esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Link Design for Quantum Key Distribution-Integrated Optical Access Networks
## 2 High-Speed PON
### WDM- and TWDM-PON  
The intensity modulation and direct detection (IM/DD)-based TDM-PON is expected to face significant challenges in meeting the required power budget, primarily due to power splitter-induced losses [2]. WDM-PON can address this issue, as shown in Figure 2a. For DS transmission in WDM-PON, the power budget requirement can be reduced, as the insertion loss of a WDM demultiplexer does not scale with the number of ONUs. This improved power margin enables the use of higher order modulation schemes, such as 4-level pulse-amplitude modulation, to increase line rates [7; 32]. Furthermore, ONUs can operate at the full bit rate of a wavelength, as they receive a continuous signal. The ability to detect continuous signals also benefits low-latency applications, such as 5G fronthaul [1; 19]. However, the high cost and complexity of WDM-PON remain the main obstacles to its widespread adoption. Each ONU in WDM-PON utilizes expensive wavelength-tunable lasers. Additionally, the use of a WDM demultiplexer for signal distribution establishes a fixed wavelength connection between the OLT and each ONU, posing operational challenges for network management. Efforts to mitigate these challenges have focused on colorless ONU transmitters, such as reflective semiconductor optical amplifiers (RSOAs), but these solutions still face difficulties in supporting high-bandwidth applications [32].  
To alleviate the wavelength management burden of WDM, a TWDM scheme has been proposed for the NG-PON2, transmitting four wavelength channels of 10G-class TDM-PON signals, as specified in ITU-T G.989 [2]. Figure 2b illustrates the structure of NG-PON2, which integrates wavelength-selected (WS) and wavelength-routed (WR) optical distribution networks (ODNs) [33]. This hybrid approach ensures compatibility with legacy TDM-PONs while reducing power budget requirements compared to a WS-only structure. When NG-PON2 was initially proposed, components operating above 10 GHz were not yet mature enough to implement a 40G-class PON [5]. However, it is likely that 50G-class PONs will continue to utilize the TDM scheme, as high-bandwidth components are expected to become commercially available [4; 5; 31]. Nevertheless, a WDM or TWDM scheme may be reintroduced for beyond-50G-class PONs to further scale capacity. Although NG-PON2 allocates the C- and L-bands for US and DS transmission, respectively, beyond-50G-class IM/DD-based WDM or TWDM-PONs are expected to operate in the O-band, as the line rate per wavelength is projected to exceed 25 Gb/s (in line with TDM-based bandwidth constraints [4]). Consequently, future high-speed PONs could allocate the C-band for QKD signaling, even when TWDM or WDM architectures are employed [1]. If the TDM architecture is to be used, an evolution toward coherent-lite PONs can be expected, as coherent receivers offer improved receiver sensitivity [8].  
Figure 2: Schematic diagrams of (**a**) WDM-PON and (**b**) TWDM-PON employing a hybrid architecture of WS- and WR-ODNs.
## 3 MCMD with Power Spectral Entropy and Grid Search (PGMCKD)
### Parameter Optimization of MCKD  
The \(L\) and \(T\) are two major parameters in MCKD, especially for \(T\). The value of \(T\) can determine the enhancement performance of MCKD. When MCKD is employed to enhance the signal, the optimal parameters for MCKD are determined according to the signal characteristics. The enhancement performance of the MCKD is decided by the value of parameters.  
In theory, the value of \(T\) can be determined.  
\[T=\frac{f_{s}}{f_{0}} \tag{10}\]  
where \(f_{0}\) is fault frequency, \(f_{s}\) is sampling frequency.  
However, in practical application, the value of T is different from the theoretical value. Therefore, the value of T is optimized. Usually, the value range of L is from 100 to 500. Due to the interaction between L and T, it is of major significance to study the adaptive selection of L and T for improving the enhancement performance of MCKD.  
If the sampling frequency \(y(n)\) is \(f_{s}\). The sampling points are \(N\). \(Y(\omega_{i})\) is obtained by Fourier transform. \(\omega_{i}=\frac{2\pi f_{s}}{N}i\) (\(i\) = 1,2,\(\cdots\), \(N\)/ 2)  
The formula of power spectral density of signal is as follows.  
\[p(\omega_{i})=\frac{\left|Y(\omega_{i})\right|^{2}}{N} \tag{11}\]  
\(p(\omega_{i})\) was normalized to \(p_{i}\).  
\[p_{i}=\frac{p(\omega_{i})}{\sum_{i}p(\omega_{i})} \tag{12}\]  
Power spectral entropy can effectively reflect the distribution characteristics of signal energy, so it is applied to optimize parameters. The expression of power spectrum entropy is described as follows.  
\[H=-\sum_{i}p_{i}*\log p_{i} \tag{13}\]  
The grid search method is a search algorithm to specify parameter values. The possible values of various parameters are arranged and combined. The possible combination results are shown to produce a \"grid\". It exists a low precision requirement, and the step size can improve the calculation speed. The search range of \(L\) is \([a,\ b]\), step is \(n_{1}\). The search range of \(T\) is \([c,\ d]\), step is \(n_{2}\). The possible values of \(L\) and \(T\) are arranged, and the generated grid is considered as \((L,T)\in[a,b]\times[c,d]\).  
The optimization evaluation of parameters using power spectral entropy is described as follows.  
\[\min_{(L,T)\in[a,b]\times[c,d]}-\sum_{i}p_{i}(L,T)*\log_{2}(p_{i}(L,T)) \tag{14}\]  
where \(i\) = 1, 2, \(\ldots\), \(n\).
# 3 GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach
## 5 Simulation Results
### End-to-End Latency  
In our simulations, we assume that the end-to-end latency is mostly affected by the propagation delay \(t_{prop}\), i.e., the amount of time required for a signal to be received after it has been sent, and the delay caused by the decoding process. We focus on these two latency parameters mainly because they are the only parameters that depend on the QoS scenarios and the decoding algorithm and because the other latency parameters are considered to be negligible compared to them. The propagation delay \(t_{prop}\) is calculated from the equation \(t_{prop}=d/c\), where \(d\) is the distance between the transmitter and the receiver and \(c\) is the speed of light. To formulate the decoding delay, we first define the decoding complexity \(Comp\). The decoding complexity for SOVA, logBCJR and maxlogBCJR is fully described in [33; 34; 35] and [22], where the authors proved that for a memory of the turbo decoder \(M=3\), the computational complexity is \(Comp_{SOVA}=76\), \(Comp_{log}=213\) and \(Comp_{max}=176\) operations/bit/iteration, respectively. Following the same methodology as in [22], we calculated the complexity of BCJR and found that it requires \(Comp_{BCJR}=228\) operations/bit/iteration. In [48; 58], the authors examined the computational complexity of LDPC, and they proved that it is calculated using the formula:  
\[Comp_{LDPC}=\frac{2d_{c}}{R}+(2d_{r}+1)\bigg{(}\frac{1}{R}-1\bigg{)}\text{ operations/bit/iteration}, \tag{5}\]  
where \(R=K/N\) is the coding rate, \(d_{c}\) is the mean column weight and \(d_{r}\) is the mean row weight of the base matrix. In our simulations, we assume that \(R=1/3\). Additionally, according to the 5G NR standard [59] and the base matrix that is compatible with the frame length we use in our simulations, we can deduce that \(d_{c}=3.7885\) and \(d_{r}=4.6905\). Therefore, we can derive that the LDPC decoding delay in our simulations is \(Comp_{LDPC}=43.493\) operations/bit/iteration. From the above analysis, it is obvious that the maxlogBCJR, logBCJR and BCJR are 1.8, 2.8 and 3 times more complex per bit and iteration, respectively, than SOVA, while LDPC is 0.57 times more complex per bit and iteration than SOVA. Polar is the only non-iterative decoding algorithm, and taking into account the extensive research conducted in [60], we conclude that it requires in total \(Comp_{Turbo}=36\log_{2}N\) operations/bit in our simulations, and it is \(0.47\log_{2}N\) more complex than SOVA per bit and iteration. Considering the computational complexity from the above analysis, the formulas
## 2 Main Disadvantage of an SDN Controller
### Centralized Controller  
The most widely used architecture for microgrid control uses a centralized SDN controller as an intelligent element within the topology. However, critical aspects such as latency, network convergence (less than 100 ms), reliability (close to 99%), and packet losses must be managed carefully [37; 38]. Achieving these properties is difficult in a centralized control scheme, especially for a large topology, due to communication devices' propagation latency and processing time. In this way, centralized SDN controllers have significant challenges, especially regarding scalability and reliability. Consequently, the controller node becomes a key target for cyberattacks. If the information stored in the controller is compromised, there is no way to recover it, resulting in a negative impact on the network and economic losses. A possible alternative is to use distributed SDN controllers as stated in [39; 40].
# Filter Media-Packed Bed Reactor Fortification with Biochar to Enhance Wastewater Quality
## 3 Results and Discussion
### Monitored Wastewater Qualities  
After 57 weeks of testing, 46 samples of the inlet and effluents had been obtained. Confidence intervals (and means with standard deviations in brackets) of the monitored parameters are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Media** & **pH** & \(\rho_{b}\) (kg/m\({}^{3}\)) & \(d\) (mm) & \(o\) & HRT (min) \\ \hline Sa & 7.4 & 1.70 & 0.1–2.0 (39.8\%) & & \\  & & & 2.0–4.0 (60.2\%) & & \\ GC & 9.1 & 0.66 & 0.1–2.0 (43.4\%) & & \\  & & & 2.0–4.0 (56.6\%) & & \\ C & 8.0 & 0.54 & 0.1–2.0 (46.2\%) & & \\ SaC & 7.8 & n.d & 0.1–4.0 & 0.43 & 105–96 \\ GCC & 8.9 & n.d & 0.1–4.0 & 0.50 & 123–91 \\ \hline \end{tabular} (\(\rho_{b}\), bulk density; d, particle diameter; \(\omega\), porosity; HRT, hydraulic retention time)  
\end{table}
Table 1: Specifications of the filter media.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Influent** & **Sa** & **C** & **GC** & **SaC** & **GCC** \\ \hline pH & 7.1–7.7 & 7.6–8.2 & 7.8–8.7 & 8.9–9.2 & 7.7–8.3 & 8.8–9.1 \\  & (7.5 \(\pm\) 0.2) & (7.7 \(\pm\) 0.2) & (8.0 \(\pm\) 0.4) & (9.1 \(\pm\) 0.2) & (7.9 \(\pm\) 0.3) & (8.9 \(\pm\) 0.2) \\ Turbidity (NTU) & 29–428 & 1–3 & 1–2 & 0–1 & 1–2 & 0–1 \\  & (150 \(\pm\) 210) & (2 \(\pm\) 1) & (1 \(\pm\) 0) & (1 \(\pm\) 0) & (1 \(\pm\) 0) & (1 \(\pm\) 0) \\ PO\({}_{4}\)3– (mg/L) & (6.1 \(\pm\) 0.8) & 3.6–4.3 & 3.1–3.8 & 0.1–0.3 & 2.9–3.6 & 0.2–0.3 \\ P\({}_{\rm tot}\) (mg/L) & (6.1 \(\pm\) 0.8) & (3.9 \(\pm\) 1.2) & (3.5 \(\pm\) 1.2) & (0.2 \(\pm\) 0.1) & (3.3 \(\pm\) 1.1) & (0.2 \(\pm\) 0.1) \\  & 8.9–10.6 & 6.4–7.4 & 4.5–5.5 & 0.0–0.6 & 10.1–11.0 & 0.1–0.2 \\ P\({}_{\rm tot}\) (mg/L) & (10.0 \(\pm\) 1.8) & (7.6. \(\pm\) 1.0) & (6.4 \(\pm\) 1.5) & (0.3 \(\pm\) 0.5) & (6.2 \(\pm\) 1.0) & (0.1 \(\pm\) 0.1) \\ DOC (mg/L) & 21.0–32.0 & 10.64–13.46 & 9.12–11.07 & 8.8–11.6 & 9.6–10–10.78 & 9.3–11.1 \\  & (25.3 \(\pm\) 2.8) & (12.1 \(\pm\) 2.3) & (10.1 \(\pm\) 1.6) & (9.4 \(\pm\) 2.1) & (10.2 \(\pm\) 1.0) & (10.0 \(\pm\) 1.5) \\ \hline \end{tabular}
\end{table}
Table 2: Confidence and mean values (with standard deviation) of observed parameters.
# A Fast Acquisition Algorithm for Hybrid Signals of 5G and BeiDou B1
## 2 Proposed Method
### Compatibility Analysis of 5G and BeiDou B1 Signal
#### 2.1.2 5G Signal System
Its multi-phase implementation schematic is shown in Figure 3.  
The filter \(P_{i}(z^{N})\) for each path in Figure 3 is obtained from the prototype filter \(P(z)\) with an \(N\) phase decomposition. The final spectrum of the FBMC is obtained by superimposing the prototype filter spectrum \(P(e^{iw})\) with the spectrum \(P(e^{iw+\frac{2\pi}{N}i}),i=1\ldots N-1\) of the other filters within the filter bank. When the time domain of the prototype filter takes a particular value of \(P(n)=\{1,1\ldots 1\},0\leq n\leq N-1\), \(P_{i}(z^{N})=1\), Figure 3 becomes the schematic diagram of the OFDM implementation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**NR Frequency Band** & \
\begin{tabular}{c} **Uplink Frequency Band** \\ **(MHz)** \\ \end{tabular} &
\begin{tabular}{c} **Downlink Frequency Band** \\ **(MHz)** \\ \end{tabular} & **Duplex Mode** \\ \hline n50 & 1432–1517 & 1432–1517 & TDD \\ n51 & 1427–1432 & 1427–1432 & TDD \\ n74 & 1427–1470 & 1475–1518 & FDD \\ n75 & N/A & 1432–1517 & SDL \\ n76 & N/A & 1427–1432 & SDL \\ n92 & 832–862 & 1432–1517 & FDD \\ n93 & 880–915 & 1427–1432 & FDD \\ n94 & 880–915 & 1432–1517 & FDD \\ \hline \hline \end{tabular}
\end{table} 
Table 6: 5G signal band near BeiDou. \begin{table}
\begin{tabular}{c c} \hline \hline
**Key Technology** & **Description** \\ \hline Duplex mode & FDD/TDD \\ \hline Basic waveform & Downlink: CP-OFDM/Uplink: CP-OFDM, DFT-S-OFDM \\ \hline Carrier bandwidth & Below 6 GHz: 100 MHz max/Above 6 GHz: 400 MHz max \\ \hline Modulation mode & QPSK, 16 QAM, 64 QAM, 256 QAM \\ \hline Subcarrier interval & Below 6 GHz: 15 KHz, 30 KHz, 60 KHz/Above 6 GHz: 60 KHz, 120 KHz, 240 KHz \\ \hline \hline \end{tabular}
\end{table} 
Table 7: 5G NR specification. The out-of-band and spurious part of 5G affects the BeiDou system, not the central signal flap part. For 5G out-of-band leakage, 3 GPP has made relevant regulations, such as the spectrum radiation template shown in Figure 4, where the operating band unwanted emission (OBUE) is the entire operating band plus 10 MHz or 40 MHz outside the upper and lower boundaries. The OB area of the signal can occupy any position in the band during communication, and the bandwidth can be configured from 5 MHz to 100 MHz. Therefore, the 5G signal frequency point can be located at the upper or lower boundary of the band. When the signal is located at the upper or lower boundary of the band, its OOB area will fall outside the assigned operating band, so the OBUE area of the spectrum radiation template contains the entire band and a specific range outside its upper and lower boundaries. Therefore, compatibility issues must be considered if other radio systems are deployed outside of a 5G operating band. For example, suppose the BeiDou B1 signal is located at about 45 MHz outside the upper boundary of the n50 band. In that case, the out-of-band radiation of a 5G signal located near the upper boundary of the n50 band will have some impact on the BeiDou B1 signal.
## 5 Fraud Risk Management in the Honey Sector--The VACCP Approach and Innovative Technologies
### Limitations and Future Directions  
While the developed system represents a significant advancement in automated honey authenticity assessment, several limitations warrant discussion. First, the current classification models were trained and validated on a dataset primarily composed of pollen images from Polish and selected international honeys. Although morphologically diverse, this dataset may not fully capture the global variability in pollen types, particularly in honeys from tropical and subtropical ecosystems, which exhibit higher botanical complexity [67].  
Second, system performance has so far been assessed under controlled R&D conditions. Real-world validation--incorporating variation in sample preparation, imaging equipment, and user proficiency--is necessary to confirm generalizability and robustness. This challenge of external reproducibility is a well-known issue in digital palynology and food image analysis [50].  
Moreover, while the use of Hjorth descriptors and deep CNNs has demonstrated high classification accuracy, certain edge cases (e.g., morphologically similar pollen grains) may require multimodal approaches. Future enhancements may include integration with spectral, isotopic, or physicochemical data to support complex authenticity profiles, in line with emerging trends in food forensics [60; 69].  
Interoperability also remains a critical concern. Despite adopting a microservices architecture and metadata harmonization protocols, successful integration into existing industrial systems (ERP, LIMS, QMS) will require further standardization efforts and compliance with sector-specific interoperability frameworks [90].  
Future research will, thus, focus on five key areas: (1) expanding the reference database through international collaboration, (2) external system validation in industrial environments, (3) model benchmarking using standardized public datasets, (4) integration of multimodal data, and (5) dissemination of technical validation results in a dedicated publication.  
Ultimately, the presented system offers a scalable, reproducible, and modular foundation for next-generation authenticity control. Its strategic alignment with Industry 4.0, food traceability, and AI-based quality assurance positions it as a promising tool in addressing the complex challenges of food fraud prevention and supply chain transparency [69].
# Effect of Working Atmospheres on the Detection of Diacetyl by Resistive SnO2 Sensor
## 3 Results and Discussion
### Gas Sensor Measurements  
First, to study the behavior of the tin oxide sensor in different working atmospheres, the sensing properties toward vapors of diacetyl in aqueous solution (0.4 mg/L) were tested in oxidant, inert and reducing atmospheres, using as carrier and regeneration flow air N\({}_{2}\) and CO\({}_{2}\).  
Tin oxide is well known as an MOS whose behavior is n-type. Indeed, the electrical resistance of the layer decrease when the sensor is exposed to reducing gas such as NH\({}_{3}\)[36], CO [37], CH\({}_{4}\)[38], H\({}_{2}\)S [39] or SO\({}_{2}\)[40] due to the oxidation of the target gas on the SnO\({}_{2}\) surface. Measurements carried out under air and N\({}_{2}\) atmospheres (Figure 3a), confirmed the n-type behavior of the SnO\({}_{2}\) sensor with R \(<\) R\({}_{0}\), whereas the measurement carried out in CO\({}_{2}\) ambient showed an inverse behavior, with R \(>\) R\({}_{0}\), proving indeed that in CO\({}_{2}\), tin oxide worked as a p-type sensor.
# Thermal Energy Storage Performance of Tetrabutylammonium Acrylate Hydrate as Phase Change Materials
## 3 Results and Discussion
### The System without Any External Forces (Static System)
Immediately after the start of the experiment, \(\dot{q}\) decreased as T\({}_{\text{out}}\) decreased, and as T\({}_{\text{out}}\) approached T\({}_{\text{in}}\), \(\dot{q}\) approached 0. After that, \(\dot{q}\) increased at t = 1.5 h and reached around 9 kW/m\({}^{3}\). This is due to the increase in T\({}_{\text{out}}\) caused by the heat generation from hydrate formation and growth, as described above. After t = 3 h, as T\({}_{\text{out}}\) descended to be tangent to T\({}_{\text{in}}\), \(\dot{q}\) descended to 0 with some fluctuations. Figure 5b illustrates the time evolution of thermal energy storage density. Since \(\overline{q}\) indicates the thermal energy density by hydrate growth and decomposition, there is no change right after the experiments start. As the hydrate grew, thermal energy storage density increased. The rate of increase gradually became slower. The hydrate formation rate got slower due to the decreased heat \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & \\ \hline Density, \(\rho_{w}\) (Water) & 999.8 kg/m\({}^{3}\) \\ \hline Volumetric flow rate, \(\dot{V_{w}}\) (Water) & 8.3 \(\times\) 10\({}^{-5}\) m\({}^{3}\)/s \\ \hline Specific heat at constant pressure, \(c_{w}\) (Water) & 4.20 kJ/(kg K) \\ \hline Density, \(\rho_{PCM}\) (PCM) & 1.0 kg/m\({}^{3}\) \\ \hline Specific heat at constant pressure, \(c_{PCM}\) (PCM) & 4.20 kJ/(kg K) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The value of each parameter in the calculation. Figure 4: Crystal growth of TBAAc hydrate on the surface of the heat exchanger coil. (**a–d**) illustrate the optical measurement of the hydrate formation at t = 2, t = 2.5, t = 4 and t = 7, respectively. conductivity around the heat exchanger coil. This is why the increasing rate of thermal energy storage density decreased. Thermal energy density reached 96 MJ/m\({}^{3}\) in 10 h.  
It is known that the solid phase fraction of ice thermal storage increases significantly with time up to around 30%, but the increasing rate decreases after about 40-50%. Therefore, it is favorable to design a thermal energy storage system that has a solid phase fraction of 40-50% [37]. For example, when the solid phase fraction is 45% in an ice thermal energy storage system, thermal energy storage density is approximately 140 MJ/m\({}^{3}\). [PERSON] et al. [33] reported that TBAB hydrate, which is the only commercially available hydrate PCM, has the heat for formation and decomposition of approximately 200 MJ/m\({}^{3}\). Assuming the TBAAc hydrate has 200 MJ/m\({}^{3}\) of heat in hydrate formation and decomposition, the thermal energy storage density corresponds to a solid phase fraction of 48%.
## 2 Study the Regional Profile and Data Sources
### Data Sources  
#### 2.2.1 Sentinel-2A Data  
The Sentinel-2A remote sensing data used in this study were obtained from the United States Geological Survey (USGS). Sentinel-2A is a high-resolution multispectral imaging satellite, equipped with a multispectral instrument (MSI) [22]. It operates at an altitude of 786 km and has a swath width of 290 km, allowing it to capture data across 13 spectral bands, providing rich spectral information. The Sentinel-2A sensor covers the visible, near-infrared, and shortwave-infrared spectral regions [23]. The data have three spatial resolutions: 10 m, 20 m, and 60 m. The detailed sensor parameters are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Wave Band** & **Central Wavelength (\(\upmu\)m)** & **Resolution Ratio (m)** \\ \hline \multirow{2}{*}{MSI} & Band 1—super blue & \multirow{2}{*}{0.443} & \multirow{2}{*}{60} \\  & (coastal and aerosol) & & \\ \hline MSI & Band 2—blue & 0.490 & 10 \\ \hline MSI & Band 3—green & 0.560 & 10 \\ \hline MSI & Band 4—red & 0.665 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sentinel-2A sensor parameters.  
Figure 1: Overview of the study area.
## 3 Results
### Dynamic Speed Tracking Validation  
RMSE and time delay are calculated to test the estimation accuracy and determine the response time during speed changes, respectively. The interval \(t_{i}\) has an effect on these  
Figure 4: Experimental setup [14] of AB dataset.  
Figure 5: Experimental equipment. (**a**) An able-bodied subject wears intelligent lower-limb prostheses and IMUs. (**b**) The treadmill for level walking.  
two performances. Based on the AB dataset, the dynamic speed tracking performances across different values of the interval \(t_{i}\) are shown in Figure 6.  
From Figure 6a, the proposed method has a good performance in both static speeds and dynamic speed tracking. The average RMSE across subjects is 0.089 \(\pm\) 0.014 m/s, 0.051 \(\pm\) 0.016 m/s and 0.045 \(\pm\) 0.012 m/s and the average time delays are 389 ms, 553 ms and 678 ms as \(t_{i}\) increases. According to Equations (13) and (15), the step frequency changes and the stride stays unchanged when \(t_{i}\) changes. Moreover, with the change in walking speed, the stride and step frequency increase or decrease at the same time. The time delays are smaller than one gait cycle which shows better responsiveness than traditional methods such as direct integration methods.  
During dynamic speed tracking, the RMSE decreases with the speed increasing as shown in Figure 7a. We guess the reason is that it is difficult to keep the step frequency stable at low speeds (from Figure 6b, AB06's step frequency at lower speeds is more variable).