esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
A broadband circularly polarized folded reflectarray antenna based on a transmissive linear-to-circular polarization converter is presented in this paper. The elements that make up the architecture of the CPFRA are designed and analyzed. The metasurface-based element on the primary reflector provides the broadband reflection and linear phase compensation. The transmissive linear-to-circular polarization converter on the sub-reflector provides the wideband polarization conversion. Compared with the conventional circularly polarized RA, the proposed FRA reduces the distance between the source and the primary reflector to half and achieves a 3 dB gain bandwidth of 27.4% (22.5-31.5 GHz) and a 3 dB AR bandwidth of 23%. The broadband low-profile circularly polarized reflectarray antenna proposed in this work has a low cost, low profile, and high bandwidth and can be applied well in millimeter-wave communication systems, especially in satellite communication systems.  
Conceptualization, Y.C.; methodology, Z.W.; software, M.D.; validation, Z.W. and Q.W.; writing--original draft preparation, Y.C.; writing--review and editing, M.Z.; supervision, Y.C.; project administration, Y.C.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under Grant 62301286, in part by the Natural Science Foundation of Jiangsu Province (Grants No BK2020602), and in part by the General Project of Basic Science Research in Higher Education Institutions in Jiangsu Province under Grant 23 KJB510023.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 6 Experiments
### Position Control  
The target position and initial position of multirotor was set to \([0,0]\) and \([-1,-1]\) on \(x\) and \(y\) axes. In the experiment, we switched to position control mode after the multirotor took off from the ground. The experimental result is shown in Figure 9. In the figure, the top graph shows position of the multirotor in \(x\) and \(y\) axis and the bottom graph shows roll and pitch angle of multirotor during the flight. The average position error was 0.0358 m on \(x\)-axis and 0.0188 m on \(y\)-axis, and it was in the region \(\pm\)0.05 m. During the flight, the attitude of multirotor (Figure 9, bottom) was in the region \(\pm\) 2 degree, therefore the body frame can be considered to be kept horizontal.  
Moreover, to verify how fine of position control of the ATD with multirotor system is possible, another experiment was performed. According to experiment result of position control (Figure 9), it is possible to perform the positioning every 0.2 m. Thus, we set the initial position of the multirotor to \([0,0]\) and increased target position by 0.2 m in every 5-s interval. The experimental result is shown in Figure 10. In the figure, the top graph shows position of the multirotor in \(x\) and \(y\) axes and the bottom graph shows roll and pitch angle of multirotor during the flight. The target position was increased from the time Second 5 in the figure. The average position error was 0.037 m on \(x\)-axis and 0.0194 m on \(y\)-axis, and it was within the region of \(\pm\)0.05 m for each target position. During the flight, attitude of the multirotor was also kept stable which was in the region \(\pm\)2 degree. Through the experiment, we verified that it is possible to perform positioning at 0.2 m, and this is the minimum possible distance.  
Figure 9: Experimental result of position control.
## 7 Towards a Simplified Optical Transceiver
### Relative Advantages of SCOH Schemes  
With reference to its counterparts such as the IM-DD and COH detections in the short- and medium-reach applications, the relative advantages of SCOH schemes are presented in the following. Compared to the COH detection, the relative benefits of the SCOH are as follows [12]:  
* There is a significant simplification in the optical hardware, mainly for the optical frontend of the receiver;
* The dual-polarized receiver is highly important in the COH system to enable 2 \(\times\) 2 MIMO polarization recovery. On the other hand, single-polarization is one of the suitable options in the SCOH for the polarization alignment for the signal and the carrier at the transmitter;
* The required DSP by the receiver is simplified (e.g., no need for the execution of sophisticated carrier recovery);
* The SCOH can be implemented uncooled, while this is challenging in the COH system. The cooling circuitry makes the COH system power-consuming and bulky;
* As an ultimate consequence of the previous itemized advantages, the SCOH-based scheme provides a cost-effective solution compared with the full COH scheme for short- and medium-reach applications.  
Figure 17: Balanced receiver-based direct detection structures: (**a**) PDM-DD; (**b**) SCI-DD; (**c**) SV-DD. PBS: polarization beam splitter; BPD: balanced photodetector; DD: delayed data stream; OD: original data stream (adapted from [9; 12; 24; 38]).  
In addition, compared to the IM-DD, the following benefits can be offered by the SCOH [9,12]:  
* The received signal in the SCOH is a linear replica of the transmitted one based on the linear channel that facilitates optical field modulation and detection;
* The copropagated carrier facilitates phase diversity realization and helps increase the receiver sensitivity;
* Since the carrier and the signal are generated through the same laser source, there is considerable assurance that the phases between them are coherent with each other at the transmitter. Consequently, the system's vulnerability to the laser phase noise is inherently mitigated in the SCOH system;
* A single-carrier IM-DD system is susceptible to ISI and has a transmission distance limitation. These issues can be addressed with the implementation of the SCOH system;
* The SCOH offers a 2D direct detection receiver that helps expand the optical spectrum efficiency. Furthermore, the RF bandwidth utilization ratio at the receiver can reach up to 100% with the DSB modulation;
* The supported optical field modulation in the SCOH system facilitates the DWDM and superchannel implementation.  
In general, an SCOH system attempts to offer a good tradeoff between the COH and IM-DD schemes, as presented in Table 8. Moreover, as detailed in Section 8, this is contingent on the employed detection dimensions and other adopted techniques.
# Automated Pneumothorax Segmentation with a Spatial Prior Contrast Adapter
## 3 Materials and Methods
### Evaluation Metrics  
To evaluate the performance of SPCA-enhanced nnUNet, we employed the Dice similarity coefficient (DSC), accuracy, precision, recall, and IoU. DSC measures the similarity between predicted and true regions, with higher values indicating better overlap [6; 32]. Accuracy calculates the overall correctness of predictions, though it may not be reliable for imbalanced datasets. Precision measures the proportion of correct positive predictions,while recall evaluates how well the model identifies all positive instances. IoU assesses the overlap between predicted and actual regions, with higher values indicating better segmentation performance. These metrics together provide a thorough evaluation of the model's performance.  
A confusion matrix is a table used to evaluate the performance of a classification model. It displays the counts of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) predictions. The evaluation matrix can be defined by using a confusion matrix.  
\[\text{Dice}=\frac{2\cdot TP}{2\cdot TP+FP+FN}, \tag{5}\]  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}, \tag{6}\]  
\[\text{Precision}=\frac{TP}{TP+FP}, \tag{7}\]  
\[\text{Recall}=\frac{TP}{TP+FN}, \tag{8}\]  
\[\text{IoU}=\frac{TP}{TP+FP+FN}. \tag{9}\]
## 2 Experiment Program
### Specimen Design
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Specimen** & \multirow{2}{*}{**L (mm)**} & \multirow{2}{*}{**L/D**} & \multirow{2}{*}{**\(t\) (mm)**} & \multirow{2}{*}{**\(a\)**} & \multirow{2}{*}{**\(f_{y}\) (MPa)**} & **Concrete** & \multirow{2}{*}{**\(f_{ck}\)**} & \multirow{2}{*}{**\(\xi\)**} \\
**Number** & & & & & & & & \\ \hline PT300-1 & 300 & 2.6 & 3 & 0.114 & 237 & PTC30 & 20.7 & 1.31 \\ BZ300-1 & 300 & 2.6 & 3 & 0.114 & 237 & BZC30 & 21.1 & 1.28 \\ PT300-2 & 300 & 2.6 & 3 & 0.114 & 336 & PTC40 & 27.3 & 1.40 \\ QZ300-1 & 300 & 2.6 & 3 & 0.114 & 336 & QZC40 & 28.5 & 1.34 \\ BZ300-2 & 300 & 2.6 & 5 & 0.202 & 231 & BZC30 & 21.1 & 2.21 \\ QZ300-2 & 300 & 2.6 & 5 & 0.202 & 231 & QZC30 & 22.4 & 2.08 \\ BZ300-3 & 300 & 2.6 & 5 & 0.202 & 332 & BZC40 & 27.9 & 2.40 \\ QZ300-3 & 300 & 2.6 & 5 & 0.202 & 332 & QZC40 & 28.5 & 2.35 \\ PT400-1 & 400 & 3.5 & 3 & 0.114 & 237 & PTC30 & 20.7 & 1.31 \\ BZ400-1 & 400 & 3.5 & 3 & 0.114 & 237 & BZC30 & 21.1 & 1.28 \\ PT400-2 & 400 & 3.5 & 3 & 0.114 & 336 & PTC40 & 27.3 & 1.40 \\ QZ400-1 & 400 & 3.5 & 3 & 0.114 & 336 & QZC40 & 28.5 & 1.34 \\ BZ400-2 & 400 & 3.5 & 5 & 0.202 & 231 & BZC30 & 21.1 & 2.21 \\ BZ400-3 & 400 & 3.5 & 5 & 0.202 & 332 & BZC40 & 27.9 & 2.40 \\ QZ400-3 & 400 & 3.5 & 5 & 0.202 & 332 & QZC40 & 28.5 & 2.35 \\ PT500-1 & 500 & 4.4 & 3 & 0.114 & 237 & PTC30 & 20.7 & 1.31 \\ BZ500-1 & 500 & 4.4 & 3 & 0.114 & 237 & BZC30 & 21.1 & 1.28 \\ PT500-2 & 500 & 4.4 & 3 & 0.114 & 336 & PTC40 & 27.3 & 1.40 \\ QZ500-1 & 500 & 4.4 & 3 & 0.114 & 336 & QZC40 & 28.5 & 1.34 \\ BZ500-2 & 500 & 4.4 & 5 & 0.202 & 231 & BZC30 & 21.1 & 2.21 \\ BZ500-2 & 500 & 4.4 & 5 & 0.202 & 231 & BZC30 & 22.4 & 2.08 \\ BZ500-3 & 500 & 4.4 & 5 & 0.202 & 332 & BZC40 & 27.9 & 2.40 \\ QT500-3 & 500 & 4.4 & 5 & 0.202 & 332 & QZC40 & 28.5 & 2.35 \\ PT600-1 & 600 & 5.3 & 3 & 0.114 & 237 & PTC30 & 20.7 & 1.31 \\ BZ600-1 & 600 & 5.3 & 3 & 0.114 & 237 & BZC30 & 21.1 & 1.28 \\ PT600-2 & 600 & 5.3 & 3 & 0.114 & 336 & PTC40 & 27.3 & 1.40 \\ QZ600-1 & 600 & 5.3 & 3 & 0.114 & 336 & QZC40 & 28.5 & 1.34 \\ BZ600-2 & 600 & 5.3 & 5 & 0.202 & 231 & BZC30 & 21.1 & 2.21 \\ QZ600-2 & 600 & 5.3 & 5 & 0.202 & 231 & QZC30 & 22.4 & 2.08 \\ BZ600-3 & 600 & 5.3 & 5 & 0.202 & 332 & BZC40 & 27.9 & 2.40 \\ QZ600-3 & 600 & 5.3 & 5 & 0.202 & 332 & BZC40 & 27.9 & 2.40 \\ QZ600-3 & 600 & 5.3 & 5 & 0.202 & 332 & QZC40 & 28.5 & 2.35 \\ \hline \hline \end{tabular} Note: PT in the sample number represents normal concrete, BZ represents half-high titanium slag concrete, and QZ represents full-high titanium slag concrete. \(L\) represents the length of the specimen; \(D\) represents the outer diameter of steel tube; \(t\) represents the thickness of steel tube wall; \(a\) represents the steel content, \(a=A_{s}/A_{c}\);\(f_{y}\) represents the yield strength of steel tube; the number of concrete is shown in Table 2;\(f_{ck}\) represents the standard value of axial compression strength of concrete, \(f_{ck}=0.67\)/\(\varepsilon_{ui}\); \(\xi\) represents the hoop factor [41], and \(\xi=\pi\)/\(\omega\)/\(\omega_{ck}\).  
\end{table} 
Table 1: Parameters of the specimens.
# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
## 7 Conclusions and Future Work  
This paper studies rainfall impacts on key signal metrics widely available in mobile terminals. The experiments are conducted in real-life scenarios. Both small-scale and large-scale variations in measurements during dry periods are analyzed by comparing them with variations in environmental factors using statistical methods. Our key findings include the identification of a daily pattern in signal strength measurements during dry days, and rainy days tend to show increased power at lower frequencies in signal measurement compared to dry days. These findings have been confirmed by data analysis across various locations and weather conditions. The results indicate novel interesting rainfall impacts on everyday mobile links, potentially facilitating precise rainfall sensing using by-product signals in our mobile devices.  
In this work, we mainly exploit the experimental results to reveal novel insights of rainfall's impacts on cellular signal metrics available in modern mobile terminals. Diverse experimental scenarios are considered to reflect the impacts of different propagation environments, though analytical modeling is not performed yet. Therefore, an interesting future work would be establishing the statistical modeling of various cellular signal metrics under different rainfall conditions as well as diverse channel models.  
Methodology, Y.X., K.W., and J.A.Z.; data collection: Y.X and J.A.Z.; writing--original draft preparation, Y.X. and K.W.; writing--review and editing, J.A.Z., Z.W., B.A.J., and Y.J.G.; funding acquisition, J.A.Z. and Y.J.G. All authors have read and agreed to the published version of the manuscript.  
This research is supported partially by the Australian Government through the Australian Research Council's Discovery Projects funding scheme (project DP210101411), and partially funded by the NSW State Emergency Service (SES). We would like to express our gratitude for their assistance, which made this work possible.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
## 4 Experimental Study
### Experimental Results and Analysis  
#### 4.2.1 Relationship Between Magnetic Induction Density and Load  
For clarity, we numbered the test conditions as shown in Table 3, where \"A, B, C, D\" represents the working conditions corresponding to maximum design loads of 100 kN, 120 kN, 140 kN, and 160 kN, respectively; \"1, 2, 3, 4\" indicate the 1 st, 2 nd, 3 rd, and 4 th loading/unloading cycles, respectively; \"I\" means loading; and \"II\" means unloading.  
Figure 7 presents an analysis of the force-magnetism relationship under condition A# during 10 loading/unloading cycles, which reveals zero-point drift during the first six cycles. Analysis suggests that this phenomenon is likely attributable to residual stress in the steel strand specimen used for testing. The residual stress within the steel strand can be minimized by subjecting it to multiple loading/unloading cycles under static load, thereby preventing zero-point drift. Figure 8 shows the force-magnetism relationship curves for  
Figure 6: Experimental platform.  
remains consistent, showing a negative correlation with the tensile force. This observed pattern aligns with the previous simulation results, further validating the accuracy of the theoretical monitoring model established through sensor measurements and indicating a clear functional relationship between \(B_{\mathrm{z}}\) and the applied load.  
The average \(B_{\mathrm{z}}\) values for each tensile cycle under different design loads were extracted, as shown in Table 4.  
Using the data from Table 4, the force-magnetism data for various operating conditions were analyzed through linear and quadratic function fitting. The results of these fittings are illustrated in Figure 9.  
Analyzing the data in the figure, it is evident that the quadratic function fitting demonstrates superior performance compared to linear fitting, with a goodness of fit exceeding 99% and approaching 1. The relative measurement error remains within 1%, confirming that the force-magnetism relationship follows a quadratic function.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Working**} & \multicolumn{5}{c}{\(B_{\mathrm{z}}\)**/mT**} \\ \cline{2-7}  & **0 kN** & **20 kN** & **40 kN** & **60 kN** & **80 kN** & **100 kN** \\ \hline A\# & 30.184 & 30.059 & 29.997 & 29.930 & 29.865 & 29.810 \\ \hline B\# & 30.186 & 30.064 & 30.002 & 29.938 & 29.880 & 29.838 \\ \hline C\# & 30.185 & 30.060 & 30.001 & 29.951 & 29.897 & 29.846 \\ \hline D\# & 30.183 & 30.050 & 29.992 & 29.922 & 29.860 & 29.794 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average magnetic induction density \(B_{\mathrm{z}}\) under cyclic tensile loading for each condition.  
Figure 9: Fitting curves of force-magnetism relationship under different design loads.
# Domain Adaptation Speech-to-Text for Low-Resource European Portuguese Using Deep Learning
## 4 Proposed System
### Data Pre-Processing  
The SpeechDat dataset, composed of \(\simeq\)186 h of audio recording collected in controlled environments, was divided into two \"versions\", defined in Definitions 1 and 2.  
**Definition 1** (SpeechDat with noise-describing tokens).: _The data pre-processing carried out in this iteration of the dataset excluded audio recordings labelled as GARBAGE or OTHER, with transcriptions being ***, empty, or containing \"or *._  
_The remaining audio recordings were re-encoded from A-LAW into WAV and re-sampled from 8 kHz to 16 kHz._  
**Definition 2** (SpeechDat without noise-describing tokens).: _In addition to Definition 1, this iteration of the dataset had the noise-describing tokens [sta], [spk], [fil], and [int] removed from the transcriptions._  
In Table 6 are shown the splits used for each dataset in the present work. The SpeechDat dataset was divided into 70% (107,158 instances) for training/transfer, 15% (22,960 instances) for validation, and 15% (22,961 instances) for testing. The MLS dataset was already split having 37,498 instances for training, 826 for validation, and 906 for testing.
## 2 Results and Discussion
### Electrochemical Corrosion Measurement
#### 2.3.3 Electrochemical Impedance Spectroscopy (EIS)  
EIS is an effective technique used to evaluate the anticorrosion performance of as-prepared coatings. The impedance spectrum was recorded on uncoated CRS and CRS coated with WPU\(-\)g\(-\)BTA\(-\)C, WPU\(-\)g\(-\)BTA\(-\)B, and WPU\(-\)g\(-\)BTA\(-\)C after 30 min of immersion in 3.5 wt% NaCl solution (Figure 4 and Table 2) at 298 K.  
It can be found from Figure 7 that for CRS, the Nyquist plot consisted of a flattened capacitive loop. The Nyquist plot consisted of a single semicircle over the immersion period, suggesting a complete capacitive and good barrier property. The radius of the semicircle increased with increasing chain length from WPU\(-\)g\(-\)BTA\(-\)A to WPU\(-\)g\(-\)BTA\(-\)C. The radius of the capacitive arc indicated the corrosion rate. The capacitive arc with larger radius had a slower corrosion rate, indicating better corrosive inhibition. The diameter of the Nyquist curve for WPU-g\(-\)BTA coatings increased with various chain extenders from A to C. The WPU\(-\)g\(-\)BTA\(-\)C coating had the largest radius, indicating excellent corrosion resistance.  
Figure 5: Cross-sectional SEM images of (**a**) WPU\(-\)g\(-\)BTA\(-\)A, (**b**) WPU\(-\)g\(-\)BTA\(-\)B, and (**c**) WPU\(-\)g\(-\)BTA\(-\)C in before corrosion (top) and after corrosion (bottom) in 3.5 wt% NaCl(aq) after 3 days of immersion.  
BODE plots are one of the most commonly used tools in corrosion science for monitoring local corrosion damages. Figure 8 is the BODE diagram of coated and un-coated  
Figure 6: SEM images (top view) of (**a**) WPU\(-\)g\(-\)BTA\(-\)A, (**b**) WPU\(-\)g\(-\)BTA\(-\)B and (**c**) WPU\(-\)g\(-\)BTA\(-\)C before corrosion (top) and after corrosion (bottom) in 3.5 wt% s after 3 days of immersion.  
Figure 7: Nyquist plots for raw CRS electrode and CRS electrode coated with WPU\(-\)g\(-\)BTA\(-\)A, WPU\(-\)g\(-\)BTA\(-\)B, WPU\(-\)g\(-\)BTA\(-\)C in 3.5 wt% NaCl(\({}_{\text{aq}}\)).  
CRS dip in 3.5 wt% NaCl for 30 min at 298 K. A small-amplitude signal was applied over a range of frequencies for EIS measurement. The impedance modulus (log Z) in the BODE plot is usually related to the coatings barrier performance; that is, a large impedance value indicates better corrosion protection. The /Z/ (Table 2) of uncoated CRS was 2.22 \(\Omega\), which was increased to 5.99 \(\Omega\) and 6.21 \(\Omega\) for WPU\(-\)g\(-\)BTA\(-\)A and WPU\(-\)g\(-\)BTA\(-\)B, respectively. WPU\(-\)g\(-\)BTA\(-\)C, which had the largest impedance modulus, exhibited the best corrosion protection. The trend in the BODE plot was similar to those observed in Tafel and Nyquist plots.
# A Multi-Head LSTM Architecture for Bankruptcy Prediction with Time Series Accounting Data
## 10 Final Analysis on the Test Set  
In light of the results achieved on the validation set, we selected the optimal single-input LSTM, the optimal multi-head LSTM, and the optimal Random Forest models. We defined the optimal model as that which met the following three conditions:  
* Lowest difference between training and validation loss to ensure that the highest AUC was not achieved as a consequence of overfitting.
* Lowest validation loss.
* Highest AUC on the validation set.  
We experimented with the best models on the previously unseen test set (companies between 2015 and 2018). We again compared the single-input LSTM, multi-head LSTM, and Random Forest classifiers. Figure 9 shows the model comparison in terms of AUC. In Table 6, we report detailed results for each model in terms of recall on the bankruptcy class, type I and II errors, and the micro and macro \(F_{1}\) scores. As expected from the previous results obtained for the AUC on the validation set, the best model was still multi-head LSTM, which achieved the best result on the test set. In addition, to gain a deeper insight into our model's performance on the test set, we leveraged the BAC (balanced accuracy) metric, which is the arithmetic mean of sensitivity and and specificity.  
Figure 8: Performance score distributions with respect to our multi–head LSTM model on the 500 runs over the validation set. Since none of the random variables was normally distributed, we decided to use the WSR test.  
Figure 9: AUC values on the test set (2015–2018) for Random Forest, single-input LSTM, and multi-head LSTM when varying the temporal window (WL).  
Since the model showed very high precision over the healthy class (Table 6), which was also the majority class in the validation and test sets, the slope differences were probably due to the higher number of correct predictions in the validation set during learning. However, it is possible to observe that no overfitting or underfitting phenomena affected our model, as is also shown by the good results on the unseen test set (Figure 9).  
Therefore, we can conclude that RNNs have an impact on bankruptcy prediction performance. However, the attention model induced by multi-head LSTM seemed to achieve better results for all the metrics (AUC and micro and macro \(F_{1}\) scores) with a temporal window equal to 4 years. On the other hand, considering the minimization of the false-negative rate, the models with WL = 5 achieved, in general, the lowest type II errors.