esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application of the Concepts: Design of an In-Line Measuring Machine
### Architecture of In-Line Measuring Station
#### 4.2.4 Speed  
A measuring cycle includes part setup and fixturing, data collection (measurement) time, and data processing time, which in this case includes calculations related to the measurements, display of the measurements, and data transfer to the Cloud. The mounting plate was designed to quickly mount and lock the casting in preparation for the measuring cycle. The part is fixed to the plate by pneumatic clamps. The total time to mount, fix, locate, extract, and dismount the part is not negligible, taking a little more than the time for the actual measurement cycle. As will be explained, the total time is competitive with the current gauging time (between 25 and 30 s in the lab), and both are much faster than any procedure used in a measuring room (with a CMM).
## 4 Experiment Results and Analysis
### Path-Loss Model and Performance of Hybrid Positioning  
The \(N\) value of each reference point is calculated by employing Equation (12). We use the reference points in room A and room B to draw three-dimensional cubic interpolation diagrams of \(N\) values at different positions. The distribution of \(N\) in two rooms are shown in the Figure 8.  
As Figure 8 shows, the calculated \(N\) value varies at different positions in both room A and room B due to noise and multipath propagation. In room A, the \(N\) value of most positions is about 20, which verifies that \(N_{0}=20\) in Equation (13). A few positions have large calculated \(N\) value. These positions are located at the corner of the wall, near the door and window where has complex propagation paths. In room B, the signal of all  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Average Error (m)** & **CDF = 80\% (m)** \\ \hline KNN without MVF & 3.00 & 4.29 \\ KNN with MVF & 2.10 & 3.21 \\ MLP without MVF & 2.28 & 2.76 \\ MLP with MVF & 1.66 & 2.41 \\ CNN without MVF & 2.54 & 3.67 \\ CNN with MVF & 1.62 & 2.26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The positioning error of KNN, MLP and CNN with or without MVF in room A.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Average Error (m)** & **CDF = 80\% (m)** \\ \hline KNN without MVF & 2.25 & 3.56 \\ KNN with MVF & 1.87 & 2.98 \\ MLP without MVF & 1.85 & 2.45 \\ MLP with MVF & 1.58 & 2.17 \\ CNN without MVF & 2.62 & 4.20 \\ CNN with MVF & 1.41 & 1.96 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The positioning error of KNN, MLP and CNN with or without MVF in room B.  
As shown in Figure 10, the hybrid positioning model performs better than CNN model though the two curves overlap at the end. Some test points in room A do not satisfy Equation (13), and their positioning results are the same under the two models. The average positioning error of hybrid positioning model and CNN alone are 1.47 m and 1.62 m respectively. The positioning accuracy of hybrid positioning model is 9.26% higher than that of CNN alone.
## 3 Results and Discussion
### Denitration Rates of the Three Biochars
#### 3.2.2 Effect of O\({}_{2}\) Concentration
The concentration of CO and CO\({}_{2}\) produced during the C-NO reduction reaction of the three kinds of biochar is shown in Figure 5. As shown in Figure 5a, with the increase of O\({}_{2}\) concentration and reaction time, the relative content of CO produced in the reduction of nitrogen oxides by RHA decreased. The concentration of CO\({}_{2}\) increased with increasing O\({}_{2}\) concentration and reaction time. The relative changes in the concentration of CO and CO\({}_{2}\) indicated that CO participated in the reduction  
Figure 5: COx concentration produced by the denitration process of the three biochars at different O\({}_{2}\) concentrations. reaction to generate CO\({}_{2}\). Figure 5b shows the concentration of COx produced by the reduction of NO by BC. The concentration of CO was the highest when O\({}_{2}\) was 1%, decreased in the presence of 0.5% O\({}_{2}\), and further diminished with 2% O\({}_{2}\). Under high temperature conditions, carbon is more likely to generate CO as the oxygen concentration decreases. However, as the oxygen concentration increases, CO\({}_{2}\) is more easily generated [22]. Thus, at the optimal oxygen concentration, there is more CO and less CO\({}_{2}\). The concentration of CO\({}_{2}\) increased with the increase of O\({}_{2}\) concentration and reaction time. As shown in Figure 5c, the COx concentration trend in the presence of SC was similar to that obtained with RHA. From the analysis of the COx concentration generated from the three biochars in the reduction of NO, some conclusions can be drawn. CO participates in the reduction reaction, and its involvement is related to O\({}_{2}\) concentration during the conditions. With the increase of O\({}_{2}\) concentration, a large amount of CO\({}_{2}\) will be generated and will inhibit the activity of the reaction sites on the carbon surface, thus suppressing the reduction of C-NO. The presence of oxygen molecules helps to promote the C-NO reaction under high temperature. However, there is a competition between C-O\({}_{2}\) and C-NO reactions, and the former is thermodynamically easier. From the energy changes of the three types of reaction involving C, it appears that the easiest oxidation of C is the one that leads to the formation of CO or CO\({}_{2}\). In addition, the enthalpy change of CO generation is lower than that of the generation of other oxidation products of C. The enthalpy changes of C directly reducing NO is higher. This means that the presence of O\({}_{2}\) causes a large consumption of carbon and lowers the selectivity of the reaction [20]. Therefore, the optimal choice of O\({}_{2}\) concentration is necessary in the reduction of C-NO. \[\mathrm{C+O_{2}\rightarrow CO_{2}}\qquad\qquad\mathrm{\Delta H=-393.5 kJ/mol} \tag{5}\]  
\[\mathrm{C+1/2O_{2}\rightarrow CO}\qquad\qquad\mathrm{\Delta H=-110.5 kJ/mol} \tag{6}\]  
\[\mathrm{C+2 NO\rightarrow CO_{2}+N_{2}}\qquad\qquad\mathrm{\Delta H=-574.1 kJ/mol} \tag{7}\]
## 3 Results and Discussion
### Cytotoxicity Assay on MCF-7  
The optimized nanosponge (IBNS4) exhibited a significant reduction in cell viability (88.24%, 74.27%, 67.10%, 52.84%, 35.55%, 24.86%, 17.12%, and 11.70% at 0.78, 1.56, 3.13, 6.25, 12.50, 25, 50, and 100 \(\upmu\)g/mL) in comparison with pure drug IBT (92.29%, 81.49%, 74.43%, 63.86%, 53.94%, 37.73%, 27.17%, and 16% at 0.78, 1.56, 3.13, 6.25, 12.50, 25, 50, and 100 \(\upmu\)g/mL), respectively, against MCF-7 cells, as shown in Figure 8[53]. The half-maximal inhibitory concentration (IC\({}_{50}\)) was found to be 13.42 and 6.84 for ITB and IBNS4. Developed NSs (IBNS4) had 1.96 times the potential compared to the pure drug (ITB). The reported mechanism of action by BTK inhibitors against the MCF-7 cells is the down-regulation of matrix metalloproteinase (MMP) 9 expression by blocking NF-kB and AP-1  
Figure 6: SEM image of optimized NS IBNS4.  
Figure 7: Cumulative (%) release of ITB and IBNS4.  
activation via the PLC\(\gamma\)/PKC/MAPK and IKK signaling pathways [2; 54; 55]. The R\({}^{2}\) value of our cytotoxicity assay was 0.9942 and 0.9929 for ITB and IBNS4, reflecting the data that best fit the regression model.
# Propagation, Scattering and Defect Detection in a Circular Edge with Quasi-Edge Waves
## 3 Experimental Study
### Specim  
The tested specimen was an aluminium tube that had an outer diameter of 200 mm, a wall thickness of 5 mm, and a height of 200 mm. These were the same dimensions as those of the FE model described in Section 2.3. A 1 mm thick through-thickness edge defect was manually created on the top edge of the specimen in a 45\({}^{\circ}\) direction. During the experiment, the depth of the through-thickness edge defect was increased from 2 to 14 mm with intervals of 3 mm.
## 4 Results and Discussion
### Height Estimation of Single Objects
#### 4.4.2 Trees
Figure 7: Height estimation of a coniferous tree with 7 m crown diameter: (**a**) Pleiades satellite image detail (left), corresponding height-colored DSM and profile location (right); (**b**) WorldView-3 satellite image detail (left), height-colored DSM with profile location (right); and (**c**) tree profile of 0.5 m width from the matched point clouds; the red horizontal line is the measured reference height.
## 3 Experimental Setup
### Phase Performance Test Using Industrial Vibration Shaker  
To verify the radar sensor's capability in industrial vibration sensing, a second measurement scenario was conducted using an industrial vibration shaker as a reference target for vibration. In this setup, a vibration test system by IMV was employed, as illustrated in Figures 7 and 8. For the measurement of very small displacements, several setups were adopted, such as the shaker machine and linear mechanical slider, as shown in Table 4. Currently, there is no mature and standardized setup for such measurements. Therefore, the experiment in this study was conducted at the IMV test center, which complies with ISO/IEC 17025 [19] standards for the competence of testing and calibration laboratories. ISO/IEC 17025 is a standard for laboratory management systems, ensuring that all equipment is well calibrated.  
The vibration test system controls both acceleration and vibration frequency, which in turn determine the displacement, defined as the peak-to-peak vibration amplitude. In this experiment, vibration frequencies of 700 Hz and 1600 Hz were used, with accelerations adjusted to generate specific small displacements at the target. The displacement amplitude ranged from 15.198 \(\upmu\)m down to 0.019 \(\upmu\)m, as described in Table 5.  
Each measurement was conducted over a 30 s period with three iterations, resulting in a total of 48 trials at 700 Hz and another 48 trials at 1600 Hz, as detailed in Tables 5 and 6.  
Figure 8: Test system setup image.  
Figure 7: Test setup with industrial vibration shaker.  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{3}{c}{**Vibration Frequency**} \\ \hline  & **700 Hz (Short PRT)** & **1600 Hz (Long PRT)** \\ \hline
**Acceleration [g]** & **Amplitude p-p [\(\mu\)m]** & **Amplitude p-p [\(\mu\)m]** \\ \hline
15.0 & 15.198 & 2.909 \\ \hline
10.0 & 10.132 & 1.939 \\ \hline
8.0 & 8.106 & 1.551 \\ \hline
6.0 & 6.079 & 1.164 \\ \hline
4.0 & 4.053 & 0.776 \\ \hline
2.0 & 2.026 & 0.388 \\ \hline
1.0 & 1.013 & 0.194 \\ \hline
0.9 & 0.912 & 0.175 \\ \hline
0.8 & 0.811 & 0.155 \\ \hline
0.7 & 0.709 & 0.136 \\ \hline
0.6 & 0.608 & 0.116 \\ \hline
0.5 & 0.507 & 0.097 \\ \hline
0.4 & 0.405 & 0.078 \\ \hline
0.3 & 0.304 & 0.058 \\ \hline
0.2 & 0.203 & 0.039 \\ \hline
0.1 & 0.101 & 0.019 \\ \hline \end{tabular}
\end{table}
Table 4: Target displacement by industrial shaker.  
\begin{table}
\begin{tabular}{c c} \hline \multicolumn{3}{c}{**Target Displacement by Shaker [\(\mu\)m]**} \\ \hline
15.2 & 370,688 \\ \hline
10.13 & 368,640 \\ \hline
8.11 & 372,736 \\ \hline
6.08 & 372,736 \\ \hline
4.05 & 370,688 \\ \hline
2.03 & 370,688 \\ \hline
1.01 & 380,928 \\ \hline
0.91 & 376,832 \\ \hline
0.81 & 374,784 \\ \hline
0.71 & 366,592 \\ \hline
0.61 & 372,736 \\ \hline
0.51 & 368,640 \\ \hline
0.41 & 364,544 \\ \hline
0.30 & 372,736 \\ \hline
0.2 & 370,688 \\ \hline
0.10 & 370,688 \\ \hline \end{tabular}
\end{table}
Table 5: Captured phase data samples by 700 Hz/Short PRT.
## 4 Conclusions  
In order to embed the stability mechanism into the CPG framework, a sensor-embedded CPG stability enhancement algorithm was proposed, which dynamically corrects the coupling weights and phase differences of the CPG through real-time feedback from IMU and foot-end force sensors, and directly embeds the stability constraints in the rhythm generation layer. The foot-end force-amplitude adaptive mechanism was designed with an IMU-driven asymmetric coupling matrix to enable fast stability recovery under terrain perturbations.  
The conclusions of the research are as follows:  
1. A simulation and experimental platform were built for wheel-footed robots, and the stability constraints were directly embedded in the rhythm generation layer by real-time acquisition of IMU and end-of-foot force sensor data, dynamic correction of coupling weights and phase differences in the CPG network, and realization of closed-loop control. Through the above closed-loop control, the rapid response capability of the robot to terrain perturbations was greatly improved.
2. The foot-end force-magnitude adaptive mechanism and the IMU-driven asymmetric coupling matrix were designed to reduce motion oscillations and center-of-mass displacement fluctuations in complex environments such as cornering, hill climbing, and gully crossing to ensure overall motion stability and meet the requirements of high-precision inspection.
3. Both simulation tests and real experiments validate the excellent stability recovery capability of the sensor-embedded CPG stability enhancement algorithm under a variety of unstructured terrain conditions, providing a reliable and efficient closed-loop control strategy for autonomous motion and environment adaptation of wheel-footed robots.
## 2 Design of Real-Time Communication Subsurface Mooring System
### System Hardware Circuit Design  
#### 2.3.1 Satellite Communication Buoy (SCB)  
The function of the SCB is to establish a complete data communication link between the underwater sensor and the receiver at the ground station, so as to realize the collection and real-time recording of data, and to make remote modifications by receiving instructions from the shore station and adjust the working parameters of the system. The SCB is mainly composed of the main control unit, the battery unit, and a floating buoy of a specific shape, as shown in Figures 1 and 2. Its overall size is small, but its unique shape and structure design improves the stability of attitude. The main control unit includes circuit modules such as the satellite communication module to realize the functions of sensor data acquisition, processing, and real-time transmission. The battery unit is equipped with lithium battery packs to provide energy. The nominal voltage of the battery pack is 11 V and effective capacity is 1544.4 Wh, which can support the system for more than 1 year.  
As shown in Figure 8, the main control unit of the SCB consists of the control module, the real-time clock module, the data acquisition module, the power management module, the satellite communication module, and the water entry detection module. The control module uses a 16-bit MSP430 microcontroller to set the program logic to control other modules to implement various workflows of the system and verify, compress, and encrypt sensor data. We used a programmable real-time clock chip to provide the system with a high-precision calendar function and timing function and also use an external temperature-compensated crystal oscillator to improve the travel time accuracy, which can ensure that in the temperature range from 0 to 50 \({}^{\circ}\)C, the annual drift of the clock is less than \(\pm\)40 s. The data acquisition module integrates the inductive modem module [21], which can collect the sensor data according to the set logic and store them in the FLASH memory chip after verifying its effectiveness. It can store no less than 10,000 sets of sensor data, which stores about 416 days of data. The power management module uses the DC-DC and LDO circuits to convert the power supply voltage and provide it to other modules through the analog switch. The satellite communication module selects the Beidou second-generation module with the short message mode for data transmission [22]. Since a single Beidou card can only transmit 70 bytes of data each time in the short message mode, this system was designed with a multi-card polling transmission mode to increase the communication bandwidth. The water entry detection module uses the double-electrode detection method to detect whether the SCB is on the sea surface. Two electrodes are installed on the outer wall of the control cabin. When the SCB is below the sea surface, the electrodes are turned on. Otherwise, the electrodes are disconnected. It provides a reference for an efficient decision data recovery strategy.
## 4 Experimental Validation
### Measurement Setup  
The mock-up of the system is depicted in Figure 11. It consists of an aluminum frame assumed to be rigid, inside which there are three moving parts. The components governing the behavior are (i) the four folded beams that provide positive stiffness in the vertical direction (\(k_{p}=4k_{s}\) in the analytical model), (ii) two pairs of curved beams that provide negative stiffness, (iii) the center mass attached to these six components. As for the soft components, the four folded beams and the two pairs of curved beams are 3D-printed. Two aluminum sliders are designed to move horizontally to induce axial force in the curved beams. The position of the sliders is adjusted using two screws and determines the initial force experienced by the curved beams. The same displacement is applied to the sliders so as to not introduce any asymmetry into the system. As for the excitation, the impact is applied to the mass, and the calculated transfer function is the mass acceleration versus the input force.  
Figure 10: Modal shapes, with exaggerated amplitudes, associated with the first four eigenfrequencies, (**a**) symmetrical bending mode with vertical motion of mass at 44 Hz, (**b**) asymmetrical bending mode with rotational motion of mass 217 Hz, (**c**) symmetrical bending mode with inverted bending of curved beams 238 Hz, (**d**) symmetrical bending mode with parallel bending of curved beams 300 Hz.  
The frame with sliders and center mass are made of structural aluminium, with mass density \(\rho=2700\) kg/m\({}^{3}\) and Young's modulus \(E=69\) GPa. After weighing the 3D-printed spring components and modal testing, the mass density of the ABS parts and the Young's modulus are estimated from the first cantilever mode. Values are reported in Table 1.
# Bacterial Foraging-Based Algorithm for Optimizing the Power Generation of an Isolated Microgrid
## 2 Description of the Isolated MicroGrid
### Design Constraints  
In order to produce a proper management of the power generation in the IMG, we considered some design constraints.  
* Power Balance: The sum of the generation power of all sources must be equal to the total load demanded by the system: \[P_{1}+P_{2}+P_{3}+P_{4}=P_{L}\] (14)
* BS Model: The output power of the solar PV generator and the load demanded at time \(t\) by the system, determine the state of charge (SOC) of the battery storage system BS. On the other hand, the SOC of the BS system at hour \(t\), SOC(\(t\)), is related to the previous hour SOC, SOC(\(t-1\)) [6]: \[SOC(t)=SOC(t-1)-\alpha_{D}P_{2}(t)+\alpha_{C}P_{3}(t)+\alpha_{C}P_{4}(t)\] (15) where \(\alpha_{D}=\eta_{D}/B_{C}^{max}\) and \(\alpha_{C}=\eta_{C}/B_{C}^{max}\), in which \(\eta_{D}\) and \(\eta_{D}\) are the battery charging efficiency and the battery discharging efficiency, respectively. Also, \(B_{C}^{max}\) is the maximum battery capacity. Using Equation (15), a general mathematical equation by the battery dynamics can be established as: \[SOC(t)=SOC(0)-\alpha_{D}\sum_{\tau=1}^{t}P_{2}(\tau)+\alpha_{C} \sum_{\tau=1}^{t}P_{3}(\tau)\] \[+\alpha_{C}\sum_{\tau=1}^{t}P_{4}(\tau)\] (16) where \(SOC(0)\) is the initial state of charge of the battery, \(\alpha_{C}\sum_{\tau=1}^{t}P_{3}(\tau)+\alpha_{C}\sum_{\tau=1}^{t}P_{4}(\tau)\) is the input power by the battery and \(\alpha_{D}\sum_{\tau=1}^{t}P_{2}(\tau)\) is the power discharged by the battery at time \(t\), respectively. Finally, at time \(t\) the state of charge of the battery must be between the minimum (\(SOC^{min}\)) and maximum (\(SOC^{max}\)) possible capacity: \[SOC^{min}\leq SOC(t)\leq SOC^{max}\] (17) In this work, the BS parameters are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**ESS Parameter** & **\%** \\ \hline Round Trip efficiency & 85 \\ Charge efficiency & 85 \\ Discharge efficiency & 100 \\ Maximum state of charge & 95 \\ Minimum state of charge & 40 \\ \hline \end{tabular}
\end{table}
Table 2: Energy Storage System parameters.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Core** & **Coating Ligand or Chelate** & \begin{tabular}{c} **d\({}_{\text{avg}}\)** \\ **(nm)** \\ \end{tabular} & \
\begin{tabular}{c} **r\({}_{1}\)** \\ **(s\({}^{-1}\)mM\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **Applied Field** \\ **(T)** \\ \end{tabular} & **Ref.** \\ \hline MnO nanoparticle & PEG 1-phospholipid & 20 & 0.21 & 3.0 & [39] \\ MnO nanoparticle & Mesoporous SiO\({}_{2}\) & 15 & 0.65 & 11.7 & [40] \\ MnO nanoparticle & Human serum albumin & 25.2 & 1.97 & 7.0 & [41] \\ MnO nanoparticle & FA-TETT 2 & 16.8 & 4.83 & 7.0 & [20] \\ MnO nanoparticle & D-glucuron acid & 2.5 & 7.02 & 1.5 & [35] \\ MnO nanoparticle & FA-PAA 3 & 2.7 & 9.3 & 3.0 & This study \\ Mn\({}^{2+}\) ion & EDTA 4 & - & 2.9 & 0.47 & [8] \\ Gd\({}^{3+}\) ion & DTPA 5 & - & 4.1 & 0.47 & [8] \\ \hline \hline \end{tabular}  
* PEG: Poly(ethylene glycol). 2 FA-TETT: folic acid conjugated N-(trimethylisopropyl) ethylene diamine triacetic acid. 3 FA-PAA: Folic acid conjugated polyacrylic acid. 4 EDTA: Ethylenediamine triacetate acid. 5 DTPA: Diethylenediamine triacetate acid.  
\end{table} 
Table 2: Comparison of the r\({}_{1}\) values of various MnO nanoparticles and metal-ion chelates. Figure 10: (**a**) Magnetic dipole-dipole interactions between the Mn\({}^{2+}\) (S = 5/2) species on the MnO nanoparticle surface and the water proton spins (s = 1/2), as indicated by the dotted lines. The FA-PAA ligands coating the nanoparticle surface were omitted for simplicity. (**b**) Stimulation of the longitudinal water proton spin relaxation from the \(\beta\)-spin (m\({}_{\beta}\) = \(-1/2\)) to \(\alpha\)-spin (m = 1/2) by the 3d-electron spins (S = 5/2) of Mn\({}^{2+}\) (here, two stimulating Mn\({}^{2+}\) species are arbitrarily shown).
## 5 Real Part of the Interference Cancellation  
Knowing the correlation relationship between the real and imaginary parts of the interference, as well as the fact that the imaginary part of the received symbols contains only the interference component, we can estimate the value of the real part of the interference \(\mathbf{J}_{m_{0},n_{0}}^{\text{Re}}\) as:  
\[\mathbf{J}_{m_{0},n_{0}}^{\text{Re}}=\sum_{t}\rho(t,m_{0})\Im\left[\mathbf{Z} _{m_{0},n_{0}+t}^{\text{A}}\right] \tag{18}\]  
In [16], the authors showed that the correlation between adjacent subcarriers appears only in the presence of a Doppler shift in the frequency of the received signal. Since this work is focused on the application in ionospheric shortwave communication systems, where the influence of the Doppler shift is minimal [11], and the gain shown by the authors manifested itself at Doppler factor values greater than 0.1, we will restrict ourselves to considering only a multipath channel with memory. Based on the values of the determinants of the correlation matrices for various shifts between the symbols used for the interference estimation, as presented in Figure 3, the set of time shifts for symbols used in Equation (16) can be limited by the values \(t=-1\) and \(t=1\). Then, the expression for estimating the real part of the interference takes the form:  
\[\mathbf{J}_{m_{0},n_{0}}^{\text{Re}}=\rho(1,m_{0})\Im\left[\mathbf{Z}_{m_{0}, n_{0}+1}\right]+\rho(-1,m_{0})\Im\left[\mathbf{Z}_{m_{0},n_{0}-1}\right] \tag{19}\]  
Figure 4: Normalized self-interference correlation coefficient versus time shift.  
The FBMC-OQAM MIMO system signal demodulation algorithm, taking into account the proposed algorithm, can be rewritten as:  
\[\mathbf{a}_{m_{0},n_{0}}=\mathrm{Re}\left\{\mathbf{y}_{m_{0},n_{0}}\mathbf{H}_{m _{0}}\right\}-\mathbf{J}_{m_{0},n_{0}} \tag{20}\]  
The block diagram of the FBMC-OQAM MIMO equalizer after calculating \(\mathbf{y}_{m_{0},n_{0}}\) with internal noise compensation is shown in Figure 5.