esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A  
The impulse response of the gammatone filter is given by:  
\[\mathrm{h(t)}=\mathrm{ct}^{n-1}\mathrm{e}^{-2\pi\mathrm{bt}}\cos(2\pi\mathrm{f }_{0}\mathrm{t}+\varphi)\mathrm{u(t)}, \tag{1}\]  
Assume that the carrier component is denoted by:  
\[\mathrm{c(t)}=\cos(2\pi\mathrm{f}_{0}\mathrm{t}+\varphi). \tag{2}\]  
Additionally, assume that the gammatone distribution function is defined by:  
\[\mathrm{r(t)}=\mathrm{t}^{n-1}\mathrm{e}^{-2\pi\mathrm{bt}}\mathrm{u(t)}. \tag{3}\]  
Hence, the impulse response of the gammatone filter can be expressed as:  
\[\mathrm{h(t)}=\mathrm{c.c(t)r(t)}, \tag{4}\]  
We can write:  
\[\mathrm{H(f)}=\mathrm{c.[C(f)*R(f)]}. \tag{5}\]where \(C(f)\) is the Fourier transform of \(c(t)\), \(R(f)\) is the Fourier transform of \(r(t)\), and \(H(f)\) is the Fourier transform of \(h(t)\). By using the Fourier transform of known functions along with Fourier transform properties [63], we can express the Fourier transform of \(r(t)\) as:  
\[R(f)=\frac{(n-1)!}{\left(2\pi b+j2\pi f\right)^{n}}, \tag{10}\]  
Alternatively, the Fourier transform of \(r(t)\) can also be expressed as:  
\[R(f)=(n-1)!(2\pi b)^{-n}\bigg{(}1+j\frac{f}{b}\bigg{)}^{-n}. \tag{11}\]  
Now, the Fourier transform of the carrier signal, \(c(t)\) can be expressed as:  
\[C(f)=\frac{1}{2}e^{j\varphi}\delta(f-f_{0})+\frac{1}{2}e^{-j\varphi}\delta(f- f_{0}). \tag{12}\]  
Substituting the Fourier transform of the \(c(t)\) and \(r(t)\) in Equation (10), we can determine the expression of \(H(f)\) as:  
\[H(f)=c.(n-1)!(2\pi b)^{-n}\bigg{(}1+j\frac{f}{b}\bigg{)}^{-n}\ast\bigg{[}\frac {1}{2}e^{j\varphi}\delta(f-f_{0})+\frac{1}{2}e^{-j\varphi}\delta(f+f_{0}) \bigg{]}, \tag{13}\]  
This expression can be further simplified as:  
\[H(f)=c.(n-1)!(2\pi b)^{-n}\bigg{[}\frac{1}{2}e^{j\varphi}\left(1+j\frac{f-f_{0 }}{b}\right)^{-n}\bigg{]}+c.(n-1)!(2\pi b)^{-n}\bigg{[}\frac{1}{2}e^{-j\varphi }\left(1+j\frac{f+f_{0}}{b}\right)^{-n}\bigg{]}. \tag{14}\]
## 2 Experiment  
A schematic diagram of the Q-switched wavelength-tunable fiber laser is shown in Figure 1. A laser diode with a pigtail fiber is used as the pump source. The diode is water-cooled and operated at a wavelength of 976 nm under a continuous wave (CW) regime. The output fiber of the diode has a core diameter of 105 um and a numerical aperture (NA) of 0.22. The pump laser is collimated by an aspheric lens L1 (\(f\) = 10 mm, \(\phi\) = 12.7 mm, NA = 0.54) and coupled into the active fiber by a plano-convex lens L2 (\(f\) = 20 mm, \(\phi\) = 12.7 mm). The active fiber is a 70,000 ppm Er\({}^{3+}\):ZBLAN fiber fabricated by Le Verre Fluore. The fiber is 3 m in length and has a core diameter of 15 um with a NA of 0.12. The inner cladding of the fiber is a truncated circular (double D-shaped) cladding with a short/long diameter of 240/260 um and an NA of 0.46. The outer circular cladding is 290 um in diameter. Both ends of the fiber are cleaved at an angle of about 10\({}^{\circ}\) to avoid any parasitic oscillation. The output laser is collimated by L3 (same lens as L2) and split by a germanium plate. The laser that passes through the germanium plate is reflected by a blazed grating (GR25-0616, Thorlabs). On the other side of the cavity, a dichroic mirror coated with reflectivity R > 99.9% at 2800 nm and transmissivity T > 98.0% at 976 nm for a 45\({}^{\circ}\) angle reflects the 2.8 um laser beam, which is focused on the Fe\({}_{3}\)O\({}_{4}\) SAM by L4 (same lens as L2).  
The refractivity of the germanium plate at a 45\({}^{\circ}\) angle is measured to be about 47.7%, which is also the coupling ratio of the output 1. Output 2 is captured by a photoconductive detector (PVI-4 TE-5, VIGO System) together with a 1-GHz-bandwidth oscilloscope (MOD3104, Tektronix) to make sure the measurements are conducted in the stable Q-switching mode. By tuning the angle of the grating, the output wavelength is tunable. Power and spectrum are measured from output 1, with a thermoelectricity power meter (S314C, Thorlabs) and a Fourier transform spectrometer (OSA207, Thorlabs) separately.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SA** & **Dopant** & **Wavelength** & **Average** & **Pulse Energy** & **Ref.** \\  & & **(nm)** & **Power (mW)** & **(nJ)** & **Ref.** \\ \hline Graphene & Er\({}^{3+}\) & 1547–1557 & 4 & 160 & [16] \\ MoS\({}_{2}\) & Er\({}^{3+}\) & 1519–1567 & 5.91 & 160 & [29] \\ MoS\({}_{2}\) & Er\({}^{3+}\) & 1484–1492 & 0.8 & 1.2 & [30] \\ Black phosphorus & Yb\({}^{3+}\) & 1040–1045 & 8.9 & 141.27 & [31] \\ Fe\({}_{3}\)O\({}_{4}\) & Dy\({}^{3+}\) & 2812–3032 & 111 & 900 & [22] \\ Fe\({}_{3}\)O\({}_{4}\) & Er\({}^{3+}\) & 2710–2810 & 188 & 1680 & This work \\ \hline \hline \end{tabular}
\end{table}
Table 1: Passively Q-switched wavelength-tunable fiber lasers.
## 4 Results and Verification  
Spatial Distribution of Surface Subsidence in the Main Urban Area of Kunming on the North Shore of Dianchi Lake  
In this study, PS-InSAR technology was used to extract surface displacements from Sentinel-1 radar images covering Kunming. The annual mean surface deformation rates along the LOS direction of both ascending and descending Sentinel-1 datasets were obtained and are shown in Figure 4. Red (negative values) indicates that the surface moves away  
Figure 3: Spatial baseline distribution map (**a**) ascending orbit and (**b**) descending orbit.  
from the satellite, while blue (positive values) indicates that the surface moves toward the satellite.  
As shown in Figure 4, obvious deformation has occurred in Xishan District and Guandu District on the north shore of Dianchi Lake in Kunming city, with the maximum deformation rate reaching \(-66.98\) mm from 2018 to 2021. These areas are distributed near the Dianchi International Exhibition Center area (S\({}_{1}\) in Figure 4) and Xiaobanqiao in Guandu District (S\({}_{2}\) in Figure 4), respectively. The deformation areas include the Dianchi International Exhibition Center, Yejia village to the north of the International Exhibition Center, an urban village near Erji Road in the east, and two communities named Yongjing Bay and Shanhahiui in the south and west. The maximum deformation rate has reached \(-20.1\) mm/a. The deformation rate in the S\({}_{2}\) settlement area is approximately \(-7\) mm/a, mainly concentrated in the five communities around Xiaobanqiao and Vanke Community. The maximum deformation rate reached \(-23.12\) mm/a.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 3 How Technologies Can Fix the Current Healthcare System
### Disruptive Health Innovations
#### 3.5.1 Ingestible Sensors  
The ingestible sensors allow physicians to monitor patient's ingestion and adherence to medicines in real time. These sensors are activated after the ingestion and transmit information about the drug's ingestion and its relation with patients' behaviours such as physical activity and physiological responses (blood pressure, heart rate and sleep quality). They are being used to monitor medication adherence [32], like adherence to tuberculosis therapy [33] and highly reliable monitoring of the timing and intake of drugs for clinical management of kidney transplant patients.
# Study on Slump and Compressive Strength of Gangue Based on Aggregate Size Gradation
## 2 Experimental Material
### Ganguue Paste Auxiliary Material  
Fly ash possesses activation and excitation properties, making it suitable for replacing a portion of fine aggregates in filling materials. The addition of an appropriate amount of fly ash can decrease pipeline transport resistance and enhance the pumping performance of the slurry. Therefore, fly ash is commonly used as an auxiliary material in traditional gangue-filling pastes. However, considering the high demand and cost of fly ash in traditional gangue filling paste materials, [PERSON] et al. [22] conducted a study on the feasibility of using gangue powder as a substitute for fly ash in filling materials. The results indicated that with a water content of 23% and cement material content of 15%, the filling materials achieved an early compressive strength of 0.743 MPa while maintaining satisfactory fluidity. The compressive strength at 28 days was 4.87 MPa, which matches the compressive strength of the backfill. Therefore, this study used gangue powder instead of fly ash as an alternative supplementary material for filling materials.
# Measuring the Centrality of DNS Infrastructure in the Wild
## 3 Preliminaries  
Name servers and resolvers are two core components of the DNS ecosystem. The name server is the maintainer of DNS records, which ensures the validity and correctness of DNS records. Depending on the content of DNS records they hold, name servers can be classified as root name servers, top-level name servers, and authoritative name servers. In general, the ADNS maintains all records for a domain name. For example, if we want to query the \(A\) record of _a1.example.com_ to obtain the IP of the domain, the response will eventually be given by the ADNS of _example.com_. The resolver is the executor of the resolution service, which acts as a bridge between users and name servers.  
With the development of the Internet and the rise of market-oriented domain name services, public DNS providers have upgraded the traditional DNS. Figure 1 illustrates the typical domain name resolution model of public DNSes. They introduce function-specific subsystems to decompose the resolution process to provide efficient and accurate domain name resolution services to users in different regions. For example, the FDNS does not perform any query to the ADNS but rather forwards the lookups along to RDNSes. RDNSes are the final executors of domain name resolution tasks, communicating directly with ADNSes. These behaviors result in the implicit cooperative resolution relationship between different RDNSes which constitute the resolver pool.  
From the user's point of view, the domain name resolution process is transparent, and it is only to observe the service IP that public DNS providers provide. Usually, public DNS providers will choose an identifiable IP as the service IP, such as the _8.8.8_ of Google Public DNS. The FDNS closest to the user receives the lookup from user devices and forwards it to the public DNS at the service IP. If the querying domain name is absent from the cache system of the public DNS, one or more optimal RDNSes are selected to communicate with the ADNS to obtain domain records. Because of the transparent nature of the public DNS service, the implicit relationship among resolvers is an unreachable area in traditional DNS measurement studies. To expose the resolver pool, [PERSON] et al. [12] proposed a resolver pool discovery method based on _CNAME_ chains, which exploited the resolution strategy of _CNAME_ records. In theory, providers will apply different resolvers to resolve _CNAME_ records to improve the efficiency of domain name resolution. However, the resolution strategy providers use is uncertain in the wild, which becomes the biggest obstacle to the method. In the subsequent section, we explore the effectiveness of this approach.
## 3 Results and Discussion
### Evolution of the Microwave Scattering Properties in Co-Based MWs Linear Arrays
Such a reduction is expected, since only the Co-based MWs are affected by the DC magnetic field. However, these are interestingly high values of microwave attenuation keeping an amplitude of the recorded scattering significantly higher than the Co-based arrays. Furthermore, regardless of the DC-modulation character, this result might also be attractive for electromagnetic shielding applications, because this combination displays a microwave scattering of \(\sim\) -11.2 dB at \(\sim\)2.38 GHz, even with an external DC applied magnetic field with intensity of 17 Oe.
conducted a study on indoor CO\({}_{2}\) concentration, thermal environment, and cooling energy consumption using fixed and differential controls in an office building modeled using building energy simulation [22]. The analysis maintained indoor CO\({}_{2}\) concentrations for both control methods below the recommended level of 1000 PPM, and indoor temperatures met the cooling set-point. The cooling energy consumption of the differential control method was approximately 4.5 kWh/m\({}^{2}\) lower than that of the fixed control. [PERSON] and [PERSON] examined the cooling energy consumption in office buildings utilizing fixed control based on dry-bulb temperature and enthalpy in the northern and southern regions of China [23]. Their analysis revealed that in the hot and humid conditions of the southern region, enthalpy-based fixed control was more effective in conserving energy compared to the dry-bulb-temperature-based control. Conversely, in the low-humidity conditions of the northern region, the dry-bulb-temperature-based control proved more effective. [PERSON] et al. evaluated the performance of economizer control by adjusting the MA temperature according to OA conditions [24]. They developed a data-driven optimal MA temperature prediction model using MATLAB 2014a and assessed cooling energy performance with TRNSYS. The results indicated an annual cooling energy consumption reduction of approximately 50.4% compared to conventional control methods. [PERSON] and [PERSON] assessed the cooling energy performance using economizer control under varying OA conditions across four regions [25]. An office building was modeled using building energy simulation with weather data from Incheon, Miami, Madison, and San Francisco. The analysis showed that San Francisco, with its lower OA temperature and humidity, achieved the most significant reduction in cooling energy consumption. On the other hand, Incheon, Miami, and Madison, which have higher OA temperatures and humidity, exhibited minimal energy savings. The studies concerning economizer control methods and MLSP in relation to OA conditions across various regions are summarized in Table 1.
## 3 Parameter Optimization and Performance Test
### Optimization Results and Verification Test of Drive Cam Installation Angle  
#### 3.2.1 Optimum Results  
Referring to JB/T 10291-2013 _Dry Land Planting Machinery_, the planting frequency of the high-speed transplanter was greater than or equal to 90 plants/(min \(\cdot\) row), the rotation angular velocity of the drive cam \(\omega\) was set to 120\({}^{\circ}\)/s, and the corresponding planting frequency was 100 plants/(min \(\cdot\) row). Since the net seedling dropping duration of the pot seedlings at the No. 1, 2, 4, and 5 positions was similar, no optimization was required. The net seedling dropping duration of the No. 3 position was 0.32 s, and the average net seedling dropping time at the other positions was 0.37 s. Substituting this into Equation (28), the installation compensation angle was calculated to be 6\({}^{\circ}\) after offsetting \(t_{\text{Q}}\). To sum up, to make the potted seedlings at each position fall at the same time interval,  
Figure 11: Key frames of seedling dropping process. (**a**) The pot seedlings begin to fall. (**b**) The pot seedlings completely fall out.  
the installation angles of the driver at the No. 1-5 positions should be \(0^{\circ}\), \(72^{\circ}\), \(150^{\circ}\), \(216^{\circ}\), and \(288^{\circ}\), respectively.
# Optical Link Design for Quantum Key Distribution-Integrated Optical Access Networks
## 4 Integration of CV-QKD Protocol in OANs
### CV-QKD Implementation Technique
#### 4.2.4 Polarization Tracking or Demultiplexing  
In optical fiber transmission, the vertical and horizontal polarization modes are degenerate, leading to time-varying polarization mismatches between the transmitted and received signals. Since CV-QKD detection requires precise polarization alignment between the LO and QKD signals, polarization tracking becomes essential. If the QKD signals and LO (or pilot) are transmitted in different polarization modes, a polarization demultiplexing technique needs to be employed. Inaccurate tracking induces increased excess noise. In laboratory conditions, manual polarization control (MPC) is sometimes used; however, for real-world deployment, dynamic polarization control (DPC) or digital signal processing (DSP)-based polarization tracking is required to ensure stability and reliability.
## 2 Materials and Methods
### WEC-Sim and Gazebo Descriptions
#### 2.1.1 WEC-Sim
In _WEC-Sim_, the pressure, volume, and temperature for nitrogen in each chamber of the air spring followed the ideal gas law:  
\[P\,V=n\,R\,T, \tag{4}\]  
where \(P\) [Pa] is the gas pressure, \(V\) [m\({}^{3}\)] is the volume, \(n\) is the mass of the gas [kg], \(R\) [J/(kg K)] is the ideal gas constant (\(R_{nitrogen}=296.8\) [J/(kg K)]), and \(T\) is the absolute temperature [K]. Spring forces were modeled with heat transfer between the chambers and the surroundings using the energy balance equation for a closed system [4]. \[\Delta\,U=Q-W \tag{5}\] \[\dot{Q_{i}}=K_{i}\,(T_{amb}-T_{i}) \tag{6}\]  
Here, \(\Delta\,U\) [J] is the change in internal energy, \(Q\) [J] the heat supplied or taken from the system, and \(W\) [J] the work done by the system, in this case, pressure-volume work related to the expansion or contraction of the gas spring piston. In (6), \(\dot{Q_{i}}\) [W] is the rate of heat transfer, \(T_{amb}\) the ambient temperature (283.15 K), \(T_{i}\) [K] the chamber temperature, and  
Figure 3: Simscape model of MBARI-WEC PTO. The different subsystems of the PTO are air spring, piston, hydraulic, electric, friction, and controller feedback. The orange ports are inputs and the green ports are outputs of the overall PTO system. Comparing to Figure 2, the Simulink PTO model takes in “PTO Velocity” and outputs “Total PTO Force” to interact with the hydrodynamic model. The input “Electric Motor Torque” is determined by controller feedback and dependent on “Motor Speed”. the heat transfer coefficient (determined in [4] by fitting the model to experimental data), where the subscript \(i\) denotes either the lower or upper chamber. Volumetric and mechanical efficiencies for the hydraulic motor were input to the _WEC-Sim_ model in tabulated form provided by the manufacturer as a function of pressure drop and motor speed. Finally, the calculation of electrical losses in _WEC-Sim_ were based on the PTO validation bench testing conducted in [4]. These values were provided to the model in tabulated form characterized by the motor shaft torque and speed.
# Optimization Study on a Novel High-Speed Oil-Free Centrifugal Water Pump with Hydrodynamic Bearings
## 4 Results and Discussion  
### Validation of Numerical Model  
The computational head and efficiency are compared with the experimental results; Figure 6 presents a good agreement between them. At the designed point, the deviation between the predicted head of 25.18 m and the experimental head of 25.13 m was 0.2%; the deviation between the predicted efficiency of 29.4% and the experimental efficiency of 28.9% was 1.7%. The numerical efficiency was larger than the measured efficiency, although their patterns agreed with each other. This is because the leak at the back gear was neglected in the numerical study. Generally, the computational model qualified for predicting the performance of the high-speed pump.  
In order to optimize the hydrodynamic performance of the high-speed centrifugal pump, the blade number of the impeller and stay vane was varied. We fixed the basic parameters of the impeller, the vane inlet angle, and inner and outer diameters. The blade number of the impeller was changed  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\boldsymbol{Q\ (\text{m}^{3}\cdot\text{h}^{-1})}\) & \(\boldsymbol{H_{hyd}\ (\text{m})}\) & \(\boldsymbol{H_{t}\ (\text{m})}\) & \(\boldsymbol{W_{i}\ (\text{W})}\) & \(\boldsymbol{\eta_{hyd}}\) & \(\boldsymbol{\eta_{t}}\) & \(\boldsymbol{\eta_{hyd}}\)-\(\boldsymbol{\eta_{t}}\) \\ \hline
5.63 & 16.60 & 14.57 & 919 & 27.67\% & 24.29\% & 3.38\% \\
5.37 & 18.31 & 16.29 & 917 & 29.20\% & 25.99\% & 3.21\% \\
4.92 & 20.57 & 18.61 & 904 & 30.49\% & 27.58\% & 2.91\% \\
4.54 & 22.47 & 20.64 & 889 & 31.21\% & 28.67\% & 2.54\% \\
3.99 & 24.78 & 23.31 & 864 & 31.13\% & 29.29\% & 1.84\% \\
3.50 & 26.54 & 25.02 & 844 & 29.99\% & 28.27\% & 1.72\% \\
3.10 & 27.55 & 25.99 & 811 & 28.67\% & 27.05\% & 1.62\% \\
2.90 & 28.00 & 26.69 & 807 & 27.36\% & 26.08\% & 1.28\% \\
2.44 & 29.04 & 28.27 & 779 & 24.75\% & 24.09\% & 0.66\% \\
2.14 & 29.72 & 29.08 & 759 & 22.75\% & 22.26\% & 0.49\% \\
1.80 & 30.28 & 29.78 & 736 & 20.17\% & 19.84\% & 0.33\% \\
1.42 & 30.76 & 30.46 & 709 & 16.71\% & 16.55\% & 0.16\% \\
0.95 & 31.51 & 30.76 & 694 & 11.76\% & 11.48\% & 0.28\% \\ \hline
**Position** & **Front** & **Rear** & **Left** & **Right** & **Average** \\ Noise (dB) & 53.96 & 54.01 & 53.79 & 53.81 & 53.89 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of high-speed pump at 7500 rpm.  
Figure 6: Comparison between computed and measured head and efficiency.  
from 4 to 8; that of the stay vane was changed from 3 to 14. Then, 60 combinations were established and numerically investigated. The influence of the matching relation between different impellers and stay vanes on the hydrodynamic performance of the high-speed centrifugal pump was herein investigated.
# A New Defect Diagnosis Method for Wire Rope Based on CNN-Transformer and Transfer Learning
## 4 Case Studies
### Training Setup  
During the experiments, a 306 Ti GPU was used to accelerate the processes of training and testing. For the programming language and deep learning platform, Python 3.6 and Pytorch 1.10 programs were used throughout all of the case studies. Furthermore, Adam was used as the optimizer to guarantee the stability of gradient backpropagation and loss decrement results. The batch size, initial learning rate, and decay rate were set to 64, 0.001, and 0.9 for all models, respectively. However, the target domain training only required 100 epochs instead of 400, which was used in the source domain training.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **A** & **B** & **C** & **D** \\ \hline Normal & 1371 & 2660 & 2593 & 2572 \\ Original Defect & 27 & 59 & 85 & 98 \\ Augmentation & Defect & 297 & 649 & 935 & 1078 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numbers of normal and defect feature images in the wire rope dataset.  
Figure 10: The overlap method of defect sample augmentation. The red box is the sampling window for obtaining 100 signal points. Sample augmentation is realized by sliding the red box from left to right. Subfigure (**a**–**c**) denote the different periods during the sampling process.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Step** & **Description** \\ \hline
1 & Original LMA signal preparation \\
2 & Data preprocessing based on the EMD method \\
3 & Defect location based on the isolation forest and local extremum method \\
4 & Data extension and segmentation \\
5 & Confirm the size of window and stride for augmentation \\
6 & Defect images generation based on the overlap method and matrix reconstruction \\
7 & End \\ \hline \hline \end{tabular}
\end{table}
Table 5: Algorithm of defect sample augmentation.
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 3 Results and Discussions
### Classification Accuracy Improvement per Noise Type  
The accuracy differences between the TL and ML models for strong variants of all types of noises across different data availabilities are illustrated in Figure 8a, representing the range of TL accuracy minus ML accuracy. This difference is positive when TL outperforms ML, and negative when ML performs better.  
The results indicate that TL consistently outperformed ML in most scenarios, except for EN under low data availability. The most substantial TL improvements were observed in scenarios with larger datasets affected by WGN and CN and reached above 30%. This suggests that TL benefits the most from greater data availability and complex noise environments. This highlights the ability of the TL to generalize under noisy conditions where ML struggles.