esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed EPP-Based Beam Scanning Approaches
### Proposed SLL/EPP-BSA  
To mitigate the SLL problem of the EPP-BSA, the SLL/EPP-based beam scanning approach, which is denoted as SLL/EPP-BSA, is introduced. In this approach, SLL reduction of the array pattern is firstly performed using our single convolution/genetic algorithm (SC/GA) technique introduced in [13] and then performance-optimized EPP in the azimuth plane. This combination between the SLL reduction and the EPP (SLL/EPP) results in a wide scanning range from the broadside to the end-fire direction with a relatively constant HPBW and minimal variations in the synthesized SLL. In addition, keeping the synthesized excitation coefficients constant without adding progressive phase shifters facilitates the manufacturing process and reduces the cost of the feeding network. The SC/GA SLL reduction technique is utilized as it provides a twofold decrease in the SLL. Consider the ULAA configuration shown in Figure 1 whose array factor \(AF(\theta)\) is defined by Equation (1), where \(a_{n}\) is the excitation coefficient of the \(n^{th}\) antenna element. The SC is used to determine the synthesized excitation coefficients, while the GA is utilized to determine the element spacing between the antenna array elements. The SC/GA technique can be summarized as follows:  
For N-element ULAA, the \(N\times 1\) excitation vector \(\text{A}_{N\times 1}=[a_{1}\;a_{2}\;a_{3}\;a_{4}\ldots a_{N}]\) is convolved by itself such that the resultant 1D convolution vector \(\mathbb{C}_{(2\text{N}-1)\times 1}\) can be expressed as:  
\[\mathbb{C}_{(2N-1)\times 1}=\text{A}_{N\times 1}\ast\text{A}_{N\times 1} \tag{22}\]  
However, the size of the resultant excitation vector \(\mathbb{C}_{I\times 1}=\mathbb{C}_{(2N-1)\times 1}\) from the single convolution process is much larger than the size of the original excitation vector \(\text{A}_{N\times 1}\). In order to synthesis the array factor \(AF_{S}(\theta)\) using a reduced number of antenna elements, we divided the vector \(\mathbb{C}_{I\times 1}=\mathbb{C}_{(2N-1)\times 1}\) into two vectors \(\mathbb{C}_{(N\times 1)}\) and \(\mathbb{C}_{(N-1)\times 1_{E}}\) that contain the odd and even excitations to implement the synthesized array factors \(AF_{SO}(\theta)\) and \(AF_{SE}(\theta)\), respectively. The odd excitation vector can be determined as follows:  
\[\mathbb{C}_{(N\times 1)}=\mathbb{C}_{I\times 1}\big{|}_{odd} \tag{23}\]  
where the elements \(\mathbb{C}_{O}(n,1)\) of the vector \(\mathbb{C}_{(N\times 1)}\) can be obtained from the elements \(\mathbb{C}(i,1)\) of the vector \(\mathbb{C}_{I\times 1}\) such that:  
\[\mathbb{C}_{O}(n,1)=\mathbb{C}(2i-1,1) \tag{24}\]  
where \(1\leq(i=n)\leq\frac{I+1}{2}\)
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
## 3 The Proposed Algorithm  
This algorithm is based on our observations following investigations that involved many singing pitch contours. From many of the plotted pitch contours, it was noticed that there is a noticeable trajectory change in the fundamental frequency when moving from one note to another. Therefore, the proposed algorithm is focused on evaluating the changes on a pitch contour to identify those meaningful changes that will signify onsets, offsets, and transitions.  
The pitch contour is selected because it is a robust indicator of onset compared to other features. For example, [PERSON] and [PERSON] [34] looked to find significant changes in the sound energy contour to find the start and the end of an isolated utterance. Their approach is based on short-time energy and zero-crossing rate. However, although in the case of a silence existing between notes, as considered by [PERSON] and [PERSON] [34], a noticeable change in amplitude contour is easy to see, it is difficult to rely on the amplitude contour as a feature when analyzing legato singing, as unpredictable variation occurs in the movement from one note to the next. In contrast, the fundamental frequency track is either erratic before the onset and then quickly becomes stable or moves smoothly from one value to the next in the case of legato singing, even when the consecutive notes are in the same pitch frequency. Thus, the proposed algorithm can be explained as seven main steps to find the onsets, offsets, and transitions, as shown in Figure 1. The steps are explained in the subsequent paragraphs.
## 8 Cost Effectiveness of Obstructive Sleep Apnoea in Primary Care Settings  
The evidence suggests that specialist services/sleep units are not necessary for all patients and should instead be focused on patients with greater severity. The value of primary care services in management of OSA is noted in the included studies.  
Cost-effectiveness of OSA management in primary care setting: several studies suggest that comprehensive management of OSA in primary care is a cost-effective and feasible alternative to standard management performed in specialised sleep units [38; 41; 42; 67].  
A multicentre, noninferiority, randomised, controlled trial with two open parallel arms and a cost-effectiveness analysis was conducted in Spain in which 20 primary care centres corresponding to six tertiary hospitals sequentially screened patients for suspected OSA. The aim was to compare primary health care areas (PHA) and in-laboratory specialised management protocols during a 6-month follow-up [41]. The results indicated a significant cost in sleep-related care per patient in the laboratory arm compared to the primary healthcare practice arm, primarily due to the higher cost of PSG. In terms of cost-effectiveness, the PHA arm was more cost-effective, with a cost difference of \(\xi\)537 per patient. As a result, the researchers conclude that primary care management utilising a prescribed protocol may provide an alternative to in-lab management for patients with high probability of OSA [41]. Participants who were randomised to the primary care practice arm underwent a home sleep study, and the general practitioner determined whether or not to recommend PAP therapy treatment based on the apnoea-hypopnoea index and clinical symptoms. The primary care nurse provided follow-up management [41]. A noteworthy contribution of this study is the use of a prescribed clinical algorithm in primary care practices that did not require extensive training of primary care nurses.  
Accordingly, the findings are consistent with those of another randomised clinical trial conducted to evaluate the cost-effectiveness and effectiveness of primary care and sleep unit models for the management of subjects with suspected OSA [38]. The results showed that among patients with suspected OSA, the primary care model did not result in a worse ESS score or Health Utilities Index than the specialist sleep unit model. The cost analysis revealed a median savings of \(\xi\)558 per patient when comparing the primary care setting to the sleep unit setting [38]. It is noteworthy that the follow-up visits were conducted by specialised nurses at the sleep unit, and in the primary care setting by general practitioners or nurses; all patients were followed up for 6 months [38].
# Quad-Band Circular Polarized Antenna for GNSS, 5G and WIFI-6E Applications
## 2 Structure and Analysis  
The geometry of the proposed antenna is schematically shown in Figure 1. The antenna is fed through a 50 \(\Omega\) microstrip feedline. The feedline is connected to an L-shaped patch, acting as field excitation and radiator. The L-shaped patch is printed on an FR4 substrate with \(\varepsilon_{\mathrm{r}}=4.4\) and \(\tan\delta=0.02\), and the frame ground is fabricated on the bottom of the substrate.  
The side length of the substrate is G, and the thickness is H. To assess the performance of this antenna, a commercial solver, ANSYS HFSS, is used for simulation. The resultant values for each parameter are listed in Table 1.  
The evolution process is illustrated in Figure 2. A square slot with side lengths of \(B\) is introduced in the ground plane, shown as Ant. 1, in Figure 2. An L-shaped stub is then protruded from the left side of the ground plane toward the center, and it is marked as Ant. 2. Following on, a rectangular patch is placed on the bottom right corner of the ground and seen as Ant. 3. At last, four slots are etched on the ground frame and shown as Ant. 4. In order to demonstrate the evolution of the antenna, the AR and impedance bandwidth of each antenna are simulated and plotted in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value/mm** & **Parameter** & **Value/mm** \\ \hline \(G\) & 80 & \(L_{\text{f}}\) & 11 \\ \(B\) & 60 & W1 & 9.5 \\ \(H\) & 1.6 & W2 & 11.5 \\ \(L1\) & 36.5 & \(W_{\text{f}}\) & 3 \\ \(L2\) & 8 & \(S\) & 18 \\ \(L3\) & 20 & \(S1\) & 30 \\ \(L4\) & 11.5 & \(K1\) & 8 \\ \(L5\) & 5 & \(K2\) & 15.5 \\ \hline \end{tabular}
\end{table}
Table 1: Dimension of the proposed antenna.  
Figure 1: Geometry and parameters of the proposed antenna.  
The basic structure is Ant. 1, which has a frame ground and an L-shaped radiator, as used in Ref. [30]. It is seen, from the simulated results, that Ant. 1 provides a wide impedance bandwidth in the range of 3.8-8.2 GHz. However, it is linearly polarized in this operation range. Even worse, the impedance matching is not good enough in the navigation bands. To improve the quality of circular polarization, an L-shaped branch is added to the upper left corner, as shown with Ant. 2 in Figure 2. Although the impedance bandwidth becomes worse in the low frequency around 1.5 GHz, the axial ratio is greatly improved. To create resonance in the navigation bands, a rectangular patch is placed at the lower right corner of the ground plane, as seen with Ant. 3. Apparently, the impedance bandwidth and axial ratio have been greatly improved. Therefore, the rectangular patch can improve impedance matching at lower and upper bands. By adding slots on the ground plane, the AR may be further improved, as seen with Ant. 4. The bandwidth does not see significant change, while the AR is greatly improved--particularly at 1-2 GHz and around 6 GHz.
## 2 Materials and Methods
### Characterization Methods  
Nanostructured pure and Pd-modified ZnO thin films were examined by scanning electron microscopy (SEM) using a Hitachi TM4000 microscope (15 kV, Krefeld, Germany). Prior to SEM observation, the samples were coated with gold using a JFC-1200 fine coater (JEOL, Tokyo, Japan). Elemental composition was determined by energy-dispersive X-ray spectroscopy (EDS) with a Bruker AXS detector (Microanalysis GmbH, Berlin, Germany). Diffuse reflectance spectra (DRS) of the pristine and Pd-modified ZnO thin films were measured in the 300-1000 nm range on an Evolution 300 spectrometer (Thermo Fischer Scientific, Waltham, MA, USA) equipped with a DRA-EV-300 Diffuse Reflectance Accessory.  
X-ray photoelectron spectroscopy (XPS) was performed using an ESCALAB MkII electron spectrometer (VG Scientific, now Thermo Fisher Scientific, 168 Third Avenue. Waltham, MA USA 02451) to determine the oxidation states and surface concentrations of oxygen, zinc, and palladium. The base pressure in the analysis chamber was maintained at 5 \(\times\) 10\({}^{-10}\) mbar, rising to 5 \(\times\) 10\({}^{-9}\) mbar during measurements. A non-monochromated Al K\(\alpha\) X-ray source (1486.6 eV) was used. The pass energy of the hemispherical analyzer was set to 20 eV for high-resolution spectra and 50 eV for survey spectra, with the defining binding energy measured at a resolution of 0.1 eV. The instrumental resolution, defined by the full width at half maximum (FWHM) of the Ag 3d\({}_{5/2}\) photoelectron peak, was approximately 1 eV. Data analysis was performed using SpecsLab2 and CasaXPS software (Casa Software Ltd. 2.3.26 PR1). Peak positions and areas were determined through Gaussian-Lorentzian curve fitting, following subtraction of X-ray satellites and a Shirley-type background correction [34]. Curve fitting was applied to track variations in palladium oxidation states with increasing UV doses used for photodeposition (2.5, 5, and 10 J/cm\({}^{2}\)). The relative concentrations of the different chemical species were determined based on normalization of the peak areas to their photoionization cross-sections (relative sensitivity factors), calculated by [PERSON]. The relative error in the concentration calculations was typically 10%.
# Coastal Wind Measurements Using a Single Scanning LiDAR
[21] also showed validation results with a distance of approximately 1.8 km at a German test site. Some of the reports have shown good agreement with observations obtained from met masts. However, details of the validation methods employed, such as the horizontal wind retrieving algorithm used in the analysis, have not been made available. Thus, the usefulness of a scanning LiDAR for wind resource assessment over coastal waters has remained ambiguous. In the current study, we conducted an offshore wind measurement campaign with a single scanning Doppler LiDAR installed at a coast in central Japan. The horizontal wind speeds and wind directions were retrieved from the LiDAR-measured radial wind speeds using the velocity volume processing method. One of the objectives of this study was to investigate the effect of the scan parameters, such as the azimuth range and elevation angle on the quality of measured wind speeds. Furthermore, the effects of the wind direction and data availability on the accuracy of the retrieved wind speeds is also analyzed. The ability of a scanning LiDAR to measure the turbulence intensity (TI)was also investigated. To that end, scanning LiDAR measurements were compared against the data from a profiling LiDAR and a sonic anemometer at the site. The paper is organized as follows. In Section 2, we describe the measurement site and devices. The measurement cases and velocity retrieval techniques are also presented in this section. In Sections 3 and 4, we present our results and related discussions. The main conclusions of this work are summarized in Section 5.
# Developing a Robust Training Dataset for AI-Driven Architectural Spatial Layout Generation
## 3 Methodology
### Result
#### 4.3.2 Results of Spatial Relationship Data Extraction  
Table 9 presents the results of the vector data extraction from the raster-based floor plan images. Further details of Table 9 are provided in Appendix B. The algorithm effectively extracts spatial relationship data and translates them into consistent vector data. Conceptually, a diagram can be interpreted as a graph with defined nodes and edges; this information is structured as an adjacency matrix. Four factors contributed to the extracted vector data, as follows:  
1. Project and Space Names: These were automatically sourced and include the project ID, layer, detected space name, class number, and a random \"N\" when there are identical space names;  
Figure 8: Results of algorithm performance.  
Figure 9: Results of spatial relationship diagram extraction.  
2. Space Size: This is determined as the percentage of the detected space area. For a total image size scaled to 100%, both the horizontal and vertical ratios were calculated. In raster images, the relative size of a space is typically inferred from the size of the door. The length of the side toward the door was standardized to 1 and the opposite side was scaled proportionally. The actual space area was derived by multiplying the total area (determined through data crawling) by the space ratio;
3. Doors and Windows: These features are coded as binary data, where 1 indicates presence within the detected space and 0 indicates absence. For example, in the detected space, a single door (class12), double door (class13), sliding door (class14), and window (class15) were designated as either 1 or 0. The numbers of corresponding classes were also identified;
4. Adjacent Spaces: These data were structured as an adjacency matrix, highlighting the connections of a particular space. If there was a wall or door connecting the space, it was marked as 1; otherwise, it was marked as 0. As per the adjacency definitions in Section 3.3, the connection weights for Cases 1, 2, and 3 were automatically set to 1, 0.5 for Case 2, and 0.25 for Case 3.  
Upon comparing the outputs of the algorithm with real measurements, a minor discrepancy of 0.6% in the space size was noted. This variance can be attributed to the fact that the actual space measurements considered the inline of the wall, whereas the detected space accounted for its outline. Nonetheless, other vector information for the detected space remains in a consistent format. This consistency confirmed the accuracy and dependability of the proposed algorithm.
## 5 Conclusions and Future Works  
The study examines the performance of hybrid features created by stacking different types of features to determine the gender of the speaker. The study uses MFCC, Chroma, Mel Spectrogram, Contrast, and Tonnetz features, which are commonly used in speech-based recognition tasks, with four different classifiers. Two of them, CNN and MLP, are deep learning models, while the other two, KNN and LDA, are traditional machine learning models. First, the gender recognition performances of five features are measured individually. Then, the features are stacked in different orders to create twelve hybrid features, and the performance of each is evaluated separately. In all experiments, a subset of 6700 speeches selected from the Turkish section of the Common Voice dataset is utilized. This dataset is randomly split into two parts in a 75:25 ratio, with the first part being used for model training and parameter optimization, and the second part for testing the models. In the study, a total of 68 models were developed--20 using five individual features and 48 using 12 hybrid features created by stacking these features. Performance measurements were made for each model. The experimental results show that using hybrid features created by stacking different features increases the accuracy of gender classification, regardless of the classifier used. Through the use of hybrid features, the CNN model increases its classification accuracy from 99.22% to 99.52%, the KNN model from 98.09% to 99.22%, the MLP model from 98.45% to 99.04%, and the LDA model from 95.94% to 97.67%. The difference in accuracy between the CNN and KNN models, which have the highest accuracies, is only 0.3%, which is negligible given the complexity of the two models. These results demonstrate that the hybrid features, created by stacking different features, significantly increase gender recognition accuracy. In future studies, new features such as Zero Crossing Rate (ZCR) and root mean square energy (RMSE) can be included in hybrid features. It is also possible to change the dimensions of the features that make up the hybrid features. Additionally, these approaches can be applied to different problems using various datasets, and their performance can be evaluated.  
This research received no external funding.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
# Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning
## 2 Analysis of Influencing Factors and Feature Selection of TBM Performance
### Feature Selection of Influencing Factors of TBM Net Driving Speed Based on Random Forest  
#### 2.2.1 Parameter Setting of Random Forest  
There are two important parameters that need to be optimized in the random forest: the number of regression trees _n_estimators_ in the sample data set extracted by the bootstrap method, and the maximum number of features max_features used on each node, which have four options, namely Auto/None, sqrt and 0.2. [PERSON] et al. (2016) proposed that in order to balance the diversity of a single decision tree and the convergence speed of the algorithm, generally, max_features select sqrt, which can be calculated according to Formula (1).  
\[\text{sqrt}=\sqrt{M} \tag{1}\]  
where \(M\) is the number of input parameters of the model.  
According to the previous analysis, there are 9 factors affecting the penetration rate of TBM. Therefore, sqrt is 3. In order to determine the best prediction model, the statistical section is divided into 170 driving units. According to the TBM performance database, the corresponding driving mileage, rock mass parameters and mechanical parameters of each driving unit are obtained. In total, 140 groups of data are used as training sets to train the RF model, and 30 groups of data are used as test sets to measure the prediction performance of the model. The prediction mean square errors of the random forest models with different combinations of the regression tree number _n_estimators_ are compared. Firstly, the random forest models with a different number of regression trees _n_estimators_ are numbered, and the value of _n_estimators_ is from 1 to 1000 in 50 steps. The mean square error (MSE) is used to evaluate the prediction accuracy of the model, and the comparison results are shown in Figure 1. With the increase in the number of _n_estimators_, the mean square error  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
(MSE) of the RF model decreases gradually, and the overall error tends to be stable when _n_estimators_ are greater than 350. Therefore, the number of _n_estimators_ is determined; that is, the number of trees in the random forest is 350.
# Random Laser Action in Dye-Doped Polymer Media with Inhomogeneously Distributed Particles and Gain
## 1 Introduction  
Random lasers (RLs) are light sources that can generate nearly coherent light with the use of scattering media with gain [1; 2; 3; 4; 5; 6]. As the optical feedback for lasing is provided by the multiple scattering of light instead of well-defined cavities, the random spatial distribution of the refractive index in the media directly affects the random lasing action. Therefore, the modes of RLs are strongly related to the scattering properties of the laser medium [7; 8]. Thus, several structures of disordered gain media for RLs have been reported. For a random gain medium of monodisperse particles, the effect of Mie resonances is observed in the emission spectrum, although the emitted light is multiply scattered among the particles [9; 10; 11]. A defect introduced in such a monodisperse medium concentrates and enhances light in the region around it [12]. The size and shape of the scattering particles affect the coherent and incoherent random lasing [13; 14; 15]. The experimental results demonstrate that the lasing threshold [13] and spectral linewidth [14] depend on the particle size, whereas the shape of the scatterers affects the peak intensity of the laser emission [14]. Considering the particle size distribution, polydispersed neodymium-doped powders exhibit a high lasing efficiency when compared with monodispersed powders [15]. The inhomogeneous distribution of scatterers also changes the efficiency of random lasing [16]; the lasing threshold of an RL with clustered particles decreases as the degree of particle aggregation is increased [17]. These studies have demonstrated that the emission properties of an RL can be improved by varying the topology of the disordered medium.  
In a previous study [18], we investigated the emission from a bubble-structured (BS) RL with coherent feedback, in which spacer particles (SPs) with gain and irregular shapes, as well as scattering particles, were randomly distributed. These SPs created non-scattering regions; thus, the distribution of scatterers was inhomogeneous. The experimental results showed that the peak intensity of the emission spectrum was enhanced when SPs with average diameters in the wide range of 53-300 \(\upmu\)m were dispersed. In the present study, we investigated incoherent random lasing from a BS RL, which, to the best of our knowledge, has not been studied before. In addition, we used different SPs and investigated their effect on the emission spectrum from both coherent and incoherent RLs. Non-scattering regions were controlled more systematically by using transparent spherical SPs with well-defined diameters.  
We examined whether the spectral peak intensity of RL emission was enhanced when the SPs were small (diameter in the range of 30-100 \(\upmu\)m) and had no gain.