esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Parameter Tuning of G-TIAG
#### 5.3.1 Hyperparameter Selection  
An important step in training GCNs is to tune the hyperparameters, which adjusts the trade-offs between model bias and classification performance. Because the number of training instances and hyperparameters in G-TIAG is large, it is challenging to find the optimal combination of hyperparameters. Therefore, we search the hyperparameters from an interval and select the best combination.  
The final selection of hyperparameters involved in G-TIAG is shown in Table 6, where the learning rate is initialized to 0.001, the training epochs are 200, and the batch size is 64. Next, the GCN involved in G-TIAG uses two convolutional layers for spatial feature extraction, with the number of hidden units in the first layer being 256 and the number of hidden units in the second layer being 128. In addition, the number of hidden units in GRU is 256, the number of hidden units in the self-attention is 256, and the time interval of graph construction is 200.
## 3 Results and Discussion
### Method Knowledge of Space
#### 3.2.1 HS-SPME Fibers
Even though the CAR/PDMS fiber has a wider range of MW, its low thickness requires a longer extraction time to reach equilibrium and a higher desorption time owing to the coating properties and configuration. Higher temperatures are also required due to their low thickness as well as a higher analyte concentration to improve efficiency [37; 39]. DVB/CAR/PDMS fiber has a highly cross-linked structure, an intermediate polarity strength, and a thinner coating layer when compared with PDMS/DVB and CAR/PDMS [37]. Considering the sample complexity, DVB/CAR/PDMS is the most suitable for complex samples, and a low concentration of the target compounds, owing to its mixed layer properties. The largest compounds are retained in the first layer (DVB), maintaining free coating to retain the lowest MW compounds. Therefore, for the next steps, the DVB/CAR/PDMS fiber was chosen because of its overall performance and acceptable values.
# Improved CycleGAN for Mixed Noise Removal in Infrared Images
## 2 Methods
### Introduction of CycleGAN  
In 2014, [PERSON] et al. [24] proposed a deep learning algorithm named GAN (Generative Adversarial Network). By training two main modules named generator and discriminator through the adversarial learning approach, GAN realized the data conversion between two domains and achieved excellent results in the field of image generation. In 2017, [PERSON] et al. [25] proposed CycleGAN, applied to style conversion task. CycleGAN can be viewed as a combination of two GAN networks which contains two generators that are responsible for converting the image in the original domain to the target domain and converting the generated image in the target domain back to the original domain. It also contains two discriminators which are used to determine whether the images generated by the corresponding generators are true or fake. In addition, CycleGAN defines a loss function named \"Cycle Consistency loss\" to ensure that the network is able to transform an image from one domain to another without changing anything except the style. Compared with other algorithms, CycleGAN can be trained under unsupervised conditions and achieve excellent style conversion results, so it has been widely used in image processing fields such as change detection, image enhancement and image translation in recent years.
# Research on Seamless Fabric Defect Detection Based on Improved YOLOv8n
## 1 Introduction
The feature extraction stage relies on complex symmetric Gabor filter banks and principal component analysis (PCA) [18]. [PERSON] et al. proposed a CNN model based on residual networks to enhance the classification of fabric defects, and the pre-trained residual network ResNet50 can be fine-tuned to classify fabric defects into four categories [19]. Table 1 shows the methods and shortcomings of the above-mentioned fabric defect detection algorithm based on deep learning methods. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Methods** & **Shortcomings** \\ \hline [PERSON] et al. [7] & Deep learning methods using U-SMR Net and multiple residual networks for fabric defect detection. & It requires high computing power and may result in false detection or missed detection in some complex backgrounds. \\ \hline [PERSON] et al. [8] & Combining deep learning and optimization algorithms to improve detection performance & The algorithm is complex, the training time is long, and high-performance hardware may be required. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Methods and shortcomings of fabric defect detection algorithms based on deep learning. The above algorithms are mainly used for fabric defect detection in ideal environments. However, in practical applications, the color diversity, light interference, and complexity of defect types in fabrics result in the weak generalization ability of the model when facing fabrics of different colors or diverse defects, making it difficult to effectively adapt to diverse detection needs. Especially in the field of small target defect detection, existing algorithms often suffer from low detection precision and high missed detection rates, making it difficult to meet the high standard requirements of industrial inspection. In addition, while pursuing higher detection precision, the detection speed of the model is often limited, making it challenging to balance real-time performance with precision. In order to solve the above problems, an improved seamless fabric defect detection algorithm based on the YOLOv8n deep learning detection framework is proposed in this paper. It aims to solve the problems encountered by existing algorithms in practical applications by optimizing feature extraction, improving small target detection capabilities, enhancing the generalization ability of the model and improving computational efficiency.
# A Peak Absorption Filtering Method for Radiated EMI from a High-Speed PWM Fan
## 2 Circuits Structure and Radiated EMI Measurement
### Radiated EMI Measurement and Analysis of Results  
According to MIL-STD-461G, the radiated emission measurement configuration of the fan was as is shown in Figure 5. The measurement layout consisted of a DC power supply, two DC line impedance stabilization networks (LISN), two power lines, a receiver antenna, an EMI receiver, and the fan under test. The measurement environment was a shielded dark room, and the receiving antenna was placed 1 m away from the device under test, as shown in Figure 6. Within the rated operating range of the fan, the EMI receiver antenna measured the amplitude of the electric field in the frequency range of 10 kHz-30 MHz.  
The measurement procedures were as follows:  
a. Ambient Condition Check: Confirm that the environmental conditions comply with the specified standards to prevent any impact on test accuracy.  
b. Equipment Initialization: Power on the measurement instruments and allow adequate stabilization time.  
Figure 4: Driving signal of each bridge arm.  
Figure 5: Radiation emission measurement configuration.  
Figure 6: Radiation emission measurement site.  
c. Device Preparation: Switch on the test equipment and ensure it stabilizes properly before proceeding.  
d. Emission Assessment: Following the configuration in Figure 5, measure the radiated emissions from the test device and its connected cables.  
The fan under test had a rated voltage of 24 V, a rated power of 103.8 W, a peak speed of 10,500 rpm, and a pole pair number of 2. Under the PWM condition, the results of the electric field radiation measurement on the fan were as shown in Figure 7. At the frequency of 15.5 kHz, the intensity of the radiated electric field measured was 72.41 dB\(\mu\)V/m, which was higher than the limit value of 57.02 dB\(\mu\)V/m required by the standard. According to the measurement results, it can be seen that there were a number of peaks of interference exceeding the standard limits in the measurement band at a series of peaks with a fundamental frequency of 15.5 kHz, which is consistent with the modulation frequency of the PWM, indicating that these frequency peaks were indeed caused by the switching of the MOSFETs in the inverter circuit with their frequency harmonics.
## 2 Dynamic Modeling of Trans-Medium Process
### Dynamic Modeling
#### 2.3.4 Control Force and Moment  
The propellers used in HAUVs are usually aviation propellers, and previous experimental studies have shown that air propellers can be used for underwater propulsion to some extent. The coaxial HAUV is equipped with two propellers and a swash plant. The control force and torque generated by them can be expressed as follows [17; 28; 29]:  
\[\mathbf{T}=\begin{bmatrix}T_{x}\\ T_{y}\\ T_{z}\end{bmatrix}=\mathbf{R}\big{(}\delta_{x},\delta_{y}\big{)}\begin{bmatrix}0\\ 0\\ k_{\alpha}\omega_{1}^{2}+\sigma k_{\beta}\omega_{2}^{2}\end{bmatrix} \tag{10}\]  
\[\mathbf{R}\big{(}\delta_{x},\delta_{y}\big{)}=\begin{bmatrix}\cos\delta_{y}&-\sin \delta_{y}\sin\delta_{x}&-\sin\delta_{y}\cos\delta_{x}\\ 0&\cos\delta_{x}&-\sin\delta_{x}\\ \sin\delta_{y}&\cos\delta_{y}\sin\delta_{y}&\cos\delta_{x}\cos\delta_{y}\end{bmatrix} \tag{11}\]  
\[\mathbf{M}_{T}=\begin{bmatrix}u_{2}\\ u_{3}\\ u_{4}\end{bmatrix}=\begin{bmatrix}-d\sin\delta_{x}\big{(}k_{\alpha}\omega_{1} ^{2}+\sigma k_{\beta}\omega_{2}^{2}\big{)}\\ -d\sin\delta_{y}\cos\delta_{x}\big{(}k_{\alpha}\omega_{1}^{2}+\sigma k_{\beta }\omega_{2}^{2}\big{)}\\ \gamma_{\alpha}\omega_{1}^{2}+\gamma_{\beta}\omega_{2}^{2}\end{bmatrix} \tag{12}\]  
where \(k_{\alpha}\) and \(k_{\beta}\) are the lift coefficients of the upper and lower propellers, respectively; \(\sigma\) is the lift loss coefficient of the lower propellers; \(d\) is the distance from the propeller center to the center of mass; \(\mathbf{R}\big{(}\delta_{x},\delta_{y}\big{)}\) represents the mapping transformation matrix of propeller thrust to the HAUV body; and \(\delta_{x}\) and \(\delta_{y}\) are the deflection angles of the swash plant.  
**Remark 4**.: _From the perspective of actual flight control and considering that the swash platform angles are very small, we can obtain \(\cos\delta_{x}\approx 1\), \(\cos\delta_{y}\approx 1\), \(\sin\delta_{x}\approx\delta_{x}\approx 0\), and \(\sin\delta_{y}\approx\delta_{y}\approx 0\). The control actuator model can be simplified, and the lateral effects caused by \(T_{x}\) and \(T_{x}\) in (10) are negligible compared to the main thrust force \(T_{z}\). For convenience, let us redefine \(T_{z}\) to be \(u_{1}\)._
# SpaFLEX: Field Campaign for Calibration and Validation of FLEX-S3 Mission Products +
## 1 Introduction  
The European Space Agency's FLuorescence EXplorer-Sentinel 3 (FLEX-S3), scheduled for launch in 2026, is a groundbreaking mission designed to remotely detect solar-induced fluorescence (SIF) in vegetation at a spatial resolution of 300 \(\times\) 300 m. With its high spectral resolution, FLEX-S3 will retrieve SIF and improve the estimation of high-level products such as absorbed photosynthetically active radiation (APAR) and non-photochemical quenching (NPQ). Together with SIF, these products are expected to significantly improve the remote estimation of the true photosynthetic capacity of vegetation [1]. Cal/Val activities are crucial to refine the FLEX-S3 products prior to their integration into vegetation models. The European Space Agency (ESA) is responsible for managing Level 1 products, while member states, such as Spain, oversee the Cal/Val plans for Level 2 products, thereby promoting interoperability.  
The SpaFLEXmp initiative, led by the Spanish National Institute of Aerospace Technology (INTA), coordinates the national calibration and validation (Cal/Val) plan. INTA contributes with its advanced airborne hyperspectral and chlorophyll fluorescence sensors, in collaboration with interdisciplinary teams from the University of Valencia (LEO-UV) and the Donana Biological Station (LAST-EBD).  
Interpretation of the weak SIF signal--10 orders of magnitude lower than reflectance--is challenging due to environmental variability. Accurate retrievals depend on atmospheric absorption characteristics and require stringent Cal/Val protocols to meet mission uncertainty targets: <30% overall, ~10% for fluorescence and irradiance, and ~1% for reflectance. Ground truth comparisons are essential to account for instrument-, algorithm-, and site-specific factors.  
As an initial approach to addressing the calibration and validation requirements of the FLEX mission products, in this work we present the design and execution of a field campaign carried out in two Holm Oak forest areas of different tree densities in Sarrion-Manzanera (Teruel, Northeast Spain) (Figure 1). In both areas two CO\({}_{2}\) flux towers and two fluorescence measurement systems (FLOX sensor (JB Hyperspectral Devices, Dusseldorf, Germany)) are installed. We analyzed spatial heterogeneity of Sentinel-2 images in order to determine the minimum number of Environmental Sampling Units (ESUs) needed to carry out intensive spectroradiometric measurements with ASD FieldSpec FR Spectroradiometer (Analytical Spectral Devices, Boulder, CO, USA), quantify leaf fluorescence with a Fluowat device [2], and overfly with the Piccolo Fluorescence sensor onboard of an UAV. For both areas, we also conducted a UAV flight campaign with the Cubert S185 hyperspectral camera for subsequent data scaling and for quantifying the number of measurements in each ESU. Results are being used to define upscaling procedures towards the FLEX-S3 300 m nominal pixel.
## 3 Results
### Analysis of Heading Precision Estimates for Flight A
These results align with the manufacturer's heading error performance specifications for the APX-18 sensor shown in Table 2. At slow horizontal velocities, this qualitative analysis suggests that a dual-antenna GNSS/INS sensor significantly improves heading precision by up to a factor of five relative to a single-antenna GNSS/INS sensor.
# Evaluation of Different Internal Designs of Hydraulic Nozzles under an Accelerated Wear Test
## 2 Materials and Methods
### The Wear Test and Flow Rate Measurement  
The accelerated wear test was undertaken, according to the ASAE S471 [20] standard, inside a device (Figure 2) that was built for this purpose. The device consisted of a 1000 L tank with a closed system to circulate the abrasive material (18.2 kg Kaolin KOM + 300 L water). The nozzles were subjected to 100 h of accelerated wear testing. Measurements of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Nozzle Type** & **Full Name** & **Nominal Flow** & **Spray Angle, \({}^{\circ}\)** & **Pressure** & **Design Feature** & **Application Rate, 1 ha\({}^{-1}\) (at \\  & **and Size** & & **The Rate, L/min** & **Range, Bar** & **Speed of** & **4 km h\({}^{-1}\))** \\ \hline  & Turbo Teejet, & & & & Twin chamber, & \\ Teejet TT 03 & Wide Angle Flat & 1.18 & 110 & 1.0–6.0 & rounded & 354 \\  & Spray & & & & internal & \\  & Turbo Twinjet, & & & & & Dual outlet & \\ Teejet TTj60 03 & Twin **T** Flat & 1.18 & 110 & 1.5–6.0 & produces dual & \\  & Spray & & & & 110\({}^{\circ}\) flat fan & \\  & Drift Guard Flat & & & & spray patterns & \\ Teejet DG 03 & Spray & 1.18 & 110 & 2.0–5.0 & Removable & \\ \hline  & & & & & pre-orifice & 354 \\ \hline \hline  & & & & & & \\ \end{tabular}  
* From the catalogue of Teejet products, Spraying Systems Co., Illinois USA. Data are based on spraying water at 70 \({}^{\circ}\)F (21 \({}^{\circ}\)C) and at 3.0 bar pressure. ** The angle between the twin spray fans of the TTj60 nozzle is 60\({}^{\circ}\).  
\end{table}
Table 1: Features of the nozzles used in the study *.  
Figure 1: Cross-section view of the tested nozzles: (**a**) TTj60, (**b**) DG, (**c**) TT (Teejet products, Spraying Systems Co., Illinois USA).  
flow rate were done after every fifth hour during the wear cycle. Measurement of spray distribution occurred after every 10 h of wear testing.  
After each wear interval, the flow rate measurements were done using a specific measuring device from ITEQ, Wallonia-Belgium. This device assesses the flow rate (quantity of water sprayed in specific time) for multi-nozzles after detaching them from the sprayer boom [21]. Tap water with a 21 \({}^{\circ}\)C temperature and at 3.0 bar pressure was used. The measurement for every nozzle of the six nozzle samples was replicated three times. The value for the flow rate after a specific wear interval and for a specific nozzle type was reported as an average for those six nozzles and replications.  
The wear rate, which represents the percentage increase in the flow rate compared with the nominal flow rate of new nozzles, was calculated from the following formula [22]:  
\[u_{i}=\left|\frac{q_{i}-Q_{nom}}{Q_{nom}}\right|\times 100\% \tag{1}\]  
where:  
\(u_{i}\)--wear rate of the ith nozzle, %;  
\(q_{i}\)--measured flow rate of the ith nozzle, L/min;  
\(Q_{nom}\)--nominal flow rate, L/min.
# Design of Multifunctional Mesosphere-Ionosphere Sounding System and Preliminary Results
## 4 Typical Experimental Results
### Mesospheric Sounding
\begin{bmatrix}\left\{\frac{\omega-\omega_{L}}{v_{m}(H)}C_{3/2}( \frac{\omega-\omega_{L}}{v_{m}(H)})\right\}^{2}+\left\{\frac{5}{2}C_{5/2}( \frac{\omega-\omega_{L}}{v_{m}(H)})\right\}^{2}\\ \left\{\frac{\omega+\omega_{L}}{v_{m}(H)}C_{3/2}(\frac{\omega+\omega_{L}}{v_{m} (H)})\right\}^{2}+\left\{\frac{5}{2}C_{5/2}(\frac{\omega+\omega_{L}}{v_{m}(H) })\right\}^{2}\end{bmatrix} ^{1/2} \tag{23}\]  
where \(\omega\) is the angular frequency of the sounding signal. \(\omega_{L}\) is the longitudinal component of angular cyclotron frequency, which can be calculated according to the magnetic dip based on International Geomagnetic Reference Field (IGRF). \(v_{m}(H)\) is the collision frequency of \(Hth\) layer and can be calculated relay to the atmospheric pressure profile according to International Reference Atmosphere (CIRA) of the Committee on Space Research (COSPAR) [36]. \(C_{3/2}()\) and \(C_{5/2}()\) can refer to [37]. When \(F(H)\) is defined as Equation (24), after calculating \(F(H)\), electron density profile of the corresponding height at \(Hth\) layer \(N(H)\) can be obtained by substituting Equation (24) into Equation (22) [36]. \[F(H)=\frac{2[K_{x}(H)-K_{O}(H)]\Delta h}{N(H)} \tag{24}\]  
Specifically, \(F(H)\) can be calculated by Equations (25) and (26). \[\left\{ \begin{array}{l}F_{O}(H)=\frac{5}{4}\frac{e^{2}}{mv_{O}v_{m}(H)}C_{5/2 }(\frac{\omega+\omega_{L}}{v_{m}(H)})\\ F_{X}(H)=\frac{5}{4}\frac{e^{2}}{mv_{O}v_{m}(H)}C_{5/2}(\frac{\omega-\omega_{L }}{v_{m}(H)})\end{array} \right. \tag{25}\]  
\[F(H)=2[F_{X}(H)-F_{O}(H)]\Delta h \tag{26}\]  
where \(e,m,\varepsilon_{0}\) represent the electric quantity, electronic mass and dielectric constant respectively. Then, the electron density profile of the corresponding height at \(Hth\) layer \(N(H)\) can be expressed as Equation (27) [36]  
\[N(H)=\frac{\left\{\ln\left[\frac{R_{X}(H)}{R_{O}(H)}\right]-\ln\left[\frac{R_{ X}(H-1)}{R_{O}(H-1)}\right]\right\}-\left\{\ln\left[\frac{A_{X}(H)}{A_{O}(H)} \right]-\ln\left[\frac{A_{X}(H-1)}{A_{O}(H-1)}\right]\right\}}{F(H)} \tag{27}\]Figure 15a shows a typical result of the inversion based on the DAE algorithm. Figure 15b shows a typical signal power distribution with height. Considering that the D layer of the ionosphere has a low electron density at night, which is difficult to reflect the MF signals, we chose the sounding data at noon for electron density inversion. From 70 km onwards, the electron density increases with the height. During the observation period, the electron density also increased slightly over time.
## 5 Simulation Results and Discussion
### Results under Varying Irradiance  
To perform simulation for varying irradiance levels, the temperature was kept constant at 25 \({}^{\circ}\)C, and the irradiance was changed as shown in Figure 11. The generated reference voltage \(V_{ref}\) using FFNN for changing irradiance profile was tracked by the proposed AOSMC. It can be seen from Figure 12 that the \(V_{ref}\) was effectively obtained using our developed controller in 0.01 s with a slight overshoot of 3 V. Similarly, in Figure 13, the PV system output power is presented with reference power curves as a result of varying irradiance levels, which depicts that the MPP was attained with minor oscillations and in a short time of 0.02 s.
## 4 Reliability Assessment Considering ME  
### Measurement of Retaining Structure Deformation  
An inclinometer system is often used to measure the deformation of retaining walls in practical engineering, which typically comprises a sensor probe, a load-bearing cable with a scaler, and a reader, as illustrated in Figure 3a,b. The sensor probe is a key component for the lateral displacement measurement in the inclinometer. A high-precision acceleration digital sensor is installed inside the probe. The tilt of the casing in four different directions, namely, A\({}_{0}\), A\({}_{180}\), B\({}_{0}\) and B\({}_{180}\) in Figure 3b, is measured by the probe. Two sets of guide wheels are located at the upper and lower parts of the probe respectively, with which the probe can slide up and down inside the inclinometer casing (c.f. Figure 3c).  
The basic idea to measure the horizontal displacement with an inclinometer is to first determine the azimuth between the central axis of the inclinometer and the vertical line of the gravity pendulum in the probe, and then to use the geometric relationship (c.f. Figure 3d) to compute the accumulative displacement at different depths [51; 52; 53]. Let the inclination angle measured at a certain depth be \(\alpha\), and \(L\) the overstep (measurement interval), as shown in Figure 3d. With this, the horizontal displacement at this depth, \(\delta\), is computed by \(\delta=L\sin\alpha\) according to the triangular relationship. Furthermore, calculating the horizontal offset from the bottom to the top, the lateral displacement at the \(j\)th measurement interval relative to the bottom, \(D_{j}\), is determined by  
\[D_{j}=\sum_{i=1}^{j}\delta_{i}=\sum_{i=1}^{j}L\sin\alpha_{i} \tag{18}\]  
where \(\alpha_{i}\) is the angle between the central axis of the inclinometer and the vertical line of the gravity pendulum at the \(i\)th measurement interval and \(\delta_{i}\) is the horizontal offset at the \(i\)th interval.