esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Earth Observation Emerging Missions and Advancements  
The advancement in spatial and spectral resolutions in new missions can significantly impact the accuracy of Evapotranspiration (ET) estimation models, providing enhanced insights into the water cycle dynamics and agricultural productivity. The Copernicus Space Component (CSC) is expected to undergo evolution in the mid-2020s. This evolution aims to fulfill key user demands that are currently not met by the present infrastructure. Additionally, it aims to enhance services by improving monitoring capabilities in the thematic areas of agriculture/forestry, CO2, and polar regions. This progression will be synergistic with the improved continuity of services for the upcoming generation of CSC. Specifically, the Copernicus program will enhance its existing capabilities via the addition of six new satellite missions. One of the missions will involve the Copernicus Land Surface Temperature Monitoring (LSTM), which will deploy a thermal infrared sensor with a high spatial-temporal resolution to gather data on land-surface temperature. The mission addresses the urgent needs of the agricultural user community to enhance sustainable agricultural production at the field level, considering the growing challenges of water shortage and unpredictability. Land-surface temperature measurements and the resulting evapotranspiration are crucial factors for comprehending and adapting to climate fluctuations, overseeing water resources for agricultural purposes, forecasting droughts, and addressing concerns related to land degradation, natural disasters like fires and volcanoes, coastal and inland water management, and urban heat island problems. In more detail, by monitoring high-resolution thermal signatures, it is possible to assess the status of crop water and overall water stress. This helps in managing irrigation and optimizing the use of limited water resources for crop production. Additionally, it allows for the derivation of essential soil parameters that cannot be obtained in the VNIR/SWIR spectral range. This promotes the use of resilient agricultural techniques, which aim to preserve ecosystems and gradually enhance the quality of land, soil, and water. As a result, it contributes to the development of more sustainable agricultural practices. High-resolution thermal imaging with precise spatio-temporal data can be used to immediately identify water conservation practices or determine if they are necessary. Unauthorized water abstractions can be identified and tracked by comparing evapotranspiration maps with water rights. Moreover, the Landsat mission will add to its constellation the Landsat Next. At present, 30-m resolution imagery of the Earth is provided by the Landsat-7 and 8 missions, with a 16-day return period per satellite. Landsat-7 was launched in April 1999 and is now in a deteriorating orbit. It also displays a scan line artifact that decreases its coverage. Landsat 8 was introduced in February 2013. Compared to Landsat-7, it has smaller spectral bands, better calibration and signal-to-noise ratios, increased 12-bit radiometric resolution,
# Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis
## 5 The ALPR System
### Detection  
The dataset images are pre-processed using image resizing, grey color conversion, banalization, and morphological operations and filtering. Next, the connected neighbors are analyzed, using the Connected Component Analysis (CCA) algorithm, to obtain the possible candidates for a license plate. The potential number of candidates are then reduced using the newly added features; vertical-edge analysis (VEA) and this is achieved by filtering some of the candidates' regions that may cause the next detection stages to be misled and hence affect the overall detection performance. The aspect and edge ratios of the image under processing are then checked to identify the correct license plate's candidate. This process passes through several stages that are outlined as follows.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Component**} & **Accuracy with Real** & **Accuracy with** & **Potential** \\  & **Inputs (\%)** & **Artificial Inputs (\%)** & **Improvement** \\  & & & **Margin (\%)** \\ \hline Detection & 82 & 82 & 40.2 \\ Segmentation & 74 & 95 & 13 \\ Recognition & 68 & 96.7 & 1.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary performance comparisons for both actual and artificial inputs for the ALPR system under study. As expected, the artificial inputs used in this analysis resulted in a significant potential for performance improvement as compared to that of the original system.
# Semantic Image Segmentation Using Scant Pixel Annotations
## 5 Experimental Results and Discussion
### Comparing SSPA with Other Methods  
We also investigated the effectiveness of the SSPA approach by comparing its performance with the state-of-the-art fully supervised and weakly supervised segmentation methods. We trained two fully supervised encoder-decoder architectures, one using U-Net and another using DeepLabV3+ with Resnet101 [53, 54]. The DeepLabV3+ model has an encoding phase which uses atrous spatial pyramid pooling (ASPP) and a decoding phase to give a better segmentation results along object boundaries. To compare our method with weakly supervised segmentation methods, we trained two U-Net models using gradient [55] and MC-WS methods, respectively, both of which generate initial pseudo-labels.  
The segmentation results of SSPA and other fully supervised (FS) and weakly supervised (WS) methods are summarized in Table 1. FS models were trained using full pixel level (\(P\)) expert labels, whereas WS models were trained using complete image-level (\(I\)) labels. The SSPA approach performs approximately equally or better on all datasets in comparison to these fully supervised and weakly supervised methods. This is despite the minimal annotation effort needed for the SSPA in comparison to the other methods.  
For the EM dataset, SSPA+LE performs equally to the two fully supervised U-Net methods (Dice scores for both are around 90.0% and IOU scores are around 82%). For this dataset, the performance of the SSPA+HE is lower in comparison to these two FS methods. The SSPA+HE and SSPA+LE perform better than both the weakly supervised methods in both the Dice and IOU measures. For the melanoma dataset, the SSPA+HE outperforms all of the FS and WS with respect to both IOU (82.4%) and Dice score (97.3%) values. For the biofilm dataset, the SSPA+HE outperforms both the FS methods as well as WS: grab-cut+UNET method. The performance of SSPA+HE and WS:MC-WS+U-NET is approximately the same. Further, we observed that SSPA+LE shows better IOU and Dice-score results on EM dataset compared to SSPA+HE version, whereas the SSPA+HE version performs well with the melanoma and biofilm datasets. Further, the performance improvement of the SSPA method on the biofilm dataset is approximately 9% compared to the supervised approaches.  
The best performing models were obtained by replacing less than 5% of the pixels summed across all iterations for all datasets (2.55% for EM with LE, 4.85% for melanoma  
Figure 10: Entropy distribution across models.  
with HE, and 4.58% for biofilms with HE). These percentages include the pixels to be replaced to generate one more model after the best performing model in order for the active learning process to terminate.
## 4 Numerical Modeling and Methodology
### Mesh and Boundary Conditions
The tie constraint feature within ABAQUS [34] establishes a connection that synchronizes both the translational and rotational degrees of freedom of the slave nodes to those of a master surface. This guarantees that both surfaces behave as a unified entity under applied loads. Tie constraints are particularly advantageous for steel plates embedded in concrete, as well as for gusset plates and anchor heads, or any components requiring complete continuity. For segments of the ACPH that necessitate rotational movement during bending, a connector is defined among the \"lugs\", allowing for shear and axial transfer while imposing limitations on specific relative displacements or rotations. The design of the connector facilitates the steel lug assembly's ability to pivot around a physical hinge, all the while maintaining shear transmission across this joint. At the finite element level, this hinge connector operates similarly to a multi-point constraint (MPC), with the distinction that the applicable rotational degree of freedom remains unconstrained, thus reflecting the practical behavior of a pinned steel shaft in real-world applications.
# A Miniaturized Design for a Terahertz Tri-Mirror CATR with High QZ Characteristics
## 3 Simulation and Measurement Results
However, when the corrugated horn feed (cross-polarization amplitude: \(-29\) dB) is used in simulations, the simulated cross-polarization amplitude (\(-28.1\) dB) differs from the measured value (\(-25.3\) dB) by only 2.8 dB, demonstrating close alignment between the re-simulation results (using measured feed patterns) and the measured results. This not only fully confirms that feed non-ideality is the dominant factor in the cross-polarization discrepancies of the CATR system but also validates the effectiveness of the proposed miniaturized tri-mirror CATR design methodology in achieving low cross-polarization performance. Finally, Table 5 lists the major characteristics between the proposed and the already-reported miniaturized CATR. As shown in Table 5, although the CATR in [19; 20; 21; 22; 23] achieves 50% QZ utilization in a relatively small space with only one mirror, our proposed miniaturized tri-mirror CATR composed of three mirrors exhibits higher QZ utilization (up to 70%), a higher upper frequency limit, lower cross-polarization (below \(-40\) dB), and a smaller system size. This miniaturized tri-mirror CATR design approach paves a new path for overcoming and reducing the high edge diffraction effects in small-aperture CATRs, as well as for constructing high-performance, flexible-mobility CATRs under constrained site space conditions.
# Multi-Layered Satellite Communications Systems for Ultra-High Availability and Resilience
## 3 MLSS Technology Assessment: Benefits and Challenges
* Ability to utilize inter-layer links: Utilization of inter-layer links (ILLs) requires sophisticated network management protocols, congestion avoidance, and the ability to utilize relatively short ILL contact times. Utilization of existing inflexible satellite infrastructure without proper ILL connectivity possibility may hinder the potential benefits of MLSS in practice. Only a limited number of links can be monitored. Faster and more accurate acquisition, tracking, and pointing (ATP) algorithms for ILLs will be necessary for the efficient utilization of ILLs ([PERSON] et al., 2018). * MLSS implementation aspects: Compact and low-cost user terminals for the multi-connectivity case require sharing a unique antenna between different layers where power and band limitations may also become an issue. Some solutions towards this start to appear, cf., Isotropic antenna technology ([PERSON] et al., 2018). * End-to-end compatibility: In the case that we want to utilize existing communication infrastructures, the possible differences between protocols at different OSI layers should be sufficiently abstracted to enable compatibility between different technologies at different layers. The abstraction methods and compatibility enablers used in heterogeneous terrestrial systems could also provide some hints for satellite systems ([PERSON] et al., 2018). * MLSS controller placement: The optimal controller locations and the functional split of access and core network protocols are difficult to decide in highly dynamic 3D networks with different delay profiles, as terrestrial network protocols do not readily tolerate delay increases and may lead to time-out effects. SDN concepts are emerging in satellite systems that can help in finding close-to-optimal places for network controllers for different delay profiles ([PERSON] et al., 2018).
## 4 AI Applications and Their Impact
### Garment Defect Detection
#### 4.1.3 General Garment Defects  
[PERSON] et al. [52] presented an advanced defect detection framework using LSNet, a CNN-based architecture tailored for multi-feature fusion. This model addressed common defects such as fuzzing and pilling, which account for a significant proportion of textile quality complaints annually. The dataset in this study comprised over 10,000 images of woven, knitted, and nonwoven fabrics, categorized into six fabric types. While achieving high accuracy in defect detection, the model's dependency on high-quality labeled data may limit its applicability across diverse fabric types.
# Enhancing Supercapacitor Simulation Accuracy Through a Novel Hybrid Modeling Approach
## 3 HyMASC Method
### Model Architecture  
The model architecture used to define the dynamics of the SCs is based on a three-RC branch equivalent circuit (Figure 6). The selection of three RC branches improves model accuracy. The choice for this case leaned towards the 3 RC branch, aligning with the methodology outlined in the Zubieta method. The proposed architecture is derived from the Zubieta and Bonert model [33] without the leakage resistance, which is considered 0.  
Figure 5: Schematic for the experimental setup.  
Figure 6: Equivalent electrical circuit model for SC.  
In the proposed architecture, the three branches describe the following physical phenomenon of an SC: the fast branch reproduces the immediate behavior of the SC using elements R1-C1, the branch is characterized by a fast response in the time domain (order of seconds); the delayed and long-term branches characterize longtime energy distribution using R2-C2 and R3-C3 elements, and the two branches are characterized with a slow response in the time domain (order of minutes for delayed branch and more than 10 min for long-term branch).  
In the fast branch, the capacitor C1 has a variable capacitance, enabling a more accurate modeling of the SC's load-voltage variation [30]. This capacitance is calculated as the sum of the base capacitance C0 and a voltage-dependent Cv1 term, as described in Equation (6). Here, Cv represents the voltage-dependent coefficient, and V1 is the voltage across C1. Consequently, the model dynamically captures the relationship between the terminal voltage and the variable capacitance of the SC in real time, providing a more precise representation of its behavior [44].  
\[\text{C}_{1}=\text{C}_{0}+\text{C}_{\text{V}}\text{V}_{1} \tag{6}\]  
where: C0--base capacitance  
Cv--voltage-dependent coefficient  
V1--voltage across C1  
The equivalent model can be described analytically using the Equations (7)-(11). The equations are determined using [PERSON]'s laws.  
\[\text{V}_{3}=\frac{1}{\text{C}_{3}}\int\text{I}_{3}\text{dt}=\frac{1}{\text{ C}_{2}}\int\frac{1}{\text{R}_{2}}(\text{V}_{2}-\text{V}_{3})\text{dt} \tag{7}\]  
\[\text{V}_{2}=\frac{1}{\text{C}_{2}}\int\text{I}_{2}\text{dt}=\frac{1}{\text{ C}_{2}}\int\frac{1}{\text{R}_{2}}(\text{V}_{1}-\text{V}_{2})\text{dt} \tag{8}\]  
\[\text{I}_{1}=\text{C}_{1}\frac{\text{d}\text{V}_{1}}{\text{dt}}=\frac{\text{d} \text{Q}_{1}}{\text{dt}}=(\text{C}_{0}+\text{C}_{\text{V}}\text{V}_{1})\frac{ \text{d}\text{V}_{1}}{\text{dt}} \tag{9}\]  
\[\text{Q}_{1}=\text{C}_{0}\text{V}_{1}+\frac{1}{2}\text{C}_{\text{V}}\text{V}_ {1}^{2} \tag{10}\]  
\[\text{V}_{1}=\frac{-\text{C}_{0}+\sqrt{\text{C}_{0}^{2}+2\text{C}_{\text{V}} \text{Q}_{1}}}{\text{C}_{\text{V}}} \tag{11}\]  
The governing Equations (7)-(11) could be further used to develop a Simulink model for an SC. In the proposed method, the Simscape blocks were used for modeling the equivalent circuit as shown in Figure 7. The input signal is the current Isc, which is applied to the SC, and Vsc is the output voltage of the SC.
## 4 Crosstalk Analysis
### Crosstalk between Power Transfer and Information Transfer  
The equivalent circuit models of crosstalk interference between power transfer and information transfer are shown in Figure 8. When analyzing the crosstalk form power loop to information loop, the magnetic coupling interface on the signal modulation and demodulation modules are equivalent to the inductance \(L_{T1}\)-\(L_{T4}\). \(R_{\text{ep}}\) and \(R_{\text{es}}\) are the equivalent implement of the full-bridge and DC source in the primary side and the secondary side, respectively.  
As shown in the Figure 8, the structures of circuit topologies are symmetrical. The noise interference of forward power transmission to the information loop signal transmission is analyzed. The noise transfer function \(G_{NT1}(\omega)\) and \(G_{NT2}(\omega)\) of signal receiving cells in the primary side and the secondary side are:  
\[G_{NT1}(\omega_{p})=\frac{V_{LT1}(\omega_{p})}{V_{p}}=\frac{j\omega_{p}L_{T1}} {Z_{p}(\omega_{p})} \tag{24}\]  
Figure 8: Equivalent circuit of crosstalk interference between power transfer and information transfer: (**a**) Simplified schematic of forward power transmission channel. (**b**) Simplified schematic of backward power transmission channel.  
Figure 7: Six-port network of the wireless information and power transfer system.  
\[G_{NT2}(\omega_{p})=\frac{V_{T2}(\omega_{p})}{V_{p}}=\frac{j\omega_{p}M}{Z_{p}} \cdot\frac{j\omega_{p}L_{T2}}{Z_{s}} \tag{25}\]  
Define \(L_{1}+L_{T1}+L_{T3}=L_{m}\) and \(L_{2}+L_{T2}+L_{T4}=L_{n}\), at this time, the voltages in the impedance of the primary and secondary loop coils are:  
\[V_{Lm}(\omega_{p})=\frac{j\omega_{p}L_{1}+Z_{rs}(\omega_{p})}{Z_{p}(\omega_{p}) }V_{p}(\omega_{p}) \tag{26}\]  
\[V_{Ln}(\omega_{p})=\frac{j\omega_{p}M}{Z_{p}(\omega_{p})}\cdot\frac{j\omega_{ p}L_{n}+R_{2}}{Z_{s}(\omega_{p})}V_{p}(\omega_{p}) \tag{27}\]  
Since the coupling coefficient of the power coil is small, the reflected impedance \(Z_{rs}(\omega_{p})\) can be approximately ignored. From (24) and (26), it draws a conclusion:  
\[\frac{V_{T1}(\omega_{p})}{V_{Lm}(\omega_{p})}\approx\frac{L_{T1}}{L_{m}} \tag{28}\]  
Similarly, the internal resistance \(R_{2}\) of the power coil on the secondary circuit is small, which has little influence on crosstalk analysis, so that it can be ignored. The voltage of power coil on the pickup side is:  
\[\frac{V_{T2}(\omega_{p})}{V_{Ln}(\omega_{p})}\approx\frac{L_{T2}}{L_{n}} \tag{29}\]  
According to Equations (28) and (29), the smaller the signal coupling inductance is, the lower the crosstalk interference is. Therefore, in the signal transmission process, the crosstalk interference generated from the power carrier can be weakened by reducing the coupled inductance.
## 4 Case Study
### ANFIS Application--Case of Bearing Seat 6  
To confirm the effectiveness of the method, an analogous procedure was performed for bearing seat 6. The correlation value of measurements HPD\({}_{\text{CMM6}}\) and HPD\({}_{\text{CNC6}}\) is 0.3 (Table 1), which constitutes a low correlation, and the values of average error and maximum error are both E\({}_{\text{6 AVE}}\) = 0.015 mm, whereas the maximum value of measurement error is E\({}_{\text{6 MAX}}\) = 0.034 mm. Inputs to ANFIS included HPD\({}_{\text{CNC6}}\) and, as in the previous model, a deviation expressed relatively, E\({}_{\text{A--2}}\). The structure of training and testing data, the structure of ANFIS, and the rule base were adopted as in the previous case analyzed.  
The obtained values of average error and maximum are \(\delta_{\text{6 AVE}}\) = 0.007 mm and \(\delta_{\text{6 MAX}}\) = 0.014 mm, respectively. On the other hand, the values of errors \(\delta_{6}\) for the data from the test set are presented in Figure 15, with error E\({}_{6}\) based on the difference between CMM and CNC measurements.  
The obtained values of average error and maximum error are again lower than the values of original errors specified on the basis of measurements carried out on the CNC machine. Error \(\delta_{\text{6 AVE}}\) constitutes 44% of error E\({}_{\text{6 AVE}}\), whereas error \(\delta_{\text{6 MAX}}\) constitutes 45% of error E\({}_{\text{6 MAX}}\).  
Figure 14: Summary of errors \(\delta_{2}\) and E\({}_{2}\) for the data from the test set for bearing seat 2.
# Comparison of Multi-Tracked Running Gears in Terms of Obstacle Negotiation Capabilities
## 3 Simulation Model
### Model Validation  
In order to validate the numerical model for traction tests, experiments were conducted using a testbed: an experimental platform, powered by electric motors, equipped with a braking mechanism with force regulation (Figure 7). Built-in incremental encoders (4) of the motor shaft speed (resolution: 1024 ppr, accuracy 0.2\({}^{\circ}\)) allowed for the determination of the drive wheel peripheral speed (Vw). Force sensors (3) EMS30-5 kN mounted on the motor reaction arms ensured the driving force (Fd) measurement. Its range was up to 5000 N, with a 2% error. The full-featured RTK Receiver (5) E-Survey E300 PRO provides platform speed (Vp) measurement, based on its position designation, with a frequency of  
Figure 6: Track–ground contact forces model scheme.  
up to 50 Hz and an accuracy of 8 mm + 1 ppm RMS. The platform's braking mechanism provided the drawbar pull (Fx) measure by compression force transducer (6) EMS30-5 kN on the drum reaction arm. The sampling frequency of the system is 10 Hz. The data collection, processing, and time synchronization are provided by the Siemens Simatic S7-1200 PLC controller.  
The measurements were taken on grassy ground--short grass with a ground moisture content of 18%. In order to obtain full traction performance, the traction force and drawbar pull were recorded over the entire slip range (determined from measured speeds \(V_{p}\) and \(V_{w}\)). An example of the parameter courses over time is shown in Figure 8. The difference between the forces (Fx and \(F_{d}\)) was also used to calculate the motion resistance coefficient f.  
The obtained measurement results are presented in Figure 9a. Based on the result points, the drawbar pull \(F_{x}\) characteristic curve was approximated as a function of slip s according to the Equation (8) from [52]:  
\[F_{x}=A\cdot[1-\frac{B}{s}.(1-e^{-\frac{e}{B}})]. \tag{8}\]  
Figure 8: Courses of measured parameters during one measurement cycle.  
Figure 7: Experimental platform: (**a**) scheme; (**b**) experimental setup.  
The correlation coefficient R\({}^{2}\) was 0.75 for parameters A = 2877 and B = 0.055. An analogous study was conducted on a tracked system model in a simulation program. The ground parameters (Table 2) were selected in accordance with the literature [49]. The simulation results (Figure 9a) were consistent with the obtained fitted curve. The regression analysis is presented in Figure 9b. The correlation coefficient R\({}^{2}\) was 0.99, and the slope of the linear regression equation was 0.97. The model data were well fitted with the experimental data; based on this, the simulation model was found to be acceptable for this type of ground. The soil parameters were then introduced into the model.