esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact, Low-Profile, Broadband Quasi-Isotropic Antenna for Non-Line-of-Sight Communications
## 2 Broadband Quasi-Isotropic Antenna Design
### Final Design of the Proposed Antenna  
Based on the observations from the parameter studies, the antenna design can be optimized for the acceptable gain deviation of a quasi-isotropic radiation pattern in a broad impedance bandwidth. In this study, the performance of the quasi-isotropic antenna is measured by the gain deviation. The previously reported broadband quasi-isotropic antenna showed a maximal gain deviation in the range of 6 to 12.7 dB [28], and we set 10 dB as the gain deviation criterion. In quasi-isotropic antenna designs, the minimal gain value is also important, as is the gain deviation. Note that the minimum gain value of the proposed work is greater than \(-\)7 dBi in the operating frequencies, which is high enough to enable reliable communication [40]. The final design values are tabulated in Table 1. As shown in Figure 7, the proposed antenna is optimized to operate at frequencies ranging from 1.8 to 4.3 GHz with a \(-\)10 dB criterion. The gain deviation ranges from 6 to 9 dB for frequencies of 1.8 to 3.6 GHz with a 10 dB reference, approaching the impedance bandwidth range. The electrical size \(ka\) of the proposed antenna is 0.94, where \(k\) is the free-space wave propagation constant and \(a\) is the radius of an imaginary sphere enclosing the antenna. The antenna profile is 0.254 mm or 0.0015A at the minimum operating frequency of 1.8 GHz.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 5 LOS Velocity Calibration and Uncertainties
### LOS Direction Evaluation
#### 5.4.1 Fitting the Lidar Response to Wind Direction  
\(\theta_{los}\) is first approximated by fitting the normalised lidar LOS velocity \(V_{LOS,norm}\) to a function of the wind direction \(\theta\). In this analysis, all wind directions sectors are valid except for site related specifications (e.g., tower shadowing, presence of obstacles, wakes from neighbouring turbines, etc.). The normalised LOS velocity is:  
\[V_{LOS,norm}=\frac{V_{LOS}}{V_{hor}\cos\varphi} \tag{4}\]  
The fitting function \(f_{fit}\) is:  
* a cosine wave for a heterodyne lidar (such as 5B): \(f_{fit_{1}}=A_{1}\cdot\cos\left(\theta-\theta_{0}\right)+B_{1}\);
* not its sign
- which translates into positive LOS velocities for any wind direction \(\theta\). In such a case, a rectified cosine must be used. The ambiguity in the fitting due to the two distinct solutions for \(\theta_{0}\) is resolved by choosing the value corresponding to the expected bearing of the LOS, e.g., using GPS coordinates;  
The least squares fitting yields gain and offset values ideally equal to 1 and 0 respectively. \(\theta_{0}\) is an approximate estimation of \(\theta_{los}\).  
Figure 7 shows the fitting results. The gain and offset values are: 0.99 and 0.01\({}^{\circ}\) for 5B; 0.97 and 0.03\({}^{\circ}\) for ZDM. The coefficients of determination are both \(>\)0.98 thus demonstrating the validity of the method. \(\theta_{0}\) values are 286.28\({}^{\circ}\) and 288.18\({}^{\circ}\) for the 5B and ZDM lidars respectively.  
Figure 7: Lidar response to the wind direction. **Left**: 5B, cosine fitting; **Right**: ZDM, rectified cosine fitting.
## 3 Results and Discussion
### Segmentation Performances  
An analysis based on the quantitative measure was performed between machine learning segmentation versus deep learning segmentation to identify the best segmentation performance for the helminth ova images. Table 3 tabulated the overall segmentation results obtained for the overall segmented helminth ova images based on accuracy and IoU analysis. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Image Illumination** & **Normal Image** & **Over-Exposed Image** & **Under-Exposed Image** \\ \hline Original Image & & \\ \hline PCS Image & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The ALO image before and after implementing PCS technique. Scale bar = 50. Figure 3: The results of deep learning segmentation technique on TTO image: (**a**) PCS on TTO image; (**b**) SLIC superpixel technique on enhanced TTO image; (**c**) ResNet-18 on TTO image; (**d**) ResNet-34 on TTO image. Scale bar = 50. Based on Table 3, different species datasets have different results in segmentation performance, which are presented in the accuracy performance and IoU performance. Deep learning segmentation, based on ResNet-34, has achieved the highest results for accuracy and IoU for the ALO species with 99.30% and 75.80% each. For the EVO species, deep learning segmentation based on VGG-16 has achieved the highest accuracy results with a value of 98.89%, and ResNet-34 achieved the highest results for the IoU with a value of 55.48%. Meanwhile, machine learning segmentation based on FCM has achieved the highest results for both accuracy and IoU with values of 99.48% and 73.25% each for the HWO species. Then, for the TTO species, deep learning segmentation based on VGG-16 has achieved the highest results for accuracy and deep learning segmentation based on ResNet-18 has achieved the highest results for IoU. Through the results obtained for accuracy, both segmentation techniques show good segmentation performances, which are around 97% to 100% in segmenting the helminth ova. Meanwhile, in IoU analysis, most of the segmentation techniques show results higher than 50%, which indicates that the helminth ova are well segmented, but there is significant contrast between the results obtained by machine learning segmentation and deep learning segmentation. Table 4 shows an example of the segmentation performance achieved by deep learning segmentation for accuracy and IoU via ResNet-34 for helminth ova. Through the example shown, the IoU analysis is more sensitive than the accuracy analysis. Therefore, a good segmented image will achieve higher IoU analysis when compared to the ground truth image. Overall, deep learning segmentation has shown better segmentation results for both accuracy and IoU compared to machine learning segmentation. The results obtained are a good benchmark in developing an automatic detection system for helminth ova in the human intestine.
## 3 System Model and Problem Formulation
### Interference Model  
There are two main types of interference considered in our scenario: (1) co-channel interference between LEO beams; and (2) interference from LEO beams to GEO ground stations. In the following subsections, we will give the model and analysis, respectively, for the two types of interference.
# Waveform Design for Space-Time Coded MIMO Systems with High Secrecy Protection +
## 5 Conclusions  
In this paper, we presented a novel secrecy-enhancing scheme by tailoring the waveform of the LD-QO-STBC scheme. The proposed scheme employed dynamic AI that was dependent on the signal, as well as the channel gain of the legitimate receiver. The dependency on the information signal was used to reduce the difference of power allocations across the transmit antennae. In addition, the dependency on the channel gain was used to cancel the AI only at the legitimate receiver, while imposing serious interference on the passive eavesdropper. As a result, the secrecy capacity was highly enhanced. The BER simulation results showed that the proposed scheme prevented information leakage to the illegal listener, even in the case when the illegal listener has a perfect CSI and the same SNR as the legitimate receiver. Because the proposed method does not incur any power loss due to the addition of AI, it can be efficiently utilized for many wireless systems using STBC, resulting in diversity gain as well as security protection. Based on the promising results of the proposed scheme, our future work can be directed to the investigation of dynamic waveform design and efficient detection schemes utilizing artificial intelligence techniques [19; 20].  
Conceptualization, S.K. and H.L.; methodology, P.S.; software, P.S.; validation, P.S., H.L and S.K.; writing-original draft preparation, P.S.; writing-review and editing, S.K.; supervision, S.K.; project administration, S.K.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript.  
The research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1B03027939).  
The authors declare no conflict of interest.
# Research on Influence of Tank Sloshing on Ship Motion Response under Different Wavelengths
###### Abstract
Tank sloshing involves gas-liquid two-phase flow, and the capture of gas-liquid interface is a difficult problem. In this paper, the volume of fluid (VOF) method is used to capture the free liquid surface. The VOF method is a popular interface capture algorithm. It was first proposed by [PERSON] (1974) [10] and further studied by [PERSON] (1981) [11]. It can be used to simulate violent sloshing. Many scholars have developed many methods to solve the sloshing problem based on the VOF method [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. [PERSON] [16] adopted the concept of some element parameters, improved the traditional VOF method, and then used the improved VOF method to carry out numerical simulations on the prismatic tank model under different filling levels and different rolling excitation periods. [PERSON] [21] numerically simulated the sloshing problem in an LNG tank using an improved VOF method. Based on the software OpenFOAM, [PERSON] [24] simulated the motion process of two-dimensional liquid and ice tanks in waves and compared the calculation results of the water-carrying tank and the ice-carrying tank with ice of equal mass. In this paper, regular Stokes waves are generated by wave-making at the velocity inlet. The pitching and rolling motions of the liquid carrier in regular waves with different wavelengths are respectively calculated. In the head sea condition, this paper explores the influence of the liquid loading rate and the wavelength of the incident wave on the pitching motion of the ship. In the transverse wave condition, the influence of the liquid loading rate on the natural frequency of the rolling motion of the ship is explored. In addition, the influence of the natural frequency of the ship's rolling motion and the frequency of the incident wave on the amplitude of the rolling motion is also analyzed.
# Experimental Study of a Piezoelectric De-Icing System Implemented to Rotorcraft Blades
## 6 Full-Length Tail Rotor Leading Edge Numerical Case
### De-Icing Scenarios and Power Estimation  
The voltage required for de-icing, based on the shear stress obtained with the small blade setup was estimated numerically for the full-length tail rotor case for modes 2, 3 and 4. This setup was used to estimate the power consumption of the system, as detailed under Section 5.3. For the power density calculation, the span was changed from 22.9 cm (9 inches) to 60 cm (23.6 inches) for a 246 cm\({}^{2}\) (37.8 in [2]) surface. Table 10 presents the number of actuators used in the numerical model for each mode (one at each anti-node), the total power required to excite the system (all actuators) and reach sufficient stress for de-icing, with and without rotation, as well as the power density and potential power reduction of the system as compared to electrothermal systems. The power density and power reduction are estimated in the same manner as in Section 5.3 for the small-scale blade profile. These values are simple basic estimates based on the various assumptions presented in the previous section and should be considered likewise. These values represent best-case scenarios within the potential voltage range. Furthermore, the exact shedding mechanisms were not fully investigated, and the de-icing prediction was based only on the maximum shear stress prediction by the numerical model. Finally, this is for a hollow leading edge of 4.5 cm, which is significantly longer than the 1 cm hollow leading edge found in the real size tail rotor blade. Despite this, the obtained results showed the power reduction potential and validated the conclusions that power density should be much lower for a larger blade. Power densities down to 1.1 kW/m\({}^{2}\) (0.72 W/in\({}^{2}\)) were estimated, which would be a significant improvement. Compared to the value found in the literature, this shows great potential for the system. Despite the important considerations that must be taken into account when judging those values, this potential reduction demonstrates the relevance of deeper investigations on the piezoelectric actuator system integrated into a full tail rotor blade.
# Enhancing Autonomous Vehicle Navigation with a Clothoid-Based Lateral Controller
## 5 Design and Validation of Lead Filter
### Design and Validation of Lead Filter  
After characterizing the inherent lag in the lateral dynamics of the TruckSim(r) vehicle dynamic model, we used loop-shaping techniques to design a lead filter to compensate for the same. The problem was formulated with the transfer function representing the lag (Equation (5)) as the plant and developing a compensator plant to increase the bandwidth of the combined system. The feed forward compensator, designed using loop-shaping techniques to increase the bandwidth of the cascaded system, consists of one pair of complex zero and three real poles as given below:  
\[G_{lead}=\frac{\omega_{p}^{3}\left(s^{2}+2\psi\omega_{z}s+\omega_{z}^{2}\right) }{\omega_{z}^{2}\left(s+\omega_{p}\right)^{3}} \tag{6}\]  
Figure 9 illustrates the time-domain validation of the lead filter's lag compensation capabilities. Figure 9a,b depicts the compensated response for a dual lane change (DLC) as well as the step input steering maneuvers, respectively, as previously depicted in Figure 8. To delve into more detail, the **kinematic** relationship that links the steering wheel angle (input to TruckSim(r)) to curvature, essentially represents a normalized input. The curvature derived from the vehicle's coordinates signifies the actual curvature of the path followed by the vehicle's rear axle center. The **'Kinematic with lag'** plot portrays the outcome of the estimated lag (Equation (5)) coupled with the kinematic model (which maps the steering wheel angle to curvature, as mentioned above, \(\tan\phi=L/R_{1}\)). As demonstrated, this plot closely aligns with the actual curvature plot (derived from the path coordinates), which was its intended function.  
The compensated response, represented by the pink-dotted-line plot, is obtained by initially filtering the steering wheel angle through the lead filter and subsequently passing it through the estimated kinematic model with lag. This plot effectively mitigates the lag and tends to align with the precise kinematic relationship output, which is the scaled-down input. This observation underscores the lead filter's role as a predictor, enhancing the signal to compensate for the lag introduced by TruckSim(r)'s lateral model. This compensation proves valuable when the lead filter is integrated with the clothoid lateral controller.  
Figure 10 shows the characteristics of the compensator (lead filter) dynamics, the original plant dynamics (estimated lag transfer function), as well as the cascaded dynamics. The increase in the total bandwidth of the system and the reduction in the phase lag in the desired frequency of interest can be inferred from Figures 10a and 10b, respectively.  
Figure 8: Characterization of a second-order-lag equation (transfer function) relating the steering wheel angle (i/p) to the realized curvature (o/p) when vehicle speed is \(\sim\)30 kmph. (**a**) Dual-lane-change maneuver. (**b**) Series of step inputs.
# Analysis of Electromagnetic Interference Effects of 5G Signals on Radio Altimeters
## 5 Conclusions  
This paper explores the mechanisms and behaviors of 5G signal interference with RAs in adjacent frequency bands through theoretical analysis, simulation, and experimental verification. We discovered that 5G signals primarily disrupt RAs by affecting the RF front-end circuitry, causing the LNA to operate nonlinearly and reducing IF signal power, which impairs altitude detection and calculation. To counteract this interference, it is essential to boost out-of-band signal suppression before the LNA. Our analysis and simulations indicate that the impact of 5G interference on RA IF output gain is proportional to the signal's power and its proximity to the RA operating frequency, with greater power and  
Figure 11: Variation in height error ratio with 5G interference signal power at different frequencies.  
closer frequencies leading to more significant gain compression. Experimentally, RA height error ratios increase with 5G signal power, with performance declining sharply beyond a 1% error threshold and leading to an NCD when errors exceed 6%. The RA interference tolerance threshold also decreases as the 5G signal frequency increases from 3800 MHz to 4200 MHz. These findings provide a basis for understanding 5G interference effects on RAs within the electromagnetic spectrum.  
The interference of 5G signals with RA receivers is caused by high-power 5G signals inducing nonlinear operation in the RA RF front-end, which impacts the processing of IF signals and output. Consequently, this issue can be mitigated by enhancing RAs' out-of-band rejection capabilities, which fundamentally solves the interference issue by reducing the LNA nonlinear response to out-of-band signals. Future work will optimize the RA RF front end for better out-of-band selectivity and reduced nonlinearity. We plan to incorporate advanced bandpass filters to counteract the impact of 5G signals, ensuring reliable receiver operation in 5G environments.  
Conceptualization, Z.D. and Z.M.; methodology, P.W.; software, K.X.; validation, Z.D., K.X. and Z.M.; formal analysis, K.X.; investigation, K.X.; resources, Z.M.; data curation Z.M.; writing--original draft preparation, K.X.; writing--review and editing, Z.M.; visualization K.X.; supervision, Z.D.; project administration, Z.M.; funding acquisition, P.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Open Fund of Key Laboratory of Civil Aircraft Airworthiness Technology under Grant No. SH202111905, in part by the Natural Science Foundation of Tianjin, Grant No. 21 JCZDJC00860.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources
## 5 Assessing Resource Sharing in the Cloud-RAN Architecture
### Mobile Network Scenario
Assigned to one of such VF, an LXC can access the network with complete traffic isolation and without relying on any Kernel features for the creation of vNICs assigned to containers or internal virtual switching for their operation. Previous evidence suggests that mechanisms based on creating virtual NIC (vNIC) like macylan or SR-IOV provide lower overhead than Kernel-based software switch mechanisms like Linux bridge or Open vSwitch (OVS) [76]. As depicted in Figure 9, the NIC used in this network scenario features two Ethernet ports that support SR-IOV (see the hardware details in Table 1). The first port, known as p1, is used in Midhaul. As all three LXCs deployed at the CU are connected to the Midhaul, three VFs are created on top of p1's FF: VF1, VF2, and VF3. Each LXC defines a vNIC called nic1, with a different VF assigned to each LXC, allowing access to the Midhaul. For example, VF1 is assigned to the nic1 of vBBU1, VF2 to the nic1 of vBBU2, and VF3 to the nic1 of A. On the other hand, the second NIC port, p2, is used in Backhaul. Since only vBBU1 and vBBU2 connect to the Backhaul, two VFs are created on top of p2's PF: VF4 and VF5. Additionally, both vBBU1 and vBBU2 define a second vNIC called nic2. VF4 is assigned to the nic2 of vBBU1, while VF5 is assigned to the nic2 of vBBU2 for access to the Backhaul. Figure 9: SR-IOV-based NIC sharing in Cloud-RAN-Deploying two different ports: p1 provides access to the Midhaul; p2 provides access to the Backhaul.
## 3 Results and Discussion
### Surface Water Retention and CN in Tillage Treatments  
Despite having differences in the runoff, CP, DP, and ripper tillage treatments had similar surface water retention with planting basins having higher (\(p=0.090\)) water retention compared to the other tillage treatments (Table 3, Figure 8). This implies that infiltration in the tillage treatments varied, and planting basins had more surface depressions that collected rainwater during the growing season. In the two conventional treatments, more surface water was retained in sandy soil than in loamy sand (Figure 9). This result suggests the slower infiltration of rainwater in sandy soil than loamy sand across experimental sites. This can be attributed to poor pore connectivity or pore geometry in the top soil layer or delayed infiltration due to air entrapment in the macropores of the sandy soil in the tilled treatments. On the contrary, sand and loamy sand soils retained similar quantities of surface water under the minimum tillage treatments. The planting basin treatment had a lower \(\mathcal{CN}\) (\(p=0.0615\)) compared to conventional and ripper practices (Figure 10). Complementary practices, such as mulching and cover cropping, could increase surface water retention and hence reduce runoff from cropping systems in rain-fed smallholder systems.  
Figure 8: Surface water retained in different tillage treatments across sites and seasons in Gwanda and Insiza districts, Zimbabwe. Means with the same letter are not significantly different (5%). The crossed blue square in each box plot represents the treatment mean.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Parameter** & **Treatment** & **DF** & **SS** & **MS** & **F-Value** & \(p\)**-Value** \\ \hline SWR & \begin{tabular}{l} Tillage method \\ Residuals \\ \end{tabular} & 3 & 11,023 & 3674 & 2.193 & 0.090 \\ \hline Curve number & \begin{tabular}{l} Tillage method \\ Residuals \\ \end{tabular} & 208 & \
\begin{tabular}{l} 3485 \\ 13,520 \\ \end{tabular} &
\begin{tabular}{l} 161.7 \\ 65.0 \\ \end{tabular} & 2.488 & 0.0615 \\ \hline \hline \end{tabular}
\end{table}
Table 3: ANOVA for tillage effects on surface water retention (SWR) and curve numbers across experimental sites. DF = degrees of freedom, SS = sum of squares, MS = mean square due to regression, \(p\)-value = probability value, and F-value = overall significance of the regression.