esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analytical Performance Prediction of EML  
To analytically predict the performance of the EML, the capacitor and bank specifications are determined. Further, the length and size of rails are determined, as listed in Table 1. Each capacitor specification is 480 V and 6800 \(\upmu\)F. One hundred capacitors are connected in parallel, and the equivalent specification of the capacitor bank is 480 V and 0.68 F; the rail size is 750 mm (l) \(\times\) 30 mm (h) \(\times\) 5 mm (w), as shown in Figure 4. These electrical properties of the capacitor bank and rails are listed in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline Copper rail size & 750 mm (l) \(\times\) 30 mm (h) \(\times\) 5 mm (w) \\ One capacitor & 450 V and 6800 \(\upmu\)F \\ Equivalent capacitor & 450 V and 0.68 F \\ Copper wire length & 10 m \\ Copper wire diameter & 10 cm \\ Projectile mass & 6 g \\ \hline \end{tabular}
\end{table}
Table 1: Electromagnetic launcher (EML) specification.  
Figure 3: EML performance prediction flow chart.  
By using these properties and Equation 8, the performance is analytically predicted. The prediction sequence is as follows:  
1. Set the initial conditions--charged capacitor voltage, projectile loaded position, and initial velocity.
2. Find Lorentz force from initial condition.
3. Find velocity (\(v(t+\Delta t)=\int_{0}^{\Delta t}\frac{F(t)_{prop}-F(t)_{Fric}}{m}dt+v(t)\)) and displacement (\(s(t+\Delta t)=\int_{0}^{\Delta t}v(t)dt+s(t)\)) from \(F_{prop}(t)=\frac{L^{\prime}I(t)^{2}}{2}\)[26]. Here,'m' is projectile mass.
4. Update resistance (\(R^{\prime}_{copper}x(t)\)) and inductance (\(L^{\prime}x(t)\)) according to projectile displacement \(s(t+\Delta t)\).
5. Find current (\(l_{t+1}\)) and voltage (\(V_{t+1}\)) through a series equivalent RLC circuit analysis after \(\Delta t\) from a previous time. The determined current and voltage will be the initial conditions for the next step.
6. Go to 2 nd step until the objective velocity or rail length are attained.  
According to this sequence, the muzzle velocity and current with the 750 mm length of rail mentioned earlier of this chapter is determined using MATLAB R2019a and Simulink Simscape Electrical. In the 3 rd step, the friction force is approximately 0.56 times the propulsion force of an EML (\(0.56F_{Prop}\)) [19].  
The muzzle velocity calculated using this approach is 252.485 m/s. Velocity as time and current as time are shown Figure 5. The reflection of inductance and resistance gradient is an extremely important point to predict EML performance.  
Figure 4: EML rail dimensions.  
\begin{table}
\begin{tabular}{c c} \hline Resistance of capacitor bank & 2.405 m\(\Omega\) \\ Inductance of capacitor bank & 14.25 uH \\ Resistance gradient of rail & 11.47 u\(\Omega\)/m \\ Inductance gradient of rail & 35.933 uH/m \\ Initial capacitor voltage & 300 V \\ Initial current on EML system & 0 A \\ \hline \end{tabular}
\end{table}
Table 2: Electrical properties of EML.
## 3 Results and Discussion  
### Surface Mildew Detection of Maize Seeds Based on YOLOv5s  
With the continuous progress of target detection technology, the YOLO series, as a representative of single target detection methods, has made remarkable achievements in the field of real-time target detection by virtue of its unique design concept and superior performance. The single-stage target detection algorithm completes the target detection task in a single forward propagation without generating candidate regions. This algorithm can predict the category and location of the target directly through a dense grid or anchor box. Single-stage algorithms usually have faster inference speed and are suitable for real-time applications or scenarios with high-speed requirements. While single-stage algorithms have advantages in terms of speed, they are generally slightly less accurate than two-stage algorithms. However, some advanced single-stage algorithms have made significant progress in accuracy [20].  
As the fifth version of the YOLO algorithm, YOLOv5 has made many innovations and improvements on the basis of the previous version and is also widely used in the current single target detection algorithm, with relatively high accuracy and detection speed. At present, YOLOv5 has five network structures: YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x, and YOLOv5n. The traditional YOLOv5 algorithm is mainly composed of input, backbone, neck, and head networks [21]. In this study, maize seed surface mildew detection was conducted using YOLOv5s, a lightweight version of the YOLOv5 series. The YOLOv5s model was trained specifically for this task.
## 3 Multi-Mode Fiber (MMF)
### Limitations of MMF  
The use of fiber optic cables has experienced significant growth and progress in the past few decades, primarily owing to their distinctive advantages in terms of suitability for operation in challenging environments. In various sensing applications demanding the utilization of high optical power, standard MMF is employed, as it possesses a notable nonlinearity threshold [43]. Conversely, employing MMF as a main alternative for multi-parameter DAS is sometimes not advisable because of the significant and frequently unpredictable intermodal coupling associated with MMF. This intermodal coupling invariably introduces noise into most MMF-based sensors [44], demanding complex control systems [45]. In terms of mode-dependent absorption sensing, the intensities of the modes in MMF undergo rapid fluctuations throughout the fiber due to intermodal coupling, even in the absence of interaction with a measured parameter. As a result, intermodal coupling's impact can significantly reduce the effectiveness of MMF mode-dependent, absorption-based sensors [46].  
Optical fiber designed for sensing implementation requires two different operation principles and systems, specifically Rayleigh scattering for DAS and Raman scattering for DTS [47; 48]. In contrast to a Raman signal, Rayleigh scattering exhibits comparatively higher intensity and does not demand the same elevated pump power levels required by DTS systems [49]. Hence, when below the fiber's nonlinearity threshold, it is feasible to use MMF for DAS measurement. However, the efficacy of DAS depends on the coherent interference of Rayleigh signals reflected by scattering centers on the cable throughout the fiber optic [48]. In this context, MMF introduces notable levels of noise within DAS images. The reason for this is that a common MMF accommodates the propagation of a significant number of modes, and each of these modes possesses a distinctive interference pattern. These patterns collectively generate a resultant Rayleigh signal that remains independent of vibrations [46].  
The type of fiber optic cables in DAS measurement has a significant impact on the acquisition of data, and each type has advantages and limitations that affect DAS data. Table 1 summarizes the trade-offs between SMF and MMF discussed above.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Fiber Optic Cable Type** & **Advantages** & **Limitations** \\ \hline \multirow{3}{*}{SMF} & Most widely used in DAS monitoring and imaging & Sensitive into acquisition optical noise \\  & Higher spatial resolution & Lower signal loss that minimizes signal attenuation & Interference fading \\ \hline \multirow{3}{*}{MMF} & Capable of DTS measurement & Unpredictable signal that can produce noisy data \\  & Lower manufacturing cost & \multirow{2}{*}{Intermodal coupling due to multiple paths of laser travel} \\  & Suitable for sort-distance DAS measurement & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of advantages and limitations for each fiber optic cable type.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 4 Results
### Sequential Estimation
#### 4.3.3 Results for the First Estimation Using HYBRID  
The results of the first estimation using HYBRID are shown in Figure 30. Among the three scenarios, the highest localization accuracy was achieved with HYBRID \(\rightarrow\) HYBRID. In terms of the median localization error (CDF 50%), HYBRID \(\rightarrow\) HYBRID exhibited the highest accuracy. However, around the CDF90 region, there was no significant difference between HYBRID \(\rightarrow\) HYBRID and HYBRID \(\rightarrow\) RSSI. This suggests that while using HYBRID for the second estimation improves the overall accuracy for a large number of sources, it does not substantially reduce large localization errors. In other words, sequential estimation may have limited corrective power when the initial estimate is already comprehensive and well-optimized.  
This limitation is discussed by [PERSON] et al. (2019), who noted that in Gaussian process-based sequential estimation, highly accurate priors can lead to limited improvement in subsequent updates. Similarly, [PERSON] et al. (2019) proposed a real-time hybrid localization system that dynamically reweights signal features based on environmental changes. They argue that static weighting strategies may fail to correct for extreme cases, especially when error sources are systematic rather than random. Our results suggest that integrating adaptive mechanisms into hybrid models--such as dynamically adjusting feature weights or selecting alternative observation paths--may be a fruitful direction for future research.  
As summarized in Figures 28-30, using HYBRID in the second stage consistently led to improved localization accuracy across different initial modalities. This improvement is attributed to the complementary nature of the features: RSSI provides coarse distance information, AOA provides angular directionality, and EOA incorporates elevation variation. By integrating these in the HYBRID model, it is possible to suppress large errors and achieve faster convergence in the cumulative distribution. These results indicate that performing a coarse initial localization followed by refined estimation using HYBRID is effective even in challenging environments with non-line-of-sight propagation. This suggests that the hybrid model's ability to leverage multiple signal dimensions allows it to recover from poor initial estimates and achieve robust performance.  
Figure 30: CDF comparison of sequential estimation outcomes starting with HYBRID. While HYBRID\(\rightarrow\)HYBRID performs best overall, marginal improvement is observed in outlier suppression compared to HYBRID\(\rightarrow\)RSSI.
## 3 Optimal Dispatch Model for DHN under CF-VT Control Strategy  
### Objective of the Optimal Dispatch Model  
The objective of the optimal dispatch model for DHN is to minimize the operation cost \(C^{\text{DHN}}\), including the cost of purchased electricity \(C_{\text{e}}^{\text{DHN}}\), the cost of purchased gas \(C_{\text{g}}^{\text{DHN}}\), and the cost of pollutant emission penalties \(C_{\text{f}}^{\text{DHN}}\), as shown in (2).  
\[\text{min}C^{\text{DHN}}=C_{\text{e}}^{\text{DHN}}+C_{\text{g}}^{\text{DHN}}+C_{ \text{f}}^{\text{DHN}} \tag{2}\]  
\[C_{\text{e}}^{\text{DHN}}=\sum_{t}\sum_{i\in N^{\text{D}}}P_{i,t}^{\text{H-D}} c_{t}^{\text{E,2}} \tag{3}\]  
\[C_{\text{g}}^{\text{DHN}}=\sum_{t}\sum_{m\in N^{\text{H}}}(G_{m,t}^{\text{CHP}} +G_{m,t}^{\text{GB}})c_{t}^{\text{G}} \tag{4}\]  
\[C_{\text{f}}^{\text{DHN}}=\sum_{t}\sum_{m\in N^{\text{H}}}\sum_{k\in\Omega^{ \text{P}}}\left(P_{m,t}^{\text{CHP}}\lambda_{k}^{\text{CHP}}+H_{m,t}^{\text{GB }}\lambda_{k}^{\text{GB}}\right)\epsilon_{k}^{\text{env}} \tag{5}\]  
where \(N^{\text{D}}\) is the set of nodes in DN; \(N^{\text{H}}\) is the set of nodes in DHN; \(\Omega^{\text{P}}\) is the set of pollutants; \(P_{i,t}^{\text{H-D}}\) is the power transmitted from the DN node \(i\) to the DHN at period \(t\); \(P_{m,t}^{\text{CHP}}\) and \(C_{m,t}^{\text{CHP}}\) are the active power and gas consumption power of the CHP unit at DHN node \(m\) at period \(t\), respectively; \(G_{m,t}^{\text{GB}}\) and \(H_{m,t}^{\text{GB}}\) are the gas consumption power and heat power of the GB at DHN node \(m\) at period \(t\), respectively; \(c_{t}^{\text{E,2}}\) is the price of electricity sold from the DN at period \(t\); \(c_{t}^{\text{G}}\) is the gas price at period \(t\); \(\lambda_{k}^{\text{CHP}}\) and \(\lambda_{k}^{\text{GB}}\) are the emission factors of category \(k\) pollutant of CHP and GB, respectively; \(c_{k}^{\text{env}}\) is the treatment cost factor of category \(k\) pollutant.  
Figure 2: Distributed cooperative dispatch framework for DN-DHN-BESS.
## 2 UWB Signal Processing Steps
### Target Localization  
The intention of this step is to find the coordinates of the target's position in a 2D space. Using a mono-static UWB radar, we can only estimate a 1D-positioning which gives the distance \(R\) between the radar and the target. However, there are many points laying at the same distance from the radar but in different positions, and these make up the circumference of a circle whose radius is \(R\). In order to obtain a 2D-positioning, we need at least two receiving antennas [32] or two mono-static radars. The distance between the two modules and their respective positions is crucial to the accuracy of the target-positioning [33]. For this regard in this work we estimate the 2D coordinates of the target using two independent monostatic radars (connected on a same PC for real-time processing). These radars are capturing one frame each at a time consecutively to avoid cross-talk between them and to allow them to capture frames in relatively close times. Data from the respective radar modules are stored as two different Radargrams which will be processed independently and will result in series of (\(R_{i}\), \(t\)) couples where \(R\) is the distance between the radar and the target, \(i=1\), 2 denotes the radar's index and \(t\) is the observation time which is herein associated with the frame number. Assuming that the coordinates of the radars are known and are (\(x_{i}\), \(y_{i}\)) (\(i=1\), 2), the 2D coordinates of the target position will be given by a geometric interpolation presented in Figure 4.
# Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern
## 4 Experiments and Results
### Verification Experiment under Simulated Smoke and Water Mist Environment
#### 4.3.1 Verification Experiment on Cross Center Positioning Accuracy of Cross Laser-Pattern  
The schematic diagram of this experiment is shown as Figure 22, the image acquisition system was placed on a PI micro translation platform, that was fixed on a tripod. A smoke generator and an ultrasonic atomizer were used to generate smoke and water mist, an acrylic tube was used to collect the smoke and water mist in the light path of the cross laser-pattern.  
Figure 21: Error after compensation.  
Figure 20: Linear error model.  
During the experiment, there was smoke and water mist in the light path of the cross laser-pattern. The cross laser was fixed as the measurement datum, and the PI micro translation platform moved at an interval of 5 mm as the reference displacement value. With the proposed measurement algorithm and the calibrated conversion coefficient, the related displacement can be measured. The relationship between measurement error and displacement value is shown as follows:  
After compensating the measured data according to the error relation curve shown in Figure 23, the compensation results are shown in Figure 24:  
According to the results, the proposed method still works under the environment of smoke and water mist. The accuracy of horizontal displacement measurement is better than  
Figure 23: Displacement error curve under smoke and water mist environment.  
Figure 22: (**a**) Experimental schematic diagram. (**b**) Experiment procedure picture under smoke and water mist environment.  
\(\pm\)10 \(\upmu\)m, relative to the conversion coefficient of 0.0359, the center positioning accuracy is better than 0.6 pixels under the simulated smoke and water mist environment.
# Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links
## 3 Experimental Datasets  
The model developed in this work makes use of the following data:  
* Ka-band rain attenuation time series derived from the received beacon power levels.
* Rain rate time series and specific attenuation of the site calculated from the drop size distribution (DSD) that was measured by the disdrometer.
* The zero-degree isotherm height, \(h_{0}\), extracted from the vertical temperature profiles produced by the European Centre for Medium-Range Weather Forecasts (ECMWF).  
The measurement period spans from 1 st January 2017 to 31 st December 2017. A preliminary selection was performed to rule out rain events when precipitation occurred along the link, but not directly over the receiver site, as well as days when at least one of the two instruments (disdrometer and the beacon receiver) was not operational. Overall, the rain events that were used in this work were collected in 70 different days.
## 3 Proposed Method
### Definition of Region of Intrusion (RoI)  
The goal of the presented ODA method is to create synthetic images to augment the existing SMART dataset with images of long-distance obstacles that are hazardous for safe rail transport. To cover the long-distance range, the length of the RoI in the target image is calculated as the farthest 25% of the total length of detected rail tracks. As illustrated in Figure 4b, once the regression lines are calculated to represent left/right rail tracks, the start and the end points of both lines are detected (points 1 and 2 for the left rail track, i.e., points 3 and 4 for the right track). If the length between the start and end points of the detected rail tracks is denoted as \(l\), the length of RoI is calculated as 0.25\(l\). For all rail track images in the SMART dataset, this definition of the RoI length corresponded to real-world distances beyond 200 m.  
In order to cover the so-called clearance region, the width of the RoI is calculated based on the standard real-world size of the rolling stock and its width outside the rail tracks. According to [30], the standard width of the rolling stock's protrusion outside the rail tracks is \(Rs=0.895\) m. Considering the standard-gauge railway with a track gauge of \(Sg=1.435\) m, and considering the projective transformation by camera imaging:  
\[r_{s}=s_{g}\cdot\frac{R_{s}}{S_{g}} \tag{7}\]  
where \(s_{g}\) is the distance between the corresponding points on the detected left and the right track (track gauge) in pixels, the pixel width \(r_{s}\) of the RoI outside the rail tracks on both the sides of the rail track can be calculated. In this way, the RoI trapezoid A-B-C-D, is defined as illustrated in Figure 4b.
## 4 Experiment
### Expert Engineer vs. BO Experiment
#### 4.2.3 Verification Test Results Table  
Table 1 summarizes the test results for the entry time and Max Vibration, comparing the results of the engineer's settings and the BO settings for the three load conditions. The table shows key performance indicators including the average (Average), maximum value (Max), standard deviation (St.Dev.), \(Z\)-value, fail count (i.e., the number of times the existing criteria were exceeded), and T-Test (two-sided) results (T-Statistics and \(p\)-value).  
Figure 10: Max vibration verification results (engineer vs. BO).  
Figure 9: Entry time verification results (Engineer vs. BO).  
Under the Light Load condition, BO significantly improved performance compared to the engineer's settings. The average entry time was reduced from 551 to 279 s, and the maximum entry time decreased from 1873 to 1348 s. Additionally, the average vibration amount increased from 6.4 mm to 7.4 mm. According to the T-Test, these differences were statistically significant (T-Statistics = 3.865, \(p\) = 0.0002 for entry time, and T-Statistics = \(-\)2.475, \(p\) = 0.0150 for Vibration).  
In the Medium Load condition, BO yielded a slight decrease in entry time from 233 to 229 s, with no significant difference, and the vibration amount remained similar (from an average of 6.4 to 6.0). The T-Test (T-Statistics = \(-\)1.015, \(p\) = 0.3150 for entry time; T-Statistics = 0.284, \(p\) = 0.7768 for Vibration) found no statistically significant difference, suggesting a trade-off may exist between faster entry time and lower vibration. The T-Test results (T-Statistics = 0.135, \(p\) = 0.8932 for entry time; T-Statistics = 0.929, \(p\) = 0.3578 for Vibration) indicate no statistically significant difference compared to the engineer's approach.  
For the Heavy Load condition, the average entry time increased slightly from 240 to 269 s when using BO, but the vibration amount decreased from an average of 4.9 to 4.7. The T-Test (T-Statistics = \(-\)1.015, \(p\) = 0.3150 for entry time; T-Statistics = 0.284, \(p\) = 0.7768 for Vibration) found no statistically significant difference, suggesting a trade-off may exist between faster entry time and lower vibration.  
BO demonstrated dehydration performance at a level comparable to the engineer in terms of entry time and vibration under Medium and Heavy Load conditions. However, under the Light Load condition, BO showed statistically significant superiority in entry time but significant inferiority in vibration, resulting in conflicting outcomes. This suggests that adjusting \(T_{t}\) (the threshold for entry time) and \(T_{v}\) (the threshold for vibration value) according to the load conditions is necessary to balance and optimize performance. The experimental results confirmed that the BO-based automation system (GAUS) achieved unbalance prevention control specifications equivalent to those of the engineer and met the dehydration performance test criteria, demonstrating its applicability to product development.
## 1 Introduction
The design of a wider impedance bandwidth antenna with compactness is challenging. A majority of the prior works lack nothing of the complete existing narrowbands. This paper presents a composite structure of a circular- and ellipse-shaped UWB antenna with a simulated and measured impedance bandwidth of approximately 113% (3.7-13.3 GHz). The proposed design exhibits two frequency band notchings: one at 5-5.6 GHz, covering the WLAN and IEEE 802.11 bands, and one at 7.3-8.3 GHz, covering X-band satellite communication. Incorporating sequences of ellipses onto the circular monopole and the lowered ground plane embedded with an arc at the edges is responsible for extended ultrawide bandwidth. The inverted U-shaped slots on the UWB antenna radiator help attain two-band notch features by evading impedance matching and disturbing the current distribution. The band-notched mechanism has a negligible influence on the radiation characteristics of the projected antenna. Further, the proposed antenna is evaluated by developing its equivalent circuit and characteristic mode analysis (CMA). The novelty of the proposed work is that there are no intricate structures used to achieve the UWB spectrum and dual-band notching. This simple structure provides an S11 curve below \(-11\) dB for almost the entire impedance bandwidth. The UWB and UWB band notch structures are compact compared to the many antenna designs in the literature, and significantly less work is reported for the complete band rejection of X-band satellite communication. The projected antenna design is also exposed to a thorough time-domain analysis. A good time-domain response signifies the proposed design's ability to incorporate it into practical applications. The projected antenna's prototype is fabricated and measured. The simulation and measured results indicate that it has good frequency and time-domain responses. This work is organized as follows: a detailed design analysis of the projected design is depicted in Section 2. Section 3 describes a comprehensive analysis of the design to find the optimal dimensions. The development of a circuit model for the proposed antenna is depicted in Section 4. Section 5 illustrates the CMA of the proposed UWB antenna. Section 6 describes the experimental and simulated findings of the antenna's design, followed by concluding remarks.