esa-sceva/satcom-chunk-collection huggingface dataset:
# The Rebound Effect of the COVID-19 Pandemic on Clinically Significant Prostate Cancer Diagnosis
## 3 Results  
A total of 2502 patients underwent prostate biopsy between January 2018 and December 2022. The median (IQR) age of patients was 68 (62-74) years. Population characteristics are described in Table 1.  
PCa was detected in 1447 (57.8%) patients, including 935 (37.4%) with csPCa. Atypical Small Acinar Proliferation (ASAP) was found in 80 patients (3.2%), while at least one biopsy core with Prostatic Intraepithelial Neoplasia (PIN) was found in another 90 (3.6%) patients. Glandular atrophy was present in as many as 1003 patients (40.1%).  
We observed a significant decrease in the number of biopsies performed during the COVID period when compared with the pre-COVID and post-COVID periods. Specifically, the median (IQR) number of biopsies performed per month was 46 (43-56), 39 (27-43) and 46 (40-58) before, during and after the pandemic, respectively (_p_ = 0.02, Table 2).  
The most significant decrease in the number of procedures performed was recorded from March to May 2020 (Figure 1).  
The decrease in the number of biopsies performed resulted in a similar decrease in csPCA diagnosed per month (median \(n\) = 12), when compared with before (\(n\) = 18) and after  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **N = 2502** \\ \hline Age, years & 68 (62, 74) \\ Any PCA, \(n\) (\%) & 1055 (42.2\%) \\ Present & 1447 (57.8\%) \\ csPCA, \(n\) (\%) & 1567 (62.6\%) \\ Absent & 935 (37.4\%) \\ ASAP, \(n\) (\%) & 2422 (96.8\%) \\ Absent & 80 (3.2\%) \\ PIN, \(n\) (\%) & 2412 (96.4\%) \\ Absent & 90 (3.6\%) \\ Standard atrophy, \(n\) (\%) & 1499 (59.9\%) \\ Absent & 1003 (40.1\%) \\ \hline \end{tabular} Any PCA: any grade prostate cancer; csPCA: clinically significant prostate cancer; ASAP: Atypical Small Acinar Proliferation; PIN: Prostate Intraepithelial Neoplasia.  
\end{table}
Table 1: Histological characteristics of prostate biopsies performed in a cohort of 2502 patients between January 2018 and December 2022.  
Figure 1: The loess curve depicting the trend in the number of biopsies with the detection rate of csPCA and non-cs PCa per month. csPCA: clinically significant prostate cancer.  
(\(n\) = 20) the pandemic (\(p\) = 0.02). A similar trend was not observed for non-csPCa, since its detection rate was similar across the three periods (\(p\)-value = 0.8, Table 2). Notably, the highest csPCa detection rate was observed in the post-COVID period (39%) as compared to the COVID (34%) and pre-COVID period (37%), but this difference did not reach statistical significance (\(p\) = 0.1, Table 2). The loess function (Figure 1) depicted the relationship between changes in the number of biopsies performed and the PCa detection rate across the three periods. We observed a significant rebound effect on the number of biopsies performed and csPCa diagnosed in the post-COVID period, which was particularly evident from November 2020 onwards. A similar rebound was not observed for ncsPCa. The distribution of csPCa and ncsPCa diagnosis for each single month within the study period is reported in Figure 2.
## 3 Results
### Clinical Trials  
Clinical trials are not a simple affair, with enormous resources required for enrolment, treatment, monitoring, and data acquisition and interpretation. One of the biggest hurdles in clinical trials, which strongly hinders both patient enrolment and the statistical value of the data observed, is patient access. It must be mentioned that the notion of 'access' is a two-sided issue, as it is composed of both patient and institutional recruitment barriers that are reflected in this study's design. For example, it is well-known that most clinical studies are conducted in large, urban academic centres. One solution to this problem is decentralisation, enabling remote data sampling and collection from smaller local hospitals and clinics or even from the patient's home, making it much simpler for health professionals, patients, and caregivers [42].  
Fortunately, most of the technology for remotely caring for patients and conducting clinical trials was present for years, but simple legislative or plain lack of trust towards more technologically orientated approaches stunted its adoption. No one could have predicted that the incentive needed to expedite this measure would come from the following unfortunate events. Despite all the detrimental effects that the COVID-19 pandemic wrought upon the world of oncology, it has provided a much-needed technical boost [43].  
The COVID-19 pandemic has posed a significant threat to non-viral clinical trials and research better to understand major global health emergencies, such as oncological diseases. The completion of many clinical trials under these difficult circumstances is mainly due to the simplicity, low prices, and ubiquitous nature of the new personal devices and the inspiring adaptability of researchers, patients, and regulators who immediately saw the potential. A significant feature of newer technologies is the remote assessment of patients, a practice encouraged by the FDA guidelines published in March 2020 [44]. These efforts have led study coordinators to include virtual check-ups in the form of telemedicine visits to analyse patients' status. Today, remote surveillance is possible through home sensors (pulse oximeters, for example). Before the COVID-19 pandemic, the potential for using this remote sensor type was mentioned in previous FDA guidelines, but many trial coordinators were concerned that the benefits could never outweigh the risks and possible inequalities. Remote surveillance also helped trial monitors keep contact and evaluate progress in individual research sites when travelling was heavily restricted [45].
# Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
## 5 Conclusions  
Our investigation focused on exploring the impact of crossband filters in the STFT domain on WPE-based speech dereverberation. We introduced two extensions to the conventional WPE that specifically accounted for crossband filtering and demonstrated their effectiveness in enhancing the dereverberation performance. Interestingly, the first extension, which increased the model's complexity, naturally improved the performance. However, the second extension maintained the same model complexity as the conventional WPE and exhibited notable performance improvements. This observation suggests that the early samples of the crossband components play a crucial role in dereverberation, surpassing the significance of the late samples from the band-to-band components. Surprisingly, the late samples of the crossband components had an unexpected detrimental effect on the dereverberation performance. To further advance this research area, future investigations can explore the impact of crossband filtering in more complex models, such as scenarios involving speaker switching or time-varying RIRs. Additionally, combining the proposed concept with other extensions of WPE, such as the Kronecker filtering extension, holds promise [26]. The combination of crossband and Kronecker filtering for WPE has the potential to reduce the computational complexity while simultaneously improving the performance, as demonstrated by the recent work on Kronecker filtering for WPE.  
Conceptualization, T.R.; Methodology, T.R., I.C. and E.W.; Software, T.R.; Validation, T.R., I.C. and E.W.; Formal Analysis, T.R.; Investigation, T.R. and I.C; Resources, E.W.; Data Curation, T.R. and E.W.; Writing--Original Draft Preparation, T.R.; Writing--Review and Editing, I.C.; Visualization, T.R.; Supervision, I.C.; Project Administration, I.C. and E.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. These data can be found in the DNS Challenge dataset--[[https://github.com/microsoft/DNS-Challenge](https://github.com/microsoft/DNS-Challenge)]([https://github.com/microsoft/DNS-Challenge](https://github.com/microsoft/DNS-Challenge)) (accessed on 1 March 2023).  
The authors declare no conflict of interest.
# An Abstraction Layer Exploiting Voice Assistant Technologies for Effective Human--Robot Interaction
## 4 The Proposed Use Case
### The Developed Applications
#### 4.1.4 Robot Action Commands Application  
The Robot Action Commands Application allows NAO to execute multiple natural language action commands spoken by the user. Commands such as _NAO, raise your left arm and sit down_ or _NAO, walk forward raising your arm_ can be given through this application. This application exploits an ontology that is fed to a natural language processing engine that has been defined in this paper. The architecture of this application consists of an HRIC and a corresponding SSSC. As soon as the application is enabled by the VAC, the HRIC sends input commands of the user to the SSSC for processing.  
The output of the SSSC is a recognized action to be sent back to the HRIC, which will instruct the robot to perform the related physical action. If the action is incomplete, the robot asks the user for the missing information. This occurs when a user misses some entity, for example in _NAO, please raise your arm_. In such a case, the Natural Language Processing engine that has been developed within the SSSC knows that there might be two possible actions, related to the two arms of the robot. The HRIC then is informed of the two choices and interacts with the user asking to specify further. Moreover, the HRIC has two modes, _STATEFUL_ and _STATELESS_. When the former is set, all the positions of the robot between different commands are maintained. When the latter is set, the robot goes back to its default position after it performs each requested action. Further details about the NLP engine, the ontology, and how they interact with each other to identify robot actions can be found in [48].
# Three-Dimensional Velocity Field Interpolation Based on Attention Mechanism
## 2 Materials and Methods
### Network Training
#### 2.3.1 Feasibility Analysis of Transfer Learning  
In many real-world applications, the act of gathering the necessary training data and reconstructing models is either costly or unfeasible. Minimizing the need and effort to recollect training data would be advantageous [36]. The task of three-dimensional velocity field interpolation is to recover complete data by interpolating sparse data. Known data are limited, and the data to be recovered are large-scale. The distribution of geoscience datasets is often uneven. This means that data in some areas may be very dense, while other areas may be very sparse. Transfer learning can obtain prior knowledge from data-dense geoscience areas and transfer it to data-sparse areas. This approach can reduce the training complexity in sparse data point work areas. Random initialization of network parameters on large-scale, high-dimensional, and irregular geoscience data may make the model difficult to converge. This paper introduces a fine-tuning strategy of transfer learning to obtain more key features about geological or geoscience phenomena in data-dense areas, so that the model can interpolate in sparse areas more accurately. Once the model gains more feature information in data-dense areas, it can generalize better to sparse areas, thereby improving the convergence of interpolation.  
At the same time, this paper introduces the attention mechanism, which is catastrophic for the training of network model parameters. On the one hand, the amount of data is too small to support the network model to learn useful feature representations. On the other hand, the method based on implicit prior information adopted in this paper transforms the interpolation solution of the three-dimensional velocity field from the model space to the parameter space, and it is particularly difficult for the parameters of the network model to find the correct iterative update direction for convergence. Therefore, we use the concept of transfer to solve these two problems.
## 3 Experimental Results and Discussion
### Ibuprofen Concentration Determination Based on THz Sensing
In addition, one can note that for both \(x\)-polarized and \(y\)-polarized incident waves, the frequency shift of the higher-order resonance is greater than that of the lower-order resonance. To further investigate the sensing performance of the device, we plotted frequency shift with change in concentration and the best-fitting line in Figure 7.  
Figure 6: Experimental transmission spectra of the metasurface filled with ibuprofen in different concentrations for (**a**) the \(y\)-polarized incidence and (**b**) the \(x\)-polarized incidence. The experimental transmission of the bare metasurface is also included as a reference. According to the experimental results, when the incident wave was \(x\)-polarized, with an increase in concentration, for lower-order resonance the resonance dip moved from 0.456 THz up to 0.525 THz when the ibuprofen concentration increased to 0.5 g/mL (Figure 7a). For higher-order resonance, the resonance dip moved from about 0.98 THz up to 1.17 THz in the same case (Figure 7b). The sample volume of each measurement was 10 \(\upmu\)L, measured by pipette. Thus, the sensitivity of the different resonance can be calculated by:  
\[S_{\Delta f}=\Delta f/\Delta m, \tag{1}\]  
where \(\Delta m\) represents the mass of ibuprofen filled onto the metasurface. \(\Delta m\) = (0.5-0.2) g/mL \(\times\) 10 \(\upmu\)L = 3 mg. The sensitivity of the metasurface is shown in Table 1. Compared with the lower-order resonance, the frequency shift \(\Delta f\) was improved from 68.75 to 181.25 GHz by higher-order resonance, and the sensitivity of the higher-order resonance was about 2.64 times that of the lower-order resonance. Figure 7c,d shows the higher-order resonance's frequency shifts and transmission changes compared between the two different polarization directions. It is worth noting that the sensing performance of the \(x\)-polarization was better. The results are shown in Table 2. The \(S_{\Delta f}\) for the \(x\)-polarization is close to two times that of the \(y\)-polarization. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Resonance** & \(\Delta m\) **(mg)** & \(\Delta f\) **(GHz)** & \(\Delta T\) **(dB)** & \(S_{\Delta f}\) & \(S_{\Delta f}\) **(GHz/mg)** & \(S_{\Delta T}\) **(dB/mg)** \\ \hline Higher-order & 3 & 181.25 & 18.81 & 60.42 & 6.27 \\ Lower-order & 3 & 68.75 & - & 22.92 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The sensitivity of the higher-order and lower-order resonance. Figure 7: Resonant frequency shift and transmittance magnitude as a function of concentration. The frequency shifts of (**a**) the lower-order resonance and (**b**) the higher-order resonance with increasing concentration for the \(x\)-polarized input THz wave. (**c**) Frequency shifts and (**d**) transmission changes (higher-order resonance) for different polarization directions. Dotted lines are linear fits to the data.
# GAE and OBE Enhanced Interference Mitigation Techniques in LDACS
## 4 Nonlinear Estimators
### OBE Enhanced Pulse Peak Limiter  
OBE enhanced PPL is an altered or upgraded form of OBE enhanced PPA. The pulse peak limiting is performed by the repeated selective attenuation of the received signal until the resulting signal holds no amplitude values greater than the threshold value \(\alpha_{th}\). As the repeated attenuation is performed only for the subcarriers which exceed the threshold amplitude value, it will not disturb the subcarriers where the information is not affected by DME interference. The OBE enhanced PPL process the input signal \(y\) and delivers output \(x_{k}(y)\) as stated in (27).  
\[\hat{x}_{|kOL|}(y)=\begin{cases}y&\text{if }|y|\leq\alpha_{th}\\ \beta_{mod}(y).y&\text{otherwise.}\end{cases} \tag{27}\]  
where  
\[\beta_{mod}(y)=\frac{\alpha_{th}}{(|y|)} \tag{28}\]Here, the value of \(\beta_{mod}(y)\) can be varied in two ways so that \(\beta_{mod}(y)\). \(|y|\) becomes equal to \(\alpha_{th}\). The two methods are detailed in Algorithms A3 and A4 included in Appendices B.3 and B.4. Using the steps in Algorithms A3 and A4 results in two types of OBE enhanced pulse peak limiters.  
From Algorithm A3, it is clear that the value of noise power components \(\sigma_{k}^{2}\) is boosted \(Q\) times so that the term the output at the nonlinear becomes equal to the threshold peak detection value. In this case, the modified scaling factor \(\beta_{mod}(y)\) can be expressed as in (29).  
\[\beta_{mod}(y)=\frac{\Sigma_{k=0}^{K-1}\rho_{mod}p_{k}G(y;\sigma_{ jkmod}^{2})}{\Sigma_{k=0}^{K-1}p_{k}G(y;\sigma_{jkmod}^{2})} \tag{29}\]  
where \(\rho_{mod}=\frac{\sigma_{k}^{2}}{\sigma_{k}^{2}+\alpha_{k}^{2}}\) and \(\sigma_{jkmod}^{2}=\sigma_{x}^{2}+Q.\sigma_{k}^{2}\).  
Algorithm A4 details the second method to perform pulse peak limiting of the selected samples. With the use of this algorithm, realization of Type 2 OBEPPL has occurred. From Algorithm A4, it is understood that \(P\) multiples of \(\beta_{O}\) are considered \(\beta_{mod}\) for limiting the pulse peak. The maximum value of \(P\) for limiting the output is derived as in (31). The steps to obtain (31) are included in Appendix A.3.  
Both of these methods are adaptable for performing operations on complex valued OFDM data signals as in LDACS. This can be stated mathematically as in (30),  
\[\hat{x}_{|kOL|}^{*}(y)=\begin{cases}|y|e^{iarg(y)}&\text{if }|y|\leq \alpha_{th}\\ \beta_{mod}(y).|y|e^{iarg(y)}&\text{otherwise.}\end{cases} \tag{30}\]  
\[P_{max}=\frac{\alpha_{th}}{\beta_{o}(y)|y_{max}|} \tag{31}\]  
It is to be noted that the function \(\beta_{mod}\) remains unaltered when the proposed nonlinear device process complex OFDM data. The variables P and Q can accept values 1, 2, 3, etc.
# Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI
## 4 Experiment and Results
### Experiments and Scenarios
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{2}{c}{**PSO**} & \multicolumn{2}{c}{**GA**} & \multicolumn{2}{c}{**BGA**} & \multicolumn{2}{c}{**QGA**} \\ \cline{2-9}  & **Channel 8** & **Channel 12** & **Channel 8** & **Channel 12** & **Channel 8** & **Channel 12** & **Channel 8** & **Channel 12** \\ \hline
1 & 60.5 & 58.7 & **61.6** & 59.4 & 60.6 & **61.1** & 60.4 & 58.1 \\
2 & 60.7 & 59.5 & 59.9 & 60.0 & **60.9** & **60.1** & 58.3 & 59.7 \\
3 & 60.3 & 62.5 & **62.0** & 61.8 & 60.9 & **63.3** & 60.5 & 61.7 \\
4 & 65.0 & **65.2** & 62.5 & 64.9 & **63.8** & 64.4 & 60.2 & 63.3 \\
5 & 59.6 & 61.9 & 59.5 & 63.5 & **60.8** & **63.9** & 58.9 & 62.3 \\
6 & 62.8 & 61.0 & **63.2** & 59.4 & 62.0 & **62.8** & 62.4 & 60.0 \\
7 & 60.7 & 64.6 & 61.8 & 66.0 & **62.2** & **67.4** & 60.2 & 65.4 \\
8 & 58.5 & 58.8 & 59.3 & 59.5 & **60.5** & **61.1** & 58.4 & 58.0 \\
9 & 59.4 & 61.6 & 60.7 & 62.4 & 61.1 & **64.6** & **61.8** & 61.3 \\ Ave & 60.8 & 61.5 & 61.2 & 61.9 & **61.4** & **63.2** & 60.1 & 61.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Feature selection accuracy of PSO, GA, and BGA with 100 features, FB5, and ELM for 100 iterations.
## 2 Materials and Methods
### Experimental Setup
The choice of such a specific coupling element was made due to the need for the spatial separation of the BWs generated by the SPPs at the out-coupling edge of this element from the parasitic BWs arising at (1) the conversion of the NovoFEL radiation into SPPs (accompanied by the formation of high-intensity diffracted BWs) [43], (2) the diffraction of the SPPs on the splitter and mirrors of the interferometer [44], and (3) the transition of the SPPs from the substrate to the out-coupling element. Moreover, an additional source of parasitic BWs was the scattering [45] of the SPPs on the roughness and optical inhomogeneities of the substrate surface, leading to the appearance of the radiative losses of the SPPs [35]. An additional and effective screen from the parasitic BWs was a foam \"gate\" (35 mm long  
Figure 1: Optical circuit (top view) of the THz SPP interferometer. and 1 mm high) placed on the substrate at the point of its contact with the conversion out-coupling element. The detection of the interfering BWs generated by the SPP beams on the free edge of the out-coupling element was done using an optoacoustic detector (Golay Cell, TYDEX, St. Petersburg, Russia) of high sensitivity (\(NEP\approx 1.4\times 10^{-10}\) W/Hz\({}^{1/2}\)[46]). The need to use a highly sensitive receiver was due to the low signal intensity, large energy losses of the SPPs during their diffraction on the circuit elements, and the SPP attenuation during propagation along the sample surface. In the BW interferometer, the beams lost energy insignificantly (only in the reflection from the splitter \(BS_{3}\)). Thus, a less sensitive single-pixel pyroelectric receiver MG-33 (Novosibirsk Plant of Semiconductor Devices Vostok, Novosibirsk, Russia) was used to record the BW interferograms [47; 48]. The radiation directed onto its \(1\times 1\) mm\({}^{2}\) sensitive element was collected by a TPX lens with a focal length of 50 mm. Since the receivers applied can register only a time-varying radiation flux, a mechanical obturator was placed at the input of the setup, which modulated the radiation intensity with a frequency of 100 Hz. The signals from each receiver were recorded by two synchronous detectors (SR-830, Stanford Research Systems, Sunnyvale, CA, USA), from the outputs of which the signals were sent to the two-channel digital oscilloscope (Handyscope 3, TiePie, Sneek, The Netherlands), which operated in the recorder mode. The measured time dependences were digitized and written to a file. The movable mirrors \(M_{2}\) and \(M_{4}\) were attached to the platforms of the motorized stages (Standa, Vilnius, Lithuania), which shifted the mirrors along the \(x\) axis during scanning with a step of 2.5 um. The scanning speed chosen was the highest possible (100 steps per second), at which the detectors had time to correctly register the recorded signals. The recording time for one pair of interferograms corresponding to a displacement of the movable mirrors by 30 mm was about 2 min.
## 2 Materials and Methods
### Establishment of Numerical Model
#### 2.3.2 Parameters of Soil Material  
There are many unsaturated parameter models of soil [16; 19; 30], the commonly used models include the Fredholm and Xing model [31], the [PERSON] model [32; 33], etc. The material of dry-stack tailings ponds is unclassified tailings, which is evenly distributed in the tailings pond and is considered as an isotropic material. In this study, the Fredlund and Xing model (Equations (12) and (13)) was used to describe the unsaturated soil properties of tailings. Since it is not for a certain tailings material, the saturated water content and saturated permeability coefficient of the materials can be taken according toexperience. The other parameters in the Fredlund and Xing model of unsaturated soil were determined by the sample function method which was provided by the Geostudio platform. The gravel material was selected for the initial dam and the saturated water content was set as 0.295. The gravel sample function was used to fit the unsaturated soil-water characteristic curve. The tailings silt was selected for the accumulation dam, and the saturated water content was set as 0.402. The silt sample function was used to fit the soil-water characteristic curve of the tailings silt. The relationship between the permeability coefficient and the matrix suction was estimated by the Fredlund and Xing model. The saturated permeability coefficient of the gravel was set as 1.25 \(\times\) 10\({}^{-4}\) m/s, and the saturated permeability coefficient of the tailings silt was set as 3.75 \(\times\) 10\({}^{-6}\) m/s. The soil-water characteristic curve and hydraulic conductivity curve of the material are shown in Figure 3.  
Figure 3: Parameter curves of the unsaturated soil: (**a**) soil–water characteristic curves (SWCC); (**b**) hydraulic conductivity curves.