esa-sceva/satcom-chunk-collection huggingface dataset:
# Plume Divergence and Discharge Oscillations of an Accessible Low-Power Hall Effect Thruster
## 1 Introduction
### Theory of Operation
\[\eta_{C}=\frac{I_{b}}{I_{d}} \tag{7}\]  
\[\eta_{\text{M}}=\frac{I_{\text{B}}}{\bar{n}_{T}Q}\frac{M_{i}}{e} \tag{8}\]  
\[\eta_{\text{B}}=cos^{2}(\lambda) \tag{9}\]  
where  
\[Q=\left(\sum_{k=1}\frac{\Omega_{k}}{Z_{k}}\right)^{-1} \tag{10}\]  
\(\eta_{C}\) represents the ratio of useful ion beam current to the total discharge current, \(\eta_{M}\) represents the ratio of the ion mass flow to the total propellant mass flow consumed by the thruster, \(\eta_{B}\) quantifies the component of the momentum lost due to the plume divergence, \(M_{i}\) is the ion mass, and \(\Omega_{k}\) and \(Z_{k}\) are the ion current fraction and charge of the \(k^{th}\) species, respectively. The Faraday probe used for this study had a collector electrode made of tungsten with a diameter of 6.33 mm. The gap distance between the collector and the guard-ring measured 0.835 mm. The guard-ring had an outer diameter of 13 mm with the overall depth of the collector and guard-ring being 10 mm. The probe was biased to \(-15\) V based off of extensive Langmuir probe sweeps that demonstrated ion saturation to occur below 0 V in the plume, as shown in Figure 1.  
Figure 1: Langmuir probe I-V traces for 44-mm-diameter Western Hall Thruster (WHT44) at 200 W and several plume locations.
## 5 Optimal Design for a Superbroad FWM Conversion Bandwidth  
Although the optimally designed waveguide above exhibits a maximal nonlinear efficiency, the bandwidth is still very limited due to a dispersion which is not small enough (164.3 ps/nm/km) at 1950 nm as shown by the calculated wavelength dependence of the dispersion in Figure 8a (green solid line). Figure 8a also shows the calculated wavelength-dependent dispersion curves for the SRN ridge waveguides with some other dimensions. The dispersion was found to be close to 0 (\(-\)6.0 ps/nm/km) at 1950 nm for the waveguide with \(h\) = 625 nm and \(w\) = 1050 nm. Such a low dispersion is expected to give a broad nonlinear bandwidth. For such an SRN ridge waveguide, the waveguide nonlinear parameter and the linear loss were calculated to be 5.24 W\({}^{-1}\)m\({}^{-1}\) and 2.88 dB/cm, respectively, thus producing a FOM of 0.0790 W\({}^{-1}\). This FOM is only a little compromised compared with the maximal one (0.0804 W\({}^{-1}\)). Furthermore, the calculated FWM CE was calculated to exhibit a maximal value of \(\sim\)36.22 dB for a waveguide length of 15 mm, when the input pump wavelength/power and input signal wavelength/power were set to be 1950 nm/500 mW and 1951 nm/1 mW, respectively. Figure 8b shows the calculated wavelength dependence of the FWM CE for the designed SRN ridge waveguide and the 3 dB conversion bandwidth was found to be as broad as 518 nm thanks to the small waveguide dispersion around 2 \(\upmu\)m. Besides, one can also find that it may be not a good idea to etch the SRN layer through, i.e., \(h\) = 800 nm, see the solid brown curve in Figure 8a, for a low dispersion in the 2 \(\upmu\)m band, which is nevertheless mostly implemented in the 1550 nm band.  
Figure 8: **(a)** Calculated wavelength dependence of the dispersion for the silicon-rich nitride ridge waveguides with various waveguide dimensions. **(b)** Calculated four-wave mixing conversion efficiency with respect to the signal wavelength for a SRN ridge waveguide (\(h\) = 625 nm, \(w\) = 1050 nm) with the zero-dispersion wavelength at 1950 nm.  
Figure 7: The calculated four-wave mixing conversion efficiency for the designed silicon-rich nitride ridge waveguide (\(h\) = 700 nm, \(w\) = 1100 nm) with a maximal figure of merit (FOM). **(a)** Dependence on the waveguide length. Here, difference of the pump and signal wavelengths is 1 nm. **(b)** Dependence on the signal wavelength. Here, the waveguide length is 14.6 mm and the pump wavelength is 1950 nm.
## 6 CMC Tracking Device and System Evaluation
### Evaluation Protocol  
The first step is to introduce the volunteer to the developed system and provide instructional cues on the system operation based on the framework presented in Figure 5. The volunteer, guided by the research team, measured their thumb parameters and calibrated the device. After calibration, the user visually verified that the ARH thumb motion resembled natural thumb movements by performing thumb circumduction a few times. After this verification, the volunteer was asked to test the device and telemanipulate the ARH. The test procedures were as follows:  
* Device operation and personalization test This test evaluated the operation of the CMC tracking device using the RPA and its ability to be personalized to different thumb sizes. The volunteer used the developed system without the flex glove and telemanipulated the \(R_{cmc}\) (the equivalent CMC joint) of the ARH thumb to oppose a cylinder (40 mm diameter and 200 mm height) suspended by a string against the palm. The opposition was deemed successful when no slippage was observed after removing the string holding the cylinder.
* Framework verification test This test verified the operation of the developed system using the proposed framework. The volunteer was asked to perform prescribed thumb motions and poses. The motions included pure flexion, extension, abduction, and adduction of the CMC joint, as well as thumb opposition poses with the index and middle fingers. An observational approach was followed in which the hand poses performed by the volunteer were observed by a research team member to evaluate the extent of the thumb reaching the target pose. Then, the volunteer was provided with four objects and asked to perform power and precision grasps. The power grasps were performed using a cylinder (diameter of 40 mm and height of 200 mm) and a cuboid (\(115\,\times\,57\,\times\,28\) mm). The precision grasps were performed using a marker pen (a thin cylinder with a diameter of 18 mm and length of 115 mm) and a sphere (radius of 25 mm). The objects used for power grasps were suspended by a string such that the object was touching the stationary ARH palm. The objects used for precision grasps were positioned and oriented by a research team member to achieve a human\(-\)like grasp. A grasp was considered successful if the object grasped by the ARH did not slip even after the string suspending the object was removed. A grasp was considered unsuccessful if the object slipped during the grasp or an ARH finger or the thumb reached their respective operational limits without adequately securing the object.
## 3 Experimental Results and Analysis
### Excess Pore Water Pressure
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Loading Sequence** & **Wave Form** & **Frequency (Hz)** & **PGA (g)** & **Duration (s)** & **Test Programs** \\ \hline \multirow{3}{*}{\(1\)} & White nose & & & & \\ \cline{2-6}  & Sine wave & 5 & 0.05 & 10 & 1. Excess pore water pressure ratio. \\ \cline{2-6}  & White nose & & & & 2. Time-history curves of the acceleration. \\ \hline \multirow{3}{*}{\(2\)} & White nose & & & & 3. Acceleration amplification factors. \\ \cline{2-6}  & Sine wave & 5 & 0.10 & 10 & 4. Bending moment of the sheet-pile wall. \\ \cline{2-6}  & White nose & & & & 5. Lateral displacement of the bridge superstructure. \\ \cline{2-6}  & Sine wave & 5 & 0.20 & 10 & 6. Lateral displacement of the sheet-pile wall. \\ \hline \end{tabular}
\end{table} 
Table 5: Loading sequence and test programs of the model test. finding is consistent with the general law of foundation liquefaction during earthquakes. In addition, from the measurement point furthest away from the sheet-pile wall to the measurement point nearest it, the excess pore water pressure ratio of the test group was 0.74, 0.66, and 0.44 times that of the conventional group, respectively. This indicates that the drainage structure can significantly reduce the development of excess pore water pressure in the foundation under the action of earthquakes. When PGA = 0.20 g, the peak excess pore water pressure ratio in the two sites was close. However, at the P5 measurement point, the peak excess pore water pressure ratio of the test group was much smaller than that of the conventional group. This is because the P5 measurement point was close to the sheet-pile wall. During the vibration excitation process, the drainage structure on the sheet-pile wall and the drainage structure on the pile worked together to effectively drain the excess pore water out of the soil. These results show that the peak excess pore water pressure ratio of the test group was always smaller than that of the conventional group under both PGA = 0.10 g and PGA = 0.20 g, indicating that the drainage structure could be effective at improving the liquefaction resistance of the foundation. Figure 6: Time-history curve of excess pore water pressure ratio.
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## 4 Evaluation
### Tag Power Consumption  
The tag's power consumption is important and determines the minimum required energy buffer size. Less energy usage for data transmission enables shorter harvesting times. The consumed energy can be distinguished in PMU self-consumption, static consumption from the MCU for driving its clock and the peripherals, and dynamic consumption for modulating the backscatter signal. The harvester's self-consumption was analyzed in Section 4.1.2. Once the cap voltage exceeds the charging threshold, the MCU is powered, dominating the total power consumption. We measure the supply current of the MCU to determine its power consumption in stop mode (inactive) and during message transmission. We also evaluate the dynamic consumption required to modulate the backscatter signal.  
The MCU provides various power-saving features, i.e., several low-power modes and clock frequency scaling. To reduce the consumed current as much as possible, we supply the MCU at only 1.8 V, scale the clock frequency down to 1 MHz, and use the STOP mode, in which the clock and most peripherals are halted, whenever possible. However, timers and a pulse-waveform generator peripheral are running during the message transmission, preventing STOP mode. Figure 12 shows the smoothed power consumption of the tag during the transmission of one message. It can be seen that the tag consumes only about 6 \(\upmu\)W when the MCU is in STOP mode, but total consumption rises strongly during message transmission. When directly comparing internal and external oscillators at 4 MHz, we can observe that usage of the external crystal consumes an additional 500 \(\upmu\)W, an increase of 80% compared to the internal oscillator. Additionally, the external oscillator requires a startup time of at least 2 ms after wakeup, and power demand spikes at oscillator startup. Given the high power requirement, we conclude that using the internal oscillator is desirable. Furthermore, when configuring the internal oscillator to only 1 MHz, which is still sufficient for backscattering data at 2 kbit s\({}^{-1}\), the MCU consumes less than 300 \(\upmu\)W.  
We also measured the consumption with and without the modulator circuit connected. The difference in power consumption reveals the dynamic power required for backscattering, i.e., switching the transistor's gate voltage. The actual backscattering only consumes about 20 \(\upmu\)W. Hence, the mostly idling MCU consumes the majority of the power. These results show that it is feasible to perform communication with ultra-low power  
Figure 11: Carrier frequency offset measured throughout three consecutive messages (differently colored lines). The supply voltage level strongly influences the generated carrier frequency when using the internal oscillator, but it also fluctuates significantly during the message. The external oscillator is independent of the supply voltage and much more stable.  
consumption suitable for wirelessly powered systems using general-purpose MCUs and off-the-shelf components.
## 2 Materials and Methods
### Overview of IoT Protocols  
In the context of IoT application protocols, including Extensible Messaging and Presence Protocol (XMPP), Constrained Application Protocol (CoAP), Data Distribution Service (DDS), Message Queuing Telemetry Transport (MQTT), and Advanced Message Queuing Protocol (AMQP), TCP and User Datagram Protocol (UDP) are the prevalent transport protocols employed [48; 49; 50]. However, in the realm of IoT applications, messaging protocols often provide additional functionality not offered by transport protocols like HyperText Transfer Protocol (HTTP) and WebSocket. Nonetheless, it is feasible to design an IoT system without relying on messaging protocols by developing application programs on a flexible transport protocol foundation like UDP and TCP [51]. In this study, which centers on vibration-based monitoring, TCP was chosen as the preferred protocol due to its crucial role in ensuring dependable data transfer from multiple end nodes to a centralized server.  
Consideration of the architecture is crucial when selecting an IoT protocol for a specific application. IoT protocols can be broadly categorized as either Client-Serveror Publish-Subscribe [52]. The MQTT protocol is widely recognized among TCP-based IoT protocols [53]. It is a lightweight messaging protocol specifically designed for IoT and Machine-to-Machine (M2M) communication. Operating on top of TCP/IP and utilizing a publish-subscribe messaging model, MQTT offers three levels of Quality of Service (QoS) to ensure message reliability. However, transmitting large acceleration data, such as in vibration monitoring, still poses a challenge [54].  
WebSocket has gained popularity due to its ability to overcome communication obstacles imposed by restrictive firewalls commonly found in enterprise and institutional networks. It has been observed that WebSocket is more suitable for applications that require near-instantaneous Round Trip Time (RTT) [55]. This protocol provides a standardized solution for full-duplex communication between web browsers and servers, and it has found applications in various domains [54]. Several attempts have confirmed the stability of WebSocket as a reliable network solution in the context of vibration monitoring [38]. WebSocket is widely employed in web-based applications, particularly in scenarios that demand real-time or interactive communication.  
On the other hand, plain sockets find utility in a broad spectrum of network applications, encompassing both web and non-web domains, where precise control and adaptability play a pivotal role. In light of the challenges associated with wireless networks for vibration-based SHM, this research leverages plain sockets for its networking requirements. Serving as a lower-level networking abstraction, plain sockets offer a general interface for network communication, affording flexibility and customizable capabilities that enable direct control over the communication process.
## 4 Practical Experiments
### Laboratory Applications with Three-Phase Voltage Source
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Voltages (V)**} & \multicolumn{2}{c}{**Currents (A)**} \\ \cline{2-6}  & \multicolumn{2}{c}{**Source**} & \multicolumn{2}{c}{**Load**} & \multicolumn{2}{c}{**Currents (A)**} \\ \cline{2-6}  & **RMS (V)** & **Angle (\({}^{\circ}\))** & **RMS (V)** & **Angle (\({}^{\circ}\))** & **RMS (A)** & **Angle (\({}^{\circ}\))** \\ \hline
**A-phase** & 230.27 & 0 & 189.83 & 5.5 & 0.782 & 5.5 \\ \hline
**B-phase** & 230.19 & \(-\)120 & 239.47 & \(-\)130.5 & 0.566 & \(-\)130.5 \\ \hline
**C-phase** & 230.32 & \(-\)240 & 267.92 & 125.5 & 0.316 & 125.5 \\ \hline \(V_{0}\)/\(I_{N}\) & 0.04\({}^{\star}\) & 82.41 \({}^{\star}\) & 44.84 \({}^{\star}\) & 156.36 \({}^{\star}\) & 0.242/0.246 \({}^{\star}\) & \(-\)23.69 \({}^{\star}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Voltages and currents measured by the Fluke 437 and SIMPELEC (\({}^{\star}\)) power analyzers for the source and load of the tested circuit under sinusoidal and balanced voltage supply (\(V_{0}\) = zero-sequence voltage, \(I_{N}\) = neutral current). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference Point** & **Analyzer** & **Apparent (VA)** & **Neutral (VA)** & **Apparent * (VA)** \\ \hline \multirow{2}{*}{\(\mathbf{N}\)} & Fluke 437 & 404.8 & 0.0666 & 404.68 \\ \cline{2-5}  & SIMPELEC & 405.126 & 0.0666 & 405.22 \\ \hline \multirow{2}{*}{\(\mathbf{n}\)} & Fluke 437 & 412.3 & 78.8976 & 412.41 \\ \cline{2-5}  & SIMPELEC & 412.827 & 78.8976 & 412.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Apparent and neutral-displacement powers measured by the Fluke 437 and SIMPELEC analyzers for the test circuit. (\({}^{\star}\)) Calculated using the neutral point of the opposite subsystem as voltage reference point. The apparent powers measured by the SIMPELEC (Figure 9) and Fluke 437 (Figure 10) power analyzers are presented in the third column of Table 5. The neutral-displacement powers were measured by the SIMPELEC power analyzer according to Equations (27), (28), and (35). If the source and load neutral points (\(N\) and \(n\)) are not available for use as voltage reference points, instrument manufacturers recommend the use of a grounding point (\(G\)) near the PCC. However, this is only an approximate solution because the apparent power measured by a power analyzer at the PCC using a grounding point as voltage reference is usually smaller than the apparent powers measured using either the source or the load neutral points (\(N\) or \(n\)) as voltage reference (third column in Table 4), especially when the source and load are unbalanced and/or distorted. Nevertheless, source and load apparent powers (\(S_{s}^{*}\) and \(S_{l}^{*}\)) could be determined (fifth column in Table 5) very close to the real values (third column in Table 5) from the apparent powers measured at PCC using the grounding point (\(G\)) as the voltage reference and the values of the neutral-displacement powers (\(S_{ns}\) and \(S_{nl}\)) indicated in fourth column in Table 5, applying Equations (37) and (39).
## 1 Introduction
In addition, by using a salt such as alkali halide, defects and traps present at the CTL/perovskite interface of PSCs were passivated, and interfacial recombination was relieved, resulting in a V\({}_{\text{OC}}\) of 1.15 V [36]. Additionally, surface treatment of SAM dipole molecules such as 2 PACz and MeO-2 PACz have been reported [37; 38]. The effect of interfacial engineering to eliminate the causes of non-radiative recombination losses by minimizing these interfacial defects, incomplete energy level alignment, and interfacial reaction losses is an important part in achieving efficient and stable PSCs. In particular, the interfacial engineering approach with the addition of PTAA polymer has proven effective in improving the energy level alignment and crystallinity of perovskite and HTL [39]. However, the effects of interfacial engineering have not been sufficiently discussed, and they remain to be understood. Moreover, although an approach of previous studies inserted a PTAA between the perovskite and the HTL, there have been no investigations into the thermal and air stability of PSCs with PTAA. In this study, we demonstrated highly thermal and air stable PSCs by replacing NiO with a PTAA/NiO bilayer that acts as an HTL. The PTAA/NiO bilayer showed a higher work function and better charge carrier mobility than the NiO film. It also inhibited interfacial recombination and accelerated charge transfer at the HTL interface, thereby increasing the V\({}_{\text{OC}}\) and current density (J\({}_{\text{SC}}\)) of PSCs simultaneously. The optimized PTAA/NiO bilayer-based PSC showed a PCE of 19.05%, which was improved over that of the NiO monolayer-based PSC with a PCE of 17.29%. In addition, the encapsulated PTAA/NiO bilayer-based PSCs showed excellent long-term stability in an environment with 85% RH and 85 \({}^{\circ}\)C temperature.
## 5 Experiments and Results  
### Experimental Setup  
Most experimental UAVs are equipped with expensive sensors and devices, such as high-precision IMUs, 3D light detection, and ranging (Lidar) sensors and differential global positioning system (DGPS), which will definitely improve the control accuracy but are unaffordable in many practical applications. To test the proposed tracking system in this paper, a customized quadrotor platform (65 cm in diameter) is used to perform all the flight experiments, which weighs 4.2 kg including all the payloads, as shown in Figure 14. The cost is much lower than the other platforms (e.g., DJI Matrice 200). The 3-axis gimbal at a dimension of \(108\times 86.2\times 137.3\) mm\({}^{3}\), weighs only 409 g, as shown in Figure 15a. The IMUs and encoders are consumer sensors at very low prices. The camera with focal lengths ranging from 4.9-49 mm has a maximum resolution of \(1920\times 1080\) at 60 frames per second (fps). The cost of the gimbaled camera is less than $400, which makes it very attractive considering its great performance. The AS5048A magnetic rotary encoder used in the gimbal system measures the angular position of each axis, which has a 14-bit high resolution output (0.0219 deg/LSB).  
Figure 14: The customized quadrotor platform.  
Figure 15: The gimbaled camera (**a**) and onboard equipment (**b**).  
As shown in Figure 15b, the quadrotor is equipped with an embedded microcontroller developed by the Pixhawk team at ETU Zurich [66]. The selected firmware version is 1.9.2.  
To achieve real-time image processing, a NVIDIA Jetson TX2 module is used as an onboard computer to implement the tracking algorithm, which is almost the fastest and most power-efficient embedded AI computing device. The output data of the vision system can be transferred from the onboard computer to the Pixhawk flight controller using serial communication, which is based on a MAVLINK [67] extendable communication node for the Robot Operation System (ROS) [68]. The control rate is at 30 Hz limited by the onboard processing speed. By using a 2.4 GHz remote controller (RC), the tracking process can be initiated by switching to the offboard mode when a target is selected.
## 5 Discussion
### Effectiveness of PCA and RPCA in Enhancing Thermal Contrast  
In LVT, both PCA and RPCA yielded similar SNRs, with only minor, sample-specific variations and no clear evidence that one consistently outperformed the other.  
For PT data, the situation was different. Here, RPCA generally provided higher SNR values than PCA across most CFRP samples, confirming its advantage in suppressing background noise and isolating defect-related signals, especially in data with complex or structured background variations. The only notable exception was the thin 2.5 J sample, in which PCA outperformed RPCA by a small margin. In all other PT cases, RPCA yielded the highest defect contrast. These results align with previous studies showing that robust decomposition techniques, such as RPCA, can offer tangible improvements in thermal contrast for PT, where spatial and temporal noise components can otherwise obscure subtle defect signatures.  
It is also important to consider the computational trade-offs associated with each method. The improvements in defect contrast offered by RPCA, particularly in PT, come at the cost of substantially higher processing times. For instance, when processing PT data consisting of 1000 frames, conventional PCA required only 1.32 s, whereas RPCA required 73.93 s for the thin 4 J sample, over fifty times longer. Given that SNR improvements with RPCA are often modest in PT, the additional computational cost should be carefully considered in practical applications. The use of RPCA may be most justified when maximum contrast enhancement is critical for defect detectability [36; 37].