esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 LR-Aided MIMO Detector Design
### ELR-Aided Detectors  
In Section 3.1 LLL-aided detectors have been described, which are capable of achieving significant performance improvement compared to detectors not using LR [10; 14]. However, this performance improvement degrades gradually and starts to deviate from the ML performance as the number of antennas increases [13; 18]. This is due to the fact that the LLL algorithm is less efficient in large-scale MIMO system [13; 18]. Recently, Element-based Lattice Reduction (ELR) algorithm [13] has been proposed to perform lattice reduction for large-scale MIMO systems with an improved performance compared to the LLL algorithm, while also requiring lower complexity [13]. Furthermore, we have proposed in [19] an ELR-aided K-Best detector that is capable of outperforming the LLL-aided detectors when used for large-scale MIMO at a reduced complexity.  
The ELR algorithm for evaluating the \(\mathbf{T}\) matrix is shown in Table 1. The ELR algorithm has been applied with ZF detector for large-scale MIMO in [13; 28], where a significant performance improvement has been attained compared to the LLL-aided detectors.  
In [19], we proposed to further improve the performance of ELR-aided detectors by employing the ELR algorithm before the K-Best detection process for large-scale MIMO. Our proposed ELR-aided K-Best detector can achieve an improved BER performance compared to the LLL-aided detectors, while requiring significantly lower complexity. The ELR-aided K-Best detection process is similar to that described in Section 3.1 with the difference that it adopts the ELR algorithm described in Table 1 in order to produce the \(\mathbf{T}\) matrix used to obtain the new channel matrix \(\widetilde{\mathbf{H}}=\mathbf{HT}\).  
The proposed ELR-aided K-Best detector can be further enhanced by employing MMSE regularization as described in Section 3.1 to obtain the ELR-aided MMSE K-Best detector. The above-proposed ELR-aided K-Best and ELR-aided MMSE K-Best detectors can perform better than the state-of-the-art detectors including LLL-aided K-Best and LLL-aided MMSE K-Best detectors, while at the same time requiring lower complexity. The reduction in the complexity is mainly due to the fact that the number of the arithmetic operations required by ELR algorithm for basis updates is lower than the LLL algorithm as explained in [13].
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 3 Antenna Design, Manufacture and Testing
### Testing of In Vitro Antenna  
The manufactured in-body antenna is verified in the in vitro antenna testing procedure using an artificially built biological environment or phantom [65]. The biological tissue properties (relative permittivity and electrical conductance) of different parts of the body at different operating frequencies are provided in the Foundation for Research on Information Technologies in Society (ITIS) website, which was established through the resourcefulness and support of the Swiss Federal Institute of Technology (ETH) in Zurich [71]. Before preparing the phantom of a particular body part, it is necessary to know its permittivity and electrical conductance at the in-body antenna operation frequency. Low-frequency band liquid phantom, as shown in Figure 5, is investigated in Refs. [70; 72], where purified water, polyethylene powder and NaCl are used as the main material, relative permittivity and conductivity generation material, respectively. Several works presented the investigation of the phantom in the ISM and MICS bands [13; 28; 73; 74]. In Ref. [13], a gel type phantom imitating the properties of muscle tissue is built with hydrophilic organic powder and degassed water, as shown in Figure 6.  
Figure 5: Liquid phantom for capsule localization investigation [70]. Reproduced with permission from an open-access article from _IEEE Access_ (CC-BY).  
A traditional method of measuring the tissue properties of a phantom is the co-axial probe, where a dielectric probe kit, such as SPEAG DAK 3.5, along with a vector network analyzer, such as Agilent 8753 ES, are used [75]. An alternative method utilized in the literature involves a dielectric resonator in close contact with the tissue [76]. These measurement techniques utilize the input reflection coefficient to calculate the material dielectric properties. Measurement uncertainty is significant for higher permittivity values, as there is less change in the measured reflection coefficient for discrepancies in material permittivity [75].
# Prediction and Control of Broadband Noise Associated with Advanced Air Mobility--A Review
## 3 Methods for Prediction of BBN
### Machine Learning  
Recent advances in machine learning have demonstrated significant potential for predicting rotor noise. [PERSON] and [PERSON] [72] developed a data-driven model based on basic rotor parameters, such as tip Mach number, collective pitch angle, twist angle, rotor solidity, and rotor radius to predict the trailing edge broadband noise of a multirotor UAM. They collected the data set by BEMT and XFOIL for aerodynamic and UCD-QuietFly for BBN. All of them were fed into an artificial neural network (ANN) (see Figure 20) for rotorcraft broadband noise training and a linear regression approach, as shown in Equations (22) and (23). When comparing the new parameter inputs with UCD-QuietFly, the artificial neural network (ANN) model demonstrated strong alignment with the results, while the linear regression model captured the overall trends effectively, though less precisely.  
\[OASPL=\alpha_{1}+\alpha_{2}M_{tip}+\alpha_{3}\theta_{0}+\alpha_{4}\theta+ \alpha_{5}\sigma+\alpha_{6}R+\alpha_{7}M_{tip}^{2}+\alpha_{8}\theta_{0}^{2}+ \alpha_{9}\theta^{2}+\alpha_{10}\sigma^{2}+\alpha_{11}R^{2} \tag{22}\]  
\[SPL(f)=\beta_{1}+\beta_{2}M_{tip}+\beta_{3}\theta_{0}+\beta_{4}\theta+\beta_{5 }\sigma+\beta_{6}R+\beta_{7}f+\beta_{8}M_{tip}^{2}+\beta_{9}\theta_{0}^{2}+ \beta_{10}\theta^{2}+\beta_{11}f^{2}+\beta_{12}\sigma^{2}+\beta_{13}R^{2} \tag{23}\]  
Similar work was conducted by [PERSON] and [PERSON] [73], who used the same machine learning (ML) model and low-fidelity methods to optimize hovering rotors. They utilized the ANOPP and ANOPP2 codes to predict the tonal noise and broadband noise (BBN), respectively. They confirmed that the machine learning (ML) model could accurately predict noise levels. To optimize the broadband noise (BBN), they identified the thrust conditions and pitch as the key factors.  
ML needs larger training data. High-fidelity data are expensive, and low-fidelity data are less accurate. To solve this problem, [PERSON] et al. [74] proposed a multi-fidelity ML model for propeller noise prediction. In their study, a deep neural network (DNN) machine learning (ML) model was employed, incorporating transfer learning (TL) to integrate data of varying fidelities. Initially, the model was trained on low-fidelity simulations that utilized blade element momentum theory (BEMT) coupled with [PERSON]'s theory. This was then further refined using a limited number of aeroacoustics wind tunnel measurements for 2-bladed propellers, applied through transfer learning (TL). The results confirmed that the TL-based multi-fidelity ML model could accurately predict noise levels when using new  
Figure 19: SPL\({}_{1/3}\) of pressure perturbations on suction surface for 64 dB–130 dB ranges [67].  
experimental parameter inputs. Moreover, comparisons with an ML model trained solely on low-fidelity or high-fidelity data demonstrated that the TL-enhanced model provided more accurate predictions.
# Review of Web Mapping: Eras, Trends and Directions
## 3 Web Mapping Eras of Development
### Web Mapping Eras
#### 3.2.3 Services Web Mapping Era
Some of the first standards issued by the OGC include Web Mapping Services (WMS) with the first version released in 2000 for web maps [47], Web Feature Services (WFS) in 2002 for vector-based features [48], Web Coverage Services (WCS) in 2005 for raster based data [49], and Web Processing Services (WPS) in 2007 for processing operations [50]. The number of geospatial standards has since increased to over 50 specifications currently released by the OGC to support web mapping and the GeoWeb.
# Simplified Fly Tower Modeling for Preliminary Acoustic Predictions in Opera Houses
## 2 Methodology  
Firstly, more common real stage house conditions are investigated to find a referring fly tower volume for preliminary prediction analysis, as a function of proscenium opening and stage dimensions; applicable conditions and limitations are included. Processes and techniques to obtain the acoustic parameters are also indicated, which follow the procedure of the room acoustic prediction for big, closed spaces, once the simplified fly tower model is added to the audience hall space, to create a single volume.  
The simplified fly tower modeling, as a fixed reference for preliminary acoustic predictions, aims to avoid checking the acoustic quality of the theater independently from the set form on stage, minimizing the effects of variable set elements.  
The simplified model can be very useful to study the acoustic field of an existing theater if measurements on-site are not available or the survey and virtual model calibration are conducted with house or fire curtains closed conditions, or to check the acoustic design development of the audience hall of a new opera house. It can be used, in fact, for the optimization of the sound quality of the stalls and galleries space, according to the evolution of the architectural requests.
## 4 Results
### ERA5 Wind Speed Correction and Its Impact on the Accuracy of VAM  
The results above indicate a significant deviation between the SAR wind retrievals and the buoy measurements. Specifically, all VAM retrievals exhibit negative biases when the wind speed exceeds 7 m/s. To explore the influence of the accuracy of background wind speed on the performance of the VAM, this study utilized observed wind speed data from 27 buoys from 2015 to 2024, which were then paired with ERA5 reanalysis data, yielding an extensive dataset comprising 5,206,485 matched pairs, as depicted in Figure 10. The analysis results show that as wind speed increases, the negative bias between ERA5 reanalysis data and buoy observations gradually increases, as shown in Figure 10b. This phenomenon is consistent with the findings of [PERSON] et al. (2018) and [PERSON] et al. (2019). Therefore, to improve the accuracy of the background wind speed and more accurately assess VAM performance, this study employed a linear function fitting approach to correct the ERA5 wind speed, resulting in an optimized background wind speed, denoted as \(V_{B\_correct}\), which can be expressed as  
\[V_{B}=0.73V_{B\_correct}+1.18 \tag{19}\]  
As shown in Figure 10b, the overall bias in the corrected ERA5 data decreased significantly from \(-0.54\) m/s to 0.21 m/s, indicating that systematic errors were effectively mitigated. Notably, when the wind speed exceeds 5 m/s, the previously dominant negative bias was substantially reduced.  
As shown in Figure 7b,d, it is particularly noteworthy that the RMSEs and biases of wind speeds generally decreased across all VAMs after background wind speed correction, with the improvement being especially significant in high-wind-speed regions, outperforming traditional direct inversion methods. Among all methods, including the direct inversion method, VAM3 (\(J_{NCRS+DCA}\)) exhibits the smallest RMSE across different wind speed ranges with a value of 1.35 m/s. Therefore, improving the background wind field speed dramatically enhanced the retrieval accuracy of VAM at higher wind speeds. However, in terms of wind direction inversion, the RMSE and bias for wind direction inversion by each method showed relatively stable performance before and after the application of background wind field correction, as depicted in Figure 8b,d.  
Figure 9: (**a**) Wind field retrieved using VAM3 (\(J_{NCRS+DCA}\)) from a SAR image acquired on 30 April 2020 along the Gulf Coast, and (**b**) the corresponding ERA5 background wind field. The arrows denote wind vectors derived from (**a**) the SAR wind field and (**b**) the ERA5 background field, respectively.
# RPKI Defense Capability Simulation Method Based on Container Virtualization
## 4 The BGP Simulation Network Deployment
### Virtualization-Based BGP and RPKI Simulation Technology  
To meet the construction requirements of the simulated target BGP network, this paper designs and implements virtual images for BGP network simulation and RPKI component simulation based on Docker container virtualization technology. This involves specific images such as BGP router images, RPKI server node images that integrate the CA and publication servers, and RP node images. Simulation image management encompasses processes such as building foundational images, creating Dockerfiles for specific images, compiling into Docker images, and synchronizing images. The specific process is illustrated in Figure 2.  
In particular, the BGP router image is built upon the open-source routing software FRRouting 8.5.2 [30], which incorporates a self-developed router initialization configuration program, an automated route configuration loading script, and a data collection program at the application layer. This results in a virtual router image that features a dynamic routing protocol configuration, ROV execution, and experimental result collection.  
The RPKI server-side node image is also based on the open-source software Krill 0.9.4. Exploiting Docker's multi-stage image construction, the Krill binary program compiled during the build stage is implanted into the base image, thereby simplifying the Docker image and helping to speed up the creation of the simulation network. To achieve a fully functional RPKI implementation in the cloud platform simulation network, it is necessary to generate a root CA. The root CA, also known as a Trust Anchor (TA), is responsible for issuing self-signed certificates for the entire IP address space and its associated AS. To simplify the deployment process and enable the instance to provide services upon startup, a modified Krill configuration file is added to the image, deploying the publication server on the certificate authority as well. Additionally, to provide high-availability access for the RPs of many ASes, a reverse proxy software, NGINX 1.18.0, is included in the image. This setup allows the clients to access services without knowing how to directly access them while handling more concurrent requests to enhance simulation stability. As the root certificate is self-signed, the manual generation and addition of a root TLS certificate for issuing subordinate certificates are also needed.  
The RP node image builds upon the base image by adding the open-source software Routinator 3000 0.12.1. During the image construction, it is necessary to install the root certificate generated by the server-side node in the RP. This enables the RP, upon deployment and startup, to automatically retrieve certificates from the publication server and verify the encrypted signatures of ROAs. All validated ROA entries are then pushed to the routers via the RTR protocol.  
Figure 2: The simulation image construction process.
# Future Directions for Electric Propulsion Research
## 4 Challenges for Electric Propulsion Development
### Moderate-Power Thrusters  
As Figure 8 shows, perhaps the most mature electric propulsion devices are those of moderate power, between 1 and 20 kW. Considering the availability of solar power on modern spacecraft, the strictures of flight-readiness, and the limitations of ground testing, most commercial and deep space missions utilizing electric propulsion in the next decade will rely on thrusters at this power level.  
Figure 8: Notional s-curves for various moderate-power electric propulsion technologies categorized with the color scheme of Figure 1, and additionally including arcjets (AJ) and pulsed inductive thrusters (PIT).  
Figure 7: A coaxial microwave thruster designed and tested at the University of Michigan based on a similar device designed at ONERA [36].
# Advanced Analysis Technologies for Social Media
## 4 Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study  
In [3], the authors propose a general framework to identify echo chambers on Online Social Networks (OSNs) built on top of features they commonly share. The paper proposes a four-step pipeline which involves the identification of a controversial issue; the inference of users' ideology on the controversy; the construction of users' debate network; and finally, the detection of homogeneous meso-scale communities. The framework has been evaluated with a real case study on Reddit, covering the first two and a half years of [PERSON]'s presidency. Results show that, even if users appear strongly polarized with respect to their ideology, most tend not to insulate themselves in echo chambers.
# A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs +
## 2 Proposed System
### Modified WaveNet Architecture
#### 2.2.2 Regularization  
While the generation process is autoregressive, during training rather than using past predictions, groundtruth past samples are used. This is a practical necessity as it allows the computations to be parallelized. However this also causes a number of issues. One issue, known as exposure bias [14], results in the model becoming biased to the groundtruth data it is exposed to during training, and causing errors to accumulate at each autoregressive generation step based on its own past predictions. In our case, such errors cause a degradation in synthesis quality, e.g., unnatural timbre shifts over time. Another notable issue is that as the model's predictions are conditioned on both past timesteps and control inputs, the network may mostly only pay attention to past timesteps and ignore the control inputs [15]. In our case, this can result in the model occasionally changing certain lyrics rather than follow those dictated by its control inputs.  
One way to reduce the exposure bias issue may be to increase the dataset size, so that the model is exposed to a wider range of data. However, we argue that the second problem is mostly a result of the inherent nature of the data modeled. Unlike raw waveform, vocoder features are relatively smooth over time, more so for singing where there are many sustained vowels. This means that, usually, the model will be able to make accurate predictions given the highly correlated past timesteps.  
As a way around both these issues, we propose using a denoising objective to regularize the network,  
\[\mathcal{L}=-\log p\left(\mathbf{x}_{t}\mid\mathbf{\tilde{x}}_{<t},\mathbf{c }\right)\quad\text{with}\quad\mathbf{\tilde{x}}_{<t}\sim p\left(\mathbf{ \tilde{x}}_{<t}\mid\mathbf{x}_{<t}\right)\,, \tag{3}\]  
where \(p\left(\mathbf{\tilde{x}}\mid\mathbf{x}\right)\) is a Gaussian corruption distribution,  
\[p\left(\mathbf{\tilde{x}}\mid\mathbf{x}\right)=\mathcal{N}\left(\mathbf{ \tilde{x}};\mathbf{x},\lambda I\right)\,, \tag{4}\]  
with noise level \(\lambda\geq 0\). That is, Gaussian noise is added to the input of the network, while the network is trained to predict the uncorrupted target.  
When sufficiently large values of \(\lambda\) are used, this technique is very effective for solving the problems noted above. However, the generated output can also become noticeably more noisy. One way to reduce this undesirable side effect is to apply some post processing to the predicted output distribution, much in the same vein as the temperature softmax used in similar models (e.g., [9]).  
We have also tried other regularization techniques, such as dropout, but found them to be ultimately inferior to simply injecting input noise.
###### Abstract  
Ground vibration induced by blasting operations is an important undesirable effect in surface mines and has significant environmental impacts on surrounding areas. Therefore, the precise prediction of blast-induced ground vibration is a challenging task for engineers and for managers. This study explores and evaluates the use of two stochastic metaheuristic algorithms, namely biogeography-based optimization (BBO) and particle swarm optimization (PSO), as well as one deterministic optimization algorithm, namely the DIRECT method, to improve the performance of an artificial neural network (ANN) for predicting the ground vibration. It is worth mentioning this is the first time that BBO-ANN and DIRECT-ANN models have been applied to predict ground vibration. To demonstrate model reliability and effectiveness, a minimax probability machine regression (MPMR), extreme learning machine (ELM), and three well-known empirical methods were also tested. To collect the required datasets, two quarry mines in the Shur river dam region, located in the southwest of Iran, were monitored, and the values of input and output parameters were measured. Five statistical indicators, namely the percentage root mean square error (%RMSE), coefficient of determination (\(R^{2}\)), Ratio of RMSE to the standard deviation of the observations (RSR), mean absolute error (MAE), and degree of agreement (d) were taken into account for the model assessment. According to the results, BBO-ANN provided a better generalization capability than the other predictive models. As a conclusion, BBO, as a robust evolutionary algorithm, can be successfully linked to the ANN for better performance.  
blasting; BBO optimization; ANN; PPV 2020 10, 434; doi:10.3390/app10020434
## 8 Discussion and Implications in the Contents of Privacy Regulations  
The real-world applications of the ShinyAnonymizer Enhanced Version may significantly affect privacy rules for individuals in managing health data storage and use. The tool allows adherence to major confidentiality laws by fully anonymizing patient data, such as the General Data Protection Regulation (GDPR) in the European Union and the Health Insurance Portability and Accountability Act (HIPAA) in the United States. They include strong protection processes and the careful storage of sensitive patient data. The upgraded ShinyAnonymizer offers an easy method for anonymizing health data, limiting legal issues and concerns. These uses include business, academia, medical studies, and scientific fields, showing the worth of privacy and confidentiality. Regulations additionally require medical facilities to establish security processes for handling modifications that impact personal data. The ShinyAnonymizer Enhanced Version helps businesses establish guidelines that prevent illicit conduct and guarantee secure data processing in every circumstance. Adherence to these rules restricts risk while enhancing trust. The enhanced version conforms with privacy rules by integrating modern privacy-preserving methods such as hashing, encryption, and anonymization. It conforms with HIPAA and GDPR criteria, covering the requirement for comprehensive security against data re-identification. It highlights the tool's essential role in ensuring privacy rules while maintaining program accuracy, using a complete security plan that utilizes numerous methods.  
The tool's user-friendly design lets investigators, healthcare facilities, and educational institutions collaborate rapidly with anonymized data, while adhering to ethical and privacy requirements. The implications of the ShinyAnonymizer Enhanced Version extend through the data lifecycle, impacting data security practices from collection to elimination. Privacy legislation frequently defines how data should be collected, handled, used, and adequately eliminated at every stage of their duration. Businesses may limit the chance of legal issues or negative reputations due to data breaches via health data security measures. The enhanced version has a simple graphical user interface encouraging responsible and ethical data management. It permits greater data-processing adaptability and indicates businesses' responsibility for maintaining privacy rights. The tool's user-friendly design lets investigators, healthcare facilities, and educational institutions collaborate rapidly with anonymized data, while adhering to ethical and privacy requirements.