esa-sceva/satcom-chunk-collection huggingface dataset:
# Rapid Estimation of TVWS: A Probabilistic Approach Based on Sensed Signal Parameters
## 6 Conclusions and Future Work  
### Conclusions  
A rapid method to estimate the amount of TVWS has been proposed, based on the probabilistic information obtained from the sensed signals. This method uses information collected by the mobile spectrum sensing station, which reads in a simultaneous manner the noise \(W(p)\), the composite signal \(Xo(p)\), the geographical coordinates, and the time stamp for each reading. From the collected information, the statistical parameters are obtained for both signals. With the statistical parameters  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PU Presence (\%)** & **Channels** & **TV-UHF Band** & **TVWS Preferred Order** \\ \hline \(1\rightarrow\ 0\) & 19, 22, 23, 24, 27, 30, 33, 34, 36, 51 & 31.25\% & 1 \\ \(20\rightarrow\ 1\) & 28, 29, 35, 46, 47, 48 & 18.75\% & 2 \\ \(40\rightarrow\ 21\) & 20, 25 & 6.25\% & 3 \\ \(60\rightarrow\ 41\) & 26 & 3.13\% & 4 \\ \(80\rightarrow\ 61\) & 21, 31, 32, 38, 39, 49, 50 & 21.87\% & 5 \\ \(100\rightarrow\ 81\) & 40, 41, 42, 43, 44, 45 & 18.75\% & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: TVWS decision matrix: PU presence, channel numbers to identify them, percentage in the band of interest, and preferred order to be assigned.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ord** & \(P_{(TVWS)}\) & \(R_{(TVWS)}\) & **Variation** & \(D_{(TVWS)}\) \\ \hline \(1\) & 52.72 & 68.75 & 16.03 & 16 \\ \(2\) & 48.30 & 71.88 & 23.57 & 15 \\ \(3\) & 44.32 & 62.50 & 18.18 & 14 \\ \(4\) & 49.31 & 62.50 & 13.19 & 15 \\ \(5\) & 42.47 & 56.25 & 13.78 & 13 \\ \(6\) & 36.37 & 53.13 & 16.36 & 11 \\ \(7\) & 48.34 & 54.12 & 4.79 & 15 \\ \(8\) & 41.26 & 53.13 & 8.74 & 13 \\ \(9\) & 41.81 & 62.50 & 20.69 & 13 \\ \(10\) & 45.83 & 62.50 & 17.07 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of the tested point.  
obtained from the multimodal composite signal \(Xo(p)\), which is not a normally distributed function, we traced a normally distributed function named \(X(p)\). This assumption allowed us to reduce the complexity of the mathematical calculations to find the probability of detection \(P_{d}\). By combining the statistical and auxiliary parameters, the amount of TVWS was estimated. These E-TVWS are allocated in the TV-UHF band by following the preferred order identified in the decision matrix. The rapid estimation method was tested and yielded 76% accuracy according to results obtained and verified. The average variation between the calculated \(P_{TVWS}\) and detected TVWS was in the range of 15%. Built on open source resources, the proposed solution can be used to reduce both complexity and cost of TV-UHF spectrum sensing for estimating the presence of TVWS with acceptable accuracy results.
## 4 Discussion
### Potential Use Case for Treating Breast Cancer Using a Kv10.1 Controlled Nanorobot  
Our hypothesis for the use of swimming nanorobots is to control ion-channel activity to specifically target cancer cells in breast tumors. We know that Kv10.1 channels in the nervous system contribute to the control of neuronal excitability. However, since activation requires strong depolarization, a single action potential would not be sufficient for activation [33]. In contrast, cancer cells are usually depolarized so that the channels are predominantly open, providing a potential target for pinpoint destruction by nanorobots. For example, overexpression of Kv10.1 channels is significantly up-regulated in invasive breast carcinoma, whereas healthy breast tissue shows hardly any expression or activity of the Kv10.1 channel [30; 33; 34; 35; 36]. Floating nanorobots applied via the blood circulation can be guided to the targeted sites and measure and control the Kv10.1 channel activity of the cells in the area of interest. Since nanorobots act as sensors and actuators, if the sensor interface detects an altered behavior of Kv10.1 channel activity, as is the case in breast cancer cells, the channels could be controlled accordingly by the Kv10.1 feedback control system of the robot (see Figures 9 and 10). Alternatively, after detecting a cancer cell based on aberrant or abnormal channel activity, such MEMS/NEMS-based nanorobot devices may also act as a nano-scalpel that mechanically destroys the targeted cell.  
Further examples of possible applications are discussed in [34; 37; 38; 39]. However, although the practical implementation of this use case has not been performed and validated, this work demonstrates a potential new, forward-looking direction for cancer detection and therapy.
## 4 Experimental Results and Performance Analysis  
### Experiment Design  
Building upon the analysis of the theory of PSSD algorithm and its anti-interference capabilities in previous sections, this section aims to comprehensively evaluate its demodu  
Figure 9: Diagram of weighted phase difference: (**a**) SGR = 6 dB; (**b**) SGR = 12 dB.  
lation performance advantages in diverse environments through a series of well-designed experiments. The experimental design is outlined bellow:  
1. Experimental environment: All experiments were conducted on a high-performance computer with Windows 10 operating system, an Intel Core i7 processor, and 32 GB of memory. MATLAB R2017b was used as the experimental platform to ensure accurate and reproducible algorithm simulations. The experimental dataset simulated eLoran signals under different SNR and CWI conditions. DAR was the main performance evaluation metric.
2. Parameter Settings: * Input signal: Continuous modulated pulse group signals. * Data modulation: Standard time-code frame data modulation was adopted. * Sampling rate: 2 MHz. * Bandpass filter: Set to 90-110 kHz, specifically designed for the EPD algorithm to filter out-of-band noise and interference. * SNR: \(-\)20-10 dB white noise environment, with a step size of 1 dB. * CWI: 0-4 single-frequency interferences (C1, C2, C3, C4) with specific frequencies and SIRs. C1: 97.2222 kHz, \(-\)7 dB; C2: 101.3333 kHz, \(-\)10 dB; C3: 103.6666 kHz, \(-\)9.21 dB; C4: 99.1111 kHz, \(-\)8.24 dB. Each interference was introduced with a random initial phase. Since most of the interference energy has been removed during preprocessing, a moderate interference power can be set. In all experiments involving CWIs throughout this paper, they are labeled and combined sequentially (e.g., \"1 CWI\" refers to C1, \"3 CWIs\" refers to the combination of C1, C2, and C3)
* Experimental reproducibility: To ensure the reliability of statistical results and reduce random errors, each set of experimental conditions was repeated 10 times, with each run consisting of 50 frames (9000 pulses to be detected).
# Possibilities of Evaluating the Effectiveness of Noise Barriers in Slovakia
## 2 Materials and Methods
### Theory of Sound Energy Propagation Through a Noise Barrier
#### 2.1.3 Sound Reflection  
The EN 1793-5 standard defines and focuses the measurement of the sound reflection index RI in the conditions of a direct sound field.  
To determine the functional characteristics of the product (noise barrier), a single number rating of sound reflection \(DL_{RI}\) is determined according to Equation (3) [27]:  
\[DL_{RI}=-10\cdot log\left(\frac{\sum_{i=m}^{18}R_{i}\cdot 10^{0.1L_{i}}}{\sum_{i=1}^{ 18}10^{0.1L_{i}}}\right) \tag{3}\]  
where:  
\(m\)--the number of the lowest reliable one-third octave frequency band; according to EN 1793-5, the lowest reliable frequency is 300 Hz, which corresponds to \(m=6\); and  
\(L_{i}\)--sound pressure level (A) of the normalized traffic noise spectrum in the \(i\)-th one-third octave band (dB).  
The maximum theoretical value of the ratio of the sums of the expressions in Equation (3) cannot be more than 1; therefore, the value is limited to 0.99.  
In general, noise barriers are made of a small number of basic elements.  
In order for a barrier to be acoustically efficient and fulfill its purpose, it is necessary for the barrier to be made precisely with high-quality individual elements, without the creation of joints and leaks. In view of this fact, after construction of the barrier, it is necessary to verify its effectiveness by measurements.  
Standard ISO 10847 Acoustics--In situ determination of insertion loss of outdoor noise barriers of all types [28] specifies two methods for determining the insertion loss of external noise barriers: the direct and indirect method.  
The Adrienne method defined in the EN 1793-5 and EN 1793-6 standards enables the comparison of design characteristics with the values of the actual structure after the completion of construction works, which is a significant advantage of this method.
## Considerations for Real-World Use of Observed Join
### Benefits of Machine Observation  
In a real-world deployment, different scenarios have different requirements for a machine-moderated join. For devices where the deployment conditions prevent the observer node from physically coming into close proximity to the device (such as an observer covering an area deployment, it may be preferable to conduct an observation via a visual method, from a longer distance than may be possible with other technologies. This may require the device to be large enough that both the display, and the device to which it is mounted, are sufficiently visible to the remote observer.  
Physical proximity can provide the best guarantee of the identity of the physical device, for scenarios where this is achievable. For example, devices being deployed via a conveyor belt-fed system, devices which are at a known and physically small location (such as passing through a door, or gateway), or devices where a human can physically access and _tap_ the device could all utilize this type of approach.  
Although designed around the IoT, and the idea of largely static devices, this approach (and the SRUP protocol in general) will work well to support broader classes of _smart_ devices, including smart vehicles. However, for the purposes of identity validation, neither of the technologies employed in this example implementation work especially well for scenarios where the device is in motion.  
For example, in a scenario where the device would only be in the observers field-of-view for a short period, careful and accurate timing would be required to ensure that the messages supporting the request have been sent (and arrived) with sufficient time for the observer to be ready to view the device. This may require, for example, that the device makes the request some period of time before it expects to be observable (although the longer the time period between the message and the observation, the greater the risk of another device being detected instead).  
Similarly, both technologies require some finite time for the read operation to complete (a visual observer would require that the entire display is visible in at least one frame of video; and an Radio Frequency-based observer would require the device to be within the operating range of the reader for at least the duration of the read operation). As such, operations to perform the join would be required to take place during a time period when the device was static (such as at a control point or barrier--prior to entering the smart system's control).
## 2 Materials and Methods
### Experimental Setup  
The model tank has a length of 1000 mm, a width of 300 mm, and a height of 1000 mm. The front is made of 20 mm thick glass fiber to display the displacement of the soil during loading, and the back and sides are made of sheet steel. Experimental studies were carried out under plane strain conditions using a strip foundation. For the model foundation, a rigid steel plate with a length of L = 300 mm, a height of H = 20 mm, and a width of B = 100 mm was used. The surfaces of the foundation in contact with the tank are covered with thick rubber material to reduce the friction coefficient. Based on the model, a hydraulic jack that can load up to 5000 kg according to the desired speed is used. To check the centric loading, two Linear Variable Differential Transformers (LVDT) were placed diagonally at the two corners of the footing plate and settlements were read. In the tests, an S-type load cell with a capacity of 5000 kg was used to determine the load values on the model base plate. During the tests, the settlements and stress values from the load cells and LVDTs were transferred to the data collection device and converted into numerical values. The schematic of the experimental setup is given in Figure 3.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Properties** & **Basalt Fiber** & **Glass Fiber** & **Macromesh Fiber** \\ \hline Fiber type & Micro & Micro & Macro \\ Length (mm) & 24 & 24/48 & 24/48 \\ Average diameter (\(\upmu\)m) & 16 & 14 & 850 \\ Specific gravity (G) & 2.65 & 2.60 & 0.91 \\ Young’s modulus (GPa) & 89 & 77 & 5.75 \\ Breaking tensile strength (MPa) & 4840 & 3400 & 750 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical and mechanical properties of basalt fiber, glass fiber, and macromesh fiber.  
Figure 2: Material diagram; (**a**) Basalt fiber; (**b**) Glass fiber; (**c**) Macromesh fiber.
## 4 Experimental Results and Comparative Analyses  
The experiments conducted in this study were designed to comprehensively validate the three key components of the methodology proposed in Section 3. Given the dynamic and complex nature of 6G technology and the limitations of existing research methods in accurately analyzing its technological evolution, our experiments aimed to provide robust evidence for the effectiveness of our approach.
## 4 Experiment Verification
### Experiment Results and Discussion
#### 4.2.2 Dynamic Characteristics Experiments
Actually, the delay time of position sensor and friction force could disturb the dynamic characteristics of on/off valve in the experiment process, which would affect the push-pull effort of the VCM and cause the dynamic response time slowing. Consequently, further improvement of VCM-DHPV dynamic characteristics by upgrading the accuracy of position sensor and reducing the friction force between valve core and valve sleeve will be the main research concerns in the future. Figure 12: Step responses of VCM-DHPV under different supply pressures (\(u=240\) V, \(\alpha=45^{\circ}\)): (**a**) \(P_{s}=2\) MPa; (**b**) \(P_{s}=4\) MPa; (**c**) \(P_{s}=6\) MPa; and (**d**) \(P_{s}=8\) MPa. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Supply Pressure**} & \multicolumn{2}{c}{**Opening Response time**} & \multirow{2}{*}{**Deviation**} \\ \cline{2-3} \cline{4-4}  & **Simulation** & & **Experiment** \\ \hline
2 MPa & 5.7 ms & 7.3 ms & 1.6 ms \\
4 MPa & 5.9 ms & 7.4 ms & 1.5 ms \\
6 MPa & 6.1 ms & 7.9 ms & 1.8 ms \\
8 MPa & 6.2 ms & 8.2 ms & 2.0 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Opening response times of VCM-DHPV under different supply pressures.
# Automatic Evaluation of Piano Performances for STEAM Education
## 4 Methodology
### Naive Bayes (NB) Approach  
Since piano sound evaluation is formulated as a multi-class classification problem, the Naive Bayes classifier (NB) can be employed to classify piano sounds in this study. In the NB framework, the feature items from one class are assumed independent of other attribute values. Our aim is to evaluate \(P(C\!\mid\!M)\), which is the probability of class \(C\) that a piano recording \(M\) belongs to, where \(C\in\{\text{Good, Normal, Bad}\}\) and \(M\) is a stream of Mel-frequency cepstral coefficients (MFCCs) extracted from piano recording. According to the [PERSON] theorem:  
\[P(C\!\mid\!M)\ =\ \frac{P(M\!\mid\!C)P(C)}{P(M)} \tag{3}\]  
and \(P(M)\) does not involve the classification; we have \(P(C\!\mid\!M)\propto P(M\!\mid\!C)\)\(P(C)\). Thus, a piano recording can be determined as one of the three classes by  
\[\hat{C}\ =\ \underset{C}{\text{argmax}}P(M\!\mid\!C)P(C), \tag{4}\]  
In this study, we used the Gaussian Naive Bayes classifier, in which \(P(M\!\mid\!C)\) is a Gaussian density function. The NB-based piano performance assessment system is shown in Figure 5.  
Figure 4: The piano performance assessment system using SVM approach.
## 2 Laryngeal Image Modalities
### Laryngeal Videostroboscopy
#### 2.1.2 Limitations  
* It does not provide a real view of the vocal folds' vibratory pattern, so it is restricted to stable and periodic vocal folds vibrations. Therefore, VS is incapable of revealing vocal fold vibratory patterns in patients with severe dysphonia [32]. In addition, VS limits scientific and diagnostic knowledge of the vocal function during voice onset and voice offset.
* It is more sensitive to camera rotation, side movement of the laryngoscope and patient movements which produce the vocal folds delocation [33; 34].
# Hybrid Quantum Neural Network Image Anti-Noise Classification Model Combined with Error Mitigation
## 2 Background  
### Convolutional Automatic Encoder  
The autoencoder (AE) represents a specialized instantiation within the broad domain of feedforward neural networks, featuring a distinctive architecture consisting of two essential components: an encoder and a decoder. The primary function of the encoder is to compress input data, facilitating a transformation into a low-dimensional code that encapsulates the latent space inherent in the input. Conversely, the decoder intricately reconstructs the output based on this condensed representation, orchestrating a process of data synthesis aimed at faithfully replicating the original input. This paradigm is typically characterized by the inclusion of fully connected layers in both the encoder and decoder modules.  
In the evolutionary trajectory of neural network architectures, CAE emerges as a refined iteration, particularly distinguished by its enhanced efficacy in extracting textural features from images [35; 36]. The CAE paradigm manifests a distinct structural augmentation, wherein the encoder is imbued with successive convolutional layers, and the decoder integrates transposed convolutional layers, all enveloping a strategically embedded sparse inner layer. The consequential outcome is an autonomously functioning CAE encoder that assumes a pivotal role in the extraction of low-dimensional representations, specifically tailored for the realms of image feature extraction and the reduction in dimensionality. The training regimen for the CAE involves exposure to raw, unprocessed image data, facilitating the acquisition of a profound understanding of a low-dimensional representation. Unlike the traditional AE, CAE deviates by utilizing convolutional layers in both the encoder and decoder networks, replacing the previous reliance on fully connected layers.  
The structural augmentation of the CAE is not arbitrary but rooted in its pursuit of heightened performance in processing image data while meticulously preserving spatial structural information. A defining attribute of the convolutional layers in the CAE is their demonstration of translation invariance, a quality that imparts a remarkable capability to capture local features and spatial relationships within the intricate tapestry of image data. This intrinsic characteristic contributes substantively to the CAE's efficacy, rendering it a potent tool in the realm of image processing and analysis. In summary, the CAE represents a sophisticated and refined instantiation of neural network architectures, with its convolutional underpinnings endowing it with a distinctive prowess in the extraction and preservation of intricate features within the visual domain. In this paper, we only utilize the encoder for image feature extraction and dimensionality reduction.
## 3 Results
### Identification of the Reaction Products in the Gas-Phase
#### 3.2.3 Detection by FTIR Spectroscopy  
The FTIR spectra recorded during reaction (R1) showed features that agree with the products observed by SPME/GC-MS and PTR-ToF-MS as can be seen in Figure 4. By comparison with the reference spectra (shown in central panels of Figure 4), the most abundant products observed were HCl, CO, 3-chloro-2-butanone, and acetaldehyde. The obtained product yields were Y\({}_{\rm HCl}\) = (92.8 \(\pm\) 12.9)%, Y\({}_{\rm CO}\) = (97.3 \(\pm\) 10.0)%, and Y\({}_{\rm 3-chloro-2-butanone}\) = (52.5 \(\pm\) 7.3)%. The quantification of acetaldehyde by FTIR spectroscopy was very imprecise due to the presence of other minor products that have similar features in the IR spectrum. The minor products that were identified are formaldehyde, acetyl chloride, methanol, methyl glyoxal, acetic acid, 2,3-butanedione, and butanone. After subtracting all these products and by comparison with the IR features described by [PERSON] et al. (2018), ketene (CH\({}_{2}\)C(O)) was also identified in the 2100-2200 cm\({}^{-1}\) range of the residual spectrum. No reference spectrum was available for ketene given its instability. Despite this identification, there are still some IR features that could be due to an unknown carbonyl compound or 2-methyl-2-butenoic acid. This acid was observed by SPME/GC-MS and PTR-ToF-MS, but no IR reference spectrum could be obtained due to experimental limitations. Nevertheless, given the amount detected by the two other techniques,  
Figure 3: Time evolution of the concentration of T2M2B (C\({}_{5}\)H\({}_{6}\)OH\({}^{+}\), on the right axis) and the products measured by proton transfer time-of-flight mass spectrometry (PTR-ToF-MS) during the _trans_-2-methyl-2-butrenal + Cl reaction. \(t\) = 0 min, depicted with a yellow dashed line, was set as the moment in which the reaction started when the lamps were turned on.  
it is expected that the concentration detected by FTIR will be very low too. During the exposure to UV light test of T2M2B, small amounts of CO, formaldehyde, and acetaldehyde were detected, but, similarly, as observed by PTR-ToF-MS, in a lesser extent than the observed during the Cl reaction.  
Figure 4: FTIR spectra. Top panel shows the spectrum obtained after 40 min of Cl reaction of _trans_-2-methyl-2-butenal with the features of this compound subtracted. The bottom panel shows the residual spectrum after the subtraction of the IR features corresponding to the detected products. HC(O)Cl spectrum was provided by Nielsen [24] and the CH\({}_{3}\)C(O)C(O)H spectrum was taken from the FTIR database of the EUROCHAMP project [25].