esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \multirow{2}{*}{**Measurement**} & \multicolumn{8}{c}{**Group EXP (n = 15)**} & \multicolumn{8}{c}{**Group CON (n = 12)**} & \multirow{2}{*}{**d**} & \multirow{2}{*}{**_p_**} \\ \cline{2-13}  & \(\widetilde{\mathbf{x}}\) & & & & & & & & & & & & & \\ \hline \multicolumn{13}{c}{Simple Reaction Time [ms]} \\ I & 308 & 30 & 305 & 261 & 358 & 10 & 306 & 34 & 314 & 256 & 348 & 11 & 2.0 & 1.000 \\ II & 303 & 34 & 300 & 239 & 355 & 11 & 304 & 22 & 308 & 267 & 338 & 7 & \(-\)1.0 & 0.961 \\ III & 287 & 25 & 290 & 221 & 319 & 9 & 285 & 25 & 290 & 224 & 327 & 9 & 2.0 & 0.608 \\ \hline \multicolumn{13}{c}{Complex Reaction Time [ms]} \\ I & 473 & 72 & 464 & 316 & 611 & 15 & 487 & 107 & 467 & 365 & 723 & 22 & \(-\)14 & 0.981 \\ II & 479 & 88 & 461 & 348 & 687 & 18 & 539 & 151 & 476 & 407 & 936 & 28 & \(-\)60 & 0.393 \\ III & 447 & 59 & 438 & 331 & 531 & 13 & 427 & 79 & 428 & 303 & 540 & 19 & 20 & 0.526 \\ \hline \multicolumn{13}{c}{Visual–Motor Coordination [ms]} \\ I & 81 & 4.4 & 81 & 72 & 88 & 5 & 80 & 6.7 & 81 & 71 & 92 & 8 & 1.0 & 0.755 \\ II & 77 & 5.1 & 77 & 68 & 88 & 7 & 77 & 6.2 & 76 & 68 & 87 & 8 & 0.0 & 0.961 \\ III & 74 & 5.5 & 75 & 66 & 86 & 7 & 76 & 5.1 & 76 & 65 & 85 & 7 & \(-\)2.0 & 0.456 \\ \hline \multicolumn{13}{c}{Spatial Orientation [ms]} \\ I & 101 & 12 & 105 & 85 & 122 & 12 & 100 & 14.4 & 97 & 84 & 135 & 14 & 1.0 & 0.719 \\ II & 94 & 12 & 94 & 75 & 119 & 12 & 96 & 9.8 & 93 & 80 & 112 & 10 & \(-\)2.0 & 0.829 \\ III & 91 & 11 & 92 & 75 & 110 & 12 & 93 & 9
# Creating Audio Object-Focused Acoustic Environments for Room-Scale Virtual Reality
## 1 Introduction
Additionally, the multimodal nature of VR must also be considered, since humans generally experience the physical world through more than one sense [16]. For this reason, interaction feedback should be provided using multimodalities, such as mapping the change in object properties to visualizations, vibration, and audio simultaneously [16]. To address the issues of interactive spatial audio for room-scale VR, this paper investigates the creation of virtual acoustic environments through the establishment of virtual objects whose behavior can be described by rules and whose audio content is spatialized in 6-DoF using 3D binaural audio exclusively. This methodology aims to reimagine the temporal structure of an acoustic environment using rule-based systems wherever possible, rather than relying on Ambisonic beds or non-diegetic music as surrounding ambiances. Firstly, this recreation allows the sound designer the injection of new, multimodal behaviors to cater to interactivity. Secondly, an object-focused, 6-DoF 3D binaural audio approach to acoustic environments allows a player to understand their proximity to the environment's objects. Similar approaches may have been used in VR games such as _Half-Life: Alyx_[17]; however, documentation of such approaches may be lacking, and this paper aims to help fill this gap. Written from a sound designer's perspective, and using the taxonomy of sounds in VR presented in [18], this paper bases its novel methodology on off-the-shelf tools and methods, while targeting mobile VR as a presentation platform. The structure of the paper is as follows: Section 2 reviews the literature to find possible solutions to the issues raised in the paper. Section 3 describes the methodology of the paper's approach. Section 4 describes the implementation of the methodology based on example applications, which are evaluated with comparable alternative solutions in Section 5. Section 6 discusses the limitations of the proposal. Finally, Section 7 concludes.
## 2 System Model
### Ofdm  
Instead of transmitting a high-rate data stream using a single subcarrier, the OFDM technique can be used, which transmits a large number of closely spaced orthogonal subcarriers transmitted in parallel, each subcarrier having a low rate. Figure 1 illustrates the operations made upon a signal to obtain the OFDM symbol.  
The input bits are coded and mapped, according to the techniques presented above, to data symbols that are complex numbers representing the modulation constellation points. A serial-to-parallel converter is applied, associating a subcarrier to each OFDM symbol. The next step is to use an Inverse Fast Fourier Transform (IFFT) algorithm on the parallel complex data, that transforms the data into the time domain from the frequency domain. The IFFT block receives \(L\) data symbols, \(L\) representing the number of subcarriers and the output is \(L\) orthogonal sinusoids that form a single OFDM symbol. After a parallel-to-serial conversion, meant to extend the transmission time for \(L\) subcarriers from \(T_{s}\) to \(T_{OFDM}\) = \(LT_{s}\), where \(T_{s}\) represents the period of the symbol, the OFDM complex signal in base band has the following form in the time domain  
\[s_{bb}(t)=\sum_{l=0}^{L-1}s_{l}(t)e^{j\ln\omega_{l}}, \tag{8}\]  
where  
\[\omega_{l}=2\pi f_{l}=\frac{2\pi}{T_{OFDM}} \tag{9}\]  
and  
\[f_{l}=f_{0}\ +\ \Delta\omega, \tag{10}\]  
where \(f_{0}\) represents the carrier frequency and \(\Delta\omega\) represents the subcarrier spacing, and the shortest duration that accomplishes this requirement is  
\[\omega_{l\ +\ 1}-\omega_{l}=\Delta\omega=\frac{2\pi}{T_{OFDM}}. \tag{11}\]  
To avoid possible occurrence of inter-symbol interference (ISI) between two consecutive OFDM symbols, a guard interval known as cyclic prefix, appended at the beginning of each symbol, is introduced. In order to create the guard interval, either the final part of an OFDM symbol is resumed at the beginning of it, or it is filled with a null signal. In this way, the length of the signal is extended with \(T_{cp}\), meaning \(T_{OFDM}\ +\ T_{cp}\), maintaining the orthogonality of the subcarriers.  
The next step is to transmit the OFDM signal \(s_{cp}(t)\) on a carrier frequency \(f_{0}\). Therefore, the output of the OFDM transmitter block is  
\[s_{cp}(t)=\text{Re}\big{[}s_{bb}(t)e^{j2\pi f_{0}}\big{]} \tag{12}\]After the signal passes through the channel, the OFDM receiver performs the reverse operations performed by the transmitter. Thus, the guard interval is removed, then a serial-to-parallel conversion is performed and the Fast Fourier Transform (FFT) algorithm is used to return to the original spectrum of the transmitted signal, bringing it again from time domain to frequency domain. By applying the parallel-series conversion, the reconstructed data sequence is obtained.
## 4 Discussion
Measurement errors can arise from several sources. Inaccurate calibration of the spectrum analyzer, antennas, and other measurement equipment can lead to erroneous readings. Issues with data-logging software or hardware malfunctions can result in incorrect data capture. Inaccurate positioning of the UAVs, despite using RTK GNSS systems, can lead to discrepancies in the measured data. The actual performance of the antennas may differ from their theoretical specifications, due to variations in the manufacturing process, which can lead to differences in antenna performance. Improper installation or alignment of the antennas on the UAVs can affect their radiation patterns. Over time, antennas may degrade, due to environmental exposure, leading to performance deviations. The experimental setup and methodology can also contribute to the observed differences. For example, variations in the UAV flight path and altitude can lead to inconsistent measurements. The algorithms used for data processing and visualization may introduce errors if not properly validated. The frequency and timing of data sampling can affect the accuracy of the measurements. The theoretical models used to predict antenna performance may have inherent limitations. Theoretical models often rely on simplified assumptions that may not fully capture real-world complexities. Variations in the dielectric properties of the ground, buildings, and vegetation can affect signal propagation in ways not accounted for in the theoretical models. To protect against distortions in electromagnetic field measurements using drones around telecommunication antennas, especially when signals are transmitted and received during UAV operations, we took several measures. Firstly, we ensured that the drone's remote-control signals and the telecommunication antenna signals operated on different frequency bands. This helped to avoid frequency overlap and reduced the likelihood of interference. Secondly, we optimized the drone placement, whereby it was strategically positioned to minimize signal overlap. We avoided flying drones too close to antennas or other sources that could cause electromagnetic interference. Also, it was necessary to consider using dual-frequency devices that operated on multiple frequencies. This provided more flexibility and reduced the chances of interference. Lastly, we continuously monitored and analyzed interference, to quickly respond to any changes. This helped maintain measurement accuracy and reduced the impact of interference.
###### Abstract  
In the era of precision medicine, increasing importance is given to machine learning (ML) applications. In breast cancer, advanced analyses, such as the radiomic process, characterise tumours and predict therapy responses. Breast magnetic resonance imaging (MRI) plays a key role in screening, staging, and treatment monitoring. Lesion segmentation on MRI is essential both to assess tumour growth and as a baseline for radiomic feature extraction. Manual segmentation is time-consuming and prone to inter-operator variability, limiting access to large labelled datasets and robust analyses. The use of ML for breast lesion segmentation on MRI has been investigated through a systematic review of PubMed, exploring studies published over the last 10 years. Results are compared in terms of performance, primarily using the Dice score. Early unsupervised methods achieved a mean Dice score of \(\sim\)0.75, surpassing traditional supervised methods (\(\sim\)0.70). In contrast, deep learning (DL) approaches based on U-Net achieved higher average scores of 0.79. Further customised supervised DL approaches reached a mean Dice score of \(\sim\)0.83. However, there is still a gap in research on unsupervised DL techniques, which could help reduce bias and human variability. Future work may also explore multiparametric and multitechnique data, integrating more representative samples, including non-mass lesions.  
machine learning; deep learning; segmentation; breast lesion segmentation; breast magnetic resonance imaging; breast cancer +
Footnote †: journal: Medical Physics Unit, Department of Pathology and Diagnostics, Azienda Ospedaliera Universitaria Integrata, Ple Stefani 1, 37126 Verona, Italy1
Solvent casting uses a single solvent (THF or dimethyl sulfoxide, DMSO) to dissolve the polymer and prepare the membrane, but the pores of the membrane are rare and ionic conductivity is rather low [19; 20]. The electrospun technique is also not significant in improving ionic conductivity performance [7; 21]. Compared with the above techniques, the phase inversion method is easier and makes pore formation in the membrane more convenient. A competitive mutual diffusion thus takes place between the solvent and non-solvent, resulting in a uniform porous structure [24; 25; 26]. In particular, the porosity of the polymer membrane can be controlled by varying the kind, content and separative condition of the organic solvent to form the membrane with a uniform porous structure [27]. Moreover, the controllable finished shape of the polymer membrane and the simple preparation process greatly contribute to realizing largescale industrial production. [PERSON] et al. [28] prepared a novel macro-porous polymer electrolyte based on PVDF/PEO (poly(ethylene oxide))-b-PMMA using the phase inversion technique. They found that the PEO-b-PMMA block copolymer could obviously optimize the porous structure and improve connectivity [28]. Generally, the solvents in the phase-inversion method are tetrahydrofuran (THF) [29], dimethylformamide (DMF) [30], and acetone [31] among others. However, the low ionic conductivity of the membrane is occurred when employing single solvents like THF or DMF, combined with off-size pores. The organic solvent is a key that affects the structure and electrochemical properties of the p(OPal-MMA) polymer membrane; however, there is little study on it. Thus, the preparation technology of GPE by phase inversion method was investigated in this study. The bi-solvent two-step phase-inversion method was applied to prepare a uniform porous polymer membrane and the pore volume and pore size of the membrane were controlled by changing the ratio of the different solvents, and hence the liquid electrolyte uptake and ionic conductivity of the membrane could be controlled as well. We used p(OPal-MMA) as material to prepare the self-supporting microporous p(OPal-MMA) membrane. The mixed organic solvents were composed of porogen (DMF or DMAC) and solvent (acetone or THF) with different concentrations. The corresponding physical and electrochemical characters of p(OPal-MMA) membrane was also investigated.
# Design of a Filtering Power Divider with Simple Symmetric Structure Using Stubs
## 3 Structure of the Proposed WPD
### Input Short-Ended Stub  
To improve the proposed power divider's frequency response, a short-ended stub is added at the input of the WPD. This short-ended stub is connected to the input port and located inside the divider structure to maintain the overall size of the device. The frequency response and layout of the basic design WPD are shown in Figure 4. As can be seen in Figure 4, adding the input short-ended stub in the basic power divider design resulted in an input return loss below the \(-30\) dB attenuation level at the operating frequency.  
Figure 4: _Cont._  
Figure 3: Structure and frequency response of the initial power divider design at the operating frequency of 2 GHz. (**a**) Layout; (**b**) \(\mid\)S\({}_{21}\mid\) and \(\mid\)S\({}_{11}\mid\).
# An Effective Off-Grid DOA Estimation Algorithm Using Difference Coarrays with Limited Snapshots
## 6 Conclusions  
This paper presented an effective coarray-based method for off-grid DOA estimation, demonstrating a superior performance in challenging scenarios. A noise elimination technique was developed to reduce the noise effect by left-multiplying a matrix to the vectorized sample covariance matrix. An angle and power iterative correction method was proposed to estimate the source angle in each iteration. The proposed method successfully addressed the conventional trade-off between the snapshot requirements and estimation accuracy, delivering precise DOA estimation with significantly reduced snapshot demands. Furthermore, we derived the CRB for the deterministic orthogonal signal model, providing a theoretical performance benchmark. Although the search-based, EPUMA, and SS-MUSIC methods can be utilized to perform off-grid DOA estimation, the proposed method provided an optimal performance, delivering a high-precision DOA estimation and excellent resolution with minimal snapshot requirements. Moreover, the RMSE of the proposed method can coincide with the CRB, and three iterations were sufficient for convergence, which manifested the effectiveness and efficiency of the proposed method for coarray-based off-grid DOA estimation for both cases of fewer sources than sensors and more sources than sensors with few snapshots. The limitation of the proposed method is that it does not consider the effects of different noise characteristics and array configurations, which provides practical guidelines for subsequent investigations in array signal processing.  
Conceptualization, Y.M., J.W. and L.C.; methodology, Y.M., J.W. and L.C.; software simulation and parameter optimization, Y.M., J.W. and L.C.; validation, P.G. and G.F.; writing--original draft preparation, Y.M. and J.W.; writing--review and editing, L.C., P.G. and G.F.; supervision, P.G. and G.F. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China, grant number 62301590. This research was also sponsored by the Beijing Nova Program (grant number 20240484571).  
Not applicable.  
**Data Availability Statement:** The MATLAB2019b code is available from the authors.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Conditions** & **Search-Based** & **EPUMA** & **SS-MUSIC** & **Proposed** \\ \hline ULA (fewer sources) (10 snapshots) & 0.475 & 0.242 & 0.255 & 0.214 \\ ULA (fewer sources) (100 snapshots) & 0.393 & 0.070 & 0.124 & 0.068 \\ NLA (fewer sources) (10 snapshots) & 0.294 & 0.118 & 0.181 & 0.110 \\ NLA (fewer sources) (100 snapshots) & 0.264 & 0.037 & 0.105 & 0.036 \\ NLA (more sources) (10 snapshots) & 0.214 & 0.160 & 0.205 & 0.140 \\ NLA (more sources) (100 snapshots) & 0.154 & 0.046 & 0.122 & 0.043 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMSE of the four methods in the case of different conditions. Unit: degree.
# A 4.1 W/mm\({}^{2}\) Hybrid Inductive/Capacitive Converter for 2-140 mA-DVS Load under Inductor +
## 2 Architecture
### Combined Converter Design  
A state machine selects between the inductive and capacitive converter based on the reference voltage with the inductive converter being selected when the reference voltage is greater than 0.55 V and capacitive converter otherwise. As discussed earlier, there is hysteresis in the loop. The switch from inductive to capacitive only occurs when output voltage drops below 0.5 V. The state machine also turns off the converter that is not in use by gating off the clock to that particular converter. The overall converter is shown in Figure 14 with the inductive on top and the capacitive on the bottom. The combined hybrid converter occupies a total area of 0.4 mm\({}^{2}\) that includes the area occupied by the decoupling capacitors on the input supply as shown in Figure 15. The filter capacitor is shared between the two converters. The input supply voltage of the converter is 1.2 V and the converter supplies a maximum power of 140 mW.  
Figure 14: Overall hybrid capacitive/inductive converter.  
Transition Circuits:The transition between the inductive and capacitive converters is decided digitally (as explained in more details later), but the effective circuit during the transition is shown in Figure 16. In capacitive mode the filter capacitor of inductive part acts as tank capacitor, and in the inductive mode the bucket capacitors of capacitive converter acts as additional filter capacitors.
# Analysis of Electromagnetic Field Characteristics of Wave Glider
## 5 Conclusions  
When the wave glider works, its electromagnetic field interference is relatively large. The peak value of the axial frequency electric field is basically between 0.5 and 0.7 mV/m, the peak value of the electrostatic field is about 0.2-0.4 mV/m, the peak value of the magnetic field is about 80 nT, and the magnetic moment is about 0.5 A\(\cdot\)m\({}^{2}\). Compared to the electromagnetic interference generated by the wobbling, the rudder works with a greater electromagnetic field interference.  
Therefore, the electromagnetic sensor is restricted by the working conditions of the wave glider and is not suitable for direct installation. From the practical engineering point of view, in order to reduce the electromagnetic interference of the wave glider itself, the following methods can be considered: 1 change the connection between the surface floating body and the underwater towing body to reduce the transmission of the swaying and rudder work amplitude; 2 change the structure of the underwater towing body material, e.g., replace it with a non-metallic material to reduce the relevant section corrosion electric and magnetic fields; 3 design the equipment pads to hang the electromagnetic sensors in the deep-water area, which can interfere with the background environment and increase the stability of electromagnetic sensor operation; 4 change the rudder material and structure, consider the use of non-magnetic materials, or other directional adjustment devices to control the main electromagnetic field signals. Determining how to use wave gliders for underwater electromagnetic detection targeting will be the focus of future research.  
Data curation, T.X.; formal analysis, J.Z. and D.X.; investigation, D.X.; methodology, J.Z., D.X., T.X. and Q.J.; writing--original draft, J.Z.; writing--review and editing, T.X. and Q.J. All authors have read and agreed to the published version of the manuscript.  
Supported by the Youth Fund of the National Natural Science Foundation of China (51509252).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Discussion
The possible mechanisms behind prenatal BPA-induced hepatic steatosis include defective hepatic mitochondrial function and activated hepatic lipid metabolism [23; 43], and also, importantly, the epigenetic regulation of novel hypomethylalted sites in the Fas, Srebp-1c, and Nrf genes [22]. Hyperlipidemia is known to cause vacuolation in renal tubules [46]. In the present study, cytoplasmic vacuolation was evident in the tubules of the renal cortex in all the study groups. [PERSON] et al. observed male histological features in the renal structure of BPA-exposed female offspring [24]. In contrast, in the present study, we did not observe such changes in the kidneys of female offspring. The conflicting views with regard to BPA exposureon nephrogenesis could be attributed to the differences in experimental conditions. More studies are required to draw a conclusive result in this regard. Perinatal Rsv supplementation significantly reduced heart weight, LVW thickness, and interstitial fibrosis and significantly prevented hepatic steatosis in the adult offspring. Further, Rsv-treated offspring mice had fewer elastin breaks and a reduced intima-to-media ratio in the BPA-exposed group, indicating the beneficial role of Rsv against atherosclerotic progression. Previously, Rsv supplementation during gestation was reported to prevent hypertension in the offspring [47]. Rsv has been shown to prevent cardiovascular diseases through Sirt1 and AMPK activation as well as promoting endogenous antioxidant enzymes [48]. Perinatal Rsv supplementation in rats inhibited hepatic lipogenesis in adult offspring by activating AMPK and upregulating Sirt 1 [49]. In another study, perinatal Rsv supplementation prevented high-fat diet-induced nonalcoholic fatty liver disease through the renin-angiotensin system [50]. The epigenetic modification or reprogramming of genes is known to be involved in the developmental or fetal origin of adult diseases. Dr [PERSON] defined epigenetics as the occurrence of heritable changes in gene expression without any alterations in the DNA sequence itself [51]. As per this definition, both DNA methylation and histone modifications are associated with epigenetic mechanisms [52; 53]. Rsv is known to exhibit epigenetic regulation through its abilities to inhibit histone deacetylase [54] and DNA methyltransferase 1 [55; 56]. Studies have proved that Rsv acts as an miRNA regulator in various disease-related therapies [57; 58]. Based on these reported facts, we hypothesize that the beneficial effect of Rsv against BPA-induced cardiovascular changes in the adult offspring could be attributed to its epigenetic regulation and/or miRNA regulation. Molecular studies exploring the effects of perinatal Rsv exposure on epigenetic mechanisms are warranted. The present study has the following limitation. The impact of the study would have been further enhanced by evaluating the epigenetic modifications and molecular mechanisms involved in the ameliorating effects of Rsv supplementation on prenatal BPA exposure-induced potentially pathological alterations in cardiovascular and liver tissues.