esa-sceva/satcom-chunk-collection huggingface dataset:
# Blind Compensation of \(I/q\) Impairments in Wireless Transceivers
## 3 Simulation Results
### Constellation Plots  
Figure 11 shows the constellations for the received and the corrected signal for a 16-QAM signal. The transceiver I/Q imbalance was applied to the signal in the presence of the AWGN channel. The degradation due to this gain and phase imbalance on the signal constellation can be seen in the figure. The proposed compensation technique is applied to the signal and the constellations are then observed. It can be seen that the proposed methodology improves the signal constellation and makes the symbol detection feasible at the receiver.  
Figure 10: BER vs. SNR for the proposed model for 16 QAM OFDM signal with 1024 subcarriers for \(g=g^{\prime}=0.5\) dB and \(\theta=\theta^{\prime}=-2\) degrees.
## 3 Results
### Correlation Analysis between the CatWalk XT(r) Parameters and Other Measurements
#### 3.4.2 Correlation between the CatWalk XT(r) Parameters and the Percentage of Preserved Tissue  
Considering that the histological assessment of preserved tissue was carried out after termination of the experiment, a correlation analysis could only be performed between the percentage of preserved tissue and the CatWalk XT(r) parameters from the last timepoint (N6). As shown in Figure 9, the correlation between the following CatWalk XT(r) parameters and the percentage of preserved tissue reached the highest \(r\) values: swing speed of the forelimbs (\(r=0.980\)), print length of the forelimbs (\(r=0.92\)), max contact mean intensity of the forelimbs (\(r=0.915\)) and base of support of the hindlimbs (\(r=-0.91\)). Interestingly, the correlation coefficients (\(r\) values) were higher in the forelimbs than in the hindlimbs in nearly all CatWalk XT(r) parameters except for the stride length (HL: \(r=0.89\) vs. FL: \(r=0.78\)) and the base of support (HL: \(r=-0.91\) vs. FL: \(r=-0.89\)). Comparison of the \(r\) values of the forelimbs and the hindlimbs, however, yielded no statistically significant difference (\(p=0.107\)). The poorest correlation could be observed between the percentage of preserved tissue and the CatWalk XT(r) parameters body speed of the hindlimbs (\(r=0.53\)) and forelimbs (\(r=0.49\)), the max intensity at \(\%\) of the hindlimbs (\(r=0.38\)) and lastly the average speed (\(r=0.34\)). A very strong correlation between the CatWalk XT(r) gait analysis and the percentage of preserved tissue was observed in 24 out of 30 (80\(\%\)) parameters and was thus considerably more common compared to the correlation with the BBB open field score (6.3\(\%\)). These findings suggested a close relationship between the spontaneous recovery after severe cervical SCI assessed by the CatWalk XT(r) gait analysis and the histologically preserved spinal cord tissue.  
Figure 8: Spearman correlation coefficients of all CatWalk XT® parameters of the hindlimbs with the BBB open field scores in SCI animals from two weeks after the injury (N2) until the end of the experiment (N6). The \(r\) values and the respective CatWalk XT® parameters are presented in a descending order from max contact area (\(r=0.88\)) to max contact max intensity (\(r=0.17\); \({}^{\star}p<0.05\); \({}^{\star\star}p<0.01\), \({}^{\star\star\star}p<0.001\); classification of the correlation in red writing).
# Performance Analysis of Dual-Hop Hybrid RF-UOWC NOMA Systems
## 3 Outage Probability Analysis
### Asymptotic Outage Probability  
A deep insight on the system performance under high SNRs regime is obtained through the derivation of the asymptotic outage probabilities. A tight asymptotic expression for the CDF of the exponential and EGG distributions at high SNR are [5]  
\[F_{|h_{w}|^{2}}(x)\simeq x, \tag{10}\]  
\[F_{|h_{i}|^{2}}(x)\simeq\frac{w}{\lambda}(\frac{x}{\mu_{r}})^{\frac{1}{r}}+ \frac{1-w}{\Gamma(a+1)}(\frac{x}{b^{r}\mu_{r}})^{\frac{\kappa}{r}}. \tag{11}\]  
Based on (10) and (11), we derive asymptotic expressions for \(OP_{1}\), \(OP_{2}\), and \(OP_{\text{sys}}\) as  
\[\begin{split} OP_{1}^{\infty}&\simeq 1-(1-\frac{\tau_{1}}{\rho_{g}d^{- \eta}})(1-\frac{w}{\lambda}(\frac{\beta_{1}}{\rho_{R}\mu_{r}})^{\frac{1}{r}}- \frac{1-w}{\Gamma(a+1)}(\frac{\beta_{1}}{b^{r}\rho_{R}\mu_{r}})^{\frac{\kappa}{ r}}),\end{split} \tag{12}\]  
\[\begin{split} OP_{2}^{\infty}&\simeq 1-(1-\frac{\tau}{\rho_{g}d^{- \eta}})(1-\frac{w}{\lambda}(\frac{\beta}{\rho_{R}\mu_{r}})^{\frac{1}{r}}- \frac{1-w}{\Gamma(a+1)}(\frac{\beta}{b^{r}\rho_{R}\mu_{r}})^{\frac{\kappa}{r}}),\end{split} \tag{13}\]  
\[\begin{split} OP_{\text{sys}}^{\infty}&\simeq 1-(1-\frac{\tau}{ \rho_{g}d^{-\eta}})(1-\frac{w}{\lambda}(\frac{\beta}{\rho_{R}\mu_{r}})^{\frac{1}{ r}}-\frac{1-w}{\Gamma(a+1)}(\frac{\beta}{b^{r}\rho_{R}\mu_{r}})^{\frac{\kappa}{r}}) \\ &\qquad\qquad\times(1-\frac{w}{\lambda}(\frac{\beta_{1}}{\rho_{R} \mu_{r}})^{\frac{1}{r}}-\frac{1-w}{\Gamma(a+1)}(\frac{\beta_{1}}{b^{r}\rho_{R} \mu_{r}})^{\frac{\kappa}{r}}).\end{split} \tag{14}\]
# Privacy and Security Issues in Online Social Networks
## 3 Privacy and Security Threats in OSNs
### Modern Threats
#### 3.2.2 De-anonymization Attacks  
De-anonymization is a strategy based on data-mining techniques, wherein unidentified information is cross-referenced with public and known data sources to reidentify an individual in the anonymous dataset. OSNs provide strong means of data sharing, content searching, and contacts. Since the data shared through OSNs are public by default, they are an easy target for deanonymization attacks [30]. In existing online services, pseudonyms are used for data anonymity to make the data publicly available. However, there are several deanonymization techniques to reidentify an individual from such data. For example, a recent work [31] claims a precise and robust deanonymization attack on social-network data.
## 2 Analysis of a New Active Quench Detection Method for High-Temperature Superconducting Magnets  
### Concept of the Proposed Method  
The quench detection method proposed in this paper has the following advantages. First, due to the transfer function characteristics of the resonance filter, the noise amplitude of the measured signal can be reduced. Second, because the proposed method detects abnormal signals in the frequency domain, it can compensate for the disadvantage of slow NZPV in the HTS. Third, the quench detection time can be shortened by adjusting the window size of the moving average when performing the FFT analysis. Figure 1 shows a flow chart of the novel fault diagnosis method for a HTS field coil of a superconducting rotating machine.  
Figure 1: Flow chart of novel fault diagnosis method for superconducting rotating machine.  
Figure 2 shows an equivalent circuit of the proposed quench detection method. The circuit consists of a superconducting coil part, a parallel resonant circuit part, a voltage measurement part, and a quench detection part. In the equivalent circuit, the superconducting stator coil is described by \(L_{ins}\) and \(R_{\theta}\), which indicate the self-inductance and azimuthal resistance, respectively, of the superconducting stator coil. The self-inductance of a superconducting rotor coil is \(L_{B}\), which is electromagnetically linked to a superconducting stator coil. The term \(V_{ac}\) represents an alternating voltage source used to transport the alternating field to the superconducting stator coil [13; 14]. Here, \(R_{div}\) is a resistor for adjusting the magnitude of the voltage induced in the superconducting stator coil and \(I_{dc}\) is a current source for applying a direct current to the superconducting stator coil. The term \(R_{R}\) is the radial resistance of the un-insulated coil [15]. Here, \(C_{var}\) is a variable capacitor for tuning the resonance frequency and \(R_{ext}\) is a resistor for measuring the voltage of the circuit. The terms \(L_{ins}\), \(C_{var}\), and \(R_{ext}\) are the components of a parallel resonant circuit with a stator coil in superconducting state. The resonance frequency of the circuit is tuned to be equal to the rotation frequency of the rotor coil.  
Figure 3 shows an equivalent circuit to explain the method of detecting the quench through \(R_{ext}\) when \(R_{\theta}\) is generated in the stator coil. The currents flowing to the stator coil and \(R_{ext}\) are called \(i_{1}\) and \(I_{2}\), respectively. The sum of the two currents is the same as \(I_{T}\), and \(I_{1}\) is equal to \(I_{T}\) because the impedance of the stator coil is zero when the stator coil is in superconducting state. However, as the stator coil transits from superconducting to normal state, \(R_{\theta}\) is generated, so that \(I_{2}\) flows toward \(R_{ext}\).  
Therefore, the voltage across \(R_{ext}\) is measured, then the quenching can be detected by performing FFT analysis because the amplitude of the transfer function is proportional to that of the output voltage.
# Cloud-Based Single-Frequency Snapshot RTK Positioning
## 2 Methodology  
This Section first introduces the general workflow of the proposed cloud-based SRTK algorithm. Then the main steps of this process and the methods used to tackle the existing challenges are described in detail. Finally, the three key parameters that influence the RTK fix rate, i.e., snapshot signal bandwidth, integration time, and baseline distances, are discussed.
## 2 Background
### Theoretical Context
#### 2.1.3 Cloud Computing  
Intelligent systems, such as, Smart City and SG, are based on the collecting and analyzing of large amounts of data obtained through a macro network of sensors distributed throughout the infrastructure, which must be processed and stored somehow. Here, data centers appear as an essential element for SG to become a settled reality and with it, the cities of the future [50].  
Emerging business prospects from the IoT are driving private, public, and hybrid cloud providers to integrate their systems with IoT devices equipped with sensors and actuators to provide a new level of service infrastructure to improve the quality and security of power supply. At the same time, with the advent of IoT, the continued evolution of cloud computing has led to a dramatic change in the design, implementation, and delivery of applications [51; 52].  
Cloud computing provides geographically distributed computing and storage resources that are essential for the analysis and management of real-time and computationally intensive data in intelligent networks, as is the case of data produced by IoT devices in an SG [53].
# Synthesize and Segment: Towards Improved Catheter Segmentation via Adversarial Augmentation
## 4 Experiments  
### Datasets  
There are currently no publicly available catheter datasets. Thus, we made two datasets for evaluations. First, X-rays dataset consisted of two-dimensional (2D) angiograms with several cranial and caudal views acquired from different patients with heart disease at Seoul National University Hospital, Korea. Figure 3 shows the dataset. For the image generation, X-rays of 100 patients not showing any inserted catheter (Figure 3a) were employed. Subsequently, we randomly composited masks (Figure 3b) with catheters to create the composited dataset (Figure 3c). Second, we considered the applicability of our method in a more challenging setting i.e., catheters in natural images. Figure 4 shows the  
Figure 2: Overview of the segmentation networks employed to train and evaluate the augmentation techniques introduced.  
samples obtained in our in-house micro-robotic research centre, which consists of \(11\) videos sequences with a total of \(11884\) catheter images (Figure 4a) where catheters exhibit varied movements in different directions in response to the magnet field alteration in coils. We composited the camera catheter mask (Figure 4c) obtained using vesseless filter [45] on the X-ray images (Figure 4b) to construct the composited X-ray dataset (Figure 4d). For evaluation, \(200\) X-ray images with manual annotations were split into train/validation/testing based on the ratios, i.e., \(140\), \(10\), and \(50\) images, respectively.
## 3 Photonic Technologies
### Optical Coherence Tomography (OCT)  
#### 3.4.1 Working Principles  
Optical coherence tomography (OCT) is an imaging technique and has been widely used in medical applications, mainly for diagnostic purposes. Compared to other conventional imaging techniques, OCT has the advantages of being non-invasive, non-destructive and does not expose patients to radiation [97].  
There are two main types of OCT: time domain OCT (TDOCT) and spectrum domain OCT (SDOCT). TDOCT can be performed by two techniques, both use two-beam interferometry: in the reflectometer technique the sample is inside the interferometer and illuminated by the sample beam only, whereas in the dual beam technique the sample is outside the interferometer and illuminated by both interferometer beams [98]. In TDOCT the path length of the reference arm is scanned in time. The interference pattern is only achieved when the optical path difference lies within the coherence length of the light source. TDOCT has been used for evaluation of indirect dental restorations, apical microleakage, monitoring the periodontal ligament changes induced by orthodontic forces, and orthodontic interfaces. In SDOCT the spectrum at the output of the low coherence interferometer is measured. The depth scan (A-scan) is calculated by a Fourier-transform from the acquired spectra, without movement of the reference arm. SDOCT has an improved signal-to-noise ratio in comparison to TDOCT, the higher the number of separate spectral windows used in the spectrometer, the larger the signal-to-noise ratio. The width of the spectral windows limits the axial scanning range, while the full spectral bandwidth sets the axial resolution. SDOCT can be also divided into swept source (SS) OCT, and camera-based, Fourier domain (FD) OCT, if it uses a narrow band or broadband optical source [99, 100, 101].  
The OCT axial resolution is related to the light source coherence length \(l_{c}\), which is a function of the light source bandwidth (\(\Delta\lambda\)). The light source coherence length can be described by Equation (3) [101]:  
\[l_{c}=\frac{2c\ln 2}{\pi}\frac{1}{\Delta v}=\frac{2\ln 2\lambda_{0}^{2}}{ \Delta\lambda}\approx 0,44\frac{\lambda_{0}^{2}}{\Delta\lambda} \tag{3}\]  
where \(\lambda_{0}\) is the center wavelength and \(\Delta\lambda\) is also known as the full width at half the maximum of spectrum. OCT systems, with appropriate broadband light sources, can exhibit excellent axial resolution, less than 20 \(\upmu\)m. The transverse resolution is decided by the final spot size on the sample. Higher transverse resolution may be achieved with a focused light. The dispersion and phase compensation are also important to an OCT system.
# Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis
## 2 Data-Driven Bearing Fault Detection  
### Data Collection  
The purpose of our fault detection method was to detect bearing faults and diagnose the fault types. Among the various fault types, inner race, outer race, and contaminant faults were considered. As an initial step, we collected two channels (the horizontal and vertical axes) of vibration signals for normal and faulty conditions using the apparatus shown in Figure 1a.  
The motors of the test bench were 3 kW three-phase induction motors with rated voltage 400 V and current 6.4 A. The configuration followed the power drive setup in [28]. We connected the motors to the controller via inverters to control the speed and torque. The running environment had a 25 Hz operation frequency and 10 Nm torque. The motors and inverters were mounted on the steel rail to fix the electronic machines.  
For data acquisition, two vibration sensors (model: MMF KS80D, range: \(\pm\)60 g, bandwidth 22 kHz, sensitivity: 0.1 V/g) were used to record vibration in the x- and z-axis (as shown in Figure 1b). An oscilloscope was connected to vibration sensors and the recorded vibration signals were stored in a server. In our experiments, the sampling rate of the vibration signal was 1 MHz. Vibration data from the induction motor were collected from the test bench under varying environment conditions over the course of a year.  
To measure faulty condition data, bearings were artificially damaged by the following methods. For inner and outer race faults, we drilled into the middle of the inner and outer raceway in the bearings after removing the metal shield and the grease in the bearing. The drilling diameter was 1, 3, and 5 mm for low, medium, and high severity, respectively. For the contaminant, we inserted metal chips in the cage. The bearing with different faulty conditions is shown in Figure 1c.
## 1 Introduction  
Liver cancer has remained a world health challenge. The most common kind of liver cancer is hepatocellular carcinoma (HCC), accounting for 90% of all occurrences [1]. HCC can be treated effectively using a number of therapies such as resection, liver transplantation, chemoembolization, and systemic medicines. Additionally, the use of herbal medicine in the treatment of liver cancer has also been documented. The Chinese and English medical literature have reported that herbal medicine could be useful in the treatment of HCC [2]. In addition, network pharmacology investigates the components, targets, and synergistic connections between drugs, targets, and diseases. These findings are applied to explain pharmacological activity [3]. Network pharmacology has enabled herbal medicine researchers to look beyond a single ingredient, target, or condition to investigate the effectiveness of herbal medicine and its mechanisms [4].  
Apoptosis, a form of programmed cell death, is considered an anti-cancer mechanism ([PERSON] et al., 2016) [5]. Apoptotic cells exhibit morphological changes including cytoplasmic shrinkage, plasma membrane budding, exposure of phosphatidylserine on the extracellular side, chromatin condensation, and DNA fragmentation ([PERSON], 2000) [6]. Apoptosis can be triggered by both intrinsic and extrinsic factors ([PERSON] 2018; [PERSON] et al., 2021) [7, 8]. Many anti-tumor drugs can target different stages of intrinsic and extrinsic pathways ([PERSON] 2017) [9]. Nowadays, some herbal medicine has been shown to be useful in cancer treatment by triggering apoptosis ([PERSON] 2022) [10].  
_Sophora flavescens_ is a medicinal plant that has recently received a lot of attention. _Sophora flavescens_ is a plant that could be used for removing heat and humidity from the body, as well as killing insects [11]. Kushen, a dried stem of _Sophora flavescens_, has been demonstrated in clinical testing to have low toxicity and negative effects [12]. Kushen has shown several therapeutic effects, including swelling reduction, immunostimulation, and anti-tumor properties [13]. _Sophora flavescens_ contains a variety of pharmacological activity compounds such as matrine alkaloids, flavonoids, and the saponin sophoraflavoside [14]. These compounds also exhibit anti-oxidant and anti-cancer properties [15, 16]. A recent study showed that the compounds from _Sophora flavescens_ exhibit anti-oxidant properties and proliferative inhibition in several cell lines (HepG2, A549, and MCF7) [16]. The prenylated flavonoid from _Sophora flavescens_ could inhibit HeLa and HL-60 tumor cell lines [17]. However, the mechanism of inhibited proliferation in HepG2 cells induced by SFA extracts has not been well characterized. Therefore, this study was conducted to estimate the capacity of matrine and oxymatrine-rich extracts of _Sophora flavescens_ in proliferative inhibition, as well as their ability to induce apoptosis in HepG2 cells.
## 1 Introduction
* The total cost, considering both the benefits (mitigating the customer interruption cost) and operation costs (Wind-DG and PV-DG generation cost, MESS transportation cost, PEV-PL operation cost, and battery maintenance cost), is proposed to evaluate the feasibility of using MESSs and PEV-PLs in DS restoration. * Internal uncertainties with DG generation sources and different load profiles are considered in the operational model via scenario-based stochastic programming, while external contingencies, including multiple line outages, are considered. * The key role of different load types is demonstrated in the model in addition to the reconfiguration of the network to show the importance of serving critical customer loads in contingency conditions according to their priorities. \begin{table}
\begin{tabular}{p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt}} \hline \hline
**Ref. No.** & **DNR** & **MESS** & **PEL-PLs** & **DGs** & \begin{tabular}{c} **Considering** \\ **MGs** \\ \end{tabular} & \
\begin{tabular}{c} **Uncertain** \\ **Parameters** \\ \end{tabular} &
\begin{tabular}{c} **Continuency** \\ **Test System** \\ \end{tabular} \\ \hline
[12,13] & \(\times\) & \(\times\) & \(\times\) & ✓ & ✓ & \(\times\) & \(\times\) & 33-bus \\ \hline
[14] & ✓ & \(\times\) & ✓ & ✓ & \(\times\) & \(\times\) & ✓ & 33-bus \\ \hline
[15] & \(\times\) & \(\times\) & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) & \\ \hline
[16] & \(\times\) & ✓ & \(\times\) & ✓ & \(\times\) & PV, wind, load & \(\times\) &
\begin{tabular}{c} 33-bus \\ 69-bus \\ \end{tabular} \\ \hline
[17] & \(\times\) & ✓ & \(\times\) & ✓ & \(\times\) &
\begin{tabular}{c} Load, wind \\ speed, solar \\ radiation \\ \end{tabular} & \(\times\) & \\ \hline
[18, 19, 20, 21] & \(\times\) & ✓ & \(\times\) & ✓ & \(\times\) & \(\times\) & ✓ &
\begin{tabular}{c} 33-bus \\ 15-bus \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison of the relevant works in the literature.
## 3 PBCS Structure  
As in all communication systems, digital information is modulated in the transmitter and demodulated in the receiver based on some method. In PBCS, the modulation is performed by using Sinusoidal Pattern Envelope Construction (SPEC), which will be explained in the following sub-section. On the other hand, the demodulation, which is another important process that determines efficiency and the performance of the system, corresponds to the offline pre-training of the ANN. This will also be explained in the Receiver Structure and ANN Design sub-section.
# UAV Path Optimization for Precision Agriculture Wireless Sensor Networks
## 2 System Model
### System Elements  
As illustrated in Figure 1, the scenario considered in this work is composed of SNs, UAVs, and a BS, while we consider the following characteristics for each element:  
Figure 1: System elements.  
Sensor nodes (SNs) are homogeneous, have fixed locations, and are known by the UAVs. We assume that they are composed of a set of sensors, processor, memory for sensory data storage, and also a Global Positioning System (GPS) interface. Besides that, we consider that SNs have a limited lifetime due to the capacity restrictions of their batteries. Thus, SNs must alternate their states between periods of activation and hibernation. We assume that there is no direct communication between SNs due to the distance between them and the limited communication capacity. Therefore, they depend on the UAV to collect data stored in memory and deliver it to the BS.  
Unmanned aerial vehicles (UAVs) have high mobility, are homogeneous, multi-rotor, and have ad hoc communication systems [26] for UAV-UAV, UAV-SN, and UAV-BS communication type. Besides gathering data from the SNs, they can exchange information with each other by copying its local database, about the scenario, directly to the new UAV on Return to Home procedure, and transfer the sensory data to the BS.  
Base station (BS) has a fixed location known by the UAVs, and it is the final destination of the information generated by the SNs. It is primarily responsible for the post-processing of the sensory data delivered by the UAVs.