esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Potential Implementation
### Condition Pre-Warning  
Condition pre-warnings are especially important for the operation and maintenance staff of in a wind farm. Using the PAM method, the T22 HV curves for the MWPTR and RPOR from 20 May 2015 to December 2017 were calculated. The HV in the MWPTR corresponds to the deviation of the actual measured data and the baseline data. Presuming a deviation of 0.05 is set as the alarm threshold, a point larger than the minimum points by 0.05 is selected as the pre-warning point. The HV curve and pre-warning line are plotted, as shown in Figure 15a, in which the minimum power point of the HV curve are marked as A, B and C. The growth rate of the HV curve after Date A is larger than 0.1. The HV of Date B is much larger than that for Date A, and the growth rate of the HV curve after date B exceeds 0.05. The HV of Date C is less than that of Date B but larger than that of Date A, of which the later growth rate exceeds 0.1. This analysis indicates that the HV curve of T22 changes considerably; in other words, the wind turbine performances are unstable. If the degradation trend after the minimum point is detected by the threshold line with HV equal to 0.05 and effective maintenance activities are carried out, a worsening in the degradation trend can be avoided. Similarly, when the minimum power point exceeds 0.1, the maintenance staff should focus extensively on investigating the reason.  
In the wind farm considered in the research, the generator power limit is 2067.6 kW, the mean power of the baseline data is approximately 2013 kW, and their difference is 54.6 kW, which is the theoretical deviation value in the RPOR area. The threshold is set as 10 kW in the RPOR, the minimum power of the HV curve is selected, and the warning line with HV equal to 10 is plotted, as shown in Figure 15b. It can be seen that after the HV curve of Date A increases gradually with a span larger than 10 kW, it decreases gradually to Date B. The continuous increasing rate after Point B exceeds 20 kW and reaches the global maximum, and the growth rate of the HV curve after Date C exceeds 10 kW. Therefore, using the proposed method to determine the HV curves and setting the threshold value line after the minimum power, an early warning can be triggered when the HV exceeds the threshold value so that further degradation can be avoided through maintenance.
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 2 Materials and Methods
### Method  
The technical flowchart of the study included five steps (Figure 2). The purpose of Step 1 was to extract the metrics of classification after Sentinel-1 and Sentinel-2 processing (Section 2.3.2). Step 2 was to generate pre-classification samples by combining multisource data and timeseries index curves (Section 2.3.3). Step 3 was to produce annual land-cover maps using a random forest classifier (Section 2.3.4). Step 4 was to map cropland abandonment with different types and degrees according to the class definition (Sections 2.3.1 and 2.3.5). Step 5 was to quantify and evaluate the accuracies and reliability of the method (Section 2.3.6).
## 4 Discussion
The inversion accuracy of soil salinization in cotton fields is affected to a certain extent by the resolution of remote sensing image data, whether the field sampling date is consistent with the satellite transit date, and whether the weather is suitable for field sampling. In this study, the 10 m Sentine-2 RS data were selected, so the classification accuracy can only be approximate. If the inversion accuracy of cotton field soil salinization is further improved, high-resolution remote sensing image data can be used, such as Planet series, domestic high-resolution series, Quickbird satellite, etc. On the other hand, the Sentinel-2 images in this study were composited from 17 July to 22 July 2022, but the soil samples were collected in the field from 15 July to 24 July 2022. There is a certain difference between the date of remote sensing images and the date of sampling. To further improve the precision of the inversion, it is necessary to calculate whether the satellite transit date and the weather on the transit date are suitable for field sampling and arrange the sampling time reasonably so that the sampling date is within the composite date of the remote sensing image.
###### Abstract  
Bearing fault diagnosis is a key factor in maintaining the stability and performance of mechanical systems, necessitating reliable methods for anomaly detection and prediction. Unlike traditional conservative maintenance approaches, the importance of predictive maintenance where real-time condition monitoring enables proactive preventive measures has been growing steadily. In this study, we propose a deep learning method that effectively discriminates between normal and abnormal bearing conditions, while predicting potential faults in advance. To achieve this, we develop a time series anomaly detection model based on a supervised learning transformer architecture. Our proposed model tackles the data imbalance issue by generating four types of synthetic anomalies from normal vibration data and incorporates a pyramid-structured attention module to reduce computational costs and enhance the handling of long-term dependencies. Experimental results on real bearing vibration datasets demonstrate improved F1-scores over 6%p compared to existing models and demonstrate a significant reduction in computational costs in specific experimental environments. By reliably identifying and predicting bearing faults at an early stage, this research contributes to reducing maintenance costs and improving system stability. Furthermore, it is expected to have wide applicability for state monitoring and anomaly detection in various rotating machinery systems.  
deep learning; anomaly detection; transformer; self-supervised; time series data +
Footnote †: journal: Applied sciences
## 5 Experimental Results  
To validate the proposed TIBuck DC/DC converter working as a VLC driver, a prototype was built to prove the concept, following the prior analysis and guidelines. Figure 17  
Figure 16: Effect of the variation of the threshold voltage of the LED with the temperature in \(\eta_{\rm t}\) at different \(\eta_{\rm bu}\).  
Figure 15: Effect of the variation of the efficiency of each individual converter in \(\eta_{\rm t}\) at different temperatures.  
shows the real prototype and Figure 18 shows the schematic and the block diagram of the control stage. The control of the TIBuck and the auxiliary Buck DC/DC converters were integrated in a FPGA Nexys A7. The FPGA implements the output-current feedback control loop by using the measured average output current, I\({}_{\rm o}\), and the lighting reference as inputs. The average output current is controlled by means of varying V\({}_{2}\), following Equation (14). Moreover, the FPGA controls the TIBuck DC/DC converter, which operates in an open loop, generating the communication signal according to the modulator block and the input bits.  
As general specifications, the TIBuck DC/DC converter supplies an HB-LED load of 8 PC-LED XLamp MX-3 in series and an input voltage V\({}_{1}\) = 28 V. The input voltage was selected as being close to but lower than the maximum voltage of the HB-LED string, based on Equation (9). V\({}_{1}\) was selected to be 12% lower, to compensate for the degradation of resolution for tracking the output voltage, due to the increase in temperature in the HB-LEDs previously shown in Figure 7. The value was selected based on the experimental behaviours of the HB-LED load. This allows us to partially counteract the temperature effect on the resolution by designing the voltage V\({}_{1}\) closer to the output voltage of the HB-LED load at room temperature. This specification can be easily met, because the maximum voltage of each HB-LED was expected to drop from 4 V to 3.5 V when the working was stabilized. The average current, I\({}_{\rm o}\), was kept at 0.25A (the middle of the HB-LED linear range) and controlled by the FPGA. The next subsections further explain the design process.
# Two-Parameter Probabilistic Model and Experimental Research on Micron Particle Deposition
## 3 Results and Discussion
### Simulation Results and Test Results
#### 3.3.4 Comparison of Simulation Results and Test Results  
In order to measure the difference between the simulation results of the two-parameter particle probability deposition model and the experimental results, collect and weigh the sedimentary particles on the lower wall of sample 2 with a relatively obvious deposition in the experimental simulation, and compare them with the simulation results (comparison range: 0 D \(\leq\) L \(\leq\) 30 D), as shown in Figure 11. \(K\) is defined as the ratio of the mass of the bottom-deposited particles per unit length D to the total mass of the whole bottom-deposited particles, \(K\)s is the simulation result, and \(K\)i is the experimental simulation result.  
It can be seen that there are peaks in both the experimental and simulation results, but the peak range of particle deposition efficiency (6 D-16 D) in the experimental simulation is more concentrated than that in the simulation results (13 D-28 D), and it is closer to the inlet of the gas transmission pipeline. The difference may be caused by the following two reasons: On the one hand, the particle collection system installed at the end of the particle migration and deposition simulation platform, using centrifugal fans to recover particles, may interfere with the wind speed field where particles migrate and deposit, resulting in a low deposition efficiency in the latter half of the particle migration and deposition pipeline. On the other hand, in order to improve experimental efficiency and obtain simulation results of the particle deposition law with a relatively large sample size, the mass of particles input should be higher than that of the clean gas transmission pipeline under actual working conditions, so that the samples after pretreatment may still be aggregated due to electrostatic force and van der Waals force, so that the peak range of particle deposition efficiency is closer to the gas transmission pipeline.  
Figure 11: Scatter plot comparing simulation results and experimental results of lower wall particle deposition for sample 2.
## 3 Analytical Approach to FGM Beam Analysis  
A simplified analytical formulation for functionally graded beam stresses analysis is presented in this section. The material properties in the FGM beam were assumed to vary in the thickness direction using power-law (P-FGM) and exponential function (E-FGM) material functions.
## 3 Results and Discussion  
### Electrical Properties and Emission Spectra of Argon and Helium Plasma  
To analyze the electrical properties, we first measured DC input voltage and DC discharged current, the breakdown voltage of argon and helium gas were 6 V and 4 V, respectively, and discharge currents as breakdown voltage were 0.29 A and 0.25 A at a 2 slm flow rate, respectively (Figure 2A). The DC input voltage conditions for transdermal permeability experiment that length of plasma plume was the longest without plasma arcing and burn in the porcine skin were 18 V and 12 V in argon and helium gas, respectively. As shown in Figure 2B, we measured two cycle waveforms of the inverted AC applied voltage and discharge current of the argon and helium plasma jet at DC input voltage 18 V and 12 V, respectively, and their frequency were 47.28 kHz and 46.67 kHz, respectively. The RMS (Root Mean Square) voltages of argon and helium plasma jets were 2.54 kV and 1.73 kV, respectively, and their RMS discharge currents were 11.01 mA and 6.21 mA at a 2 slm flow rate, respectively. Additionally, the calculated AC power of the argon and helium plasma jet were 4.85 W and 1.86 W, respectively, while DC power was 5.22 W and 3 W, respectively. As previously reported, this difference seems to be caused by argon being relatively easy to ionize because the ionization threshold energies of argon and helium are 15.76 eV and 24.59 eV, respectively [27], even with the increased voltage, the generation of radicals is much higher in argon than in helium plasma [28]. We examined various active species that are observed in argon and helium plasma. The emission spectra of argon and helium plasma indicate that OH (309 nm) and N\({}_{2}\) (310 to 370 nm) were observed in both argon and helium plasma, but NO (283 nm) and N\({}_{2}\)+ first negative bands (391 nm) were observed only in helium plasma (Figure 3). As the ionization threshold energy of N\({}_{2}\) is 14.53 eV, N\({}_{2}\) is likely to be ionized by helium plasma with a metastable energy level of 19.82 eV compared to that of argon plasma (11.5 eV) [29].
# Microscope-Based Optical Frequency Comb Generator for 200 GHz Spaced WDM Data Transmission System
## Abbreviations  
\begin{tabular}{l l} OFC & optical frequency comb \\ WGM & whispering gallery mode \\ WDM & wavelength-division multiplexing \\ PON & passive optical network \\ FSR & free spectral range \\ TM & transverse magnetic \\ TE & transverse electric \\ CW & continuous wave \\ DKS & dissipative Kerr soliton \\ FWHM & full width at half maximum \\ BER & bit error rate \\ NRZ & non-return-to-zero \\ OOK & on-off keying \\ IM/DD & intensity modulation direct detection \\ NG-PON2 & Next-generation PON \\ ASE & amplified spontaneous emission \\ ASI & amplified spontaneous emission light source \\ AWG & arrayed-waveguide-grating \\ LPF & low-pass filter \\ MZM & Mach-Zehnder modulator \\ OBPF & optical band-pass filter \\ WGMR & whispering gallery mode resonator \\ MUX & multiplexed \\ de-MUX & demultiplexer \\ PIN & photodiode \\ SMF & single-mode fiber \\ B2B & back-to-back \\ WR & wavelength-routed \\ QoT & quality of transmission \\ ITU-T & International Telecommunication \\ Union-telecommunication standardization sector \\ OCNR & optical carrier-to-noise power ratio \\ CO & central office \\ \end{tabular}
# High-Speed VCSEL-Based Transceiver for 200 GbE Short-Reach Intra-Datacenter Optical Interconnects
###### Abstract  
The soaring demand for higher speeds in datacenters to address the relentless growth of the global IP traffic places optical interconnects in the spotlight. In this manuscript, we present a high-speed optical transceiver for intra-datacenter connectivity. The transceiver is based on single-mode, single-polarization high-speed vertical-cavity surface-emitting lasers (VCSELs), a VCSEL driver chip, and a linear receiver. Following a step-by-step approach, we present the architectures, assembly processes, and experimental results from the different modules. More specifically, we demonstrate (1) a data transmission experiment at 80 Gb/s using PAM-4 (four-level Pulse Amplitude Modulation) modulation for a reach of up to 500 m by employing a single-mode VCSEL module, and (2) a full-link experiment proving up to 64 Gb/s per lane capacity using PAM-4 signaling of the VCSEL-based optical transceiver test vehicles in back-to-back configuration and up to 56 Gb/s for 500 m and 2 km transmission distances. The acquired experimental results verify the suitability of the optical transceiver for intra-datacenter interconnects' applications.  
short-reach optical interconnects; intra-datacenter connectivity; VCSEL-based transmitter; VCSEL; linear receiver; high-speed optical transceiver; 200 GbE applications
# A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time
## 3 System Architecture
### Architecture Layers  
Each layer performs a specific function and communicates with the other layers through APIs. The system can handle multiple cameras and traffic lights simultaneously and can adapt to different scenarios and environments. The system architecture is as follows:  
1. Data Acquisition Layer: This layer gathers the data from the camera and breaks it into frames.The frames are then sent to the next layer for processing. This layer is responsible for interfacing the external data sources, such as the camera, to the system. It also performs some preprocessing tasks such as filtering, scaling, or sampling the data.  
2. Ambulance Detection Layer: This layer runs the YOLOv8 model on every frame that is received from the data acquisition layer (i.e., for more details about the YOLOv8 model, see Section 3.2). It determines if an ambulance is present or not in each frame. If an ambulance is detected, it posts a detection signal to its API. The ADL is connected through the cloud layer where the processing takes place.  
3. Monitoring Layer: This layer fetches the detection signal from the ambulance detection layer and processes it. It displays the signal for the admin or the traffic light system so that they can open the road for the ambulance. This layer also stores the signal in the cloud for a future analysis and reporting. This layer provides a user interface for the admin to monitor and control the system. It also communicates with the traffic light system to send commands to change the traffic signals according to the detection signal. This layer uses cloud services to store and access data securely and efficiently.  
4. Cloud Layer: This layer provides a platform as a service (PaaS) and infrastructure as a service (IaaS) for the other layers. It enables the communication and storage of data among the layers through the internet. It also provides scalability, security, and reliability for the system. PaaS is a cloud computing model that provides tools and services for developing and deploying applications without managing the underlying infrastructure. Meanwhile, IaaS is a cloud computing model that provides computing resources such as servers, storage, or networks on demand without managing them.
# Universal Behavior of the Image Resolution for Different Scanning Trajectories
## 2 Scanning Methods
### Scanning Trajectory Characteristics  
Unlike the unidirectional Cartesian trajectory, the BC trajectory propagates in both directions, as seen in Figure 1a. BC is the trajectory in the Cartesian family that has the highest resolution [24]. Compared to sinusoidal and triangular approaches, the resolution and regularity are lower. Moreover, the image reconstruction with BC scanning is also challenging [7]. The waveforms that make up the TL trajectory are both triangular and saw-tooth. It is created by combining two distinct triangular signals, each with a different frequency [26]. Despite the TL trajectory being quite uniform and homogeneous throughout the FOV, as observed in Figure 1b, the image resolution produced is not better than the SL trajectory [7, 8]. SL is a non-Cartesian sinusoidal scanning trajectory that provides acceptable scanning duration and resolution (Figure 1c). The irregularity of the forming pattern is the primary disadvantage of this trajectory, particularly in the center of the FOV where large gaps are located. As a result, this trajectory not only exhibits an uneven distribution of scanning density and image quality, but it also has low resolution in the FOV's center [29]. RL is another non-Cartesian sinusoidal scanning trajectory with an extremely dense center and moderately dense borders. Depending on the amplitude values selected, the trajectory has an elliptic or circular shape, as implied by its name. RL is a member of the Lissajous family, and its trajectory formulas are similar [24]. In this study, \(f_{x}\) was set to 25 kHz for all cases, and the FOV was set to be 3.2 cm long by 1.6 cm wide [8]. Changes in image resolution within the field of view can be observed by adjusting \(N_{P}\). In other words, a higher number of \(N_{P}\) denotes a denser pattern within the FOV.
# BeiDou Augmented Navigation from Low Earth Orbit Satellites
## 2 Advantages of Navigation with LEO Constellation
### Integer Ambiguity Resolution  
For high accuracy positioning using the carrier-phase measurements (e.g., PPP), the positioning accuracy is rely on if the ambiguities can be correctly fixed to be integer. Hence, the convergence speed of ambiguity solution has essential impact on the positioning solution. Normally ambiguities are solved at the same time with the other parameters such as station coordinates, receiver clocks, troposphere delay, and so on. With the change of observation geometry, the multipath and atmospheric delay is estimated quickly, making that the ambiguities can be separated with those parameters. In this case, a reliable ambiguity solution can be obtained. The probability of ambiguity solution is dependent on the satellite types and altitude and the empirical formula can be expressed as (Kang and K
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Sponge-Like Graphene ECG Electrodes
#### 2.6.1 Graphene Sponge ECG Electrode  
A graphene sponge is a highly compliant and porous material that is structurally stable and flexible. [PERSON] et al. proposed a graphene sponge ECG electrode fabricated by freeze-casting [75]. First, GO prepared by [PERSON] method was partially reduced by ascorbic  
Figure 4: Modified-graphene based electrodes. (**a**) The schematic illustration of the preparation processes (above) and photos (below) of chemically-modified graphene-CNT ECG electrodes. Reprinted with permission from Ref. [68]. Copyright 2019, MDPI. (**b**) Preparation processes of the environmentally friendly, chemically modified graphene ECG electrode. Reprinted with permission from Ref. [69]. Copyright 2017, ICPscience. (**c**) Fabrication processes and photo of the intercalation doping graphene ECG electrode. Reprinted with permission from Ref. [71]. Copyright 2020, American Chemical Society. (**d**) Preparation of the glycerol-modified, graphene-oxide ECG electrode. Reprinted with permission from Ref. [72]. Copyright 2022, ICPscience. (**e**) Fabrication processes of hybrid copper nanoparticle–graphene oxide and its photos. Reprinted with permission from Ref. [74]. Copyright 2022, MDPI.  
acid. Second, the mixture was boiled, followed by anisotropic freeze casting and a second reduction process. Finally, the acquired hydrogel was freeze-dried and annealed to create the sponge-like structure of graphene. The graphene sponge is ultra-lightweight (5 mg/cm\({}^{3}\)), has a stable cellular structure and high resilience (with stable elastic properties over 1000 cycles of compression), and is ultra-soft (its elastic modulus is 1,000,000 times lower than that of PDMS). As an ECG electrode, the graphene elastomer electrode shows a significantly low skin-electrode impedance and a high signal-to-noise ratio.
## 4 Materials and Methods
### Western Blot Assay  
Western blot assay was conducted to detect the effects of SOE on the expression of different antioxidant enzymes, i.e., catalase, SOD, and GPx. The cells treated with glucose and SOE (0, 62.5, 125 and 250 mg/mL) were collected, washed twice with chilled PBS, and lysed with RIPA buffer (Sigma-Aldrich). The protein concentration was determined using the Bradford Protein Assay Kit (Bio-Rad, California, CA, USA). Equal amounts of protein extract were first resolved by 10% SDS-PAGE gels, and the separated proteins were transferred to nitrocellulose (NC) membranes. Following a blocking step, the proteins were probed with different primary antibodies (purchased from Sigma-Aldrich) against different antioxidant enzymes. The primary antibodies, including dilution conditions, were as follows: rabbit anti-catalase (1:1000), rabbit anti-GPx (1:500), mouse anti-SOD (1:500), and anti-\(\beta\)-actin (1:5000), which acted as an internal control. Then, the membranes were treated with secondary antibodies (1:15,000) conjugated with horseradish peroxidase (HRP) for 1 h at room temperature. Enhanced chemiluminescence (ECL) detection reagents (Sigma-Aldrich) were applied to detect the protein expression. The analysis of relative protein expression was conducted using ChemiDoc XRS+ System (Bio-Rad).