esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Single-Phase NPC Converter Structure and Conventional MPC Approach
Because the two reference voltages \(v_{diff}^{a}\) and \(v_{diff}^{b}\) have the same magnitude with opposite polarities, there are four possible cases depending on the reference voltage positions, as shown in Figure 2.  
Figure 2: Four possible situations depending on the location of the difference-mode reference voltages, leading to switching state of: (**a**) (1, \(-\)1); (**b**) (0, 0) with the \(a\)-phase reference voltage \(v_{diff}^{a}>0\); (**c**) (0, 0) with \(v_{diff}^{a}<0\); (**d**) (\(-\)1; 1). Thus, an optimal switching state in each phase, calculated by the cost function in Equation (21), can be determined as a function of the difference-mode reference voltage by:  
\[S_{i}=\left\{ \begin{array}{c}1\left(\frac{\
u_{dc}}{4}\leq v_{diff}^{i}\leq \frac{\
u_{dc}}{2}\right)\\ 0\left(-\frac{\
u_{dc}}{4}<v_{diff}^{i}<\frac{\
u_{dc}}{4}\right)\\ -1\left(-\frac{\
u_{dc}}{2}\leq v_{diff}^{i}\leq-\frac{\
u_{dc}}{4}\right)\end{array}  \right. (i=a,\ b). \tag{23}\]  
It can be seen from Equation (23) that the reference voltage positions in Figure 1(a)-d result in \((1,-1)\), \((0,0)\), \((0,0)\), and \((-1,1)\) as the optimal operating states, respectively. The switching states determined by the difference-mode reference voltage can lead only to the three operating states, \((1,\ -1)\), \((0,\ 0)\), and \((-1,1)\), none of which can contribute NP voltage balancing because they force both the capacitor voltages to rise or fall together as shown in Table 1. As a result, in cases that capacitor voltages deviate from the nominal value, resulting in NP voltage unbalance, the MPC approach cannot minimize the differences between two capacitor voltages. Furthermore, it can be noted that only three-level line-to-line voltages with \(\pm V_{dc}\) and zero can be produced by the difference-mode reference voltages term. In this paper, a common-mode voltage term will be considered to add to the reference voltage in order to carry the capacitor voltages' balancing task. In the next section, a common-mode voltage to regulate the NP voltage balance is added to the difference-mode voltage, which is for sinusoidal source current generation.
## 4 A320 Cabin Interference Path Loss Measurement  
### Experiment Preparation  
The measurement experiment was conducted on an A320-232 aircraft, which has a fuselage width of 3.95 m and a length of 37.57 m. The aircraft is equipped with 40 rows of windows and the positions of these windows align with the simulation model shown in Figure 1. In the simulation, the magnitude of \(S_{21}\) between the two antenna ports is used as the IPL value. However, it is not feasible to directly measure the \(S_{21}\) parameter using a vector network analyzer, due to experimental limitations and other factors. Instead, IPL is measured as the difference between the received power \(P_{R}\) at the GPS antenna and the transmit power \(P_{T}\) of the PED. The schematic diagram of the experimental setup is shown in Figure 5.  
The vector signal generator (ROHDE SCHWARZ SMW-200A, ROHDE SCHWARZ, Munich, Germany) was connected to a power amplifier (OET-GF-1-6-20200, OET, Beijing, China). The output of the amplifier was then connected to an antenna, simulating the use of PED inside the aircraft cabin, as shown Figure 6. It was difficult to directly measure the received power near the GPS antenna, since it is located on the upper surface of the A320 aircraft. The GPS antenna is actually connected to the GLU-920 multi-mode navigation  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Influencing Factors** & **Min IPL** & **Max IPL** & **Avg IPL** & **SD** & **95\% CI** \\ \hline PED at the left side of the 4 th row of windows (1500–1600 MHz) & 61.93 & 106.39 & 71.36 & 5.11 & [71.04, 71.68] \\ PED at the center of the 4 th row of windows (1500–1600 MHz) & 62.16 & 105.58 & 71.81 & 5.63 & [71.46, 72.16] \\ PED at the right side of  
receiver in the forward electrical equipment bay via internal antenna cables. To facilitate measurement, the receiver was removed and a high-shielding effectiveness tested cable was connected at the \"avionics rack connection\" in order to measure the received power of the GPS antenna. The other end of the tested cable was connected to the spectrum analyzer as shown in Figure 7. The position of PED was varied from front to back within the cabin, positioning it at the left or right side of the same row of windows to assess its effect on the received power.  
The experimental setup consists of two main components: (1) A signal generator is connected to a power amplifier, which supplies RF power to the PED. (2) A spectrum analyzer is connected to the \"avionics rack connection\" through tested cables or directly measuring the received power near the aircraft antenna. If the test cables could be connected directly to the aircraft antenna, the measurement results would be more accurate. However, due to the location constraints of the GPS antenna, the tested cables can only be connected to the \"avionics rack connection\", which inevitably introduces some cable losses. The actual measurement setup used in the experiment is shown in Figures 6 and 7.  
Figure 5: Schematic of the experiment.  
Figure 6: Signal transmission setup.
Satellite-based inundation observations have been extensively improved and have recently been applied in major flood event monitoring with both traditional segmentation algorithms and automated mapping techniques based on deep learning [26; 27; 28]. However, a prevalent practice in prior research studies, that is, relying solely on rainfall-related indicators to measure hazards in the data-driven risk model, may cause divergence with respect to the actual situation, resulting in inaccurate risk assessment [29; 30; 31; 32; 33; 34]. Multiyear historical inundation data, as a more realistic tool than precipitation statistic indices, are important for measuring risk. However, previous studies have rarely considered historical inundations in data-driven risk models, despite the maturity of satellite monitoring techniques. In this context, in this study, we developed a risk analysis framework based on historical (especially extreme) and future scenarios by integrating multi-source Earth Observation data. This novel framework improves the traditional data-driven flood risk assessment frameworks by broadening risk perception, which is achieved by integrating flood inundation-prone and high-risk areas. With this new framework, more potential threats to flooded areas could be foreseen for preparedness purposes while also providing a reference for decision-makers to identify priorities for optimizing resource allocation. Therefore, the framework can better address the uncertainty caused by climate change than previous methods based on the single analysis standpoint of high-risk determination. The detailed application of the framework is illustrated with the case of the Poyang Lake Eco-Economic Zone (PLEEZ) in Jiangxi Province, China.
# Vocabulary Retention Under Multimodal Coupling Strength Index (MCSI): Insights from Eye Tracking
## 6 Limitations and Future Work  
This study has several limitations that warrant consideration. Firstly, the sample was restricted to university students (a common population in psychological research), whose cognitive strategies and neuoplasticity differ substantially from children or older adults, potentially biasing estimates of the pedagogical effectiveness of audiovisual stimuli. Secondly, the artificial laboratory setting may not reflect authentic learning conditions, where factors like rapid task engagement and emotional states (e.g., test anxiety) could influence outcomes but were not accounted for in our analyses. Thirdly, we did not assess long-term memory retention through delayed testing, limiting our understanding of whether the observed effects persist over educationally relevant timescales (month to years). Fourthly, the experimental design focused exclusively on word-level memorization, leaving open questions about how multimodal stimuli affect higher-level language processing (phrases, syntax, or discourse), where such interventions might potentially cause interference rather than facilitation. Additionally, manual audio presentations may have introduced minor temporal inconsistencies that could affect individual memory accuracy.  
To address these limitations, future research should (1) incorporate more diverse age groups and geographical backgrounds in sampling, (2) implement delayed retention tests to evaluate long-term memory effects, (3) expand stimulus materials to include phrases and sentences for investigating multimodal effects on complex linguistic processing, and (4) employ automated stimulus delivery systems to ensure precise timing control.
## 4 Discussion  
A great variety is observed in ship dynamics modeling. The situation is much easier when we deal only with simulations. However, it is complicated when the controller is applied to a real plant/floating ship. It turns out that there were unmodeled elements of the object dynamics. Failure to take them into account in the predictive model causes the oscillations that were observed during experimental data validation. The obtained results proved this theory and showed that there was a perfect fit to the simulation validation data and a poor fit to the experimental validation data. In the case of simulating validation data, no deterioration in the prediction quality was observed along with the length of the prediction horizon. Nevertheless, such a deterioration was recorded during model validation based on real-time experimental data gained during lake trials. It can be concluded that the input data used for linearization are much more important than the structure of the model itself. The presented method allows for maintaining a simple structure of the model while tuning its parameters to the real plant/ship.  
During identification based on simulation data, a linear model is created by mapping mathematical, not physical, relationships. Not all cross-couplings between several  
Figure 10: Regularized GA model: 5-, 10-, and 20-step prediction comparisons based on real-time lake validation data; (**a**) 5-steps ahead prediction, (**b**) 10-steps ahead prediction, (**c**) 20-steps ahead prediction.  
channels are taken into account. Thus, the proposed method gives the opportunity to take into account the most important, from the prediction point of view, cross-couplings and allows for the model to be tuned to the real dynamics of the object, not only its mathematical description.  
Widely used in ship control theory and applications, the Nomoto model is insufficient for prediction purposes. Hence, there is a need to develop a different methodology for building a simple predictive ship model. The proposed algorithm for linear model identification in state-space combined with GA tuning is the answer to this need in real applications.  
Neural-network and LSSVM models are vert extended ones. Their identification is time consuming, and usage in MPC is limited, due to the computation power of the on-board ship controller. The proposed model is a very simple, linear state-space one, which is easy to implement in real-world applications and may be used in MASS predictive control systems.
# Meander Line Super-Wideband Radiator for Fifth-Generation (5G) Vehicles
## 4 Results and Discussion  
The finalized design of Ant4, which has a super wideband of 53.4 GHz from 6.79 GHz to 60 GHz, and its simulated S-parameter and gain plots are displayed in Figure 6. Additionally, Ant4's patch and ground surface current at 24 GHz, 26 GHz, and 28 GHz are shown in Figure 7, which shows that the 24 GHz surface current distribution exhibits more radiation at the center of the patch in Figure 7a, while the 26 GHz surface current distribution shows more radiation at the center and top corner edges of the patch in Figure 7b. Similarly, the 28 GHz surface current distribution displays more radiation at the patch's center and top center edge in Figure 7c. Interestingly, all three surface current distributions exhibit more radiation at the center of the continuous ground plane, as shown in Figure 7. Implementing the Ant4 design of the continuous ground, adding \(0.5W_{p}\) meander line width, and adding \(6.5h_{s}\) to the substrate length and width report remarkable results, and the bandwidth is improved compared to the Ant2 and Ant3 designs. This component can benefit wideband applications in the 5G and 28 GHz mm-wave bands. The proposed meander line antenna's performance is evaluated by comparing it with several antennas inspired by meander line designs, as detailed in Table 2. The results demonstrate that the proposed antenna exhibits gain, bandwidth, and compactness on par with those reported in the existing literature. The proposed antenna exhibits a comparable response and is suitable for its intended application.  
Figure 4: Design flow of 5G FR2 super-wideband meander line antenna for Ant2 to Ant4 (dimensions all in mm).  
Figure 5: Design flow of 5G FR2 super-wideband meander line antenna for Ant2 to Ant4 (**a**) S-parameter and (**b**) gain.  
Figure 6: Gain and S-parameter of Ant4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Ref. [44]** & **Ref. [45]** & **Ref. [46]** & **Ref. [47]** & **This Work** \\ \hline
**Substance** & RTS880 & RTS880 & FR4 & polymide & FR4 \\
**Permitivity (\(\epsilon\),)** & 22 & 22 & 44 & 3.5 & 4.4 \\
**Volume (mm\({}^{3}\))** & \(38\times 35\times 1.87\) & \(100\times 80\times 3.175\) & \(58\times 58\times 1\) & \(30.5\times 4.6\times 0.05\) & \(34\times 20\times 1.6\) \\
**Feeding** & QWTATS & Corporate MS & 4-port MS & Offest MS & MS \\ \(\epsilon_{0}\) (GHz) & \(5.5/8.5/12.8\) & \(14.5/16.5\) & \(3.5/5.8/13.13\) & \(0.915\) & \(7.5/8.5/12.8\) \\
**BWR** & \(8.33\):1 & \(1.39\):1 & \(13.79\):1 & \(10.1\) & \(9.09\):1 \\
**Gain (dBi)** & 6.4 & 10 & 13.5 & \(-21.5\) & 7.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of proposed design.  
Figure 7: Ant4 patch and ground surface current distribution: (**a**) 24 GHz, (**b**), 26 GHz, and (**c**) 28 GHz.
###### Abstract  
This paper investigates the performance of deep neural network (DNN)-based signal detection in multiple input, multiple output (MIMO), communication systems. MIMO technology plays a critical role in achieving high data rates and improved capacity in modern wireless communication standards like 5G. However, signal detection in MIMO systems presents significant challenges due to channel complexities. This study conducts a comparative analysis of signal detection techniques within both the single input, single output (SISO), and MIMO frameworks. The analysis focuses on the entire transmission chain, encompassing transmitters, channels, and receivers. The effectiveness of three traditional methods--maximum likelihood detection (MLD), minimum mean square error (MMSE), and zero-forcing (ZF)--is meticulously evaluated alongside a novel DNN-based approach. The proposed study presents a novel DNN-based signal detection model. While this model demonstrates superior computational efficiency and symbol error rate (SER) performance compared to more conventional techniques like MLD, MMSE, and ZF in the context of a SISO system, MIMO systems face some challenges in outperforming the conventional techniques specifically in terms of computation times. This complexity of MIMO systems presents challenges that the current DNN design has yet to fully address, indicating the need for further developments in wireless communication technology. The observed performance difference between SISO and MIMO systems underscores the need for further research on the adaptability and limitations of DNN architectures in MIMO contexts. These findings pave the way for future explorations of advanced neural network architectures and algorithms specifically designed for MIMO signal-processing tasks. By overcoming the performance gap observed in this work, such advancements hold significant promise for enhancing the effectiveness of DNN-based signal detection in MIMO communication systems.  
5G; DNN; signal detection; MIMO; wireless communication; machine learning; Artificial Intelligence +
Footnote †: journal: _telecom_  
_ Article_
# Evaluation of Bol Corrosion Degree Based on Non-Destructive Testing and Neural Network
## 4 Multi-Scale Convolutional Neural Networks for Anchor Bolt Corrosion Recognition
### Multi-Scale Convolutional Neural Network Model Design
#### 4.2.2 MS-CNN Network Architecture  
Each pathway in the CNN network has a similar structure. It consists of four convolutional layers, four pooling layers, and two fully connected layers, as shown in Figure 9. To reduce overfitting during training, a dropout layer with a dropout rate of 50% is introduced between the remaining two fully connected layers.
# Influence of the Microwave Heating Time on the Self-Healing Properties of Asphal Mixtures
## 3 Results and Discussion
### Crack-Size of Asphalt Samples Healed by Microwave Radiation  
Figure 10a shows the frequency histogram for the average crack size measured using an optical microscope of all the semi-circular samples after the first three-point bending test. It can be seen that the average size of the open cracks before healing is 0.431 mm, and that the values ranged from 0.141 mm and 0.781 mm. Additionally, with the aim of knowing if the amount of fibres added to asphalt mixtures have an influence on the crack size, a probability analysis has been developed. In this way, Figure 10b presents the Normal probability-probability plot for all the crack size data obtained from the samples regardless their amount of fibres added to the mixtures. It can be seen that the relationship between the Normal probability and the data percentiles align in a straight line with a 1:1 slope, which means that it can be concluded that the amount of fibres added to the asphalt mixture has not a significant influence on the size of the cracks produced in the semi-circular samples by the three-point bending test.  
Moreover, Figure 11a shows the box-plot graph of the crack size measured in all the samples before (shown as 0 s) and after healing by microwave heating at different times (10 s, 20 s, 30 s and 40 s), regardless the amount of fibres added to the mixtures. Observing this Figure, it can be concluded that the longer is the heating time, the smaller are the crack sizes measured on the samples. In addition, with shorter heating times (10 s and 20 s), the measured cracks presented more dispersed values, that can be related with the dispersion of the healing level results, see Figure 6a,b. In this way, crack sizes measured on the healed samples ranged from 0.035 mm to 0.38 mm, and from 0.032 mm to 0.206 mm, in the case of samples heated during 10 s and 20 s, respectively. While samples heated during 30 s and 40 s presented crack apertures ranged from 0.018 mm to 0.072 mm, and from 0.014 mm to 0.058 mm, respectively.  
Figure 9: Healing levels of asphalt mixtures with 4% of fibres, depending on the heating time (includes results of healing level at 60 s, 80 s and 100 s from the previous research of [PERSON] and [PERSON] [17]).  
Furthermore, a crack has been considered healed when its aperture after healing is half of the minimum aperture measured before healing, which is under 0.07 mm. In view of that, it can be concluded that only the cracks of the samples heated during 30 s and 40 s were healed, which reaffirms the results obtained from the measurements of the self-healing levels of asphalt mixtures, see Figure 6c,d. In contrast, although samples heated during 10 s and 20 s can heal some of their cracks, they are more likely to obtain wide crack sizes, not being able to heal most of the open cracks and consequently losing a high percentage of the initial mechanical resistance, see Figure 11b.
# Terahertz Single-Pixel Imaging Improved by Using Silicon Wafer with SiO\({}_{2}\) Passivation
###### Abstract  
We demonstrate terahertz single-pixel imaging is improved by using a photomodulator based on silicon passivated with SiO\({}_{2}\). By exploring various SiO\({}_{2}\) thicknesses, we show that the modulation factor of the as-fabricated terahertz photomodulator can reach 0.9, three times that based on bare silicon. This improvement originates from chemical passivation, as well as anti-reflection. Single-pixel imaging experiments based on the compressed sensing method show that reconstructed images adopting the new photomodulator have better quality than the conventional terahertz modulator based on bare silicon. Since the passivation process is routine and low cost, we expect this work will reduce the cost of terahertz photomodulator and single-pixel THz imaging, and advance their applications.  
terahertz single-pixel imaging; passivation; photomodulation; compressed sensing +
Footnote †: journal: Applied Physics Letters
## 5 Results and Discussions
### Power Consumption  
Power input is a major cost in industrial-scale stirred tanks. In the present work, the power consumption of different impeller types was calculated by the torque monitor applied to the impeller walls. The power input was mainly used to overcome the pressure gradient. A reduction of the pressure difference drag force and projected area on the novel impellers were the dominant factors explaining the lower power consumption. Furthermore, many small-scale vortices were formed around the apertures and gaps of the novel impeller. The pressure in the center of these vortices was relatively low, which gives some extra driving force to the impeller and can also reduce the energy consumption to a certain extent. The variations of the power consumption of different impellers at the same speed re tabulated in Table 4, which shows that the novel impellers can reduce power consumption to varying degrees. The sequence of decreasing power consumption at the same impeller speed is: PBT > PBPT > BCBT > BCPBT. Of note was the sharp power consumption drop of 17% when the BCPBT was employed instead of the PBT.  
Figure 11: Contour plots of the solid volume fraction on horizontal planes at different vessel heights (i.e., \(z=0.1H\), \(0.4H\), \(0.7H\)). (**a**) PBT, (**b**) PBPT, (**c**) BCBT, (**d**) BCPBT.  
Figure 12: Effect of impeller type on local axial solid concentration profile.
## 5 Deployment and Integrability
### Server Integration with PACS-RIS Service  
Local integration in a server connected within the hospital/clinic network allows seamless PII/PHI management (as the data does not leave the hospital network) while requiring minimum hardware upgrades (only the GPU requirements according to the number of acquired chest X-rays).  
The model would be exposed as a stateless micro-service that accepts studies via standard DICOM protocols (C-STORE), processes them, and returns the result, either as a DICOM Structured Report to the referring DICOM node, or as an HL7-FHIR _DiagnosticReport_ resource. This design allows any PACS to dispatch studies and receive triage scores without modifying the existing radiological or clinical workflow.  
This approach, along with containerization, has the benefit of allowing multiple parallel models for different clinical use in the same server (e.g., model detection, segmentation, x-ray quality assurance, other pathologies, etc.).  
The disadvantage of this implementation is the lack of automatic prioritization for the high-probability pneumothorax studies, as their score would be sent to PACS directly.