esa-sceva/satcom-chunk-collection huggingface dataset:
# ScientIST: Biomedical Engineering Experiments Supported by Mobile Devices, Cloud and IoT
## 3 Proposed Approach
### Technical Evaluation  
ScientIST builds upon the background technologies described in Section 2, and proposes a workflow (Figure 4) in which biomedical sensors with an analog output are powered from the Arduino SCB and connected to its analog inputs. A log of the experiment is created on a mobile device using GSJ (including biomedical sensor data visualization and recording), and the resulting data are exported to CSV files, to conduct post-processing. Given the emphasis of our work on biomedical data, the BioSPPy toolbox [11] is used for signal processing and feature extraction.  
Figure 4: Proposed ScientIST workflow for biomedical engineering experiments supported by mobile devices, cloud and IoT, illustrating a use case of Photoplethysmography (PPG) data acquisition.  
Figure 3: Example of the notes and photographs taken by the students during a Histology experimental class.  
The Arduino SCB does not comply with the Nyquist theorem requirements (see Section 4.1), and lacks the \(V_{CC}/2\) reference voltage required by some of the more common biomedical sensors, as such, we illustrate this workflow with an experiment devised for a course on Introduction to Biomedical Engineering using Photoplethysmography (PPG). This is a noninvasive technique to monitor blood perfusion as a result of arterial translucency, in which an LED illuminates the skin surface using a light source in a wavelength capable of penetrating it, ideally reaching the hypodermis where arteries can be found (i.e., red to infrared) [12]. A photodetector captures the amount of reflected or transmitted light (depending on the operating mode).  
In each cardiac cycle, blood is vigorously pumped during the diastole and traverses the blood vessels across the body to the periphery, while during the diastole blood slowly flows back to the heart. Since part of the light emitted by the LED is absorbed by blood cells, the higher the blood volume, the higher the amount of light absorbed. Measuring the amount of light reaching the photodetector over time, enables the recording of the blood volume changes throughout the cardiac cycle. The dynamics of the resulting signal is a process with relatively low frequency [12].  
To support the case study we use a PulseSensor ([[https://pulsesensor.com/](https://pulsesensor.com/)]([https://pulsesensor.com/](https://pulsesensor.com/))) PPG sensor. Power is provided by the Arduino SCB using the 0-3.3 V pins and the analog output is connected to the analog input on the SCB; all the connections are made via the Molex Sherlock pins on the SCB. Within the GSJ mobile app, a new journal is created with the information about the experiment and procedure. A subject is asked to sit down with the PulseSensor placed on his index finger, enabling a visualization and recording of the PPG time series on the journal. The next step is to export the data to a CSV file, and run the post-processing Python script, by means of a Jupyter Notebook, to process the data and compute the heart rate using the BioSPPy toolbox.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### SPDT Integration with X-Band ON and K/Ka-Band OFF  
In a switchable wideband receiver front-end configuration, only one band is active while the other is pinched off (OFF). In this section, the X-band LNA path is switched ON, and the K/Ka-band path is switched OFF.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & \(\mathbf{S_{11}}\) **(dB)** & \(\mathbf{S_{12}}\) **(dB)** & \(\mathbf{S_{21}}\) **(dB)** & \(\mathbf{S_{22}}\) **(dB)** \\ \hline
[47] & \(\leq\)\(-11\) & \(-25\) & \(-0.75\) & – \\
[48] & \(\leq\)\(-14\) & \(-30\) & \(-1.0\) & – \\
[49] & \(\geq\)\(-13\) & \(-28\) & \(-1.1\) & – \\
[50] & \(\leq\)\(-13\) & \(-44\) & \(-0.8\) & – \\ \hline
**This Work** & \(-16\) & \(-70\) & \(-3\) & \(-15\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Wideband SPDT Switch Design Comparison at 10 GHz.
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 8 Acoustic Emission Technique
### Additive Manufactured Materials  
In addition, to conventional structural materials, the possibility of using AE technique damage assessment has also been examined for additive manufacture (AM) metals [104]. This study evaluated whether phenomena like the Kaiser effect and waveform parameters would influence the results concluding that the damage condition of AM structure can be correctly estimated via AE and other NDT methods.
# Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm
## 2 Hardware Structures for Linear Operation  
ONN is formed by passive optical waveguide interconnects and has natural advantages for the linear operation. Due to the different computing tasks, the ONN is mainly classified into three types: feedforward neural network (FNN), convolutional neural network (CNN), and recurrent neural network (RNN). The networks can be implemented by optical waveguides. Among them, the response function of a single MZI can be expressed as a Jones matrix [9] and thus, the MZI array is an excellent candidate for linear matrix multiplication [10; 11; 12]. In the case of SOA, modulation and amplification are used to control the intensity or phase of the input signal and complete the matrix multiplication [13; 14]. MRR can also be operated according to different wavelengths. In MRR, optical signals of different wavelengths are modulated, and the calculation is completed [15; 16; 17]. Furthermore, complex 3D-routed waveguides are created by two photon polymerization [18; 19], which efficiently connects many IO channels. When the channels are added by a combiner, the 3D waveguide works as a summator. Above all, these waveguides are deployed for ONN, intending to realize linear operation. For the three types of ONN, the scheme is shown in Table 1.
## 3 Depth Estimation of Artificial and Natural Defects
### Depth Estimation Using Artificial Defect Specim  
In order to derive the relationship between the depth and the phase of the defect, as shown in Figure 10, the artificial defect specimen was constructed with four squat defects on the UIC60 sample. The depths of the defects were 2, 4, 6, and 8 mm, and the lengths and widths were all 10 mm.  
Figure 11 shows the results of the scan of the artificial defect specimen. Figure 11a is the X data (resistance) of the eighth sensor, and Figure 11b is the Y data (reactance). As shown in Figure 11a,b, detrending and denoising were performed through signal processing. As a result of applying calibration, it was confirmed that two peaks were generated when the detector passed through the defect. These two peaks are the start- and endpoints of the defect, and the length of the defect can be measured using the two peak points.  
Figure 10: Artificial defect depth change of the specimen.  
Figure 9: Equipment used in the field.  
The phase was used to estimate the depth of the defect. Figure 12 shows the relationship for estimating the depths of the defects, and Equation (1) shows the relationship between depth and phase. As a result of the measurement, it was confirmed that the phase increased at a constant rate as the depth of the defect increased.  
\[\text{Depth}=1.5593\times\text{Phase}-57.864 \tag{1}\]  
Figure 13 shows the results of estimating the depth of artificial defects according to the depth change through a total of six repeated experiments. The red dotted line in Figure 13 is an error bar of \(\pm 1\) mm, and it can be observed that there was no point beyond the error bar. Therefore, it is possible to estimate the depth of the surface defects of different specimens using the correlation between the depth and the phase of the derived defect.  
Figure 11: Ch 8 Data: (**a**) X data (resistance) and (**b**) Y data (reactance).  
Figure 12: Correlation between depth and phase.
## 3 Findings and Discussion
### The Effect of the Number of Helixes  
The effect of the number of helixes (N = 0, 1, 2) was investigated in this section considering the physical model tests and numerical analyses. In the case of the multiple helical pile, the spacing ratio between helixes was constant at S/D\({}_{1}\) = 3, which is the optimum spacing ratio between helixes. The results for load-displacement obtained from the physical model tests and 2D/3D finite element models are presented in Figure 7; also, the failure mechanism results of the 3D finite element model, which is in better agreement with the physical model test results, are given in Figure 8.  
The capacity of the straight pile (N = 0; with no helix) was determined to be approximately 86 N, and the effect of the presence of  
distance between the helixes [49; 50; 51]. With the transition from a single helix to multiple helixes, the pile capacity increased by approximately 55%. The level of this increase was directly related to the distance between the helixes and the location of the upper helix plate (buried depth). Hence, it was recommended to choose an optimum distance to achieve maximum efficiency.  
From the physical model test results, it could be seen that the capacity of the helical pile increased significantly compared to the straight pile. When all results were examined, it was determined that the physical model test and 3D finite element results had better agreement; on the other hand, the 2D finite element results were slightly below the physical model test results.  
The effect of the number of helixes on the failure mechanisms obtained from the 3D finite element model is presented in Figure 8. In the case of N = 1 (single helical pile), the displacement intensity on the failure mechanism occurred around the helix and exhibited deep foundation behavior. At N = 2 (i.e., multiple helical piles), the displacement intensity decreased significantly and dropped by almost half compared to N = 1. It can be expressed that the use of multiple helixes caused a cylindrical formation on the failure mechanism between the helixes. The presence of the upper helix plate ensured that the failure mechanism was effective almost along the shaft and the mechanism moved towards the soil surface. When the failure mechanisms of single and multiple helical piles were  
Figure 8: The effect of the number of helixes on the failure mechanism.  
Figure 7: The effect of the number of helixes on the load–displacement relationship.  
compared, it was observed that the upper helix plate belonging to the multiple helical piles was positioned where the displacements originating from the single helical pile disappeared. As a result of this, it was understood that the upper helix in the multiple helical pile was placed at the optimum distance where the displacements were damped in the failure mechanism.
## 2 Proposed Structure: Analysis and Design Methodology  
### Analysis of the Structure  
A 3-D view of the proposed multilayer structure for balanced dual-band operation is depicted in Figure 1. The two dimensional layout for each layer is depicted in Figure 2. The pattern etched on the top layer was used in [26] for the design of a single-band balanced BPF. It basically consists of a pair of open-loop resonators that are magnetically coupled (under DM operation). The middle layer also consists of a pair of open-loop resonators that are magnetically coupled when operating in DM. The difference between the two coupled resonant structures lies on the type of excitation and the values of the resonance frequencies. Thus, top layer resonators are excited via the gap capacitance associated with the gap width \(s_{2}^{t}\), while the middle structure is excited by means of the broadside capacitance created between the top feeding lines and the middle layer conducting strips. Figure 3 illustrates the overlapping section between layers which originates the broadside capacitance (indicated by the shaded region). By setting the value of \(l_{\text{over}}\), it is possible to control the excitation level of the middle-layer resonators, which is critical to implement the response of the differential passband created by this pair of coupled resonators. Figure 4 plots the simulated electromagnetic response (simulations have been carried out using _ADS Momentum_ from Keysight, stationed at Santa Rosa, California, United States of America, USA) of the entire multilayer structure for both DM and CM excitations. Two differential passbands with good CM rejection can be appreciated in such figure, demonstrating the expected behavior of the proposed structure. The design process of the multilayer balanced dual-band bandpass filter that leads to such response is described in detail in the next subsections.  
Figure 1: Deployed 3-D view of the proposed planar multilayer balanced dual-band bandpass filter.  
Figure 2: 2-D view of the two printed planes of the structure (not to scale). (**a**) Top layer; (**b**) Middle layer.
# Random Frequency Division Multiplexing
## 2 Preliminaries
### Inspirations and Beginnings  
CS is a signal processing technique that reconstructs a signal from a small number of measurements by exploiting its sparsity [10]. It relies on three principles: (1) the signal \(\mathbf{s}\in\mathbb{C}^{N\times 1}\) is sparse or can be represented with fewer non-zero coefficients in some specified domains, with at most \(K\) non-zero components. (2) This sparse or approximately sparse signal can be undersampled by a matrix \(\mathbf{A}\) with specific properties. Since it projects the signal in a low-dimensional subspace spanned by the rows of the sensing matrix \(\mathbf{A}\), \(\mathbf{A}\) is either randomly generated or designed based on the restricted isometry property (RIP) or its incoherence with a representation basis [11]. Much effort has been made in the signal processing community to design \(\mathbf{A}\) such that the structure of the data is preserved in the low-dimensional subspace. There are many matrices which satisfy the RIP, such as Gaussian random matrices, Bernoulli random matrices, and so on. (3) The reconstruction algorithm can uniquely solve the underdetermined equation by minimizing the following \(\ell_{0}\)-norm:  
\[\min\|\mathbf{s}\|_{0}\ s.t.\ \mathbf{z}=\mathbf{A}\mathbf{s},\]  
where \(\mathbf{z}\) is the received signal [12]. The nature of CS is to recover the original high-dimensional signal from a small number of measurements, inspired by CS principle; it is natural to apply this idea to the communication system [13]. Then, we propose the RFDM scheme. At the transmitter, the signal is compressed to lower dimensions and can be reconstructed at the receiver.
## 5 Results
### Validation by Comparison with Experiments  
In order to evaluate the quality of the simulation, it was compared against performance data of real lidars under different weather conditions. Several publications ([11; 12; 51; 52]) reported on physical experiments in a large environmental testing facility, called Cerema [53], which is capable of generating different rain and fog scenarios for **A**dvanced **d**river-**assistance **s**ystems (ADAS) testing. Out of these publications, [11; 12] were chosen for comparison, as the first publication compares the performance of five different lidar systems (lidars benchmarked: Livox Horizon, Velodyne VLP-32, Ouster 128, Cepton 860, and AEye 4 SightM) under adverse weather situations. The second publication also benchmarks the Valeo lidar considered in this work.  
To recreate the real experiment in [12], which used a white target with a surface of 1 m\({}^{2}\) and reflectivity of 80% at a distance of 23 m, a similar scenario was set up in the simulation. Here, a car with 80% reflectivity was placed at a distance of 23 m in front of the simulated lidar (see Figure 12). As the detected number of points, which refer to the object, is normalized, a similar result is expected. The experiment of [11] was performed with a white car at a distance of around 18 m, so the results can be compared, but it is expected to perform slightly differently. However, as the reflectivity of the white car is unknown, the experiment could not be recreated. To obtain a real-world reference with lidars of similar performance, the comparators for the Scala 2 simulation experiments were the Livox and Aeye data [12] and the Scala 2 experimental data [11].
# Influence of the Quality of Consumer Headphones in the Perception of Spatial Audio
## 2 Headphones Measurements and Virtual Headphone Simulation
### Headphone Selection  
Different headphones were selected in order to represent a range of commercial and readily-available headphones. According to this principle and the scope of the study described in previous sections, seven different headphones were selected plus a high quality reference one. A Sennheiser HD800 (Sennheiser, Wedemark, Germany) was chosen as the reference headphone (REF). The reason for this selection is due to its great fidelity, response, low distortion and accurate timbral reproduction. The other seven headphones were selected to cover a wide range of possible common uses. The brands and models of the rest of the headphones will be omitted, as they are not necessary for the result analysis.  
The headphones used in the study were classified as:  
1. REF, Reference, Sennheiser HD800 headphone (open and circumaural)
2. HQop, High Quality open headphone (circumaural)
3. MQcl, Medium Quality closed headphone (circumaural)
4. BDso, Big Diaphrag semi-open headphone (circumaural)
5. LCmul, Low Cost multimedia headphone (supra-aural)
6. AirL, Airline headphone (supra-concha)
7. Woh, Wireless open headphone (circumaural)
8. LCmul2, Low Cost multimedia headphone 2 (supra-aural)  
The reference headphone was the only one that participants used, saw and had contact with during the tests. The rest of the headphones were simulated through the reference one. Then, all of the participants performed the test using the same high quality reference headphone (REF, Sennheiser HD800). The resulting signals for the rest of the headphones (used in Tests 2, 3 and 4 and described in their sections) were simulated by means of proper signal processing algorithms and heard through the reference headphone.
## 4 Proposed Neural Networks for GEQ Design
### Training the Neural Network for the Bark GEQ Design (NSGEB)  
The neurally-controlled Bark band equalizer NSGEB was trained according to the same principles as NSGE3. This time, there were no earlier models available, so the training of the network had to start from scratch. Furthermore, due to the larger number of EQ bands, the prior training data could not be used, and so, the training data had to be created as well. The new training data included the seven special cases and 1493 random input-output gain pairs for the 24 Bark EQ filters, 1500 cases in total. This dataset was used to train the first version of the NSGEB.  
After successful convergence of the training algorithm, the network performance was validated, and it was discovered that although the performance error was reasonable, the network gain prediction did not achieve the largest individual filter gain values accurately enough. Note that although the user-set command gain values were limited to \(\pm 12\,\mathrm{dB}\), the actual EQ filter gains could have values larger than \(30\,\mathrm{dB}\) with certain command gain settings. Thus, a new training dataset was created containing 48 extreme command gain settings that produced EQ filter gains greater than \(30\,\mathrm{dB}\) on some EQ bands. These extreme cases consisted of command gain values of \(-12\,\mathrm{dB}\), \(0\,\mathrm{dB}\), and \(12\,\mathrm{dB}\). Additionally, a new set of random gain input-output pairs was created, so that the total number of data pairs was 1500.  
The first version of the neural network was retrained using the second training dataset, which, after convergence, was more accurate than before. This neural network was chosen to be the one used in NSGEB. The performance of SGEB and NSGEB methods is evaluated in the next section.
# Student Behavior Prediction of Mental Health Based on Two-Stream Informer Network
## 4 Experimental Results and Analysis
### Basic Settings
#### 4.1.2 Comparison Methods  
We chose the following deep learning models as benchmarks:  
* Fully Convolutional Network (FCN) and Residual Network (ResNet) [27], which are the best deep learning models in multivariate time-series classification tasks [23]. Multilayer Perception (MLP) also serves as a simple baseline.
* Universal Neural Network coder (Encoder) [38].
* Multi-scale Convolutional Neural Network (MCNN) [28].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Train Cases** & **Test Cases** & **Dimensions** & **Length** & **Classes** \\ \hline AUSLAN & 1140 & 1425 & 22 & 45–136 & 95 \\ ArabicDigits & 6600 & 2200 & 13 & 4–93 & 10 \\ CMUsubject16 & 29 & 29 & 62 & 127–580 & 2 \\ CharacterTrajectories & 300 & 2558 & 3 & 109–205 & 20 \\ ECG & 100 & 100 & 2 & 39–152 & 2 \\ JapaneseVowels & 270 & 370 & 12 & 7–29 & 9 \\ Libras & 180 & 180 & 2 & 45 & 15 \\ UWave & 20 & 4278 & 3 & 315 & 8 \\ Wafer & 298 & 896 & 6 & 104–198 & 2 \\ WalkvsRun & 28 & 16 & 62 & 128–1918 & 2 \\ STU & 6159 & 2054 & 13 & 32 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The multivariate time-series classification archive.  
* Multi-Channel Deep Convolutional Neural Network (MCDCNN) [29].
* Time Convolutional Neural Network (Time-CNN) [39].
* Time Le-Net (t-LeNet) [40].
* Time Warping Invariant Echo State Network (TWIESN) [30].
* Gated Transformer Network (GTN) for multivariate time-series classification [32].
## 3 Natural Products: A New Source of Molecules with Antitumor Activity
### Terpenes
#### 3.3.1 Geraniol  
Geraniol is a monoterpene that is found mainly in essential oils of aromatic plants and used in perfumes. It has been used as an active ingredient in many drugs since it exhibits analgesic and anti-inflammatory activity [114]. This compound presents preventive and therapeutic activity in many types of cancer, carrying out its effects by acting on the regulation of different signaling pathways such as PI3K/AKT/mTOR, MAPK/ERK1/2, or NF-\(\kappa\)B, and modulating the expression of different molecules such as cyclins, CDKs, interleukin, and different growth factors [115].  
In the Caco-2 cell line, it has been observed that this compound triggers the inhibition of cell proliferation by inducing an S-phase cycle arrest (IC\({}_{30}\) = 200 uM after 7 days). In addition, it was shown that it is able to induce apoptosis in in vivo models, showing a decrease in the expression of the anti-apoptotic protein BCL-2 in tumor tissue after treating mice with a dose of 250 mg/kg for 4 weeks. Its oral administration in in vivo models prevents the development of CRC, reducing the number and size of precursor lesions [116].  
Furthermore, its chemosensitizing effect was observed in Caco-2 and SW620 cell lines, and in murine models originated with 5-FU-resistant TC-118 tumor cells, having shown that co-treatment sensitizes tumor cells to 5-FU [117,118]. However, these studies are scarce, which requires further analysis to understand the effects of geraniol on resistance phenomena.