esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach
## 3 Machine Learning HRTF Restoration
### Model Enhancement
#### 3.3.6 Training With More Data  
To shorten the training time to compare across different methods and considering the limited size of RAM, the models discussed above were trained with 50,000 randomly sampled HRTFs from different angles of the training and validation HRTF sets. However, the best way to reduce over-fitting is to increase the size of the training set. To investigate what the model capable of with more data, a baseline model was trained with 633,000 HRTF measurements. Training this amount of data can take a lot of time per epoch. To speed up the process the batch size for training increased to 32, whilst the validation and test sets remained the same at eight for better comparison.  
Figure 10: Right channel L1 loss during training (blue: training loss, orange: validation loss, green: hold out test data Subject 20 from SADIE II, red: Bernschutz KU100), which shows that the lowest point in the curve is at around 110 epochs.  
Figure 9: Left channel L1 loss during training (blue: training loss, orange: validation loss, green: hold out test data Subject 20 from SADIE II, red: Bernschutz KU100), which shows that the lowest point in the curve is at around 110 epochs.  
Two models, the baseline model (Table 7 Model G) and the model with weight decay and dropout were trained with extra data (Table 7 Model H). The baseline model trained with extra data showed major improvement with the Bernschutz dataset, alongside the training and validation sets. However, there was also a noticeable trade-off with the SADIE II Subject 20 test data. It is believed that the improvement in the Bernschutz dataset is the result of the model having more examples of the KU100 HRTFs with different measurements at different angles, so it can generalise the measurement method better. The trade-off in the SADIE II Subject 20 test data may have been caused by the increased batch size, which can induce worse performance [57; 58]. Nevertheless, as the extra data were within the same distribution, it is quite unlikely they could have provided any noticeable performance improvement with unforeseen HRTF measurement subjects.
# A Novel Scheme for MIMO-SAR Systems Using Rotational Orbital Angular Momentum
## 2 An OAM-Based MIMO-SAR System Model  
The geometry of OAM-based MIMO-SAR system under a platform coordinate is depicted in Figure 1. The system, which is working in stripmap mode, moves in the X-direction with velocity \(v\). Assuming that there is an ideal point target \(P(x,y,z)\) in the detection area, for simplicity, a \(2\times 2\) MIMO-SAR system with two uniform circular array (UCA) antennas mounted in the along-track direction is discussed in this paper. The UCA antennas, as shown in the lower right corner of Figure 1, is used to transmit vortex EM waves of different OAM modes [19]. In the UCA antenna, the \(N\) array elements are located equidistantly around the circle, and each element is fed by a different phase, i.e., \(\varphi_{n}=2\pi ln/N\) (\(n=1,2\ldots N\)), where \(l\) is the OAM mode number, and \(n\) is the element number. A single antenna (the red dot) set at the array center is used to receive the echoes. Figure 2 presents the distribution of the azimuth phase centers of the OAM-based MIMO-SAR system. The vertical axis denotes different azimuth times, and the horizontal axis denotes different equivalent phase centers. At every azimuth time, each receiving antenna can receive echoes from both Tx1 and Tx2. The red dot denotes the equivalent phase center of Tx1/Rx1 or Tx2/Rx1, while the blue triangle denotes the equivalent phase center of Tx1/Rx2 or Tx2/Rx2. There exist three equivalent observation channels at every azimuth time. In the case of SAR imaging, a wider image swath width can be obtained, due to a reduction of the pulse repetition frequency (PRF) by a factor of three compared to a single-channel system. Besides, the additional phase centers yield extra and longer baselines for the ground moving target indication (GMTI) and along-track interferometry (ATI) applications [2]. Moreover, in contrast to plane EM waves, vortex EM waves possess a wider beam width in both range and azimuth directions. Consequently, the OAM-based MIMO-SAR system can simultaneously realize the high azimuth resolution and the wide swath width with better performance. Assume that the OAM mode is pure, and that each element of the UCA antenna transmits the Linear Frequency Modulation (LFM) signal. The center of the \(2\times 2\) array is located at the origin when the azimuth time \(t_{m}=0\), and the target azimuth position is \(x-(p-1.5)L_{a}\) with respect to the \(p\)th (\(p=1,2\)) UCA antenna. According to Ref. [12], the signal transmitted from the \(p\)th Tx antenna can be expressed as:  
Figure 1: Geometry of the orbital angular momentum (OAM)-based multi-input multi-output (MIMO)-SAR system. Figure 2: Distribution of the equivalent phase centers in the OAM-based MIMO-SAR system.
# A 2-V 1.4-dB NF GaAs MMIC LNA for K-Band Applications
## 3 Proposed Circuit
\[Z_{S1}=\left(Z_{in}\right)^{*}=50\cdot\left(1.043+j\cdot 1.399\right)\left( \Omega\right) \tag{7}\]  
\[\Gamma_{L}=\left(S_{22}+\frac{S_{12}S_{21}\Gamma_{S}}{1-S_{11}\Gamma_{S}} \right)^{*} \tag{8}\]  
\[Z_{L1}=\left(Z_{out}\right)^{*}=50\cdot\left(1.366+j\cdot 1.261\right)\left( \Omega\right) \tag{9}\]  
\[Z_{S2,3A}=50\cdot\left(0.329+j\cdot 0.709\right)\left(\Omega\right) \tag{10}\]  
\[Z_{L2,3A}=50\cdot\left(0.537+j\cdot 0.955\right)\left(\Omega\right) \tag{11}\]  
The input matching network can be implemented by a very low-inductance, high-Q inductor since it is critical that it presents a very high Q to avoid a significant degradation of the NF [14; 15]. However, in practice, the inductance required is so small that it can be easily implemented with a series TL and a shunt stub. The same applies to the inductor between stages 1 and 2, which has been replaced with a series TL and a shunt stub. The matching networks between stages 2 and 3 and 3 and 4 were implemented using two inductors (520 pH and 510 pH, Q \(\approx\) 15) and two open stubs (O1, O2) in series. The layout of these inductors, the current density distribution and the inductance and Q values obtained from the EM simulations are shown in Figure 5. The open stubs O1 and O2 are sized 80 \(\times\) 280 \(\upmu\)m\({}^{2}\) and 80 \(\times\) 270 \(\upmu\)m\({}^{2}\), respectively, and they provide an extra capacitance required for proper matching. Finally, a series TL, a shunt stub, and a DC Block capacitor of 0.58 pF form the output matching network. The layout of the circuit is depicted in Figure 4, with a total size of 3300 \(\times\) 1800 \(\upmu\)m\({}^{2}\) including pads.
## 2 Methods
### Study Risk of Bias Assessment  
The risk of bias was judged by two evaluators using the National Heart, Lung and Blood Institute (NHLBI, 2019, [[https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools](https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools)]([https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools](https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools)) (accessed on 3 March 2023)) study quality assessment tools and crucial appraisal recommendations (15). The judgment tool contained ratings of 'poor', 'fair', or 'good'. In the case of a disagreement, a third independent arbitrator would have been appointed to decide it. The tool has been used previously (15), and its development methodology is rigorous. Additionally, it accounts for various study designs. The criteria they employed for rating were as follows: A study that produces valid results with minimal bias is considered 'good'. A study that may be susceptible to bias, but not enough to invalidate findings, is considered 'fair'. A 'poor' study indicates a significant risk of bias. Refer to Table 1 for complete NHLBI quality assessment results. In the event of a disagreement, a third independent arbitrator would have been appointed to adjudicate the dispute.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline  & **[PERSON] et al.**, **2019 [16]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON] et al.**, **2016** & **[PERSON] et al.**, **1998 [21]** \\  & **2019 [16]** & **[**17**]** & **[**18**]** & **[**19**]** & **[**13**]** & **[PERSON] et al.**, **2010 [20]** & **[PERSON] et al.**, **1998 [21]** \\ \hline Q1: Was the research question/objective  clearly stated in this paper? & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline Q2: Was the study population clearly  specified and defined? & Yes & Yes & Yes & Yes & Yes & No & Yes \\ \hline Q3: Did at least 50\% of eligible persons  participate? & NR & NR & NR & NR & NR & NR \\ \hline Q4: Were all subjects from the same  population and recruited uniformly (including the same period)? Were inclusion  and exclusion criteria applied consistently to all participants? & Yes & NR & Yes & NR & Yes & NR \\ \hline Q5: Was information provided on sample  size, power, variance, and effect estimates? & No & No & No & No & No & No \\ \hline \end{tabular}
\end{table}
Table 1: Quality Analysis of Case-Control Studies.
## 4 Conclusions  
A new sample preparation method was developed that used C18 SPE (1000 mg) for the pre-concentration of the target analytes from surface and wastewater samples. Target analytes more prone to stability issues included bisphenol A, 4-octylphenol and 4-nonylphenol. Strong retention on C18 SPE was also shown for 4-octylphenol and 4-nonylphenol. To reduce the elution volume required to elute the target analytes from the SPE sorbent and to improve their stability over the time required for analyses, the elution solvent was selected to be acetonitrile with no chemical additives. The presence of additives such as formic acid or ammonia in the sample preparation or mobile phase used for the separation greatly reduced the MS sensitivity. For all target analytes (diethylstilbestrol, 17\(\beta\)-estradiol, estriol, estrone, 17\(\alpha\)-ethinylestradiol, prednisone, and prednisolone) negative ion mode electrospray ionization was used for detection in selected reaction monitoring mode with one transition used for quantitation and an addition transition used for confirmation. Even though the matrix effects were often determined to be moderate to severe for all sample matrices, a linear standard addition calibration curve could be obtained with similar LOQs to solvent-based calibration. The LC-ESI-MS/MS method was demonstrated to be reliable for the detection and quantitation of the target analytes.  
The following are available online. Figure S1: Quantitative and confirmation SRMs chromatographs of EDCs and internal and surrogate standards, Figure S2: The selected reaction monitoring chromatograms for BPA and internal standard BPA-d\({}_{16}\) for raw and treated wastewater in comparison with similar levels of standard, Table S1: Linear regression equation of the best-fit curve and the R\({}^{2}\) values for all the analytes from WWT9 (for quantitative SRMs).  
Conceptualization, R.R.-F. and G.A.; methodology, R.R.-F. and G.A.; validation, G.A.; formal analysis, G.A.; investigation, G.A.; resources, R.R.-F. and O.T.; data curation, G.A. and R.R.-F.; writing--original draft preparation, R.R.-F. and G.A.; writing--review and editing, R.R.-F. and G.A.; visualization, R.R.-F.; supervision, R.R.F.; project administration, R.R.-F. and O.T.; funding acquisition, O.T. All authors have read and agreed to the published version of the manuscript.  
This research was supported by funding from Water Security Agency, Government of Saskatchewan, grant number WSA-2014A-0011.  
The authors wish to acknowledge the support in the collection of water samples for this study and technical support for the sample collection from [PERSON] and [PERSON] of WSA, and staff at the Wastewater Treatment plants in Saskatchewan.  
The authors declare no conflict of interest. The funders had no role in the analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Materials and Methods
### Data and Methodology
#### 2.2.3 Mangrove Cover Change and Fragmentation  
After the accuracy assessment of the 2000 and 2019 classification results, the classified mangrove areas were extracted from the classified map. Because the classification maps are in raster form, we converted the maps into vector polygons using the Raster to Polygon function in the ArcGIS software. Change detection was performed based on the polygons using ArcGIS software to detect the unchanged, gained, and lost mangrove areas in IM  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Type/Value** \\ \hline Kernel type & Radial Basis Function (RBF) \\ Gamma in kernel function & 1.00 \\ Penalty & 100 \\ Pyramid levels & 0 \\ Region of Interests (ROI) & 140 (20 polygons for each class) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for Support Vector Machine (SVM) classification.  
between 2000 and 2019 by superimposing the mangrove cover from 2000 onto the results of 2019.  
The Patch Analyst extension in ArcGIS 10.5 was used as the tool for fragmentation analysis. Patch Analyst is based on the FRAGSTATS [46], which can be used for analysis and to provide attributes of the fragmented patches. Several indices were selected in this study to represent the fragmentation of the mangrove area in IM, including Class Area (CA), Total Landscape Area (TLA), Number of Patches (NP), Mean Patch Size (MPS), Total Edge (TE), Edge Density (ED), Mean Shape Index (MSI), Average Weighted MSI (AWMSI), Mean Perimeter Area Ratio (MPAR), and Mean Patch Fractal Dimension (MPFD). The group and details of each index is listed in Table 2. Because mangrove is the focus of the study, the fragmentation analysis was performed for mangrove areas only.
###### Abstract  
Laser-driven radiation pressure acceleration (RPA) is one of the most promising candidates to achieve quasi-monoenergetic ion beams. In particular, many petawatt systems are under construction or in the planning phase. Here, a stable radiation pressure acceleration (SRPA) scheme is investigated, in which a circularly-polarized (CP) laser pulse illuminates a \(CH_{2}\) thin foil followed by a large-scale near-critical-density (NCD) plasma. In the laser-foil interaction, a longitudinal charge-separated electric field is excited to accelerate ions together with the heating of electrons. The heating can be alleviated by the continuous replenishment of cold electrons of the NCD plasma as the laser pulse and the pre-accelerated ions enter into the NCD plasma. With the relativistically transparent propagation of the pulse in the NCD plasma, the accelerating field with large amplitude is persistent, and its propagating speed becomes relatively low, which further accelerates the pre-accelerated ions. Our particle-in-cell (PIC) simulation shows that the SRPA scheme works efficiently with the laser intensity ranging from \(6.85\times 10^{21}\) W cm\({}^{-2}\) to \(4.38\times 10^{23}\) W cm\({}^{-2}\), e.g., a well-collimated quasi-monoenergetic proton beam with peak energy \(\sim\)1.2 GeV can be generated by a \(2.74\times 10^{22}\) W cm\({}^{-2}\) pulse, and the energy conversion efficiency from the laser pulse to the proton beam is about 16%. The QED effects have slight influence on this SRPA scheme.  
laser driven ion acceleration; radiation pressure acceleration; near-critical-density plasma +
Footnote †: journal: Applied Sciences
# Low-Complexity Synchronization Scheme with Low-Resolution ADCs
## 4 System Analysis and Simulation Results
### Quantization Effects of Timing Synchronization  
In Figure 5, we plot the autocorrelation property for different quantization resolutions. We can see from Figure 5 that each curve has a maximum value at the same location. This means that the autocorrelation of the PN sequence is not affected by low-resolution quantification. Since the starting point of the OFDM symbol is determined based on the location at which the maximum autocorrelation property occurs, we can conclude that the performance of timing synchronization is not degraded by low-resolution quantization. As shown in Figure 5, with the increase of quantization resolution, the peak of each curve decreases gradually. Among these curves, the peaks under low-resolution quantization are all above those at full resolution. The reason for this phenomenon is because the small amplitude signal is amplified by the low-resolution ADCs. Furthermore, the lower the resolution is, the more serious the signal amplitude is amplified; thus, the peak under one-bit is the highest compared to other cases. Combining the above analysis, we can see that the timing synchronization based on one-bit is achievable. Therefore, the simulation results and analysis below are obtained by the frequency offset estimation module after the timing synchronization with one-bit ADCs is completed.
## 4 Discussion
### Model Adaptability  
The optical characteristics of inland water bodies are complex and vary widely both regionally and seasonally, leading to strong regional and seasonal limitations in the remote sensing retrieval model for water quality parameters. This limits the advantages of using satellite remote sensing for long-term and large-scale water environmental monitoring. The FUI model was derived from optical remote sensing data based on the principle of optical color theory; thus, it was not affected by water body regions and seasons.  
The lakes and reservoirs in Guangzhou and Shenzhen were distributed widely throughout the respective regions. The optical properties of inland water are relatively complex. The uncertainty of traditional empirical and semi-empirical methods in different regions and water is large; therefore, conducting large-scale and long-term analysis of spatio-temporal variations in water quality is difficult [13]. The FUI is an optical parameter, which is invariant with the optical characteristics of inland water, and does not have regional or seasonal limitations. Although there is a relationship between the FUI and two parameters (i.e., water clarity and trophic status), this relationship is not clear. However, we can approximate the overall quality of water using the FUI. In addition, the FUI has a strong anti-interference ability regarding aerosol types and observational conditions [18]. Therefore, the FUI was selected as the index for evaluating water quality in lakes and reservoirs larger than 0.5 km\({}^{2}\) in Guangzhou and Shenzhen, and their spatio-temporal variations were analyzed.
## 4 Experimental Results and Analysis
### Comparison of the Classification Effects of Different Parameters  
In the BERT-GCN model, the hyperparameter setting is essential. In the process of constructing a strongly linked relation graph, the threshold of _TF-IDF_ affects graph construction. An appropriate threshold can filter out effective co-words, which facilitates the GCN model to extract effective co-word features. Setting different hyperparameters in the model will produce different classification results, such as the number of neurons in the hidden layer. The number of neurons in the hidden layer affects the performance of the text classification model, and fewer neurons may lead to underfitting and poor performance of the model. A moderate quantity usually strikes a balance between capturing features and avoiding overfitting, providing better classification results. Although an excessive number of neurons can improve the expression ability of the model, it may lead to overfitting of the training data, resulting in good performance on the training set but poor performance on the test set and decreased generalization ability. The model may also fall into local optima, which can affect the classification results. Therefore, selecting the appropriate _TF-IDF_ threshold and number of hidden units requires experimentation and tuning to optimize the classification performance of the model. To further improve the accuracy of the BERT-GCN model, we selected a series of _TF-IDF_ thresholds and hidden unit numbers for experimental analysis on the dataset in Zhuji City. Tables 6 and 7 show the classification accuracy of the model under different _TF-IDF_ thresholds and hidden unit numbers, respectively.  
According to Table 6, the classification results of the model are best when the _TF-IDF_ threshold is 0.03. When the threshold is 0.01, a total of 275 words are selected as co-words. Therefore, the relation graph composed of co-words is too dense, which can easily lead to associations between enterprises that are not in the same category. When the _TF-IDF_ threshold is 0.05, only 32 words are selected as co-words, and this time, the co-word relation graph is too sparse to form effective connections and associate enterprises of the same type.  
Figure 4: Visualization of minor industrial category features. (**a**) Xiuzhou City. (**b**) Zhuji City.  
We selected 16, 32, 64, 128, 256, and 512 as the hidden layer neuron formats for the GCN experiments, and the classification accuracy is shown in Table 7. When there are 128 neurons in the hidden layer, the model's enterprise industry recognition results are optimal. Therefore, we selected 128 neurons for model training.
## 3 Results  
### Construction of Peptide-Linked Magnetic Bead  
To construct a reusable adsorbing system, the magnetic-core bead enabling magnetic separation from water was used as a basic frame to bind the peptide on the bead surface. Since the peptide has an amine and a carboxylic group on both ends, the lead-binding peptide can be linked via EDC/NHS chemistry to the bead functionalized with NH\({}_{2}\)/COOH on its surface. Meanwhile, depending on the surface characteristic of the bead, the lead ion can be adsorbed to the bare surface of the bead that is not occupied with the peptide. To examine the lead removal performance that is mainly caused by the lead-binding peptide, and not by the raw bead, various kinds of commercially available magnetic beads were tested to select the bead with the lowest affinity to lead. Among the four types of beads shown in Figure 1, the AccuBead(tm) COOH magnetic bead and the Cytodiagnostic Zepto(tm) Mag bead adsorbed less lead than the two other types of bead. Since the AccBead(tm) COOH magnetic bead was cheaper to produce and separated better by magnet, it was selected as the frame with which to bind peptide.  
The amine group of peptide was linked to the carboxylic group on the beads, where the previously screened lead-binding peptide (NH\({}_{2}\)-TNTLSNN-COOH, [17]) and the reverse sequence of the peptide (NH\({}_{2}\)-NNSLINT-COOH) were used to examine the effect of the spatial position of the peptide sequence on the lead binding performance. After each peptide was linked to the magnetic beads, the remaining peptide concentration of each supernatant was measured to determine the amount of peptide bound to bead. Slightly more peptide (0.89 \(\pm\) 0.05 mg peptide/g bead for NH\({}_{2}\)-TNTLSNN-COOH, 0.79 \(\pm\) 0.05 mg peptide/g bead for NH\({}_{2}\)-NNSLINT-COOH) was bound to the bead in the case of the originally screened peptide sequence (NH\({}_{2}\)-TNTLSNN-COOH), where the NH\({}_{2}\) group of threonine (T) reacted with the COOH group of the bead. The amine groups of threonine (T) and asparagine (N) in each peptide involved the binding reaction with the bead. Each amino acid has a similarly polar uncharged side group, but asparagine has a longer side chain than threonine. Thus, the higher peptide binding efficiency may be attributable to the smaller steric hindrance of threonine during the EDC/NHS reaction. The amount of adsorbed lead was 34.1 \(\pm\) 1.3 mg lead/mg bead for NH\({}_{2}\)-TNTLSNN-COOH and 30.9 \(\pm\) 1.6 mg lead/mg bead for NH\({}_{2}\)-NNSLINT-COOH. The lead binding efficiency in each peptide sequence was similar in terms of the amount of adsorbed lead per peptide mass on the bead. Thus, the adsorption capacity of the peptide bead would increase if more peptide were further attached on the bead surface.