esa-sceva/satcom-chunk-collection huggingface dataset:
# Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)
## 4 Design Analysis
### Required Thruster  
To calculate the required thruster power, it is assumed that USV only moves straight or longitudinally. The USV Equation of Motion (EOM) when moving in the longitudinal direction is served in Equation (8). By neglecting the lateral speed, and yaw rotation, the EOM is expressed in Equation (25).  
\[(\rm{m}-X_{\rm{u}})\dot{u}=F_{0}-X_{u}u \tag{25}\]  
To calculate the minimum required thrust, Equation (25) is calculated during stationary conditions. As a result, Equation (25) is derived as  
\[0=F_{0}-X_{u}u \tag{26}\]  
\[F_{0}{=}X_{u}u \tag{27}\]  
where \(X_{u}u\) is the total linear drag force in u direction calculated using Equation (28).  
\[X_{u}u=2X_{uHull}u+X_{uFrame}u \tag{28}\]  
where \(X_{uFrame}u\) is defined as linear drag force that works around the Frame of USV in u direction, while \(X_{uHull}u\) is linear drag force around each hull of the USV in the u direction. These are calculated by Equations (29) and (30).  
\[X_{uHull}u=Cd_{Hull}\frac{1}{2}\rho uA_{Hull}u \tag{29}\]  
\[X_{uFrame}u=Cd_{Frame}\frac{1}{2}\rho uA_{Frame}u \tag{30}\]  
where Cd\({}_{Hull}\) is the drag coefficient of USV Hull, \(\rho\) is the density of water, g is the gravity acceleration, u is the longitudinal velocity of USV, \(A_{Hull}\) is the frontal area of USV's Hull, \(A_{frame}\) is the frontal area of USV's Frame. In this work, the desired longitudinal velocity is 1.3 m/s, \(\rho\) = 997 kg/m\({}^{3}\), g = 9.81 m/s\({}^{2}\), \(A_{Hull}\) = 0.031 m\({}^{2}\), \(A_{Frame}\) = 0.007076 m\({}^{2}\), Cd\({}_{Hull}\) = 0.42 [24], Cd\({}_{Frame}\) = 1.05 [24].  
The calculation results show that \(X_{uHull}u=10.97\) N and \(X_{uFrame}u=6.08\) N. Consequently, the sum of linear drag force (X\({}_{u}\)u) and the total thrust force of USV that is requiredwhen sailing in 1.3 m/s is 28.02 N or should be around 6.29 lbf. According to this result, the T-100 thruster from Blue Robotics meets this criterion since the maximum thrust of a single thruster is 5.2 lbf.
## 3 Results
### Typical Models
#### 3.2.1 Deep-Depth Model  
As depicted in Figure 6, the media consists of three layers, with the second layer containing a 3D anomaly. The resistivities of the three layers are 500 \(\Omega\cdot\mathrm{m}\), 100 \(\Omega\cdot\mathrm{m}\), and 40 \(\Omega\cdot\mathrm{m}\), respectively. The first layer has a thickness of 10 km, while the second layer has a thickness of 38 km. The dimensions of the 3D anomaly are 8 km \(\times\) 8 km \(\times\) 31 km (in the x-, y-, and z-directions, respectively) with a top depth of 18 km. Four models are generated by varying the resistivities within the embedded anomaly region. The Anis1 model represents an axial conductive anisotropic model with three principal resistivities set at 50 \(\Omega\cdot\mathrm{m}\), 200 \(\Omega\cdot\mathrm{m}\), and 400 \(\Omega\cdot\mathrm{m}\), respectively; the Anis2 model also exhibits axial conductive anisotropy but with three principal resistivity values adjusted to 50 \(\Omega\cdot\mathrm{m}\), 200 \(\Omega\cdot\mathrm{m}\), and 40 \(\Omega\cdot\mathrm{m}\). The Iso1 model corresponds to a conductive isotropic model, with the resistivity value set at 50 \(\Omega\cdot\mathrm{m}\), while the Iso2 model represents another conductive isotropic model, with the resistivity value adjusted to 200 \(\Omega\cdot\mathrm{m}\). The frequency used for computation is 0.001 Hz.  
Figure 4: Three-dimensional anisotropic model: (**a**) section view; (**b**) plan view.  
Figure 5: Comparison with the edge-based FE method: (**a**) the left column corresponds to the \(\mathbf{A}-\phi\) method; (**b**) the middle column corresponds to the edge-based FE method (VFEM); (**c**) the right column corresponds to the relative errors.  
For the four models, Figures 7 and 8 depict the real and imaginary components of the horizontal electric fields induced by two sources, i.e., Source A (Ex = 0, Ey = 1, Ez = 0) and Source B (Ex = 1, Ey = 0, Ez = 0) on the ground surface. The first to fourth columns correspond to the Anis1 model, the Iso1 model, the Iso2 model, and the Anis2 model, respectively. In Figure 7, the first to fourth rows correspond to the real parts of Ex induced by Source A (RExA), Ey induced by Source A (RExA), Ex induced by Source B (RExB), and Ey induced by Source B (REyB), respectively. In Figure 8, the first to fourth rows correspond to the imaginary parts of Ex induced by Source A (IExA), Ey induced by Source A (IExA), Ex induced by Source B (IExB), and Ey induced by Source B (IEyB), respectively.  
Figure 6: Deep-depth model: (**a**) the section view; (**b**) the plan view of the 3D anomaly.  
Figure 7: The real parts of the electric fields at the ground surface for the Anis1 model, the Iso1 model, the Iso2 model, and the Anis2 model.
# Numerical Simulation Analysis Method for Rockburst Control in Deeply Buried Caverns
## 2 Mechanism and Control Measures of Rockburst  
The occurrence of rockburst is related to various factors, such as complex geological conditions, cavern layout, and excavation disturbance. Previous studies on rockburst phenomena [17; 18; 19; 20; 21; 22] have led to a relatively deep understanding of the diversity of rockburst, the mechanism of occurrence, and the relationship between rockburst occurrence and the physical and mechanical properties of surrounding rock. Many effective methods for controlling rockburst have been proposed by summarizing the mechanism and evaluation methods of rockburst.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Outcome**} & \multirow{2}{*}{**Day**} & \multirow{2}{*}{**Games**} & \multicolumn{5}{c}{**Average**} & \multicolumn{5}{c}{**60 s PD**} \\ \cline{3-10}  & & & **Estimate** & **SE** & **95\% CI** & **Estimate** & **SE** & **95\% CI** \\ \hline RD & Sat & 1 & 78.8 & 3.2 & 71.2 & 86.4 & 130.7 & 4.3 & 120.3 & 141.2 \\ RD & Sun & 1 & 79.9 & 1.7 & 76.6 & 83.2 & 133.2 & 4.7 & 122.3 & 144.2 \\ (m\(\cdot\)min\({}^{-1}\)) & Sat & 2 & 78.5 & 1.7 & 74.8 & 82.1 & 121.3 & 7.0 & 105.1 & 137.5 \\  & Sun & 2 & 74.4 & 1.8 & 70.6 & 78.1 & 133.5 & 3.2 & 127.0 & 140.1 \\ \hline  & Sat & 1 & 3.7 & 0.3 & 3.0 & 4.5 & 19.5 & 2.2 & 14.2 & 24.7 \\ HSR & Sun & 1 & 4.5 & 0.4 & 3.6 & 5.4 & 22.0 & 1.8 & 18.1 & 25.9 \\ (m\(\cdot\)min\({}^{-1}\)) & Sat & 2 & 3.5 & 0.4 & 2.6 & 4.3 & 21.1 & 1.8 & 17.1 & 25.1 \\  & Sun & 2 & 3.8 & 0.3 & 3.0 & 4.5 & 22.3 & 2.0 & 17.8 & 26.7 \\ \hline  & Sat & 1 & 1.2 * & 0.3 & 0.7 & 1.8 & 3.1 & 0.90 & 1.8 & 5.5 \\ ACC & Sun & 1 & 0.9 * & 0.1 & 0.7 & 1.2 & 3.3 & 0.61 & 2.3 & 4.7 \\ (n\(\cdot\)min\({}^{-1}\)) & Sat & 2 & 0.8 & 0.1 & 0.6 & 1.1 & 2.5 & 0.60 & 1.6 & 4.0 \\  & Sun & 2 & 0.8 & 0.1 & 0.6 & 1.0 & 3.3 & 0.40 & 2.6 & 4.2 \\ \hline \end{tabular}  
* different from Sunday games (\(p<0.05\)). ACC: maximal accelerations; DEC: maximal accelerations; HRR: high-speed running; PD: peak demands; RD: relative distance; Sat: Saturday; Sun: Sunday. arb. U.: arbitrary units. SE: standard error; 95\% CI: confidence interval.  
\end{table} 
Table 3: Physical performance and wellness estimates for players competing in 1 or 2 games per weekend.
## 3 Results
### Comparison of Model Performance Experimental Results  
In the study, five models--UNet, DeepLabv3+, HRNet, PSPNet, and MHC-PSPNet--were compared and tested. Their IoU, accuracy, and F1 score were evaluated using the test set from a custom-built dataset, with the results presented in Table 5.  
As shown in the table, while the UNet model, with VGG16 as its feature extractor, performs well in metrics such as MIoU, MPA, and F1 score, it slightly lags behind other models in the performance comparison. DeepLabv3+, utilizing Xception as its feature extractor, outperforms UNet but still has room for improvement across various metrics. The HRNet model, with hrnetv2_w18 as its feature extractor, achieves high scores due to its distinctive high-resolution feature fusion and multi-scale processing mechanisms. The PSPNet model, using ResNet50 as its feature extractor, demonstrates strong overall performance, although its MPA value is slightly lower than that of HRNet. The MHC-PSPNet model, which employs the lightweight MobileNetV2 as its feature extractor, outperforms all other evaluated models and shows enhanced effectiveness in complex scenarios [26]. This demonstrates the success of the design strategy in improving both model performance and adaptability.
# A 28-nm CMOS Low-Power/Low-Voltage 60-GHz LNA for High-Speed Communication
## 5 Conclusions: Main Achievements, Pioneering Research and Future Directions
This work is among the few in the literature to demonstrate a differential, ESD-protected 60 GHz CMOS LNA that combines low power, low voltage and wide bandwidth, all within a compact and integration-friendly layout. The use of the _PE-SNIM_ technique within a pseudo-differential structure under such strict power constraints represents a significant advancement for energy-efficient mm-wave front-end circuits. However, some limitations and challenges must be acknowledged. The performance figures are based on post-layout simulations, and further silicon validation is essential to confirm the results under real measurement conditions. Moreover, while the custom-designed transformers and MIM capacitors offer excellent performance, their implementation requires tight control over layout density rules, parasitic modeling and process variation, which can introduce yield or integration issues when porting to other foundries or scaling to system-on-chip (SoC) platforms. These factors may also affect matching, gain flatness or ESD tolerance if not carefully managed. Looking ahead, several research directions are envisioned:  
1. Integration with OOK demodulator to evaluate complete receiver performance in terms of sensitivity and energy per bit. 2. Exploration of reconfigurable bandwidth or gain control via tunable passive elements for adaptive communication environments. 3. Investigation into co-design strategies for ESD and matching, to reduce area and further improve robustness without degrading performance. 4. Scalability to higher frequencies by redesigning transformers and layout techniques while managing increased losses and EM coupling. In conclusion, the proposed LNA represents a promising and practical solution for next-generation mm-wave wireless systems, offering a compelling combination of performance, power efficiency and integration readiness. Conceptualization, M.E. and E.R. ; validation, M.E., A.B. and E.R. ; formal analysis, M.E. and E.R. ; methodology, M.E. and E.R. ; project administration, E.R., G.G. and S.P. ; supervision, E.R., G.G. and A.B. ; writing--original draft, M.E. and E.R. ; writing--review and editing, M.E., G.G., A.B., S.P. and E.R. All authors have read and agreed to the published version of the manuscript. This work was partially supported by the European Union under the Italian National Recovery and Resilience Plan (PNRR) of Next Generation EU, partnership on \"Telecommunications of the Future\" (PE0000001--program \"RESTART\"). The data presented in this study is available on request from the corresponding author.
## 1 Introduction
### State-of-the-Art Study  
#### 1.1.1 Wireless Sensors  
In this section, we briefly present the proposed concept of the intelligent wireless sensors. Our goal is not to completely change the structure of existing wireless sensors, but rather to expand their functionalities. A typical wireless sensor consists of several components listed below [5; 6; 7; 8]:  
* front-end sensor used to collect analog data from the environment;
* anti aliasing filter and filters used to remove the noise from the signal;
* ADC used to convert measured analog signal into its digital counterpart; and RF communication block.  
The requirements as well as the structure of particular components of the sensors depend on the application for which they are designed. In an in-door environment, for example in some medical applications [9], the robustness on extreme conditions may be not so important as in the case of the sensors to be used in the out-door environment, e.g., in automotive applications [10]. On the other hand, in the case of medical application, a larger miniaturization and lower energy consumption is required.  
The first element in the presented data processing chain is the front-end sensor used to collect data from the environment. Depending on spectrum of the input data, a low-pass analog filter may be required to avoid aliasing that may occur during the following operation, which is the analog-to-digital conversion. The signal after the ADC may be further processed in various ways in a data processing unit.  
In the proposed concept of the intelligent sensors, we assume a direct analysis of the collected signals by the use of an ANN integrated together with other components of the sensor. To make this possible, we propose to extend the scope of functionality of the processing unit by adding to it an artificial neural network and a block responsible for creating a training set. The structure of such a sensor is shown in Figure 1. The aim of this approach was to move some part of signal processing tasks from the external base station to the sensor itself. As a result, the sensor would communicate less frequently with the base station and thus consume less energy.  
Since the key block in the described concept is a miniature ANN, the state-of-the-art should be more related to the neural networks themselves, instead of the sensors.
# A New Windage Loss Model for S-CO\({}_{2}\) Turbomachinery Design
###### Abstract  
A supercritical CO\({}_{2}\) (S-CO\({}_{2}\)) Brayton cycle is a compact and simple power conversion system with competitive efficiency. However, the strong real gas effects of S-CO\({}_{2}\) pose challenges to the design of a cycle and its components. In particular, designing turbomachinery for expansion and compression processes has to accurately reflect real gas effects. Windage loss is one of the major losses that affects the motor load and heat generation in turbomachinery. The windage loss has a substantial impact on the overall turbomachinery efficiency especially in an S-CO\({}_{2}\) power cycle since the windage loss is reported to be the dominant loss mechanism due to high fluid density and high rotational speed. Therefore, an accurate windage loss model reflecting the real gas effect of S-CO\({}_{2}\) is essential to obtaining an optimal design of turbomachinery as well as maximizing the performance of an S-CO\({}_{2}\) power cycle. In this study, existing windage loss models are first compared to the recently obtained data from S-CO\({}_{2}\) windage loss experiments conducted by the KAIST research team under S-CO\({}_{2}\) conditions in order to understand the turbomachinery performance uncertainty caused by the windage loss models. This is followed by proposing a new windage model which explains data better windage loss  
supercritical carbon dioxide Brayton cycle (S-CO\({}_{2}\) Brayton cycle); turbomachinery; windage loss
## 2 OFRD Method
### Fiber Bending Test  
Optical fiber installation often involves right-angle or parallel bends. Excessive bending causes light attenuation, which significantly reduces the signal-to-noise ratio (SNR) and renders measurements unreliable. This study determined the minimum allowable bending radius. Test Procedure: Printed circles with radii of 0.5 cm, 1 cm, 1.5 cm, 2 cm, 2.5 cm, and 3 cm on A4 paper. Tested fibers by adhering them starting from the largest radius to identify the minimum viable bending radius. Validated results through parallel bends (180\({}^{\circ}\) curvature). Key Findings: (1) Plastic sleeves were installed at non-critical stress concentration zones to protect the fiber during testing; (2) The bending radius must exceed 1 cm at all turn points.
# Interspectral Error Tracking and Compensation of DSDT in Satellite Internet of Things
## 5 Simulation and Experimental Results Analysis
### Experimental Results and Analysis of Multiple RF Channels  
To validate the performance of the algorithm, experiments were conducted using the NI-USRP wireless transceiver platform. The experimental parameters are shown in Table 3.  
The sub-spectrum of the signal after four segments is shown in Figure 14. Figure 15 shows the constellation diagrams before and after using the sub-spectrum error tracking and adaptive compensation algorithm. It can be observed that the constellation diagram after pilot synchronization and the direct combination of sub-spectrums is more dispersed and exhibits some rotation, thus indicating the presence of residual frequency and amplitude phase errors in the sub-spectrum signals. After processing with the proposed algorithm, the combined signal's constellation diagram is greatly focused with almost no scatter points, thus demonstrating the high-precision error synchronization and tracking performance of the algorithm. Figure 16 shows the error convergence curve, thus indicating that the signal achieved error convergence within approximately 20 symbol periods, and the mean square error ended up being generally within 0.05 after convergence, thus reflecting the algorithm's fast error convergence and tracking performance.  
Finally, Gaussian white noise was introduced using the built-in controls of LabVIEW, and the BER curves before and after applying the proposed algorithm were compared at different SNRs, as shown in Figure 11.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Modulation & QPSK \\ bandwidth/MHz & 2 \\ Sample rate/MHz & 100 \\ Number of sub-carriers & 4 \\ Sub-spectrum bandwidth/MHz & 0.5 \\ Subspectral roll-off factor & 0.5 \\ Antenna carrier frequency/GHz & 2.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental parameters under multiple RF channels.  
Figure 13: Inter–spectral synchronization error compensation in various modulation types in presence of \(A\in[1,1.05]\), \(\Delta f\in[0,5]\), and \(\Delta\phi\in[0,0.05]\).  
Figure 17 shows the measured BER of the DSDT; it can be observed that at an SNR of 10 dB, the reconstructed signal's BER is close to \(1\times 10^{-5}\).  
After applying the proposed algorithm, the BER further decreased compared to direct reconstruction after pilot synchronization, thus validating the conclusions of the simulation experiments.  
Figure 16: Mean squared error convergence curve.  
Figure 14: Spectrum of DSDT signal received by real\(-\)time spectrograph.  
Figure 15: Constellation diagram comparison in practical measurement. (**a**) Constellations after pilot synchronization. (**b**) Constellations after proposed method.
## 1 Introduction  
During pregnancy, the umbilical cord acts as a lifeline for the foetus connecting it to the placenta. The cord can insert into the placenta in a variety of locations, referred to as the placental cord insertion (PCI) site. In most pregnancies, the cord inserts toward the middle of the placenta (NCI); sometimes, it inserts towards the placental edge (MCI) and occasionally into the foetus membranes instead of the placenta (VCI). The latter two types of PCI are associated with potential maternal and foetal complications, and as such, the PCI location should be assessed during routine antenatal ultrasound examinations. In 2023, [PERSON] et al. reviewed the current practice of PCI documentation in Australia, discussing the associated challenges [1]. Whilst the PCI can be efficiently evaluated with two-dimensional and colour Doppler ultrasound imaging [2; 3], ultrasound assessment of the PCI site can be complex, particularly when the cord insertion is abnormal, and this requires proficient sonographer spatial perception. Despite the availability of excellent two-dimensional (2D) resources, visualising anatomical spatial relationships remains challenging [4]. The production of three-dimensional printed models (3 DPMs) can address this problem by providing clear visualisation of the spatial relationships [5].  
Three-dimensional printing technology is showing great potential in medical applications; in particular, it has been shown that 3 DPMs offer exceptional dimensional awareness [6]. Three-dimensional anatomical models are now widely used in many aspects of medicine [7; 8; 9; 10; 11] including education [12; 13; 14], pre-surgical planning [15; 16; 17; 18; 19], and patient communication [20; 21; 22], due to the advancements in 3D printing technology, reduction in costs, and ease of access to 3D printing facilities [7; 18]. Patient-specific or personalised 3D models can be printed at a 1:1 scale based on imaging datasets, mainly computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound [15]. Further, these models can be printed with different materials, depending on their usefulness for clinical applications [20; 23].  
The current literature describes the production of a variety of 3 DPMs for a diverse array of medical applications with superior advantages reported when compared to the use of standard 2D or 3D image visualisations [7; 21; 24]. In the field of obstetrics, raw data obtained from 3D ultrasound imaging is being used to create 3 DPMs to facilitate antenatal assessment, maternal and paternal foetal attachment, and presurgical planning for foetal malformations [25; 26]. To the best of our knowledge, no studies have described the creation of 3 DPMs that depict the PCI. This study aimed to address this research gap by assessing the potential value of 3 DPMs of the PCI in ultrasound education. Our 3 DPMs are patient-specific, based on images of selected patient cases; thus, the models reflect realistic physiological and pathological processes. We hypothesised that our 3 DPMs would serve as a useful adjunct to the currently available PCI site educational resources of 2D ultrasound images and videos.