esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Previous Approaches to Multi-Pitch Estimation
The CGP system was built using a previously developed toolbox for MatLab called CGP4 Matlab [48] (Download available: [[https://github.com/tiagoinacio/cgp4](https://github.com/tiagoinacio/cgp4) matlab]([https://github.com/tiagoinacio/cgp4](https://github.com/tiagoinacio/cgp4) matlab), accessed on 21 March 2021), which is particularly suited for signal processing and image processing problems and is available for general use. That technique was then improved using a CGP multi-pitch estimation system with the aid of a spectral harmonic mask [49], resulting in better accuracy results. However, there is still room for improvement, and this led us to what we present in this paper. We propose an improved version of the algorithm, with some innovations and an extended MPE system based on Cartesian genetic programming for piano music. This system was developed for an AMT frame-based usage and takes advantage of the authors' knowledge previously acquired during other research [46; 48]. Our system is the first white-box MPE-based approach that uses GP, in particular Cartesian genetic programming. The main innovations of this new CGP system are the use of a new onset detector, real-time computing performance using a parallel system architecture, and an artificial data augmentation process for the training stage using the onset detector, which resulted in accuracy improvements. We can summarize the main advantages of our approach and implementation as follows:  
* The accuracy results are in line with the state-of-the-art approaches that use other non-evolutionary methodologies. * It works with different piano models (upright and grand pianos) and with both digital and acoustic pianos. * It does not depend on harmony rules, western music rules, or chords construction rules, which was typically the case for previous approaches. * It uses improved onset detection. * It provides white-box analyses, which allow other researchers to study the generated models and to use them as an optimization baseline. * It achieves real-time computing performance. * The technique allows us to perform MPE for other instruments besides piano. * The significant accuracy results improved compared to our previous approach. The rest of this document is structured as follows: Section 2 overviews the proposed system; Section 3 describes the implemented onset detection algorithm; Section 4 describes Cartesian genetic programming as a genetic algorithm technique; Section 5 describes our CGP system and its main features; Section 6 shows our experiments and results; and, finally, Section 7 presents our conclusions and discusses future work.
# On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies
## 1 Introduction
These mechanisms are intended to make efficient use of the available battery energy for, respectively, asynchronous and synchronous communications, thereby prolonging the battery life. For all the aforementioned reasons, IEEE 802.15.5 can be, a priori, a suitable and representative WMSN solution for the transmission of video in mesh topologies. However, according to our knowledge, there are very few studies in the scientific literature combining mesh topologies with multimedia applications for low power devices. Under these circumstances, the purpose of this paper is to contribute to the development of multimedia applications in the WMSN arena, fulfilling the WSN/WMSN specific requirements, assuring the QoS of the multimedia service and achieving a reasonable performance of the network in terms of, for instance, lifetime and data transmission reliability. To achieve these goals, we have designed a novel tool, available for download, that evaluates, by means of computer simulation, the performance of video transmissions using the SES energy-saving mechanism of IEEE 802.15.5. For the evaluation of the standard, Quarter Common Intermediate Format (QCIF) video sequences are encoded using the H.264/AVC compression standard and transmitted through WMSN mesh networks of different sizes. The main results will be discussed, highlighting the most important metrics such as latency, jitter, time between packets, message delivery ratio, power consumption, lifetime and Peak Signal-to-Noise Ratio (PSNR). The rest of this paper is organized as follows: Section 2 introduces related work in the field of WMSNs. Section 3 provides the necessary technological background about video encoding with H.264 as well as WMSN operation based on the IEEE 802.15.5 standard. Section 4 describes the simulation tool and simulation environment. Section 5 shows and analyzes simulation results, which are further discussed in Section 6. Section 7 concludes this work.
## 2 Experimental
### Experimental Method  
The liquid phase adsorption of cationic neutral red (NR) dye (pH 5) on the sample was measured with a UV-1100 spectrophotometer (Shanghai Mapada Instruments Co., Ltd., Shanghai, China). Briefly, 20 mg of sample was added to 30 mL of a 10, 25, or 50 mg/L NR aqueous solution (pH 5). Adsorption was carried out with continuous stirring at 700 rpm at room temperature. At certain time intervals, 5 mL of the mixture was withdrawn and separated by centrifugation (5000 rad min\({}^{-1}\)). All of the test data wererepeated 3 to 5 times and averaged, and the data error was controlled within 5%. The concentration of NR in the solution was determined at 530 nm. The adsorption capacity and removal efficiency of the samples were calculated by the following equations:  
\[Q_{t}=\frac{(C_{0}-C_{t})\times V}{m} \tag{1}\]  
\[E=\frac{(C_{0}-C_{t})\times 100\%}{C_{0}} \tag{2}\]  
where \(Q_{t}\) (mg/g) is the amount of NR adsorbed on the adsorbent at time \(t\), \(C_{0}\) (mg/L) is the initial NR concentration, \(C_{t}\) is the NR concentration at time \(t\), \(V\) (L) is the volume of the solution, \(m\) (g) is the mass of the adsorbent, and \(E\) is the removal efficiency of the sample.  
Herein, to investigate the kinetics of the adsorption process, pseudo-first-order and pseudo-second-order adsorption models were chosen.  
Pseudo-first-order equation [31]:  
\[\ln(Q_{e}-Q_{t})=\ln Q_{e}-k_{1}t \tag{3}\]  
Pseudo-second-order equation [32]:  
\[\frac{t}{Q_{t}}=\frac{1}{k_{2}Q_{e}^{2}}+\frac{t}{Q_{e}} \tag{4}\]  
where \(Q_{e}\) (mg/g) is the theoretical equilibrium saturated adsorption of NR, \(Q_{t}\) (mg/g) is the amount of NR adsorbed on the adsorbent at time \(t\), \(k_{1}\) (g mg\({}^{-1}\) min\({}^{-1}\)) is the pseudo-first-order rate constant of adsorption, and \(k_{2}\) (g mg\({}^{-1}\)min\({}^{-1}\)) is the pseudo-second-order rate constant of adsorption.
# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 1 Introduction
### Paper Overview  
Section 2 outlines the IEEE 802.11p and then introduces the channel models considered in the simulation. Then, the practical channel estimation schemes used by the proposed scheme are introduced in Section 3. Section 4 presents the adaptation algorithm using the long preamble for adaptive channel estimation. The error performance of the simulation results for the proposed method are analyzed with respect to the results of other channel estimation schemes in Section 5. Finally, Section 6 concludes this paper.
## 5 Conclusions  
The challenge of dynamically selecting wireless backhaul links between UAV BSs and LEO relays in IoT scenarios is resolved. A robust multiple UAV-LEO candidate backhaul link association model that enhances the adaptability and efficiency of the network is established. The optimization problem inherent in IoT applications, taking into consideration their multiservice nature, and a corresponding heuristic algorithm are proposed. In this problem, an adjustable performance evaluation indicator is adopted that encompasses both the remaining service time and the system capacity. Moreover, comparative analyses against conventional methods are presented. The results show that the proposed algorithm is more balanced and efficient.  
Finally, the limits of the present work and directions of research are listed as follows:  
Figure 5: Association pattern of the UAV-LEO.  
The problem of backhaul link selection between UAVs and LEO satellites is considered. However, how to select global links for the satellites, UAVs, and sensors has not been researched. In the future, a three-level model of satellites, UAVs, and sensors could be established for joint optimization to obtain an optimal link selection algorithm.  
The free space path loss model is adopted for capacity calculation, and the impact of factors such as weather and season on capacity are ignored. These factors may affect the optimization results. They could be considered by using the corresponding path loss model in our algorithm in future work.  
Based on our proposed link selection algorithm, further consideration needs to be given to resource allocation issues such as bandwidth and power. The optimization problem could be modeled and solved according to the various traffic demands of the LEO-UAV links.  
Conceptualization, R.C. and W.W. ([PERSON]); methodology, R.C.; software, R.C.; validation, R.C., W.W. ([PERSON]), and W.W. ([PERSON]); formal analysis, R.C.; investigation, R.C. and W.W. ([PERSON]); resources, W.W. ([PERSON]); data curation, R.C.; writing--original draft preparation, R.C.; writing--review and editing, R.C. and W.W. ([PERSON]); visualization, R.C.; supervision, R.C.; project administration, R.C.; funding acquisition, W.W. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by Postgraduate Research and Practice Innovation Program of Jiangsu Province(Grant No. KYCX21_0724) and Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications (Grant No. NY222118).  
Not applicable  
Data availability Statement: Data are contained within the article.  
Author [PERSON] was employed by the company Jiangsu Broadcasting Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 State-of-the-Art Models for Binaural Localization  
A machine learning approach is typically employed in modern computational models for binaural localization. The topology of these models typically includes an audio feature extractor followed by a classifier. Such features as the interaural level difference (ILD), interaural time difference (ITD), and interaural coherence (IC) [26] are typically calculated by the feature extractor. Then, the extracted features are fed to the input of classification algorithms, e.g., Gaussian mixture models [4]. More recently, deep learning techniques have been employed in the models [5; 6; 7; 8]. It was recently demonstrated that the binaural localization models can benefit from using a source separation algorithm [12].  
Binaural signals are normally subject to some forms of signal preprocessing prior to being used at the input of the deep neural networks. For example, instead of using the raw waveforms directly (such as in [9]), the standard features, such as ILD, ITD, and IC, could feed the network input [4]. Alternatively, the signals could be converted to spectrograms, which constitutes a common approach in the case of CNNs [27]. Due to the difficulty in the accurate estimation of ITD, particularly under noisy or reverberant conditions, raw values of the interaural cross-correlation function can be exploited at the input of the deep learning algorithms [6; 8].  
While the performance of the recently developed binaural models has been greatly improved, they still exhibit considerable errors attributed to a front-back confusion effect. To circumvent this issue, micro-head movements were modeled in some of the algorithms [4]. This solution is referred to as an \"active\" sound localization modeling [28]. Such an approach substantially reduces the front-back localization errors. However, it limits the applicability scope of the method, as it can only be employed in algorithms that allow for an adaptive synthesis of binaural signals [4], or it can be applied to systems equipped with robotic heads, such as those in [29]. For this reason, the method proposed in this paper was developed under the head-stationary condition (\"passive\" localization).  
While the state-of-the-art models are capable of localizing speakers in concurrent speech binaural signals, they require a priori information on the \"number\" of audio sources present in a complex scene and their \"characteristics\" [4; 5; 6; 7]. Such information is normally unavailable in the repositories of binaural music recordings on the Internet. Consequently, the method proposed in this paper is assumption free (\"blind\") in terms of the number of music sources and their characteristics.
# Image Quality Assessment to Emulate Experts' Perception in Lumbar MRI Using Machine Learning
## 3 Results
The agreement between each neuro-radiologist and the classification by SVM, being the best learning case, was evaluated and is displayed in Table 5. Agreement in all cases \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metric** & **Image** & **LDA** & **QDA** & **LogReg** & **SVM** & **MLP** \\ \hline \multirow{3}{*}{Accuracy} & Sagittal \(T_{1}\) & 0.740 & 0.713 & 0.721 & 0.763 & 0.731 \\  & Sagittal \(T_{2}\) & 0.713 & 0.632 & 0.689 & 0.772 & 0.649 \\  & Axial \(T_{1}\) & 0.767 & 0.634 & 0.747 & 0.726 & 0.726 \\ \hline \multirow{3}{*}{Precision} & Sagittal \(T_{1}\) & 0.731 & 0.467 & 0.518 & 0.673 & 0.635 \\  & Sagittal \(T_{2}\) & 0.769 & 0.686 & 0.717 & 0.737 & 0.686 \\  & Axial \(T_{1}\) & 0.717 & 0.537 & 0.700 & 0.622 & 0.670 \\ \hline \multirow{3}{*}{Recall} & Sagittal \(T_{1}\) & 0.625 & 0.275 & 0.583 & 0.817 & 0.692 \\  & Sagittal \(T_{2}\) & 0.675 & 0.515 & 0.600 & 0.890 & 0.605 \\  & Axial \(T_{1}\) & 0.775 & 0.642 & 0.725 & 0.750 & 0.675 \\ \hline \multirow{3}{*}{F1 score} & Sagittal \(T_{1}\) & 0.631 & 0.340 & 0.535 & 0.719 & 0.646 \\  & Sagittal \(T_{2}\) & 0.705 & 0.553 & 0.640 & 0.797 & 0.735 \\  & Axial \(T_{1}\) & 0.725 & 0.578 & 0.693 & 0.674 & 0.648 \\ \hline \multirow{3}{*}{AUC ROC} & Sagittal \(T_{1}\) & 0.727 & 0.777 & 0.746 & 0.792 & 0.801 \\  & Sagittal \(T_{2}\) & 0.710 & 0.716 & 0.763 & 0.759 & 0.735 \\ \cline{1-1}  & Axial \(T_{1}\) & 0.791 & 0.740 & 0.780 & 0.747 & 0.792 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Metrics of classifier models with respect to subjective Mean Opinion Score (MOS) attributed by experts. Figure 4: Correlation coefficients calculated between wMOS and each of the different features extracted, applied to the three image types. was moderate, showing the best results between SVM and NR\({}_{1}\). The exam type that showed the best agreement between expert, Sagittal \(T_{2}\), showed one of the best agreements between SVM and NR, 0.48 \(\pm\) 0.05. Conversely, Axial \(T_{1}\) images, which showed the lowest kappa index between NR, are also the ones that present the lowest kappa index between SVM and neuro-radiologist, of 0.37 \(\pm\) 0.04. In comparison with what is shown in Table 4, results are slightly improved for LDA, but no significant change is seen in SVM (data not shown).
## 3 Materials and Methods
### Framework Overview
#### 3.2.1 Physical Model  
The physically based component of the hybrid framework was implemented using HEC-HMS (Hydroologic Engineering Center's Hydrologic Modeling System). The model was configured to simulate streamflow for the selected catchment based on sub-basin delineation derived from a high-resolution Digital Elevation Model (DEM) combined with hourly meteorological inputs, including precipitation and temperature.  
The watershed delineation was based on the HydroSHEDS [44] void-filled digital elevation model at 15 arc-second resolution. The stream network was defined using the Copernicus Land Monitoring Service's European River Network Database [45], which provided accurate reach delineation for the interconnected lake system. The HEC-HMS built-in GIS tools were used for terrain preprocessing, including terrain reconditioning, sink treatment, drainage preprocessing, and stream network identification.  
Standard modeling components were employed, including the SCS Curve Number method [46] for surface runoff estimation, the Clark Unit Hydrograph [47] for transform modeling, and Muskingum routing for channel flow [48]. The model was run at an hourly timesteps to capture short-term hydrological dynamics relevant for discharge forecasting.  
The meteorological model incorporated rainfall data from spatially distributed weather stations located in Zazari and Amynttaio to account for precipitation variability across the watershed's sub-basins. Additionally, air temperature, wind speed, relative humidity, and evapotranspiration from Amynttaio station were given as inputs (see Section 2.2).  
Model calibration was performed using observed discharge data from the basin outlet over a yearlong period. Calibration focused on minimizing discrepancies between observed and simulated discharge using performance metrics such as the Nash-Sutcliffe Efficiency (NSE) and the Kling-Gupta Efficiency (KGE) [49; 50]. While the model was effective in capturing general runoff trends, discrepancies were noted during irrigation periods, particularly during dry seasons--highlighting the need for subsequent bias correction.  
Importantly, irrigation activities and other anthropogenic influences were not explicitly modeled in HEC-HMS, and their absence is addressed in the next stage of the framework through a data-driven correction process.
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 3 Proposed ACS Architecture
### ACS Functions  
The ACS functionality relies on a locally developed SDN path-computing application and the MPTCP transport layer. The application runs on top of the controller, and it is focused on reducing the delay of the communication and providing path diversity. Meanwhile, MPTCP runs in the OBE and the CCE with a scheduler that uses all the available network interfaces to obtain a redundant T2G wireless communication.
## 4 Discussion  
### Influence of Satellite Orbit Accuracy on Positioning Performance  
Satellite orbit parameters are fixed with final precise ephemeris products in this study, which are the most strongly recommended processing strategy in processing of regional or local GNSS network [19]. Reference [37] showed that the radial accuracy of orbits for BDS satellites can reach to 10 cm. Reference [38] also concluded that the overlapped RMS on the radial component of the orbits for BDS satellites are 10 cm. Reference [39] proved that the 3 days-session overlapped radial RMS can reach up to 1.8 m and 0.3 m for GEO and IGSO/MEO, respectively, however, the radial accuracies are 10 cm for all kinds of BDS satellites.  
To clarify the influence of ephemeris for BDS satellites, we acquired two sets of baseline solutions on GAMIT based on Wuhan University and GFZ ephemeris respectively, and made comparisons between them. In Figures 8 and 9, we present the mean and RMS of the differences between these two sets of baseline solutions. The mean of the differences has a scope of from \(-\)6.9 to 5.4, from \(-\)8.1 to 4.4, and from \(-\)26.6 to 15.0 mm on the north, east and up components, respectively. The average value of the mean of the differences is below 1 mm on all three components (see in Table 4), indicating no systematic discrepancies between Wuhan University and GFZ results. The variation of the RMS of differences gets amplified with the increasing length of baselines. The RMS of differences are 6.3, 7.6, and 23.5 mm on the north, east and up components, respectively, indicating no significant differences in view of the accuracy of BDS positioning results, and no systematic discrepancies between results resolved from two types of satellite ephemeris.  
Figure 7: The velocity field derived from BDS/GPS stations derived from GPS, BDS and BDS+GPS data in International Terrestrial Reference Frame 2014 (ITRF2014). (**a**) Horizontal component. (**b**) Vertical component.  
Figure 8: The variations on average values of baseline differences caused by different satellite ephemeris and whether or not including MEO satellites in BDS processing, with respect to baseline length.  
Figure 9: The variations on RMS of baseline differences caused by different satellite ephemeris and whether or not including MEO satellites in BDS processing, with respect to baseline length.
## 4 Algorithm Implementation  
The proposed algorithm is designed to image raw data. So, clutter suppression, target detection and target signal extraction are necessary before refocusing moving targets. The flow chart is shown in Figure 4. The processing mainly includes clutter suppression, target detection and target signal extraction, target motion parameter estimation and focus imaging. The detailed processing process is as follows.  
Step 1: Clutter suppression and target signal extraction  
Before imaging the moving target, we need to remove clutter in the signal by pretreatment. The moving-target signal is extracted after baseline delay compensation, channel equalization processing, STAP clutter suppression, and target detection operations on the multi-channel SAR signals. These pretreatments are shown in reference [31].  
Step 2: RCMC  
Hough transform and the SOKT are used to complete the range walk and the range curvature correction, so as to concentrate the signal energy into a range cell.  
Step 3: Motion parameter estimation  
A DCCF is constructed in the range-frequency domain and azimuth-time domain. An azimuth chirp signal is extracted at the cross-correlation peak range cell. The SAC algorithm is used to accurately estimate \(a_{3}\), and the residual range migration is further corrected with \(a_{3}\). Then, 2D-FFT transform is used to estimate the values of \(a_{1}\) and \(a_{2}\).  
Step 4: Imaging Processing  
The accurate range migration compensation function and the azimuth matched function are constructed according to the estimated \(\widehat{a_{1}}\), \(\widehat{a_{2}}\) and \(\widehat{a_{2}}\) to obtain focused imaging and accurately position the moving targets.  
Step 5: Target Relocation  
After obtaining the precise motion parameters, we can correct the position of the target by Equation (37).