esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Meteor Observations with Photodiode Detectors
## 3 Concept
### Baseline Drift Correction  
The final stage in the detector (Figure 3) is the DC voltage correction circuit. During the night, due to the effects of various interference light sources (moonlight, dusk/dawn effect, changing atmospheric transparency and light pollution), some DC components may appear at the output of the transimpedance amplifier (marked in Figure 3 as U2). This additional, unwanted DC drift can saturate the second amplifier (marked in Figure 3 as U3) in the Sallen-Key low-pass filter. To avoid this, a PI (proportional-integral) regulator was realized in the microprocessor circuit, which observes the DC voltage at the input of the A/D converter and develops a DC voltage at the output of the DAC. This output voltage from the PI regulator controls the current source (G1 in Figure 3), which is connected to the divider at the amplifier's inverting input. The current source has no effect on the gain of  
Figure 6: Characteristics of IIR digital filter implemented in ADuC7020 microprocessor.  
the circuit and only on the DC component at the output of the filter (marked in Figure 3 as Out from U3). Additionally, the value of the DC component from the output of the PI controller contains information about the background level (average intensity of the sky illumination) and is collected as auxiliary data during acquisition.  
The PI-based reduction in offset is activated only when the A/D converter measures values above \(\pm 200\) mV and is deactivated once the ADC reads below \(\pm 5\) mV. This threshold-based operation compensates for the offset for only a very short period, typically a few seconds, allowing for undisturbed signal observation for most of the time. In practical applications, the PI controller is employed only during dusk and dawn when the total brightness of the sky changes gradually. It is also utilized during nights with a full moon, additionally 2-3 times per night. These occasional corrections are easily distinguishable from meteor detections by either signal processing or a human investigator since they occur when the signal exceeds a well-defined 200 mV threshold.
## 4 Numeric Analysis
### Effects of seawater Turbulence and Beam Parameters on the BER  
Figure 2 shows variation curve of the BER of the random double-Gaussian optical vortex carrier communication link with the dissipation rate of the mean-squared temperature \(\chi_{T}\) and topological charge of the RCDPPGV vortex. The variation curve shows that with the increase in transmission distance \(z\), the BER of this link increases. When the transmission distance is less than \(50\,\mathrm{m}\), BER increases rapidly and, then, tends to be flat. Additionally, a larger \(l_{0}\) means a higher BER. The physical reason for this result is that when the topological charge (orbital angular momentum quantum number) of OAM signal mode is larger, the OAM energy level of the signal is also higher. Therefore, the probability of transition form to another OAM energy level is also higher when it is disturbed.  
Under certain topological charge of vortex laser, we use Figure 3 to show the variation curve of BER of random double-Gaussian optical vortex carrier communication link with dimensionless parameter \(\alpha\). From Figure 2, we can see that BER has two minimum values  
Figure 2: BER of the coaxial double-position power Gaussian vortex carrier communication link versus dissipation rate of the mean-squared temperature \(\chi_{T}\) for different topological charge of vortex.  
at \(|\alpha|=1\). This result indicates that the link with a single-position power Gaussian vortex has minimum BER.  
In Figure 4, we investigate the effects of the \(\varepsilon\) and \(l_{0}\) on the BER of RCDPPGV carrier link for given topological charge of vortex. From Figure 3, we can see that the value of the BER decreases with an increase in the \(\varepsilon\). The reason for this phenomenon is the increase in \(\varepsilon\), the turbulence strength of seawater decreases, and the influence of turbulence on the transmission vortex decreases.  
In Figure 5, we investigate the effects of dissipation rate of the mean-squared temperature \(\chi_{T}\) on the channel capacity of the RCDPPGV carrier link for the given topological charge of vortex. It is clear from Figure 5 that the BER of the channel capacity increases with increasing \(\chi_{T}\). The reason for this result comes from that with the increase in \(\chi_{T}\), the turbulence strength of seawater increases, and the influence of turbulence on the transmission vortex increases.  
Figure 4: BER of the coaxial double-position power Gaussian vortex carrier communication link versus dissipation rate of kinetic energy per unit mass of fluid \(\varepsilon\) for different topological charge of vortex.  
Figure 3: BER of the coaxial double-position power Gaussian vortex carrier communication link versus dimensionless parameter \(|\alpha|\) for different topological charge of vortex.
# A PMSG Wind Energy System Featuring Low-Voltage Ride-through via Mode-Shift Control
## 5 Simulation Results  
A PMSG-based WEC grid-tied system is considered in simulation work for validating the proposed approach. Simulated system-rated parameters are listed in Table 2, where the wind turbine is driven at a fixed wind speed of 12 m/s and constant blade pitch angle \(\beta\) = 0. The turbine drives a 1.5 MVA, 80 poles, 11.5 Hz, gearless PMSG at 690 \(\surd\)3 V line-to-line generator voltage, and fixed rated speed \(\omega_{r}\) of 1.8 rad/s. The diode rectifier converts the generator voltage to a DC voltage, which is then boosted by the boost chopper of 5 kHz switching frequency. During normal operation, the boost chopper controller guarantees MPPT, whereas the three-phase VSI, operating at 5 kHz carrier frequency, is responsible for DC-link voltage regulation to 5500 V and WECS integration to a 480 V, 50 Hz utility grid at unity power factor and 4% THD. For wind speed of 12 m/s, the tracked power, which is injected into the grid during normal conditions, reaches almost 765 kW.  
The system has been simulated using MATLAB/Simulink during two voltage sag cases: 30% and 70% grid voltage dips. For comparison, the WECS was simulated once without the proposed technique, then when applying the swap proposed control methodology. Table 3 briefs simulation results during both dip cases.
# Optical Technology for NFV Converged Networks
## 3 Silicon Photonics Devices in Optical Transport: Challenges and Opportunities
### Integration Issues of Optical Amplifiers and Active Sources in Silicon Photonics Devices  
One issue with silicon is that it is a bad light emitter, due to its indirect bandgap, making it very difficult the realization of light sources and optical amplifiers. The integration of silicon with materials of the III-V groups, such as Indium Phosphide (InP), is a solution commonly adopted for the realization of Semiconductor Optical Amplifiers (SOA) and lasers on chip. Amplifiers provide additional power budget and allows to compensate for or mitigate the device insertion loss, which is primarily due to the coupling with input and output optical fibers. The high insertion loss is an issue that so far prevented silicon photonics to become widespread in optical networks, although it offers tremendous opportunities for cost reduction and enhanced flexibility. CMOS-compatible ROADMs are an example of silicon-based optical device enabling such a new network [26]. The integration of silicon with III-V materials for achieving optical gain is made it difficult by the mismatch of the crystal lattice in the two materials. Despite this, techniques have been developed for integrating InP SOAs on silicon [26]. In the so-called flip-chip integration, micro-packaged SOA assemblies are individually aligned by a flip-chip bonder with a silicon grating coupler, by means of active alignment techniques that use an auxiliary laser beam. A disadvantage of the flip-chip bonding is that a sub-micrometric alignment precision is required for coupling the laser beam, slowing down the production process and leading to significant impact on the device cost. Silicon III-V bonding at wafer level is an alternative integration technique. It consists of molecular bonding of III-V materials on top of a patterned Si-on-Insulator (SOI) layer. This process is more efficient than flip-chip bonding because it can be performed at die or wafer level, but it complicates the device structure and requires enhancements to the standard CMOS-compatible processes.
# Path Tracking of Agricultural Vehicles Based on 4 WIS-4 WID Structure and Fuzzy Control
## 2 Materials and Methods  
### Configuration of Test Prototype  
In this paper, a 4 WIS-4 WID vehicle prototype is used. Sizes of the prototype, including the length, width, and altitude, are 1200 mm, 700 mm, and 700 mm, respectively. The entire platform is made of 40 mm \(\times\) 40 mm aluminum profiles, with an axle track of 540 mm, wheelbase of 1040 mm, and clearance of 600 mm, as illustrated in Figure 1. The prototype is driven by four-wheel motors (8 inches, 24 V) independently, and four servos (DH-03X, 120\({}^{\circ}\)/s, 38 N-m) are connected to the driving wheels through a coupling mechanism, controlling each driving wheel to achieve independent steering in the range of \(-90^{\circ}\) to \(90^{\circ}\). The prototype is powered by a 24 V, 20 Ah lithium battery.  
The wireless positioning system consisted of four ultra-wideband (UWB) base stations, and the UWB (I-UWB LPS PA) mobile tag installed at the center of the test prototype receives the UWB signals from the base stations and decodes them into the position information of the prototype vehicle. The position and orientation information of the prototype is provided by a 9-axis electronic gyroscope (WT901C), and the error of the position and orientation information is \(\pm\)50 mm and \(\pm\)0.1\({}^{\circ}\), respectively. The STM32F103 is used as the central controller of the test prototype. It receives the position coordinates (\(x\), \(y\)) of the prototype from the UWB mobile tag and information of the heading angle \(\varphi\) from the electronic gyroscope and then converts them into the lateral deviation \(d\) and heading deviation \(\theta\) of the vehicle relative to the target path.  
The path-tracking algorithm proposed in this paper calculates the speed \(v_{1}\)-\(v_{4}\) and the steering angle \(\delta_{1}\)-\(\delta_{4}\) of each wheel. The PWM signals the motor required to execute the speed and angle commands mentioned above are input to the motor driver (AQMD6015 BLS) and the steering servo to execute the corresponding driving and steering actions to achieve path tracking. The prototype is also equipped with an ultrasonic sensor to detect obstacles and issue an alarm and an emergency brake when an obstacle reaches a dangerous distance. The system control rule is shown in Figure 2.
## 5 Contract-Based Energy-Efficient Resource Allocation for UAV-Assisted FL  
In the incentive stage, the UAVs have already incentivized VUs to sign the corresponding optimal contract based on their willingness, and the contract determines the VUs' contributed data quantity \(q_{j}\) for local training. During the training stage, the VUs utilize the results of the contract for local training. In this section, we design a resource allocation problem based on the contract's willingness to minimize the VUs' total energy consumption in training.  
UAVs broadcast the applications to VUs in their coverage, and the responding VUs are further scheduled by the UAVs to form federations. VUs use local data for training and upload the trained model to the current UAV. UAVs aggregate when receiving all models and then distribute new model parameters. The whole process repeats a number of rounds to reach convergence.  
Assuming that the initial model is defined as \(\omega(0)\), \(c_{j}\) trains local models according to its assigned dataset \(D_{j}(t)\) in each iteration \(t\). If the local model of \(c_{j}\) at \(t\) timeslot is denoted as \(\omega_{j}(t)\), the local training process can be expressed as  
\[\omega_{j}(t+1)=\omega_{j}(t)-\frac{\epsilon}{|b|}\
abla f(\omega_{j}(t),D_{j }(t)) \tag{32}\]  
where \(\epsilon\) is the learning rate, \(\epsilon\in[0,1]\), \(|b|\) is one batch size, and \(f(\omega_{j}(t),D_{j}(t))\) is the loss function of \(c_{j}\).  
In each iteration \(t\), VUs upload local models to the current UAV. The UAV uses the federated averaging algorithm (FedAvg) to aggregate all models. Assume the number of VUs involved is \(J\), where \(J\leq N\), then the aggregation process can be expressed as  
\[\omega(t)=\frac{1}{J}\sum_{j=1}^{J}\omega_{j}(t) \tag{33}\]
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 2 Advantages and Challenges of mmWave Industry 4.0 and beyond Communication
### Advantages of mmWave Communication
#### 2.1.5 Size Miniaturization  
The advancement in complementary metal oxide semiconductor (CMOS) technology and integrated circuits (IC) designs in recent decades have paved the way to realize compact 60 GHz transceivers and radio designs [59]. Since the Federal Communication Commission (FCC) and other governing organizations released the 60-GHz spectrum, great progress has been made toward the realization of highly integrated 60-GHz radios for low-cost, potentially ubiquitous consumer adoption. Some comprehensive surveys on 60 GHz radios, transceivers, on-chip antennas, and other related circuitry are presented in [59; 66; 70]. The advantages of 60 GHz mmWave communication are summarized in Figure 4.
## 4 Results
### Extraction of Feature Bands Using SCARS Algorithm  
In this study, the SCARS algorithm was applied to extract the feature bands from the spectral data after eight spectral transformations. Specifically, the feature bands of the original spectrum N, after SG smoothing and denoising, were selected for further analysis. As shown in Figure 9a, the number of variables selected by SCARS decreases exponentially with the number of runs in 250 samples. The overall trend exhibited a rapid decrease, followed by a gradual stabilization. As shown in Figure 9b,c, the root mean square error of RMSECV exhibits a gradual decrease overall, indicating that the eliminated bands during the screening process are not significantly related to the SOM content. The RMSECV reaches its minimum value of 2.599 in the 173 rd iteration, with a total of nine variables screened. After this point, the RMSECV starts to increase. Figure 8: Correlation analysis between organic matter content and different spectral transformation forms. (**a**) Visualization of spectral correlation coefficients after FD, SD, RFD, and RSD transformations for N. (**b**) Visualization of spectral correlation coefficients after LRFD, LRSD, RLFD, and RLSD transformations for N. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Spectral Transformation Form** & **N** & **FD** & **SD** & **RFD** & **RSD** & **LRFD** & **LRSD** & **RLFD** & **RLSD** \\ \hline Maximum value & 0.01 & 0.55 & 0.35 & 0.38 & 0.37 & 0.24 & 0.33 & 0.43 & 0.40 \\ Minimum value & \(-0.10\) & \(-0.38\) & \(-0.33\) & \(-0.54\) & 0.42 & \(-0.51\) & \(-0.30\) & \(-0.53\) & \(-0.39\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Correlation coefficient values between SOM content and different spectral transformation forms. In order to provide a detailed comparison of the characteristic bands selected using different spectral transformation methods using the SCARS algorithm, Table 4 presents a comprehensive list of the selected characteristic bands for each spectral transformation. According to Table 4, after applying the SCARS algorithm, the N, FD, SD, RFD, RSD, LRFD, LRSD, RLFD, and RLSD transformations yielded 10, 12, 32, 7, 19, 18, 22, 16, and 16 characteristic bands, respectively. The selection of characteristic bands compressed the input bands to less than 14.29% of the total number of bands. It effectively extracted significantly different spectral features and reduced the data dimensionality, thereby improving the speed of model execution. Overall, the characteristic bands selected by the eight spectral transformations mainly concentrated in the near-infrared spectral range of 1100-1500 nm. This may be attributed to the absorption valleys caused by the chemical components in the soil in the near-infrared region. Among them, the SD transformation resulted in the highest number of selected characteristic bands, capturing relevant information in each wavelength range. On the other hand, the RFD transformation yielded the fewest characteristic bands, and it can be observed from the table that some bands were included in the characteristic bands selected by the RLFD
## 3 Vitamin A, Its Vitaminoids and Carotenoids' General Profile and Health Benefits
### Anti-Aging and Photo-Protective Properties of Vitamin A, Retinoids, and Carotenoids
#### 5.1.1 Anti-Aging Effect--Skin Regeneration
Lycopene, as a very strong antioxidant present mainly in tomatoes, can also act as a natural photo-protective agent towards photo/early aging [133]. The anti-aging activity of different vitamin A forms derives mainly from their antioxidant ability. Besides being photo-protective agents externally in the skin by extensive UV\({}_{\text{R}}\), carotenoids act as anti-antioxidants as well, by scavenging ROS and covering excited forms of singlet oxygen and triplet-state molecules that could lead to inactivation of vital antioxidant enzymes, lipid peroxidation, and DNA damage, leading to internal protection [117]. Vitamin A deficiency has long been connected to delayed epithelialization and wound healing, which is confirmed by poor wound closure, lower collagen synthesis rates, and lower levels of cross-linking of newly generated collagen. The efficacy of retinoids or carotenoids' topical application and/or oral consumption appears to be comparable. As previously stated, retinoids function by binding to certain receptors both in the cytoplasm and in the nucleus, which remarkably impact RNA, protein synthesis, lysosome-membrane stability, cell growth, differentiation, and finally division. A widely recognizable sensitivity impact of retinoids is their exceptional capacity to counteract the anti-inflammatory steroids' inhibitory effects on the healing process, aside from wounds' contraction. Administering a retinoid therapy partially but significantly reverses the adverse effects of the inflammatory response, tensile strength, and collagen build-up in cutaneous wounds in individuals following steroid treatment [118]. Recent research outcomes supported that topical retinol applied to aged human skin has been pointed out to dramatically increase epidermis thickness by inducing epidermal keratinocyte proliferation, as well as to enhance endothelial cell and blood vessel proliferation in the papillary dermis, by thickening the epidermal layer and leading to the formation of new blood vessels in the dermis [111]. Type I collagen, along with tropoelastin and fibronectin expression, and the formation of collagenous ECM in aged human skin in vivo due to activating dermal fibroblasts, are drastically elevated when topical retinol therapy is applied. Reportedly, vascularity and epidermal thinning are important factors that contribute to skin weakness and restrain rapid wound healing in trauma cases in aged skin. Topical retinol improves the dermal microenvironment by encouraging the growth of vasculature via endothelial cell proliferation in aged human skin, in addition to boosting ECM's generation. Several reports on age-related decrease in the cutaneous vasculature have been recorded, where topical retinol treatment was confirmed to promote dermal vascularity, which is able to enhance skin blood flow, as well as dermal and epidermal homeostasis [111].
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 4 Video and Audio Distortions
### Video Degradations
#### 4.1.3 Frame-Freezing  
For this study, a frame freezing without skipping was selected; that is, pauses of the video did not discard any of the incoming frames. To recreate a frame freezing event, the following parameters were considered: (a) number of events, (b) position in the sequence, and (c) length of the event. For each sequence, a maximum of three freezing events was inserted. These events were inserted at the \"start\", \"middle\", and \"end\" of the sequence. As for the length of the events, they were set to 1, 2, and 4 s.  
Five levels of discomfort were established from the combination of these three parameters (number, position, and length of the events). The levels were labelled as \"S1\", \"S2\", \"S3\", \"S4\", and \"S5\", scaling from a low annoyance effect (S1) to a high annoyance effect (S5). Table 4 presents the parameter combination for these five annoyance levels.  
It is important to mention that, since frame-freezing is only present in video services (Video-on-Demand or YouTube) based on reliable transport mechanisms (e.g., Transmission Control Protocol--TCP), a user of these services does not experience packet-loss distortions, which are common in services based in non-reliable transport mechanisms, such as User Datagram Protocol (UDP). Therefore, since frame-freezing and packet-loss degradations do not appear in the same types of scenarios, in our experiments, they were not inserted simultaneously into a single video sequence.
# An Adaptive Multitask Network for Detecting the Region of Water Leakage in Tunnels
## 2 Methods
### The Multilevel Transformer Encoder
#### 2.2.3 Self-Attention Mechanism  
For the multi-head self-attention layer in ViT, there remains a heavy computational workload. The amount of computational workload can be calculated by using Equation (2).  
\[\mathcal{O}=4 NC^{2}+2C(N)^{2}\sim N^{2} \tag{2}\]  
where \(\mathcal{O}\) represents the complexity, \(N\in\mathcal{R}^{N\times C}\) indicates the product of the width and height of the feature map, and \(C\) denotes the number of channels. According to Equation (2), the complexity of the self-attention layer is proportional to the square of the product of the width and height of the image, and the complexity of multi-head self-attention layers is denoted as \(\mathcal{O}\big{(}N^{2}\big{)}\). Inspired by Segformer [30], the attenuation ratio \(R\) is introduced into the self-attention layer of the multilevel transformer encoder, and the attenuation ratio is used to reduce the product of image width and height, thus lowering the complexity of the self-attention layer. The calculation process is shown in Equations (3) and (4).  
\[\hat{K}=\textit{Reshape}\bigg{(}\frac{N}{R},\ C\cdot R\bigg{)}(K) \tag{3}\]  
\[K=\textit{Linear}(C\cdot R,C)\big{(}\hat{K}\big{)} \tag{4}\]  
where \(\hat{K}\) represents the intermediate variable, and \(K\) indicates the dimension of the input matrix, which varies from \([N,C]\) to \(\Big{[}\frac{N}{R},R\cdot C\Big{]}\). Then, the dimension of \(K\) is changed to \(\Big{[}\frac{N}{R},C\Big{]}\) through the _Linear_ layer. When the input matrix changes to \(V\), the dimension of \(V\) is changed to \(\Big{[}\frac{N}{R},C\Big{]}\) by Equations (3) and (4). To clearly explain Equations (3) and (4), we draw Figure 4. The orange sequence in Figure 4 is sequence \(K\), and Figure 4 describes how to reduce the length of sequence \(K\). Similarly, this way can reduce sequence length \(V\). At this time, the size of the input matrix \(Q\) remains unchanged. Finally, the matrix \(Q\), \(K\), \(V\) is sent into Equation (1) for calculation.  
Equation (5) shows the formula used to calculate complexity after the introduction of \(R\).  
\[\mathcal{O}=4\frac{N}{R}C^{2}+2C\frac{(N)^{2}}{R}\sim\frac{N^{2}}{R} \tag{5}\]  
Figure 4: Flow diagram of decreasing the length of sequence \(K\).  
Figure 3: Interaction of overlapping patches. Two patches overlap to exchange information.  
As shown in Equation (5), the complexity of the self-attention layer of the multi-level Transformer encoder decreases to \(\mathcal{O}\Big{(}\frac{N^{2}}{R}\Big{)}\). From stage 1 to stage 4, \(R\) is set to (64, 16, 4, 1), respectively.