esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact Monopole Wideband Antenna Based on DGS
## 3 Simulation Results and Analysis
### Parametric Analysis on G-Shaped Patch Antenna  
Parametric optimization and analysis were conducted to assess the effects of the dimensions of the G-shaped patch antenna's performance. Various parameters controlling the resonant characteristics and bandwidth were adjusted. This subsection presents key parameters influencing the antenna's overall performance. In this study, one parameter was varied at a time while keeping others fixed to optimize the antenna structure. The analysis focused on the width of Lt, VW, and K of the G-shaped patch antenna to investigate their effects on key performance metrics, including reflection coefficient (S11) and bandwidth.
These technologies enable more efficient data traffic routing, improved system scalability, and greater flexibility in responding to dynamic network demands, all while potentially lowering operational costs and simplifying maintenance procedures [21]. While these software-defined, programmable networks present myriad benefits, they face a new set of performance, security, reliability, and scalability challenges. To tackle these challenges autonomously while ensuring that the industrial service level agreements are met, organizations like the European Telecommunication Standards Institute (ETSI) and the Open Network Foundation (ONF) have introduced the concept of _intent-based networking (IBN)_. IBN aligns network operations to meet the specific business goals or _\"intents\"_ of the application (in this case wind farm operation), both initially and for future changes [22]. Intents are declarations of desired outcomes or operational states. These intents are translated into network configurations and policies using an abstraction module. IBNs continuously monitor the current state to ensure it aligns with the defined intents [23]. Future networks will use _\"AI-driven autonomous networking\"_ concepts where an AI/ML approach is deployed in the network to predict its behavior and suggest optimization techniques to self-configure, self-heal, and self-optimize these networks. When performance anomalies or security breaches occur, these AI-driven autonomous networks trigger self-healing modules to correct faults without human intervention.
# A Graph Convolutional Network-Based Method for Congested Link Identification
## 4 Performance Evaluation
### Evaluation Metrics  
Congested link identification algorithms are designed to increase the detection capability of congestion and decrease the likelihood that a regular link will be falsely recognized as a congested link. Accordingly, similar to [26], we evaluated the performance of the algorithm using the metrics of the detection rate (DR) and false positive rate (FPR). The DR indicates the probability that a congested link is accurately identified as congested, and we calculated the DR as follows:  
\[\mathbf{DR}=\frac{\left\|\mathbb{C}\cap\{l|l\in\mathbf{L};x(l)=1\}\right\|}{ \left\|\{l|l\in\mathbf{L};x(l)=1\}\right\|} \tag{16}\]  
The FPR indicates the probability that a normal link is incorrectly identified as congested, and we calculated the FPR as follows:  
\[\mathbf{FPR}=\frac{\left\|\mathbb{C}\cap\{l|l\in\mathbf{L};x(l)=0\}\right\|}{ \left\|\{l|l\in\mathbf{L};x(l)=0\}\right\|} \tag{17}\]  
where \(\mathbb{C}\) represents the set of links estimated to be congested and \(x(l)\) represents the actual state of link \(l\).
# Analytical Model to Calculate Radial Forces in Permanent-Magnet Synchronous Machines
## 3 Analytical Model
### Magnetic Flux Density Created by Coils in a Slotless Machine
\[F_{MM}(t,\theta)=\sum_{n=-\infty}^{n=+\infty}F_{an}e^{-jnt_{p}\theta}ie^{jN_{ e}t}\cdot\bigg{[}1+2\text{cos}\bigg{(}\frac{2\pi}{3}(n-1)\bigg{)}\bigg{]} \tag{32}\]  
Figure 4: Star of slots: (**a**) Q48p8, (**b**) Q36p15. The expression (32) is referenced to a fixed stator frame. In order to compute the magnetic flux density created by the coils on the rotor surface, it is necessary to change from a fixed stator frame to a moving rotor frame. The new expression for the magnetomotive force in the moving rotor frame is the following:  
\[B_{g}^{a}(t,\theta)=\mu_{0}\cdot\frac{F_{MM}(t,\theta)}{g+\frac{h_{m}}{\mu_{m }}} \tag{26}\]0  
As can be seen from (32) and (33), there are some harmonics of the \(F_{MM}\) which are annulled when all the magnetomotive forces are summed. It can be easily deduced that in three-phase machines, all harmonics of the total FMM that are multiples of 3 do not exist.
## 7 Robustness
### Evaluation Metrics and Baseline Performance
#### 7.2.3 Filtering Attacks  
The watermarking algorithm was tested under median and average filter attacks with different kernel sizes (3 \(\times\) 3, 5 \(\times\) 5, 7 \(\times\) 7). For smaller kernel sizes, the watermark was fully recovered across all image dimensions. However, as the kernel size increased to 7 \(\times\) 7, the performance decreased, although the NC value remained high.  
Under average filter attacks, the method achieved full recovery. The smaller kernel sizes (3 \(\times\) 3 and 5 \(\times\) 5) maintained a perfect NC value of 1.0 for 500 \(\times\) 500 images and above. At the 7 \(\times\) 7 kernel size, the NC value dropped to 0.8442, showing only a slight performance loss but still proving robust.  
The algorithm preserved high NC values during Gaussian Low-Pass Filter (LPF) attacks with a 7 \(\times\) 7 kernel.
# ATC Human Factors Involved in RPAS Contingency Management in Non-Segregated Airspace
## 2 Method
### Nominal RPAS Conditions and Contingencies into Non-Segregated Airspace
#### 2.3.2 Loss of the Command-and-Control Link Contingency
According to the need to pre-plan the RPAS reaction to a potential C2 lost-link situation, three different operational aspects need to be determined:  
Airport Selection: The whole area of the RPAS operation includes the selection of a landing area in case the RPAS suffers an emergency contingency. RPAS should use its nominal airport base as a return location where the IAF hold could be employed as a safe location to attempt the re-acquisition of the C2-Link before landing. Alternative airports for early termination of the mission must be used in case the C2 lost-link contingency degenerates into any other contingency that degrades the airworthiness of the RPAS. Alternative trajectories: Given that a lost command-and-control link does not immediately imply a failure of the FMS system, RPAS should be operated under the assumption that once the contingency happens, the vehicle retains the capability to safely operate autonomously as pre-planned. Following ICAO standard procedures, the lost-link contingency should be considered a basic radio failure. That procedure implies that the initial flight plan must be completed, bringing the RPAS to its intended destination airport. RPAS operations, however, may not entirely fit in that standard scheme due to two factors:  
1. Mission duration: the RPAS may operate over an area of interest for an extremely long time. In that case, executing an early termination of the mission may be advisable, cancelling that part of the mission. However, that cancellation may require moving directly from the departure or mission part of the operation to the return trajectory. 2. Mission extension: the RPAS may execute an extremely extensive mission. Cancelling the mission may require the RPAS to operate thousands of miles in the outbound trajectory and return to the intended destination airport. Under that scenario, it becomes unclear if completing that extensive round trip with the RPAS suffering a communication failure is advisable. Therefore, early mission cancellation would require using pre-established turnaround points that have been adequately coordinated with the ATC authorities. Table 2 describes the operational procedure we define in the case of C2 lost-link contingency [24].
# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 1 Introduction
The Boolean search terms used in the Web of Science database were: (\"agriculture\") AND (\"computer vision\") AND (\"deep learning\"), with the time frame limited to articles from the past five years. The study initially screened nearly 3000 articles based on four thematic aspects. Subsequently, the top 10% most relevant articles were selected, and a manual screening was conducted based on their abstracts, resulting in a final selection of 100 articles (Figure 1). Notably, the number of publications on deep learning-based computer vision applications in agriculture has shown a consistent annual increase, as illustrated in Figure 2.  
Prior to our work, several studies have reviewed research in this field. Ref. [20] focused specifically on tea crops, summarizing applications of computer vision and machine learning across three key stages: cultivation, harvesting, and processing. Ref. [21] introduced eight types of image segmentation methods and reviewed deep learning applications in agricultural image segmentation. Ref. [16] analyzed deep learning models and discussed their use in plant disease and pest detection. The study by [17] reviewed computer vision-based weed detection methods, with a particular emphasis on traditional image processing techniques and deep learning approaches for agricultural weed identifi  
However, our study makes three primary contributions:  
First, we categorize agricultural production into three key phases, with particular focus on applications of computer vision and deep learning in the crop growth management stage. We systematically review four critical aspects: crop identification, crop grading, disease monitoring, and weed detection. Second, the paper organizes relevant computer vision technologies and deep learning-based model algorithms, providing a classified review of methods based on their utilization of visual information. Third, we examine current research challenges in the field and discuss future development trends. This literature review is structured into six sections. The remaining five sections are organized as follows: Section 2 introduces computer vision and synthesizes related technologies. Section 3 reviews several categories of widely used deep learning-based models. Section 4 provides a detailed examination of applications of computer vision and deep learning in agricultural growth management. Section 5 discusses current challenges of computer vision in this field. Finally, Section 6 presents conclusions and future research directions.
# Design and Experimental Study of the General Mechanical Pneumatic Combined Seed Metering Device
## 3 Key Component Parameter Design and Analysis  
### Structure Design of Feeding Drum  
The feeding drum was a key component of the seed meter, and its size could determine the size of the seed meter, the maximum number of seed filling holes, as well as the upper limit of the seed metering speed [19]. The diameter \(D\) of the feeding drum, the linear velocity of the drum \(v_{1}\) and the number of the circumferential holes \(Z\) are expressed below:  
\[\left\{\begin{array}{l}v_{1}=\pi Dn\\ \pi D=Z\Delta l\end{array}\right\} \tag{1}\]  
where \(v_{1}\) denotes the linear speed of the feeding drum, \(m/s\); \(D\) is the diameter of the drum, mm; \(n\) is the rotating speed of the drum, \(r/s\); \(Z\) represents the number of circumferential holes, each; \(\Delta l\) expresses the hole distance, mm.  
Figure 1: Sketch map of the general mechanical pneumatic combined seed metering device. (**a**) Front view; (**b**) Axonometric view. 1. Seed cleaning nozzle; 2. Seed filling hole; 3. Seed filling box; 4. Seed unloading nozzle; 5. Unloading seed pore; 6. Seed cup; 7. Seed unloading duct; 8. Seed cleaning duct; 9. Seed strips; 10. Protective seed plate; 11. Seed collection box; 12. Feeding drum.  
According to Equation (1), increasing the diameter of the drum could set more holes in the circumferential direction of the drum, thereby improving the seed filling efficiency, whereas at a constant drum speed, the larger the drum diameter, the greater the linear speed of the drum would be. At the relative speed exceeding the filling limit, the seeds could not be filled normally [20]. Under an excessively small diameter of the drum, the curvature of the drum would increase, and the seed filling efficiency would decrease. Accordingly, the design of the drum diameter should be considered comprehensively. In this study, given the research experience of pneumatic seed metering devices worldwide [21], the diameter \(D\) of the feeding drum of the seed metering device was determined as 104 mm. To make the unloading air flow more concentrated on the seeds attached to the bottom of the seed filling hole, an inverted cone-shaped unloading seed pore was set on the seed feeding drum, and the respective seed filling hole was correspondingly provided with an unloading seed pore, thereby increasing the unloading efficiency.
# Multi-Color Channels Based Group Sparse Model for Image Restoration
###### Abstract  
The group sparse representation (GSR) model combines local sparsity and nonlocal similarity in image processing, and achieves excellent results. However, the traditional GSR model and all subsequent improved GSR models convert the RGB space of the image to YCbCr space, and only extract the Y (luminance) channel of YCbCr space to change the color image to a gray image for processing. As a result, the image processing process cannot be loyal to each color channel, so the repair effect is not ideal. A new group sparse representation model based on multi-color channels is proposed in this paper. The model processes R, G and B color channels simultaneously when processing color images rather than processing a single color channel and then combining the results of different channels. The proposed multi-color-channels-based GSR model is compared with state-of-the-art methods. The experimental contrast results show that the proposed model is an effective method and can obtain good results in terms of objective quantitative metrics and subjective visual effects.  
image restoration; group sparse model; multi-color channel; nonlocal self-similarity; adaptive parameter +
Footnote †: journal:  
Article
## 3 Results and Discussion
### The Firing Temperature Analysis Using FTIR and XRD
The presence of wollastonite and feldspar (albite, orthoclase, and microcline) in the Pratima Garh, Padumpukhuri, Rukmini Garh, and Mouramora-3 brick samples indicates that the bricks were fired at temperatures above -800 \({}^{\circ}\)C. The presence of the iron oxide minerals hematite and magnetite in the same sample, on the other hand, indicates that they were fired at temperatures higher than 650 \({}^{\circ}\)C, as indicated by the FTIR and XRD studies. In their analytical study of ancient pottery, the authors of [72] discovered that feldspars decompose at temperatures ranging from 900 to 950 \({}^{\circ}\)C. The presence of feldspars in the samples indicates that they were all fired at temperatures lower than 900 \({}^{\circ}\)C. This clearly shows that the Pratima Garh, Padumpukhuri, Rukmini Garh, and Mouramora-3 brick samples were fired at temperatures lower than 900 \({}^{\circ}\)C in oxidizing kiln atmospheric conditions. The estimated firing temperature of the samples is presented in Table 2. However, research concentrating on Thermogravimetry-Differential Thermal Analysis (TG-DTA), apparent porosity, water absorption, and other pertinent aspects are required to establish the accurate fire temperature of the investigated bricks.
## 3 Approximation by Removing the Number of Paths with Smaller Coefficients
### Approximation of Gram Matrix
#### 3.1.2 Applications to Solve Systems of Linear Equations  
A system of linear equations [39] is, in general, defined by \(\mathbf{y}:=A\mathbf{x}\). In the theoretical sense, the solution of this equation can be found by computing the inverse \(A^{-1}\): i.e., \(\mathbf{x}=A^{-1}\mathbf{y}\) (In practice, this would not be the most efficient way). In some cases, since \(A\) may be a singular matrix, instead of solving the original equation, the equation is converted into the following normal equation: \(A^{T}\mathbf{y}:=A^{T}A\mathbf{x}\). Then, the solution is found by the inverse \((A^{T}A)^{-1}\). Here, \(G=A^{T}A\) is the same as the Gram matrix generated by the column vectors of \(A\). Therefore, the analysis in Figure 2 is also the same for this matrix. If this matrix can be approximated with one term: \(G\approx\alpha Q_{1}\otimes\cdots\otimes Q_{n}\), then the inverse can be found by the following:  
\[G^{-1}\approx\alpha^{-1}Q_{1}^{-1}\otimes\cdots\otimes Q_{n}^{-1}. \tag{13}\]  
The inverse in this form would be directly represented since each \(Q_{i}\) is of dimension 2.
## 4 Results  
Prototypes of the band-pass filter with second harmonic suppression (henceforth referred to as BPF-DGS) and without harmonic suppression using one unit-cell resonator, have been manufactured using laser micromachining (see Figure 7).  
The results of measured transmission coefficient for the BPF-DGS and BPF prototypes are depicted in Figure 8a. The measured 3 dB bandwidth goes from 6.16 to 6.62 GHz, signifying 460 MHz (7.18%) for the BPF prototype. There is good agreement between measurement and simulation results for the filter without harmonic suppression.  
Regarding the BPF-DGS prototype, a 3 dB passband of 210 MHz (3.32%) at the center frequency 6.32 GHz is obtained in simulation whereas, in measurements, 175 MHz (2.76%) passband at 6.33 GHz resulted. In the stopband regions some slightly differences between measurement and simulation can be observed. The fact that commercial MoM software considers infinite extension of the dielectric substrate, together with manufacturing process tolerances and cables' and connectors' losses, explains the differences in the stopband region. Nevertheless, the simulations and measurement results are in good agreement and they meet the application requirements in the C band range. The filter with DGS exhibits approximately 50% less pass bandwidth than the BPF without harmonic suppression because of the reduced coupling generated.  
Figure 6: Parametric analysis of the DGS.  
The absence of multilayer substrates and vias make the filters suitable to be used not only in satellite/terrestrial communication applications, but also in wearable applications where the prototypes could be shape adapted and bended. From Table 2, it can be easily seen that the size of the proposed BPF is minimized compared to band-pass filters with similar performance and substrate as used in [31; 32; 33; 34], whereas the BPF-DGS shows a higher quality factor.  
Figure 8: (**a**) S21 simulation and measurement results; and (**b**) S11 simulation and measurement results.  
Figure 7: Manufactured prototype band-pass filter with second harmonic suppression (BPF-DGS) (**a**) top view; (**b**) bottom view; and (**c**) cross-section view.  
The miniaturized BPF-DGS is demonstrated at 6.33 GHz, which is a higher frequency than the reported filter in [35]. If the proposed filter would be made to operate in the same frequency band as in [35], the filter would have a smaller size compared to [35].  
In [37], the proposed filter renders a quality factor of 6.26, which is approximately three or six times lower compared to 17.4 of the BPF and 36.38 of the BPF-DGS. In [39], the upper conducting layer of the filter is connected through the bottom ground plane using vias. Even though in [39] a 0.1\(\lambda_{0}\)\(\times\) 0.023\(\lambda_{0}\) miniaturized band-pass filter is shown, with a lower quality factor, it has the disadvantage of high cost and a difficult manufacture process due to the use of vias which are vulnerable to environmental influences such as being insufficiently plated through or filled with solder. This may cause the delamination or cracking of the vias.
## 4 Application  
To demonstrate the advantages of this system, it is applied to real car stability testing. This test is based on a computer simulation. The part control structure of Simulink in MATLAB is shown in Figure 7. Carsim is also used in this computer simulation.  
Figure 6: Sideslip angle combination algorithm block.  
The real vehicle stability test was performed at the Heilongjiang Institution of Technology campus. The route of the vehicle is a square shape. The car runs around a big building. Therefore, the GPS signal is not stable, and the GPS navigation system is not very precise. The GPS/INS integration navigation system can overcome the shortcomings of GPS in a city. Figure 8 shows the course of the test car. Figure 9 is the picture of the test car and the equipment of the testing system.  
This work uses the HV2 dual antenna dual function GPS receiver [25]. HV2 can provide accurate directions, with the GPS positioning accuracy up to the sub-meter level, a 0.1 degrees heading precision and a 20 Hz data update rate. In this paper, the INDAS-5000 embedded system is the data acquisition system. INDAS-5000 is on a printed circuit board (PCB) integrated real-time embedded processor, a field programmable gate array (FPGA) and an analog and digital I/O.  
Figure 8: Testing course.  
Figure 7: Simulink diagram.  
The vehicle sideslip angle measured by GPS/INS, which is fused by a two-stage Kalman filter, is shown in Figure 12. From Figure 12, it can be seen that the vehicle sideslip angle is smoother after the Kalman filter algorithm.  
In Figure 13, the vehicle sideslip angle, which is measured by the RT3102 navigation system sensor calibrates the sideslip angle measured by the GPS/INS. Although the curves are similar, the sideslip angle measured by the RT3102 navigation system has great precision. The testing error is small.  
From the experimental data, it can be seen that using the Kalman filter algorithm, especially the second order Kalman filter can solve the problem of the GPS signal loss well, and the INS signal accumulation error becomes large during vehicle stability testing. This method can meet the real-time and accuracy requirements for the measurement of the vehicle stability key parameters well.