esa-sceva/satcom-chunk-collection huggingface dataset:
# T-Type Vertical Wall for Decoupling and Pattern Correction of Patch Antenna
## 3 Result Analyses
### Pattern and Realized Gain  
The radiation patterns are measured in the anechoic chamber, as shown in Figure 7, which includes the measurement environment and the fixed detail for the patch. The Agilent N5244A network analyzer, working in 10 MHz-43.5 GHz, is employed in the measurement. The corresponding patterns at the corrected frequencies are presented in Figure 8. The corrected frequency for the simulated result of the proposed structure is 5.80 GHz, while the measured frequency is 5.81 GHz. In any case, the co-polarizations of the no-decoupling structure are also presented for comparison purposes.  
Figure 5: Antenna prototype: (**a**) 3D view and (**b**) back view.  
Figure 6: S-parameters comparisons between the simulation, measurement and the no-decoupling.  
Figure 7: Measurement in the anechoic chamber.  
In order to state the pattern-corrected direction, we used the negative degree to indicate the radiation tilts to the left of the antenna, as shown in Figure 1a, and the positive degree means the right hand. The radiation patterns are shown in Figure 8 when the left antenna is excited.  
Owing to the arrangement of patches along the H-plane, it is easy to judge that the close spacing influences the H-plane radiation pattern, while the E-plane pattern is unaffected. Thus, the H-plane patterns shown in Figure 8 indicate the decoupling effect, while the E-plane is unaffected. The H-plane beam tilts by 28\({}^{\circ}\) with no decoupling structure and is corrected to the broadside direction by the T-type wall.  
The measurements agree well with the simulation for both the E- and H-plane patterns. Both the co- and the cross-polarizations have good consistencies for the E-plane pattern. A difference is noticed in the co-polarized H-plane patterns, where measured and simulated beamwidths are \(-\)35\({}^{\circ}\)-41\({}^{\circ}\) and \(-\)60\({}^{\circ}\)-42\({}^{\circ}\), respectively. The measurement environment affects the left beam, especially the foam and tape.  
Simulated and measured realized gain comparisons are depicted in Figure 9. Here, the corresponding result of the no-decoupling structure is also included. The realized gain decreases by over 2 dB, owing to the existence of the proposed decoupling structure, but the measurement has a good agreement with the simulated results. The maximum difference is 0.28 dB, where the simulated maximum is 5.02 dB at 5.77 GHz and the measured 5.30 dB at 5.81 GHz. The fabrication causes this difference because we cannot mount the vertical wall precisely, as in the simulation.  
Figure 8: Pattern comparisons between the simulation and measurement: (**a**) E-plane and (**b**) H-plane.  
Figure 9: Realize gain comparisons.
## 3 Results and Discussion
### Fluorescence and ASE Spectra  
On the other hand, B-co-MP's fluorescence spectra in THF at various concentrations (1, 200, and 400 \(\upmu\)M) pre-irradiation were recorded. The result shows that there was only one fluorescence peak around 535 nm. The spectrum's wavelength was red-shifted and fixed at 580 nm when the concentration increased, as shown in Figure 5.  
Figure 4: Photon energy (\(h\
u\)) and (\(\alpha h\
u\))\({}^{2}\) of B-co-MP in THF at a concentration of 1 \(\upmu\)M at different gamma doses.  
After that, a solution of 1 uM was irradiated with four different doses under a constant temperature of 25 \({}^{\circ}\)C. The results showed that the spectrum exhibited a blue shift as the \(\gamma\)-irradiation dose increased. For example, at the 5 kGy dose, the profile of the fluorescence spectrum blue-shifted 9 nm. Once the exposure dose increased to 10 kGy, the fluorescence spectrum shifted about 16 nm. Eventually, at a dose of 15 and 20 kGy, the fluorescence spectrum moved 26 and 35 nm, respectively, as shown in Figure 6. These findings were consistent with the absorption profiles as described earlier.  
B-co-MP's fluorescence peak in THF for the five concentrations (1, 10, 100, 200, and 400 uM) with four different doses (5, 10, 15, and 20 kGy) are presented in Figure 7. The  
Figure 5: The fluorescence spectra of B-co-MP in THF measured for different concentrations pre-irradiation.  
Figure 6: Fluorescence spectra of B-co-MP in THF at a concentration of 1 uM at different \(\gamma\)-irradiation doses.  
study revealed that the solutions with high concentrations had more substantial blue shifts than diluted ones under the same operational conditions (see the slope of the equations displayed in Figure 7). This result was in line with our hypothesis that the B-co-MP is an excellent material for gamma detectors that can be used for dosimetric purposes.
# An Information Entropy Based Event Boundary Detection Algorithm in Wireless Sensor Networks
## 5 Simulation Results
### Efficiency Evaluation  
Overall, communication consumption is the most important energy consumption of WSNs. The communication complexity of each stage of the LFEBD is \(O(d)\), because the EEBD only receives the information report of the neighbour node when the boundary detection is carried out. In the implementation of the LFEBD algorithm, nodes and neighbours need to exchange data twice, first the sensor data, secondly to evaluate the difference between the calculated data. Moreover, data exchange needs to choose a different neighbourhood. So in the detection, the proposed algorithm saves at least half of the communication consumption. In terms of computational complexity, the proposed algorithm only performs simple statistics and comparisons. In the LFEBD algorithm, each node needs to divide its neighbourhood according to the geographic coordinates into two or three parts and then update the difference, so the computational complexity of the LFEBD algorithm is higher compared to EEBD. Because each node needing its three-hop neighbouring sensing data, the computational complexity of DBD algorithm is higher compared to EEBD.  
Figure 14: Marine experiment scenario diagram.  
Figure 15: Visualized simulation result of marine event boundary.  
The proposed algorithm can customize the setting of the boundary width in a certain range. This allows us to control the number of boundary sensors while ensuring the boundary detection rate and reduce the communication consumption of the boundary nodes to report sensing data to the sink node.  
It can be seen from the above discussion that the EEBD algorithm can achieve better detection results with less traffic and lower computational complexity, especially in low density, in the range of reasonable false alarm rate.
## 4 Experiment Results and Discussion
### Simulation Results
The driving samples of Driver 1 and Driver 2 are used to train two supervisor networks, and then these two supervisors are adopted for the SRL training process to obtain two different SRL control policies. Recall that Driver 1 has an aggressive driving style, whereas Driver 2 is non-aggressive. As shown in Figure 7, both SRL policies can control the host vehicle to follow the preceding vehicle stably and accurately, and the distance error and relative velocity are regulated in a small range. The variance of distance error of the policy trained by Driver 2 (SRL-Driver2) is larger than SRL-Driver1, as listed in Table 4. The velocity curve and the acceleration curve of SRL-Driver2 are smoother than SRL-Driver1. The fluctuation range of the acceleration for SRL-Driver 1 is also larger than that of SRL-Driver 2, especially at 20 s to 40 s and 60 s to 80 s. The acceleration distribution for the data samples and simulation results are depicted in Figure 8a,b, respectively. From the acceleration distribution of the collected driving data for the two human drivers shown in Figure 8a, it can be seen that for driver 2, whose driving style is non-aggressive, the acceleration distribution is more concentrated in the areas around zero. For driver 1, the distribution probability when the absolute value of acceleration is larger than 0.5 is greater than that of driver 2. This means driver 1 tends to give larger acceleration or braking command, whereas driver 2 tends to give smaller and smoother acceleration or braking command. The distribution shapes for the data samples and the simulations results of the two drivers are clearly similar. In detail, the SRL  
Figure 6: Simulation results with the cycle of sinusoidal velocity: (**a**) velocity, (**b**) acceleration, and (**c**) distance. control policy trained by Driver 2, which inherits the acceleration characteristics from the data samples of Driver 2, is more likely than SRL-Driver 1 to give small acceleration commands. It can be concluded that the SRL-based control policy learns the driving style from the samples of the human driver while guaranteeing control performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Control Policy** & **Maximum (m)** & **Average (m)** & **Variance** \\ \hline SRL-Driver1 & 1.39 & 0.08 & 0.13 \\ SRL-Driver2 & 1.93 & 0.05 & 0.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Maximum, average, and variance of distance error, with the real driving cycle. Figure 7: Simulation results with the real driving cycle: (**a**) velocity, (**b**) acceleration, and (**c**) distance.
# Multi-Scale Universal Style-Transfer Network Based on Diffusion Model
## 3 Proposed Method
### Loss Function  
This paper trains the network in two stages. In the first stage, the coarse style-transfer network is trained using Laplacian loss \(L_{m}\), style loss \(L_{s}\), and cycle consistency loss \(L_{c}\). The Laplacian loss function is defined as follows:  
\[L_{m}=\frac{1}{N}\sum V_{c}[I_{cs}]^{T}MV_{c}[I_{cs}] \tag{4}\]  
where \(N\) is the number of image pixels; \(V_{c}[I_{cs}]\) denotes the vectorized stylized image in channel \(c\); and \(M\) denotes the matched Laplacian matrix of the content image \(I_{c}\).  
The style loss function is defined as:  
\[\mathcal{L}_{s}=\sum_{i=1}^{l}\lVert\mu(\phi_{i}(I_{cs}))-\mu(\phi_{i}(I_{s}) )\rVert+\sum_{i=1}^{l}\lVert\sigma(\phi_{i}(I_{cs}))-\sigma(\phi_{i}(I_{s}))\rVert \tag{5}\]  
where \(I_{s}\) denotes the style image; \(I_{cs}\) denotes the stylized image; \(\phi_{i}\) represents the first several layers (from \(ReLU_{1}^{1}\) to \(ReLU_{4}^{1}\)) of the VGG-19 network; and \(\mu\) and \(\sigma\) denote the mean and standard deviation of the feature maps, respectively. However, invertible networks may suffer from numerical errors, which can lead to noticeable artifacts. Therefore, this paper introduces a cycle consistency loss to enhance the robustness of the network, which is defined as follows:  
\[L_{c}=\left[I_{c}^{{}^{\prime}}-I_{c}\right] \tag{6}\]  
where \(I_{c}^{{}^{\prime}}\) denotes the process of converting the style-transferred image back to the original content image.  
In the second part, the Stable Diffusion (SD) model is pre-trained and fine-tuned. We use sd-1.5 as the base model, retaining the original hyperparameter settings of SD. Due to the latent space of CLIP's text embeddings, our optimization process benefits from strong guidance. We adopt a multi-layer cross-attention learning approach. The input artistic image is first fed into the CLIP image encoder, which produces corresponding text embeddings. The segmentation model also automatically provides the corresponding mask information. By applying multi-layer attention to both the text embeddings and masks, the model can quickly capture the key information of the image. During training, the model is guided solely by the corresponding text embeddings and mask constraints. To prevent overfitting, a dropout strategy is applied at each cross-attention layer, with a rate of 0.05. In addition, the generative model is fine-tuned to ensure it can reliably generate images in the desired style during the refinement and repainting process.  
Figure 5: Refined style-transfer module.
# A Multi-Year Study on Rice Morphological Parameter Estimation with X-Band Pulsar Data
## 3 Datasets
### SAR Data  
During the study, the Ipsala test site is monitored with the data acquired from the TerraSAR-X satellite with an average incidence angle of 31\({}^{\circ}\). The TerraSAR-X satellite has the central frequency of 9.65 GHz and temporal resolution of 11 days. The acquisition dates are given in Figure 4. The data were delivered in single look complex format and were spatially and temporally co-registered by bilinear interpolation.
## 5 Handling Stability Simulation and Comparison
### Simulation and Comparison of the Ramp Steer Tests  
In a ramp-steer analysis, time-domain transient response metrics can be obtained [43]. The most important quantities to be measured are: the steering wheel torque, the yaw rate, the truck sideslip angle, and the truck roll angle. During a ramp-steer analysis, Adams ramps up the steering input from an initial value at a specified rate, as shown in Figure 11. The simulations of the ramp steer tests were conducted in Adams based on the two truck models, respectively. The simulation settings are as follows: the initial steer value = 0\({}^{\circ}\), the initial velocity 40 km/h, the ramp = tan(\(\theta\)) = 15.0, the start time = 3.0 s, and the end time = 15.0 s. A comparison of the simulated dynamic responses is shown in Figure 12.  
Figure 11: The curve of the input versus the time for the ramp steer test.  
Figure 12: _Cont._  
From Figure 12a, it can be seen that the steering wheel torques from the two truck models are almost the same from the beginning to about 10.0 s. Moreover, in the later stage, with the increasing of the steer angle, the steering wheel torque of the multi-leaf spring truck model increases more obviously. In other words, when the steer angle is very small, the two truck models show almost the same steering portability. However, when the steering angle is very large, the truck with the taper-leaf spring suspension has better steering portability. From Figure 12b, it can be seen that the two truck models show almost the same yaw rate. The result shows that replacing the multi-leaf spring with the steering wheel torque, the steering wheel torque is almost the same as the steering wheel torque.  
Figure 12: A comparison of the simulated dynamic responses obtained from the ramp steer tests: (**a**) The steering wheel torque, (**b**) the yaw rate, (**c**) the truck roll angle, and (**d**) the truck sideslip angle.  
the taper-leaf spring has almost no effect on the yaw rate. From Figure 12c, it can be seen that the truck roll angles on the basis of the two truck models are almost the same from 0.0 s to 5.0 s. With the increase of the steer value, the truck roll angles on the basis of the two truck models begin to deviate. The truck roll angle of the truck with the taper-leaf spring suspension is obviously larger than that of the truck with the multi-leaf spring suspension. The results show that the truck with the multi-leaf spring suspension has the stronger stability. From Figure 12d, it can be seen that the two truck models show almost the same sideslip angle. The result shows that replacing the multi-leaf spring with the taper-leaf spring has almost no effect on the truck sideslip angle during the ramp steer test.
## 2 Circuit Implementation
### The Continuous-Time Input Common-Mode Current-Feedback Loop
#### 2.1.2 Buffer and Window Comparator  
A schematic of an amplifier utilizing a buffer and window comparator is shown in Figure 5.  
The amplifier is implemented in three stages in terms of the input range, driving CM detector, and high enough gain. Since the input CM signal can be over the supply rail, the buffer and window comparator must have a wide input range. For this reason, the rail-to-rail input stage is implemented.  
In the case of the input buffer, the input buffer must drive the CM detector, and the open-loop gain should be large for high buffer linearity. To gain a heavy load driving capability and high open-loop gain, folded cascode stage merged floated class-AB biasing and a class-AB output stage are implemented.  
The phase margin is the key feature in terms of the stability of an amplifier. In the case of a three-stage amplifier, the number of poles is increased, which leads to difficulties in achieving enough phase margin. Since nested miller compensation (NMC) can be a good solution in this case, NMC is implemented in the amplifier.
# A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
## 4 Experiments Results
### Comparison with State-of-the-Art Methods
#### 4.4.1 Evaluation on Synthetic Test Dataset  
In this paper, a total of 29 synthetic low-light images as the test samples are selected from the open dataset LIVE1 [30]. Figure 8 shows the results for four synthesized low-light images, which marked as (a)-(d) from top to bottom. Where, (a), (b) are uniformly illuminated low-light images, and (c), (d) are unevenly illuminated images. As, it is shown in Figure 8 for (a), (c) and (d), Dong method always generates serious black edge. For example, in Figure 8a, originally under the hat, a shadow is seen that indicates, serious contour lines appear after enhancement. Although, SRIE method preserves the image color and no false contour is appeared, but there is a limitation for the brightness improvement. LIME method produces over-enhanced results for bright regions and it possibly leads to different degrees of color distortion. For example, in Figure 8c,d, the flowers and plants are yellowish, whereas in ground truth they are green. The proposed algorithm not only improves the image brightness and contrast, but also preserves the image color information too close to the ground truth image. Meanwhile, like other algorithms, it is not able to enhance the dark regions of a large white area in the actual scene.  
As ground truth is known for the used synthetic low-light images, we can compare the performance of different algorithms in terms of objective criteria. So, for performance evaluation, peak signal-to-noise ratio (PSNR), structural similarity (SSIM), mean square error (MSE) and lightness order error (LOE) [31] were used. PSNR reflects the distortion degree of an image, so high PSNR value indicates less distortion. SSIM characterizes the integrity of image structure information, so high SSIM value shows more similar structural features between the enhanced image and the corresponding ground truth. MSE reflects the difference between the enhanced image and the ground truth, so the small MSE means that the enhanced image is similar to the original normally illuminated image. LOE mainly evaluates the naturalness preservation ability of the enhanced image, so the small LOE value indicates that the brightness order of the image is reasonably preserved and the image is more natural. Table 2 gives the average objective parameters for different enhancement algorithms where LIVE1 is used as the test dataset.  
Figure 8: The subjective visual comparison of different algorithms for synthetic low-light images. (a) “caps” from LIVE1, (b) “carnivaldolls” from LIVE1, (c) “cemetry” from LIVE1, (d) “building2” from LIVE1.  
According to the results written in Table 2, the proposed algorithm outperforms other approaches in terms of PSNR, MSE and LOE, only it got less SSIM sin comparison with SRIE algorithm. The achievements indicate that the enhanced image by our algorithm is much closer to the original image, it is more realistic and natural, and the distortion is small.
###### Abstract
Therefore, the main objective of this study is twofold: first to explore the capability of C-band S-1 SAR imagery to capture multi-seasonal wet-snow dynamics over Mediterranean mountain areas and second, to apply a hydrological approach to assess the connection between these wet-snow dynamics and streamflow response. In this work, the main innovative parts are (i) the use of terrestrial photography in combination with multitemporal S-1 SAR imagery to understand SAR backscattering at the plot scale; (ii) the use of S1-SAR imagery to monitor the full snow season, thus highlighting different melting cycles; (iii) the use of S1-SAR imagery to foresee changes in streamflow dynamics at catchment scale. The Sierra Nevada mountain range (southern Spain) is selected as a pilot Mediterranean mountain area to carry out this study. The paper is structured into five remaining sections. Section 2 presents the test sites at a local and catchment scale. It also includes the meteorological data available as well as the remote sensing information used. In Section 3, we present the different steps of the proposed approach to detect Wet-snow conditions and the definition of melting cycles. The results are rendered in Section 4, focusing on local and catchment scales. In Section 5, we discuss the main results. Finally, Section 6 draws the conclusions of this work by providing information on how the proposed methodology can be extended to other semi-arid mountains.
## 4 Spectrum of Heterogenous Services for Transport Means and the Communication of Information  
Service provision for vehicles by using VANET vary in a broad spectrum. By summarising types of services provided in works [10; 24; 25; 26; 27], we would like to give the classification structure of services in Figure 2.  
In Figure 2, we have used some standard abbreviations: GIS--Geographic information system, GPS--Global positioning system, RSU--Response Support Unit/Reunification Services Unit.  
Services can be divided into two main classes: for transport safety and vehicle readiness for cargo transportation. For supporting high-quality services, some parameters are essential and have considered by evaluating such data as the rate, packet delivery efficiency and collision rate. The analysis shows systematic data quality requirements for different services for vehicle communication networks (Figure 2) based on the data presented in several studies [1, 11, 12].  
Safety measures improve the protection of drivers by sending and receiving information related to vehicle safety.  
Typically, these alerts are generated by GIS, video cameras, and sensors, such as collision alert, lane departure alert, emergency video streaming, and incident management received by the automatic active safety system or reported to drivers by GPS [28].  
The vehicle readiness category is related to freight applications. It aims to improve vehicle availability for cargo carriage, increase drivers and heavers comfort level (make the journey more pleasant) and enhance cargo routing efficiency level.  
Information flow is modelled to cover internal and external needs and separated into network-related components (1-2) and carriage-related element (3):  
Figure 2: The classification of heterogeneous services for transport means based on wireless networks.  
* The information flows in the context of traffic jumps;
* The information flows in the context of accidents;
* The information flows in the context of tracking.  
Information about network conditions, vehicle parking and another status is collected by exchanging messages in real-time and could include various identifiers:  
* The licence plate of cargo vehicle--registration numbers of vehicle visible for everyone, could be removable or transferable,
* VIN--identification of the vehicle, i.e., 17-digit numbers stamped on various parts of the vehicle during production, which remains on vehicle from production to the end,
* Cargo identification--unique identification of assignment transported by carrier on behalf of the customer, usually referring to the global shipment identification number,
* Route code--unique identification number of the route.  
Below it is a detailed presentation of separate components of information flow. These identifications will be used to access particular data.