on dynamic patched-grid technology coupled with a three-dimensional N-S equation solver, the numerical study of the aerodynamic interaction effects between the rotor and fuselage on the drag performance and flow field of the fuselage of a light civil helicopter is carried out under the forward flight condition. The conclusions can be summarized as follows:  
1. The comparison of the drag performance between the rotor-off and rotor-on configurations shows that the total drag increases by 57.9% while the drag of the fuselage and horizontal tail increases by 124.1%.
2. The cycle of the drag fluctuation is observed in one full revolution of the rotor with a change in amplitude of \(\pm\)20%. The change in the azimuth angle of the rotor mainly affects the drag performance of the fuselage and horizontal tail.
3. The aerodynamic interaction effects between the rotor and fuselage on the drag performance are mainly reflected in the influence of the backward-developing rotor wake on the flow field of the rear part of the fuselage. Rotor rotation induces an additional afterbody vortices system.
4. The vorticity magnitude of the rotor-on configuration near the rear fuselage/tail boom increases significantly, suggesting that strong flow interactions occur between the rotor wake and the fuselage wake, forming a vortices system with a wide range. This vortices system causes a large reverse pressure gradient on the rear surface of the fuselage, which contributes to the increase in the pressure drag.  
Although the basic features of the aerodynamic interaction effects between the rotor and fuselage can be reflected by the RANS, which is an effective tool for the prediction of general performance, it should be pointed out that due to the turbulence being totally modeled and averaged, the time-dependent nature of the flow field cannot be considered precisely reflected; thus, the application of advanced turbulence simulation approaches such as the hybrid RANS/LES method is necessary in further studies.  
Figure 21: Comparison of the vorticity magnitude along the Z-axis between the rotor-off and rotor-on configurations.  
**Author Contributions:** Conceptualization, H.Z.; methodology, W.S.; software, W.S.; validation, W.S.; formal analysis, W.S.; investigation, W.S.; resources, Y.L.; data curation, Y.L.; writing--original draft preparation, W.S. and Y.L.; writing--review and editing, H.Z.; visualization, Y.L.; supervision, H.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Scientific Research Fund of Young Teachers in Xi'an Technological University.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available on request due to restrictions of privacy.  
**Conflicts of Interest:** The authors declare no conflict of interest.