esa-sceva/satcom-chunk-collection huggingface dataset:
# Gathering Big Data in Wireless Sensor Networks by Drone +
## 6 Algorithm for Big Data Gathering during Drone Movement
### Verifying If a Sensor Node Can Send Data during the Drone Flight  
This subsection presents how to verify if a sensor node \(s_{i}\) can send data to a drone flying over the path between the hovering points \(p_{a}\) and \(p_{b}\), as exemplified by Figure 9. We consider that the area covered by a sensor node's radio is a circle with radius \(r\) and center in the sensor node position. The time an object takes to travel a given distance at constant speed is the distance divided by the speed. Hence, we define \(trs\) as the minimal distance the drone has to fly inside the area covered by a sensor node to enable it to send data during the drone movement. The length of \(trs\) depends on the drone speed (\(d_{speed}\)), the link bandwidth (\(m\)), and the amount of data stored in the memory of each sensor node. Consequently, \(trs\) is calculated with the following equation:  
\[trs=\frac{m}{b}\times d_{speed} \tag{4}\]  
Let \(ds_{i}\) be the part of the path \(p_{a}\)-\(p_{b}\) covered by the radio of the sensor node \(s_{i}\). The length of \(ds_{i}\) is easily calculated by the following equation:  
\[ds_{i}=2\sqrt{r^{2}-(dist_{i})^{2}} \tag{5}\]  
where \(dist_{i}\) is the Euclidean Distance of the sensor node \(s_{i}\) to the line \(p_{a}-p_{b}\). Equation (5) is a manipulation of the Pythagorean theorem, with \(a=r\), \(b=dist_{i}\) and \(c=\frac{ds_{i}}{2}\). We can see these variables in Figure 9.  
Figure 8: Analyzing nodes to send data to the drone during movement.  
The sensor node \(s_{i}\) can send data to drone flying over the path between \(p_{a}\) and \(p_{b}\) only if \(ds_{i}\geq\mathit{trs}\). Given a tour composed of a sequence of hovering points, the algorithm proposed here calculates \(ds_{i}\) for every sensor node for each path between two hovering points and compares them to \(\mathit{trs}\). Hence, the algorithm creates a subset of sensor nodes for each path and guarantees that the drone will stay a minimum time inside the sensor nodes' radio range. The algorithm is described in the following subsection.
## 3 Recent Developments and Progress
### Environmental Remote Sensing by GNSS-R
#### 3.5.1 Water Level Monitoring  
Currently, the use of ground-based GNSS-R for monitoring water levels has been extensively validated. Floods seriously affect human production and life, often causing major economic losses. In cases of extreme weather, the river water level can rise sharply in a short time, which seriously threatens the houses and farmland on both sides of the river. The problem of how to monitor the water level change in real time and timely obtain the water level information for the river is very important. GNSS-R technology instruments have been developed to a high degree in water level monitoring because of their high resolution and the possibility of convenient deployment. [PERSON] et al. [50] employed a weighted iterative least-squares method to estimate high-frequency water level change with multiple systems based on GNSS-R data. By utilizing measured GNSS observation data, a high-frequency water level with impressive centimeter-level accuracy was estimated using multi-frequency and multi-system GNSS-R based on the Signal-to-Noise Ratio (SNR) method. Moreover, the temporal resolution of their findings was remarkably high, with measurements captured at intervals of just five minutes (Figure 12).  
Figure 12: Data processing flow chart for high-frequency GNSS-R water level monitoring (Reprinted with permission from Ref. [50]. 2022 [PERSON], [PERSON]).  
[PERSON] et al. [51] found that GNSS-IR was not as accurate as post-processing in real-time accuracy, but RMSE was significantly improved under a 1 h post-processing delay. In addition, by using a simple tropospheric correction model, the average deviation between GNSS-IR observations and measured data was effectively eliminated. [PERSON] et al. [52] proposed the use of a lower-cost GNSS-IR sensor, that is, to replace the commonly used high-precision GNSS receiver with an electronic device--the mobile phone that we often use. Although the final accuracy was slightly lower than for the control device (CHCANV P5 GNSS receiver BT560 antenna), the experiment verified the feasibility of the Android smartphone as a GNSS-R sensor.
# Tunable RF Filters Based on Liquid Crystal for Space Applications
## 3 Qualification Tests and Results
### Test Results
#### 3.2.2 Power Handling Test  
The RF performance measurements taken during the power handling test are shown in Figure 10.  
The initial measurement at the start of applying 0 dBm RF power (label \"A\", dark blue) and the second measurement after having applied this power for 60 minutes (\"B\", red) coincide. Just after having increased this power to 20 dBm, the measurement \"C\" (green) shows some drift towards higher frequency and increased loss at center frequency. Sixty minutes later, after continuous application of this power, this effect is more pronounced (\"D\", brown). Having reduced the power back to 0 dBm, measurement \"E\" (light blue) shows an even larger drift towards higher frequency but no longer increase in center frequency insertion loss. At the end of this final period of application of 0 dBm, measurement \"F\" (orange) has returned back to the original performance. \"F\" overlays exactly with \"A\" and \"B\" and is therefore hardly distinguishable.
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
### QKD Protocols
#### 1.3.1 B84  
Named after creators, [PERSON] and [PERSON] in 1984, BB84 encodes data in single-photon polarization states, such that [PERSON] and [PERSON] can communicate limited information and generate identical, secret cryptographic keys [9; 12].  
A linearly polarized photon at angle \(\theta\) in the x-y plane is represented by \(\left|\theta\right\rangle=cos(\theta)\left|0\right\rangle+sin(\theta)\left|1\right\rangle\), where the \(\left|1\right\rangle\) and \(\left|0\right\rangle\) represent the vertical and horizontal polarization states, respectively [13]. A single photon polarization is measured with respect to a certain direction. BB84 uses two bases: rectilinear and diagonal. The rectilinear basis can be thought of as \(0^{\circ}\) (horizontal) and \(90^{\circ}\) (vertical); the states can be written as \(\left|0_{+}\right\rangle,\left|1_{+}\right\rangle\). The diagonal basis can be thought of as a superposition of the horizontal and vertical states, or more simply, \(45^{\circ}\) and \(135^{\circ}\). These states can be written as \(\left|0_{\times}\right\rangle,\left|1_{\times}\right\rangle\). Using these four states, [PERSON] and [PERSON] can communicate by encoding data in photon polarization.  
The BB84 protocol works as follows: first, [PERSON] randomly generates a stream of bits and a random sequence of polarization bases, either rectilinear or diagonal. Next, [PERSON] encodes her random bits in photon polarization states by polarizing the photons according to a pre-specified encoding scheme. For example, in traditional BB84, she will send a binary 0 with a 0\({}^{\circ}\) (rectilinear) or 45\({}^{\circ}\) (diagonal) polarized photon; and she will send a binary 1 with a 90\({}^{\circ}\) (rectilinear) or 135\({}^{\circ}\) (diagonal) polarized photon. She sends the polarized photons to [PERSON] via the quantum channel (e.g., fiber optic or free space). For each photon he receives, [PERSON] randomly chooses one of the bases to measure the photon and interprets the resulting bit based on the same encoding chart [PERSON] used. Next, [PERSON] and [PERSON] communicate their bases choices via the classical channel (e.g., internet), and they discard all bits in positions where mismatching bases were used. If [PERSON] did not interfere, their keys should contain low error and in theory, should be identical. To confirm, [PERSON] and [PERSON] share a subsection of their keys and compare to check for low error. If the error is low, the remaining bits are used as the final cryptographic key; otherwise, they conclude that [PERSON] interfered, discard their keys, and try again. Note, in BB84, a portion of the key must be sacrificed and exposed in order to estimate the presence of interference.
# The Antimicrobial Effectiveness and Cytotoxicity of the Antibiotic-Loaded Chitosan: ECM Scaffolds
## 3 Results  
### Scaffold Properties  
Representative images of the prepared scaffolds are shown in Figure 1. Endotoxin testing showed that the prepared chitosan solution contained 5.03 EU/mL. The prepared scaffolds therefore contained endotoxin proportional to the amount of chitosan used in their fabrication. All the scaffold formulations that consisted of 100% chitosan or 80:20 and 50:50 chitosan:ECM hydrogel formed a fibrous sponge like scaffold following lyophilization. The 100% chitosan scaffolds had a firm and dense, but flexible, structure that was easily cut with a biopsy punch to obtain test samples. The scaffolds incorporating ECM hydrogel had a softer consistency than 100% chitosan, with the softness increasing with increasing ECM hydrogel percentage. Obtaining biopsy punch samples for testing was still possible. The scaffolds that incorporated particulate ECM had a firmer, less flexible structure than those made with ECM hydrogel or chitosan alone. The 10% ECM scaffolds had a solid inflexible structure that was very easy to cut and showed very little compression. Addition of the rifampicin and minocycline antibiotics colored the scaffold either orange or yellow respectively showing that the antibiotic was distributed homogenously throughout the scaffolds.  
When hydrated, notable differences between the scaffolds were observed. The 100% chitosan scaffolds retained their structure when hydrated and showed no signs of degradation of dissolution of the fibrous structure over time. The 80:20 chitosan:ECM scaffolds softened when hydrated becoming slightly adhesive to any surface upon which they were placed and over time formed a semitranslucent gel. The 50:50 chitosan:ECM scaffolds quickly formed a translucent gel (approximately 5-10 min) when hydrated and were more adhesive to a surface that the 100% or 80:20 scaffolds. Over time, the 50:50 chitosan:ECM scaffolds lost their fibrous structure and after 24 h had turned completely to hydrogel that could not be picked up with forceps. The 1% ECM and 10% ECM scaffolds both retained their structure following hydration but swelled in volume over a 24 h period. While the 1% scaffolds retained their shape after 24 h, the 10% ECM scaffolds began to crumble with increasing hydration time eventually forming a paste-like putty after 24 h that could still be handled by forceps but could be spread like a cream when lateral forces were applied. Scaffolds made from 100% ECM hydrogel were also made, while these scaffolds could be cut with a biopsy punch to obtain test samples, they had a very soft, friable structure that was easily disrupted and when hydrated these scaffolds rapidly reverted to hydrogel. These handling characteristics made them unsuitable for testing and they were excluded from the subsequent experiments.
# On Transform Domain Communication Systems under Spectrum Sensing Mismatch: A Deterministic Analysis
## 3 Spectrum Sensing Mismatch  
### Mathematical Model for Spectrum Sensing Mismatch  
In wireless systems, the transceivers are often deployed separately from each other and additional interferences will be involved during such a process, which is even severe in IoT systems. It has been defined in IEEE802.22 [29] that channel uncertainties are likely to introduce a mismatch between the spectrum sensing results. Atypical example is depicted in Figure 5, which is named Spectrum Sensing Mismatch (SSM) in this work, where spectrum sensing results forany certain spectral bins are different in the transceiver.  
Figure 4: An example of a PCF vector (with noise).  
Figure 5: An example of spectrum sensing mismatch.  
Figure 3: Block diagram of the TDCS receiver (two modulation methods in one general diagram).  
In these available spectral bins, there are total three of them in the presence of SSM:  
* \(\left\{A_{k}=1,k\in\Omega^{Tx}\right\}\) for those bins available only at the transmitter side, with \(\|\Omega^{Tx}\|=N_{Tx}\);
* \(\left\{A_{k}=1,k\in\Omega^{Rx}\right\}\) for those bins available only at the transmitter side, with \(\|\Omega^{Rx}\|=N_{Rx}\); and
* \(\left\{A_{k}=1,k\in\Omega^{C}\right\}\) for those bins available at the same time both at the transmitter and the receiver, with \(\|\Omega^{C}\|=N_{C}\).  
For any spectrum sensing results, it satisfies:  
\[\left\{\begin{array}{l}\Omega^{C}=\Omega^{Tx}\cap\Omega^{Rx}\\ 0\leq N_{C}\leq(N_{Tx},N_{Rx})\leq N\end{array}\right. \tag{10}\]  
When the TDCS is essentially a multi-carrier system, the mismatched spectral bins will significantly lead to the loss of signal power. The Simplified block diagram of TDCS transceivers is depicted in Figure 6. Therefore, we have:  
* \(\mathbf{D}=\mathbf{A}^{Rx}\cdot\mathbf{P}\) for receiver's local reference signal, where \(\left\{A_{k}^{Rx}=1,k\in\Omega^{Rx}\right\}\);
* \(\mathbf{R}_{1}=\mathcal{F}(\mathbf{r})\) for the received signal in the frequency domain (after equalization);
* \(\mathbf{R}_{2}=\mathbf{R}_{1}\cdot\left(\mathbf{D}\right)^{*}\) for those after multiply with local reference signal \(\mathbf{R}_{1}\); and
* \(\alpha\) for the proposed mismatch factor, (\(0\leq\alpha\leq 1\)).
# Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals
## 2 EEG Recording and Preprocessing  
EEG signals from 14 patients with paranoid SZ, comprising seven males and seven females, with average ages of 27.9 \(\pm\) 3.3 and 28.3 \(\pm\) 4.1 years, respectively, were collected from the Institute of Psychiatry and Neurology in Warsaw, Poland [24]. The exclusion standards involved patients with severe neurological aliments such as Alzheimer's, early stage SZ, and epilepsy, amongst other considerations, such as pregnancy and existence of a general medical condition. Fourteen healthy subjects within the same age group and gender proportion were recruited for the study from the same institute as well. Each participant provided informed consent to participate in the study upon receiving the study protocol.  
As participants remained in a relaxed state with eyes closed, fifteen minutes of EEG data was collected at a sampling rate of 250 Hz. Data was obtained via the typical International 10-20 System. The electrodes used were Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2. The signals acquired were then divided into segments, in which the signals can be considered to be stationary. Each segment consisted of a 25 s (6250 sample) window length and was normalized with Z-score, before feeding to the one-dimensional deep convolution network for training and testing. A total of 1142 EEG segments were used and each segment consisted of 6250 \(\times\) 19 sampling points. Normalization was employed to scale the signals to a standard range of values, hence allowingfaster convergence by the deep learning model during training. For subject-based testing and non-subject-based testing, 50 epochs and 70 epochs were fed into the network, respectively. An epoch is the dataset that passes forward and backward through a neural network once. An epoch of training for deep learning lasted between 2 to 3 s. For subject-based testing, the validation of the system is executed in three phases: training the data, validation, and testing of data, respectively. During the training phase, k-fold validation is employed, wherein the full data pool is split into fourteen equal parts (subjects). Of these subjects, twelve were used for training, one subject for validation, and one subject for testing, respectively. This process was repeated fourteen times so that all of the fourteen subjects were subjected to the training, validation, and testing phases. In non-subject based testing, the system is validated through the training and testing phases. During the training phase, ten-fold validation is employed, whereby the entire data is split into ten uniform parts. Of these, nine are used for training the model and the remaining one part is used to test. This process is reiterated such that each of the ten portions is involved in both the training and testing phases. Thereafter, 20% of the cross-validation training data is set aside for validation of the model. Figure 1 illustrates an example of EEG recordings from normal and SZ patients.
## 3 Efficient Network AOS Comprehensive Multiplexing Algorithm
### Asynchronous Real-Time Traffic
#### 3.2.2 Virtual Channel Scheduling Algorithm
Different VC priorities are assigned according to the data's importance level. High-priority VCs are able to preemptively occupy scheduling resources under certain channel conditions. Virtual channel scheduling algorithm based on comprehensive channel status:  
Based on the four channel status parameters mentioned above, the comprehensive channel status parameter for the \(j\)-th VC at the \((r,k)_{sche}\) scheduling moment can be calculated as follows:  
\[U_{s\_j}(r,k)=u_{s\_j}(r,k)\cdot m_{s\_j}\cdot(\alpha_{1}\cdot U_{s\_j}(r,k)+ \alpha_{2}\cdot U_{s\_j}(r,k)+\alpha_{3}\cdot U_{s\_j}(r,k)) \tag{10}\]  
where \(u_{s\_j}\) is a Boolean function that determines whether the \(j\)-th VC can participate in competing for the physical channel at the \((r,k)_{sche}\) scheduling moment. Its value is determined by the following formula:  
\[u_{s\_j}(r,k)=\left\{ \begin{array}{ll}0,&\text{if there is no frame at the $(r,k)_{sche}$ moment}\\ 1,&\text{if there is a frame at the $(r,k)_{sche}$ moment}\end{array} \right. \tag{11}\]The virtual channel scheduling state parameter, transmission frame scheduling state parameter, and traffic prediction state parameter are weighted by coefficients \(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\), respectively, to balance their contributions in the transmission function. By adjusting these parameters, optimal matching with the channel can be achieved. At any \(\left(r,k\right)_{sche}\) scheduling moment, the virtual channel with the maximum comprehensive channel state parameter value is denoted as \(n_{s}\). At this time slot, the \(n_{s}\)-th channel is selected as the current scheduling channel. The scheduling algorithm can thus be expressed as follows:  
\[Z_{s}(r,k)=n_{s}(r,k) \tag{12}\]  
In the equation, \(Z_{s}\) represents the VC channel number that gains the scheduling right at the current moment, where \(n_{s}\) satisfies the following condition:  
\[U_{s\_{n_{s}}}(r,k)=\max(U_{s\_0}(r,k),U_{s\_1}(r,k),U_{s\_2}(r,k)\ldots\ldots \ldots U_{s\_N_{s}-1}(r,k)) \tag{13}\]
## 4 Wireless Soil Moisture Sensing Applications
### Applications in Remote Sensing and Geophysics  
In the past decade, soil moisture WSNs contributed to several validation activities of satellite missions such as the SMAP (Soil Moisture Active and Passive) or SENTINEL missions by providing reference data [18; 98]. In addition, soil moisture data from WSNswere used for the calibration and validation of simultaneous passive/active microwave airborne campaigns in the Rur catchment [99; 100] to analyze various radiometer-radar fusion methods for retrieving improved soil moisture data products [101] and to develop methods for downscaling SMAP radiometer data [17].  
Soil moisture WSNs have also been used to support unmanned aerial vehicle (UAV) applications for soil moisture mapping [102]. For instance, [PERSON] et al. [103] showed that a combined UAV and WSN instrumentation can be used to better capture the soil moisture of a certain area by optimizing the UAV pathway planning. Other studies combined a UAV platform with a WSN to monitor, in real time, important parameters that characterize the growth conditions [104] and the thermal stress in vineyards [105].  
Soil moisture data from WSNs were also used to validate field-scale soil moisture measurements with non-invasive geophysical instruments. For instance, soil moisture data from SoilNet have been used to test novel cosmic-ray neutron sensors (CRNS) that enable non-invasive soil moisture measurements at the field scale [106; 107; 108; 72]. On the other hand, soil moisture data from SoilNet were used to demonstrate the strong influence of biomass on CRNS measurements [109]. In another study, [PERSON] et al. [110] used SoilNet data to investigate the potential of mapping soil moisture with electromagnetic induction (EMI) measurements in a forested headwater catchment. They found limitations for the use of EMI to monitor spatiotemporal soil moisture changes due to low electrical conductivity of the forest soils and the spatiotemporal variability of the pore water electrical conductivity.
# High-Gain Waveguide-Fed Circularly Polarized Spidron Fractal Aperture Antenna
## 4 Conclusions  
A high-gain rectangular waveguide-fed fractal aperture antenna with circular polarization operation was fabricated and tested, and the design and results are presented. A Spidron fractal aperture was employed to generate circular polarization and was fed directly by a WR-90 waveguide-to-coax adapter. To realize the high-gain characteristics, a superstrate was applied at an appropriate distance above the antenna. The experimental results proved that the proposed antenna has a \(-10\) dB reflection bandwidth of 9.89-11.58 GHz (15.74%), a 3 dB AR bandwidth of 2.95% (10.68-11.00 GHz), and a maximum RHCP gain of 9.59 dBic. Unlike most antennas reported in earlier work [4], the proposed waveguide-fed Spidron fractal aperture antenna radiates CP waves. Therefore, it can be feasibly integrated into satellite communications and radar systems for X-band applications.  
The presented work was carried out in collaboration of all authors. [PERSON] and [PERSON] performed the simulations. [PERSON], [PERSON], [PERSON], and [PERSON] participated to the conception, fabrication and experiment. [PERSON] wrote the paper which was edited by all co-authors.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (2018R1C1B6005024).  
The authors declare no conflict of interest.
## 3 Elastic Foundation Beam Model Used in the Small Radius TBM Interval  
### Model Description  
The elastic foundation model is the most widely used theoretical model in the study of tunnel longitudinal deformation, mainly it has three categories: (a) Winkler foundation model [12], the foundation is regarded as a series of independent springs. The soil properties are manifested by the stiffness of the spring. (b) Pasternak model [13], an incompressible shear layer, only having horizontal shear deformation, is added in the Winkler foundation model. The shearing between springs is considered in the Pasternak model. (c) Kerr model [14], the spring layer, is added based on the Pasternak model. In contrast, the parameters in the Winkler foundation model are straightforward. When the compressible strata are thin and a hard layer exists, the calculation results based on the Winkler foundation model agree well with the real present situation. Therefore, the Winkler foundation model is selected to calculate the horizontal axis deviation of a small radius TBM tunnel subjected to construction loading. The lining structure of TBM segments in the small radius interval is simplified as the continuous circular curved beam on the Winkler foundation based on the equivalent elastic foundation beam theory (Figure 3). \(q_{r}\) is the radial linear load on the curved beam; \(q_{t}\) is the tangential linear load on the curved beam. \(\varPhi\) is the arbitrary angle on the circular curved beam; \(p\) is the concentrated force.