esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Parameter Setup  
The system simulation parameters were selected from the LEO-based non-terrestrial networks (NTN) scenario (C2&D2), specified by the third-generation partnership project (3 GPP) [53; 54]. The parameters are summarized in Table 2, where the subcarrier spacing is \(\Delta f=120\) kHz, the total number of subcarriers (delay bins) is \(M=1024\), and the number of time slots (Doppler bins) is \(N=16\). The number of zero-padding subcarriers is \(M_{p}=24\), and the number of subcarriers that can be allocated to navigation and communication is \(M_{n}+M_{c}=1000\). The signal frame duration is \(NT=0.133\) ms. The duration of the CP should be greater than the maximum delay spread of the multipath channel given in Table 3, set to \(T_{\text{CP}}=1.482\) us. The navigation message and communication data mapping are BPSK and 4 QAM, respectively. The satellite communication channel adopts the tapped delay line (TDL) model. The signal strength in the multiple satellites scenario is based on the actual OneWeb constellation at an orbital altitude of 1200 km, with a user longitude of \(112.99^{\circ}\) and latitude of \(28.22^{\circ}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Configuration** \\ \hline Orbit altitude & 1200 km \\ Carrier frequency & 20 GHz \\ Signal bandwidth & 120 MHz \\ Subcarrier spacing \(\Delta f\) & 120 kHz \\ Number of subcarriers \(M\) & 1024 \\ Number of time slots \(N\) & 16 \\ Navigation data mapping & BPSK \\ Communication data mapping & 4 QAM \\ Communication channel model & TDL-C \\ \hline \hline \end{tabular}
\end{table}
Table 2: System simulation parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tap** & **Delay [us]** & **Power in [dB]** & **Fading Distribution** \\ \hline
1 & 0 & \(-0.394\) & LOS path \\
1 & 0 & \(-10.618\) & Rayleigh \\
2 & 1.48124 & \(-23.373\) & Rayleigh \\ \hline \hline \end{tabular}
\end{table}
Table 3: Channel parameters of TDL-C.  
The power allocation of the navigation and communication signals has a significant impact on the performance of the INAC signal. In this paper, we define the communication-to-navigation power ratio (CNPR) as the indicator of power allocation:  
\[\text{CNPR}=\frac{\sum\limits_{k=0}^{M_{c}-1}P_{c,k}}{\sum\limits_{k=0}^{M_{a} -1}P_{n,k}} \tag{29}\]  
For ease of analysis, the power of the navigation and communication subcarriers is equally distributed separately. The larger the CNPR, the higher the power allocated to the communication subcarriers. To fairly compare the performance between DDBDM, ZC-NOMA, and PN-OTFS, we evaluate their performance with the same SNR of the received INAC signal.
# Digital Signal Processing of the Inharmonic Complex Tone
## 1 Introduction
The component \(rest[k]\) contains random noise and other artifacts, including quasi-sinusoidal components with frequencies other than expected partial values. Those additional sinusoidal components can arise from different phenomena specific to each of the instruments. Variation of the partial frequency, often referred to as pitch glide, is typically small and is usually neglected in rough signal analyses. This assumption is supported by empirical findings in the analysis of plucked string instruments, where pitch glide occurs primarily during the note onset and decays rapidly, often falling below perceptual thresholds [9; 20]. For many DSP applications, including spectral analysis and partial tracking, this small, transient variation can be neglected in models. In this paper, a set of digital signal processing procedures developed for the analysis of string instrument musical tones with prominent inharmonicity is proposed and presented. These procedures are organized in the form of the MuPI (Musical Signal Processing with Prominent Inharmonicity) application, comprising three major modules. The application, developed in MATLAB 2024, is focused on the quick inspection of recorded inharmonic tones following a processing chain. The application is intended for users with a good understanding of musical signal phenomena and basic DSP skills. Each module is designed as a Graphical User Interface (GUI) application. The approach presented in this paper relies on observing the signal in both time and frequency domains in parallel. As a result of the application, additional information can be extracted from the signal and included in its description, such as the fundamental frequency, number of prominent partials, and inharmonicity coefficient \(B\). The last submodule of the application assumes that processing of the subchannel signals corresponds to the frequency bins of each prominent partial. Additional features of the time envelopes of single partials can be extracted from the subchannel analysis. For the decomposition of the signal, a multichannel doubly complementary filter with an additional phase correction block is proposed, which improves the time alignment between the signals of the subchannels. To position the proposed MuPI application within the landscape of existing audio signal analysis tools, a comparative evaluation was conducted against three well-established frameworks: LTFAT [21], Essentia [22], and MIRtoolbox [23]. The comparison was performed across several criteria relevant to time-frequency analysis, feature extraction, signal manipulation, and system flexibility (Table 1).
# Recovery and Characterization of Orbital Angular Momentum Modes with Ghost Diffraction Holography
## 4 Conclusions  
In conclusion, we have developed a technique for the recovery and characterization of OAM modes with a ghost diffraction holography system. The approach utilizes the spatial correlation of intensity fluctuations of the speckle pattern for the recovery of scrambled vortex modes. The applicability of the technique is validated by simulation and followed by experimental demonstrations for the recovery of integer orders of various OAM modes with positive or negative helicity. Additionally, we have demonstrated the recovery of phase distribution beside the amplitude distribution of OAM modes and highlighted the advantage of simultaneous determination of sign and magnitude of the topological charge from the direction of rotation and azimuthal order of the helical phase structure. Moreover, the encoding of the OAM modes in random field and the quantitative recovery of scrambled modes from speckle pattern may find significant interest in the sorting of superposed optical vortex modes. In addition, the technique is expected to find important applications in optical communications, multi-dimensional imaging, ghost imaging, coding, imaging through turbid media, etc.  
The mathematical derivations are available online at [[https://www.mdpi.com/article/10.3390/app112412167/s1](https://www.mdpi.com/article/10.3390/app112412167/s1)]([https://www.mdpi.com/article/10.3390/app112412167/s1](https://www.mdpi.com/article/10.3390/app112412167/s1)). References [28, 31, 32, 33, 34, 37] are cited in the supplementary materials.  
Conceptualization, V.R.V.; methodology, Y.H. and V.R.V.; formal analysis, Y.H., T.S., R.K.S. and V.R.V.; investigation, Y.H. and V.R.V.; data curation, Y.H. and V.R.V.; writing--original draft preparation, Y.H. and V.R.V.; writing--review and editing, V.R.V., Z.C., R.K.S. and J.P.; supervision, J.P.; funding acquisition, Z.C. and J.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) under grant number 62005086; Dongguan Introduction Program of Leading Innovative and Entrepreneurial Talents under grant number 201927879; Science and Engineering Research Board (SERB) India, grant no. CORE/2019/000026; Council of Scientific and Industrial Research (CSIR), India, grant no. 80 (0092)/20/EMR-II; and the Department of Biotechnology (DBT), India grant no. BT/PR35587/MED/32/707/2019.  
Not applicable.  
Figure 6: Quantitative comparison of experimental and simulation results: (**a**) variation of inner and outer radii with the topological charge (\(r_{1}\), inner radius and \(r_{2}\), outer radius); (**b**) variation of area of dark core (Ad, dark core) and area of bright annular ring (Ar, annular ring) of the recovered OAM modes.  
Data Availability StatementThe data that support the findings of this study are available from the corresponding author, [PERSON], upon a reasonable request.  
The authors declare no conflict of interest.
## 3 Results  
### Predicting Land Surface Temperature to Fill in Missing Satellite Data and Variable Importance  
To assess the effectiveness of RF models in predicting LST to fill the gaps of missing LST data, we meticulously designed an experiment incorporating both temporal and spatial variables. Our RF models to predict LST missing data include both temporal (day of the year (DOY) and year) indicators and spatial variables (\(T_{\text{max}}\), \(T_{\text{min}}\), elevation, built-up land, agricultural land, forest land, water bodies, and miscellaneous land), allowing for an evaluation of feature importance specifically for predicted LST.  
To implement this approach, we systematically divided the study area into a grid with 1 km intervals around each of the 10 meteorological stations. Subsequently, we selected four points along cardinal directions to generate predicted LST models for both daytime and nighttime. Following the creation of these LST prediction models, we further divided the area into a grid with 5 km intervals, covering all seven provinces with weather stations. This resulted in 11 training samples and 1250 testing samples. Notably, only 248 points within this grid overlapped with the targeted study area encompassing Bangkok, Pathum Thani, and Saraburi provinces. This design ensures a rigorous evaluation of the RF models' predictive capabilities, accounting for both temporal and spatial factors, while also considering the specific features of the targeted study areas.  
According to the RF results, a concise overview of the model's feature importance for LST modeling is presented in Figure 3. The factor DOY shows the highest explanatory rates for daytime LST as in MOD11A1_day (Figure 3a) and MYD11A1_day (Figure 3b), whereas the \(T_{\text{min}}\) affects most LST night as in MOD11A1_night (Figure 3c) and MYD11A1_night (Figure 3d). In predicting LST day, \(T_{\text{max}}\) and \(T_{\text{min}}\) rank second, followed by year, elevation, and land use. For MOD11A1 data, the water body from the land use factor is essential, while the built-up area is vital for MYD11A1 data. Later, in the case of MOD11A1 and MYD11A1, \(T_{\text{min}}\) emerges as the second most influential factor for predicting nighttime LST, following the elevation of the land and the extent of built-up areas.  
A summary of the LST model statistical measurement between the observed LST and predicted LST is given in Table 3. The result demonstrates that MOD11A1 night has the greatest satisfied RMSE (2.09 \({}^{\circ}\)C) and the best linear relationship (\(R^{2}=0.64\)), followed by MYD11A1 night (\(R^{2}=0.48\)), and MOD11A1 day (\(R^{2}=0.45\)). On the other hand, MYD11A1 day is an unsatisfactory model due to the highest RMSE (5.02 \({}^{\circ}\)C) and lowest \(R^{2}\) (0.29).  
These results confirm the ability of the RF machine learning algorithm, particularly the MOD11A1 night model, in estimating LST. Conversely, the other predicted LST models indicate a tendency to overestimate the data.
## 4 Influence Factors on Dynamic Responses of Vehicle-Track-Subgrade System in Transition Section
### Law of Dynamic Response along the Depth  
Figure 13 indicates the rule that dynamic response changes with depth when the transition section of the positive trapezoid and inverted trapezoid is adopted at Section 3 m from the abutment. It can be seen that the elastic strain firstly increases sharply with the increase in depth, which increases nearly five times from the surface of the bed surface to the bottom of the bed surface, increases nearly 1-2 times from the surface of the bottom bed to the bottom of the bed bottom, and then gradually decreases from the bottom of the bed bottom to the subgrade, as shown in Figure 13a. On the contrary, the dynamic stress decreases sharply with the increase in subgrade depth, which decreases by 44% from the surface of the bed surface to the bottom of the bed surface, decreases by 36.32% from the surface of the bed bottom to the bottom of the bed bottom, then the subgrade slowly decreases below the bottom of the subgrade bed, as shown in Figure 13b. Thus, we can infer that the bed layer bears the primary dynamic response, and the type of filling has a significant influence on the dynamic response in the transition section.  
The curves of dynamic stress and vertical elastic strain in Figure 13a,b indicate that the major difference only occurs within the layer of approximately 2-7.5 m (bed and shallow subgrade). In detail, from Figure 13a, for the inverted trapezoidal embankment, the vertical dynamic elastic strains of the bed bottom and the subgrade are smaller than those of the trapezoid. The response of dynamic stress is just the opposite as shown in Figure 13b, that is: the dynamic stress of the inverted trapezoidal embankment is greater. After that, the two curves gradually coincide when the depth exceeds 7.5 m. The above phenomena, due to the rigidity of the transition section filling, are higher than that of ordinary subgrade filling and the corresponding stress has a better diffusion effect. Therefore, the analysis result of this paper considers that the structural design of inverted trapezoid is more reasonable than that of the positive trapezoid transition section.  
Figure 12: Effect of transition length on dynamic response of surface layer of subgrade bed: (**a**) dynamic stress; (**b**) dynamic displacement.  
Figure 13: Variation of dynamic response with depth: (**a**) vertical elastic strain; (**b**) vertical dynamic stress.
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 6 Learning Paradigms
### Closed/Open Set Classification  
Most ASC tasks in public evaluation campaigns such as the DCASE challenge assume a closed-set classification scenario with a fixed predefined set of acoustic scenes to distinguish. In real-world applications however, the underlying data distributions of acoustic scenes is often unknown and can furthermore change over time with new classes becoming relevant. This motivates the use of open-set classification approaches, where an algorithm can also classify a given audio recording as an \"unknown\" class. This scenario was first addressed as part of the DCASE 2019 challenge in Task 1C \"Open-set Acoustic Scene Classification\" [66].  
[PERSON] et al. proposed the multi-class open-set evolving recognition (MCOSR) algorithm to tackle open-set ASC [67]. Unknown samples are first rejected by a recognition model before the algorithm tries to identify underlying (hidden) classes in these samples in an unsupervised manner. Finally, the recognition model can be updated using the novel classes. [PERSON] and [PERSON] combined a closed-set classification algorithm and an outlier detection algorithm based on deep convolutional autoencoders (DCAE) to recognize unknown samples in an open-set ASC scenario [68]. [PERSON] et al. evaluated the model's classification confidence to identify unknown samples [69]. Therefore, a threshold was applied on the highest logit value at the input of the final neural network layer.
## 4 Results and Discussion
### Article Selection  
Through the application of designated search strings across five key academic databases, a total of 1441 articles were initially identified. The first searches were conducted in July 2023. The selection process, guided by the specific inclusion and exclusion criteria and a thorough review of abstracts and full texts, narrowed this down to 78 articles for detailed analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Criterion** & **Description** & \multicolumn{2}{c}{**Score**} \\ \hline \multirow{3}{*}{**QC1**} & \multirow{3}{*}{Publication Venue} & The article has been published in a reputable and high-quality venue & Full & 1 \\ \cline{3-4}  & & & Partial & 0.5 \\ \cline{3-4}  & & No & 0 \\ \hline \multirow{3}{*}{**QC2**} & \multirow{3}{*}{Framework/Methodology Proposed} & Full & 1 \\ \cline{3-4}  & & & Partial & 0.5 \\ \cline{3-4}  & & No & 0 \\ \hline \multirow{3}{*}{**QC3**} & \multirow{3}{*}{Proof of Concept/Performance Assessment} & Full & 1 \\ \cline{3-4}  & & & Partial & 0.5 \\ \cline{3-4}  & & No & 0 \\ \hline \multirow{3}{*}{**QC4**} & \multirow{3}{*}{Patent Dataset Size} & Full & 1 \\ \cline{3-4}  & & & Partial & 0.5 \\ \cline{3-4}  & & No & 0 \\ \hline \multirow{3}{*}{**QC5**} & \multirow{3}{*}{Study Limitations Defined} & Full & 1 \\ \cline{3-4}  & & & Partial & 0.5 \\ \cline{3-4}  & & No & 0 \\ \hline \multirow{3}{*}{**QC6**} & \multirow{3}{*}{Citations} & Full & 1 \\ \cline{3-4}  & & The article has been appropriately cited & Partial & 0.5 \\ \cline{3-4}  & & No & 0 \\ \hline \multirow{3}{*}{**QC7**} & \multirow{3}{*}{Future Direction Defined} & Full & 1 \\ \cline{3-4}  & & & Partial & 0.5 \\ \cline{3-4}  & & No & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quality criteria and scoring process.  
The methodology and results of this filtering process are detailed in Table 5 for each database, and the PRISMA flow diagram depicted in Figure 3 meticulously outlines the steps of article identification and screening. In the identification phase, a total of 561 articles were removed after applying the initial exclusion criteria such as date, language, and article type. Additionally, 37 duplicates were removed. In the first part of the screening phase, 631 articles were further removed after reading the title and the abstract. These removed articles focused on techniques for patent trend analysis, topic modeling, non-textual retrieval, etc. A total of 212 articles were sought for full access; however, 45 out of those did not have full access which led to their removal from the list of the shortlisted articles. After reading the full texts, a further 89 articles that did not qualify as per our outlined exclusion criteria were removed. Articles discussing patent retrieval techniques in general that could help in the patent prior art search were included in the final list of 78 shortlisted articles.  
Figure 3: PRISMA flow diagram for article extraction process.
# Circularly Polarized Vivaldi Antennas Integrated with Septum-like Polarizer
## 2 Materials and Methods  
### Design Approach  
Figure 1 depicts a comparison of different polarizers to generate circularly polarized waves. While the typical septum has selective excitation of either one of two ports, the proposed polarizer requires simultaneous excitations of both orthogonal ports. Although they have different configurations for port excitations, similar magnitudes and 90\({}^{\circ}\) phase differences can be obtained. The typical septum polarizer is placed in the transition section between the feed network and antenna aperture. However, for the Vivaldi antenna, the antenna gain is determined by the shape of the two exponential slot edges. If the proposed polarizer is situated in the radiating edges, the antenna performance may be degraded. Therefore, the proposed polarizer is located within the slotted section or feed network to avoid this adverse effect. A septum polarizer shaped like the teeth of a wood saw has been presented for a low-profile parallel plate waveguide. The proposed polarizer is different from the previous septum polarizer in terms of the shape of each element and its array configuration. A simplified pulse train is used, and the optimal spacing between each element is studied to obtain a wider bandwidth. Furthermore, the proposed polarizer is advantageous because it is more suitable for a planar antenna, which is printed on a single substrate.
# Audio Anti-Spoofing Based on Audio Feature Fusion
## 2 Methods
### Data Augmentation  
Data augmentation (DA) alleviates the issue of insufficient data in deep learning, which was initially widely used in the image domain and then extended to NLP, achieving success in many tasks. DA can reduce overfitting, thus improving generalization [30; 31; 32; 33], and is particularly effective in LA scenarios involving large amounts of encoding, transmission, and acquisition equipment. Although many DA solutions have been proposed, such as in [34] and SpecMix [35], they are only suitable for detection models operating on 2D front-end representations.  
The model here used RawBoost DA [33] tools, a data augmentation and enhancement method used to design more robust models that directly takes raw waveform inputs, meeting the needs of the model. RawBoost has developed various data augmentation methods. Mode 1 is the processing of data through convolutional noise, mode 2 processes the data through impulse noise, mode 3 processes the data through colored additive noise, and mode 4 is where the three algorithms of modes 1, 2, and 3 are concatenated together for data processing. Mode 5, conversely, is the data processing of two algorithms of modes 1 and 2 in series. Based on [13], we conducted experiments on our model using modes 3 and 5.
## 2 The Uncertainty Models of Renewable Energy and Load Demand
### The Model of Uncertainty for the Output of Wind Power Generation System  
Wind speed is the main factor affecting the output of the wind turbine generator system. By predicting the wind speed of wind power farms, the predicted value \(P_{wt}\), the active power provided by wind power generation system, can be represented as [18]:  
\[P_{wt}=\begin{cases}0&0\leq v\leq v_{c}\text{ or }v\geq v_{f}\\ P_{r}\frac{v-v_{c}}{v_{r}-v_{c}}&v_{c}\leq v\leq v_{r}\\ P_{r}&v_{r}\leq v\leq v_{f}\end{cases} \tag{5}\]  
where \(P_{r}\) is the rated output power of the wind power generation system, and \(v\), \(v_{r}\), \(v_{c}\) and \(v_{f}\) are the predicted wind speed, rated wind speed, cut-in wind speed, and cut-out wind speed, respectively.  
Typically, lots of scholars employ various distributions, such as the Weibull distribution, normal distribution, and Rayleigh distribution, to model the probability density function of wind speed. In this work, the well-established bimodal Weibull distribution [19] is adopted to characterize the probability density of wind speed, and its corresponding probability density function can be represented as follows:  
\[f(v_{wind})=\frac{k_{shape}}{c_{scale}}\bigg{(}\frac{v_{wind}}{c_{scale}}\bigg{)} \exp\bigg{[}-\bigg{(}\frac{v_{wind}}{c_{scale}}\bigg{)}^{k_{shape}}\bigg{]} \tag{6}\]  
where \(k_{shape}\) and \(c_{scale}\) are the shape parameter and distribution parameter of \(f(v_{wind})\), respectively.  
According to the wind speed probability model and the wind power generation model, the active power output and the maximum deviation of the active power output of the wind power generation system can be obtained. Then, the active power output of the wind power generation system considering the uncertainty can be represented as:  
\[PU_{wt}=P_{wt}+PD_{wt}\epsilon_{wt} \tag{7}\]  
where \(PU_{wt}\) is the active power output of wind power considering the uncertainty, \(P_{wt}\) is the forecasted active power output provided by the wind power generation system, \(PD_{wt}\) is the maximum deviation of the actual wind power generation active power value relative to the active power forecast output, and \(\epsilon_{wt}\) is the fluctuation degree of the wind power output.