esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Experimental Comparison with Other Deep Learning Models  
To evaluate the identification performance of the proposed CC-GAN, we conducted comparative experiments with several KVNNs and CVNNs. The architectures and parameters of the models to be compared were designed to be as consistent as possible with the CC-GAN. The RVNNs include a real-valued conditional adversarial generation network (RC-GAN), real-valued generative adversarial denoised network (RGADNet) [25], real-valued deep residual network (RRResNet) [35], and a real-valued deep residual shrinkage network (RDRSN) [36]. The CVNNs include a generative adversarial network based on deep complex U-net (DCUnet-GAN) [37] and a complex-valued deep residual shrinkage network (CDRSN). The detailed architectures of these models are shown in Table 1.  
The RC-GAN consist of two components, a generator and a discriminator, with a architecture identical to that of the CC-GAN. In detail, the 1D complex-valued convolutional layer, the 1D complex-valued transposed convolutional layer, and the 1D complex-valued global average pooling layer in the generator and the discriminator are replaced with a 2D real-valued convolutional layer, a 2D real-valued transposed convolutional layer, and a 2D real-valued global average pooling layer, respectively. The complex-valued batch normalization layer, complex-valued dense layer, zReLu activation function, complex-valued Tanh activation function, complex-valued sigmoid activation function, and complex-valued softmax activation function were replaced with the corresponding real-valued components. The RGADNet consists of two components: a generator and a discriminator. The residual unit A, as shown in Figure 9a, has been incorporated into the generator in addition to the upsampling and downsampling structures. The structure of the discriminator is consistent with the RC-GAN. Figure 8: The individual identification accuracies with different denoising algorithms. The architectures of the RResNet and the RDRSN are consistent with the structure of the discriminator of the CC-GAN. Most importantly, the feature extraction unit in the original discriminator needed to be replaced with the respective residual unit B and residual shrinkage unit of each network, and the specific structures of the two units are shown in Figure 9b,c. The remaining components of the two networks refer to the RC-GAN, replacing the complex-valued components in the original discriminator with the corresponding real-valued components. The architecture of the CDRSN is consistent with that of the RDRSN, which merely requires replacing the components in the original real-valued model with their corresponding
## 3 Results and Discussion
### Parameters Optimization in Fabricating PES Electrospun Nanofibers
#### 3.1.2 Effects of Electrospinning Parameters on Applied Voltage  
The effects of the electrospinningning parameters on the applied voltage were considered physicochemically based on the response surface function for applied voltage obtained. A droplet of polymer solution in the tip is exposed to the capacitor-like electrical field during the electrospinningning process. When the tip-to-collector distance is much larger than the needle diameter, the electrical field strength applied to the charges in the droplet can be estimated as follows [18]:  
\[\mathrm{E}\ \sim\ \frac{V}{d} \tag{7}\]  
where E is the electrical field strength, V is the applied voltage, and d is the tip-to-collector distance. Equation (7) indicates that more applied voltage is needed for stable electrospinningning as the tip-to-collector distance increases. This supports our statistical finding that the linear coefficient of the tip-to-collector distance (2.125) was positive in Equation (6) and had the highest value. Our previous statistical analysis indicates that the LiCl concentration exerts the corresponding influence on the applied voltage compared with the tip-to-collector distance. Since the linear coefficient of the LiCl concentration (2) was positive in Equation (6), the applied voltage increases with an increasing LiCl concentration. Many researchers have reported the interactions of LiCl and polar aprotic solvents, such as N-methyl-2-pyrrolidone (NMP), dimethylformamide (DMF), and dimethylacetamide (DMAc), with polymers [19; 20; 21]. When LiCl is dissolved first in NMP, Li\({}^{+}\) cations form the positive charged complexes with the amide groups in NMP under the solvation process. Li\({}^{+}\) cations may also form the positive charged complexes with the sulfone groups in PES following the addition of PES in the LiCl-NMP system. In other words, the addition of LiCl changes the solution environment, inducing the bulky solvated NMP molecules and the electrostatic interactions among the formed complexes and Cl\({}^{-}\) anions. As a result, the two chemical reactions improve the viscosity of the mixed solvent [21]. This phenomenon means that the more LiCl that is added, the more applied voltage is needed to overcome the electrostatic attractions.
## 2 System Model
Therefore, considering the photoelectric conversion efficiency of the transmitter and receiver, the DC gain, and the path loss, the underwater channel gain model can be expressed as follows:  
\[h=\eta_{t}\eta_{r}H(0)L_{loss}=\eta_{t}\eta_{r}\frac{m+1}{2\pi}\cos^{m}(\phi) A_{eff}(d,\psi)e^{-c(\lambda)d}, \tag{5}\]  
where \(\eta_{t}\) and \(\eta_{r}\) represent the conversion efficiency of the transmitter and detector, respectively. The simulation values of the above key parameters are shown in Table 2[20]. Since the light source can only emit non-negative optical signals with real values, the signals generated by traditional QAM are no longer applicable to the UWOC system. Therefore, this paper adopts the asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) system [22] and adds a feedback link for the selection of the modulation mode and the adjustment of the LCLV. A block diagram of the adaptive system used in this paper is shown below.
# Packed Bed Photoreactor for the Removal of Water Pollutants Using Visible Light Emitting Diodes
## 3 Materials and Methods
### Photocatalytic Tests with the Packed Bed Photoreactor at Semi-Pilot Scale  
The experimental setup (Figures 1 and 5) consists of a packed bed photocatalytic reactor, an external stirred aerated tank containing the polluted solution, and a peristaltic pump to provide a continuous flow of water along the photocatalytic reactor.  
The packed bed reactor included a cylindrical Pyrex tube to maximize the incident radiation intensity (40 cm long, 5 cm external diameter) and N-TiO\({}_{2}\)/PS structured photocatalyst (325 g) as packing material. The photocatalytic experiments were conducted in both continuous mode (Figure 5) and batch mode (Figure 1). For both working conditions, the liquid flow rate was varied in the range 6.3-74.15 mL/min. In the case of batch mode, the total volume of the treated solution was 500 mL. The external body of the photoreactor was irradiated by an LEDs strip (produced by LED lighthouse; electrical power: 81.6 W) closely enwrapped around the external surface of the photoreactor (Figure 10). The emission spectrum of the LEDs lies in the range from 400 to 700 nm (Figure 10 insert).  
The photocatalytic tests were carried out using MB at 10 mg/L initial concentration. Additional photocatalytic tests were carried out using aqueous solutions containing CAF, PC, and CFX at 5 mg/L initial concentration. The reactor was left in dark condition for 2 h before switching on visible LEDs.  
All the photocatalytic tests were performed at the spontaneous pH of the polluted solutions, and the temperature was in the range 20-30 \({}^{\circ}\)C.  
Liquid samples were analyzed by spectrophotometric measurements (Perkin Elmer UV-Vis spectrophotometer) in order to determine the concentration of MB (at \(\lambda\) = 663 nm), CAF (at \(\lambda\) = 273 nm) [55], PC (at \(\lambda\) = 246 nm) [50], and CFX (at \(\lambda\) = 254 nm) [56].  
The TOC contained in a fixed volume of the solution was measured by the high temperature combustion method on a catalyst (Pt-Al\({}_{2}\)O\({}_{3}\)) in a tubular flow micro-reactor operated at 680 \({}^{\circ}\)C. The solution was injected in the catalytic reactor fed with air to oxidize the organic carbon into CO\({}_{2}\), whose concentration in gas-phase was monitored by a continuous analyzer (Uras 14, ABB) [6].
## 4 Discussion  
The fluoride source and inclusion of PEG clearly affect the morphology of the Yb:LLF crystals. The (112) planes seen in all crystals synthesized via the PEG-assisted method indicate that the PEG lowers the surface energy of that plane, changing the Wulff construction of the crystal and allowing it to form during the hydrothermal synthesis. In spite of this additional plane, the stoichiometric 1:1:4 product is formed, as confirmed by XRD. This is of particular interest as other RELiF\({}_{4}\) materials, such as NaYF\({}_{4}\), do not form a stoichiometric phase. [32] Additionally, this extra plane does not impact the crystals' cooling abilities, which are comparable to those shown by crystals formed using the ammonium bifluoride synthesis. It can then be concluded that this synthesis method is not only effective, but a safe and scalable alternative to the ammonium bifluoride synthesis when attempting to synthesize large amounts of Yb:LLF crystals. This synthesis generates high amounts of  
Figure 4: Powder XRD pattern of crystals following PEG-assisted hydrothermal synthesis at: **(a)** 100 \({}^{\circ}\)C, **(b)** 120 \({}^{\circ}\)C, **(c)** 130 \({}^{\circ}\)C, **(d)** 150 \({}^{\circ}\)C, **(e)** 180 \({}^{\circ}\)C, and **(f)** 200 \({}^{\circ}\)C, for 24 h.  
Figure 3: Laser cooling of Yb:LLF crystals. **(a)** PL spectra of the Yb:LLF crystal on a SiO\({}_{2}\) optical fiber in air under 1020 nm diode laser excitation measured at different irradiances. **(b)** Cryostat calibration of PL spectra of the Yb:LLF crystal on a SiO\({}_{2}\) optical fiber in air under 1020 nm diode laser excitation measured at different irradiances. **(c)** Laser refrigeration of the Yb:LLF crystal in deionized water measured via cold Brownian motion analysis at different irradiances.  
product while avoiding the hazards of hydrofluoric acid formation and hydrogen fluoride gas generation.  
Compared to the fluoride source, the temperature and length of the reaction do not significantly impact crystal morphology but, instead, are key factors in forming a pure and highly crystalline Yb:LLF phase, as well as impacting the size of the crystals formed. The number of precursors remaining in solution at shorter time points, specifically undissolved or unreacted LiF, could be eliminated by allowing the reaction to proceed for longer times, further improving the yield of the reaction. The size of the crystals can also be controlled by the concentration of precursors in an aqueous solution. By combining the size control demonstrated by changing the reaction temperature and the size control demonstrated by the precursor concentrations, it could be possible to finely control the size of the crystals.
## 3 Results
### Effect of Parameters on Extraction Efficiency
#### 3.4.3 Effect of Extraction Time  
The performance of HS-SPME is based on the equilibrium between the adsorbent, headspace, and sample solution. Therefore, the diffusion of the analytes through this triple-phase system is essential. The time taken to reach equilibrium is usually long and the extraction is often involved in non-equilibrium conditions. The influence of extraction time was studied by varying the exposure time of the fiber to the headspace of the sample solution in the range of 3-45 min. Figure 5C shows that the extraction efficiency increased by  
Figure 5: Optimization of effective parameters on extraction efficiency of OCPs. (**A**) Effect of salt (\(w\)/\(\gamma\) %), (**B**) extraction temperature (\({}^{\circ}\)C), (**C**) extraction time (min), (**D**) desorption temperature (\({}^{\circ}\)C), and (**E**) desorption time (min).  
increasing the extraction time to 10 min, and was thereafter reduced. This short equilibrium time could be attributed to the high surface area and porosity of the nanofibers. Therefore, 10 min was selected as the optimum extraction time.
# Using Common Spatial Patterns to Select Relevant Pixels for Video Activity Recognition
## 3 CSP-Based Approach  
Similar to Reference [1], the use of CSP is the main idea of the presented approach, although this time the signals to be processed are composed by temporal sequences of pixel. In this section, the used algorithm is explained, as well as the approach that is being introduced.  
The Common Spatial Pattern (CSP) algorithm [20], a mathematical technique applied in signal processing, has been widely used in Brain Computer Interface (BCI) applications for electroencephalography (EEG) systems [21; 22; 23]. Research has also been published applying CSP in the field of electrocardiography (ECG) [24], electromyography (EMG) [25; 26] or even in astronomical images for planet detection [27]. CSP was presented as an extension of Principal Component Analysis (PCA) and it consists of finding an optimum spatial filter which reduces the dimensionality of the original signals. Considering just two different classes, a CSP filter maximizes the difference of the variances between the classes, maximizing the variance of filtered signals of EEG of one of the targets while minimizing the variance for the other.  
It must be clarified that, although the CSP algorithm has been used mainly with EEG problems, in this paper a new application is presented, the use of CSP filters for feature extraction in the human action recognition task. In our approach, each video represents a trial and each pixel is treated as an EEG channel, so the videos are taken as time series where the pixels are the channels which change over time. This is an application outside the usual field of analysis of physiological signals, somehow justified by the successful use in astronomical image processing [27], but here it is extended to videos depicting actions.  
The full process can be seen in Figure 1. The first step consists of selecting the most relevant pixels of the frame sequences, that will be used to feed the CSP. In order to select the most relevant pixels, those which have the biggest variance are chosen, that is, the pixels that change most in the frame sequence. Once the pixels are selected and, hence, the signals are formed, the CSP is computed in order to separate the classes according to their variance.
# A Review of Time-Scale Modification of Music Signals +
## 7 Applications
### Pitch-Shifting
The spectral envelope is therefore scaled as well, relocating the positions of the formants. This leads to a (usually undesired) change in timbre of the singing voice. One strategy to compensate for this change is outlined in [17]. Let \(X\) and \(X^{\text{Shift}}\) be the STFTs of a given input signal \(x\) and its pitch-shifted version \(x^{\text{Shift}}\), respectively. Fixing a frame index \(m\), let \(X(m)\) and \(X^{\text{Shift}}(m)\) be the frequency spectra of the \(m^{\text{th}}\) frames in \(X\) and \(X^{\text{Shift}}\). Furthermore, let  
Figure 17: Frequency spectra for a fixed frame \(m\) of different versions of a singing voice recording. The spectral envelopes are marked in red. (**a**) Original spectrum \(X(m)\) with spectral envelope \(\Gamma_{m}\); (**b**) Pitch-shifted spectrum \(X^{\text{Shift}}(m)\) with spectral envelope \(\Gamma_{m}^{\text{Shift}}\); (**c**) Pitch-shifted spectrum \(X^{\text{Mod}}(m)\) with adapted spectral envelope. \(\Gamma_{m}:[0:N-1]\to\mathbb{R}\) and \(\Gamma_{m}^{\text{Shift}}:[0:N-1]\to\mathbb{R}\) denote the spectral envelopes of \(X(m)\) and \(X^{\text{Shift}}(m)\), respectively. Our goal is to compute modified spectra \(X^{\text{Mod}}(m)\) that have the frequency content of \(X^{\text{Shift}}(m)\) but the original spectral envelopes \(\Gamma_{m}\). To this end, we normalize the magnitudes of \(X^{\text{Shift}}(m)\) with respect to its spectral envelope \(\Gamma_{m}^{\text{Shift}}\) and then scale them by the original envelope \(\Gamma_{m}\):  
\[X^{\text{Mod}}(m,k)=X^{\text{Shift}}(m,k)\ \frac{\Gamma_{m}(k)}{\Gamma_{m}^{ \text{Shift}}(k)}. \tag{36}\]  
In Figure 17c we see the frame's spectrum from Figure 17b after the envelope adaption, leading to a signal that sounds more natural. There exist several approaches to estimate spectral envelopes, many of them either based on _linear predictive coding_ (LPC) [40] or on the _cepstrum_ (the inverse Fourier transform of the logarithmic magnitude spectrum) [41; 42]. Generally, the task of spectral envelope estimation is highly complex and error-prone. In envelope or formant-preserving pitch-shifting methods, it is often necessary to manually specify parameters (e.g., the pitch range of the sources in the recording) to make the envelope estimation more robust.
# Review of Ka-Band Power Amplifier
## 3 Topologies
### Doherty Power Amplifier
To address these challenges, some approaches have been proposed. [PERSON] [105] proposed a combiner based on the analytical synthesis methodology, which performs the DPA impedance modulation and output matching simultaneously to achieve lower loss. Fabricated in 45 nm SOI CMOS process, the PA chip achieves Psat and peak PAE at 22.4 dBm and 40%, respectively. In [106], a DPA with distributed-active-transformer (DAT) power combining is reported. The measured results demonstrate that Psat and peak PAE are 28.3 dBm and 30.4% at 28 GHz. A summary comparison with Doherty PA measurements is given in Table 5.  
The Doherty power amplifier is an attractive option to improve back-off efficiency due to its structure. As we can see from Table 5, the 6 dB back-off PAE is much higher than its classical counterparts (10%-15%); the highest PBO factor in DPA was 32%. It is illustrated that the PAE and PBO of the PAs on GaAs are clearly much higher than that on Si and SiGe, but the area is also much larger than that on Si CMOS.
## 2 Materials and Methods
### Sensor Models  
Sensor models are defined by specifying rules for calculating the visibility for each voxel surrounding the sensor within the environment. Proposed sensor models employ a probabilistic coverage model. There are two main types of sensors that differ in their sensing capabilities: sensors with isotropic sensing and sensors with directional sensing.  
The visibility \(v_{vs}\) between each voxel \(v_{i}\) and the sensor \(s_{j}\) is determined as the product of three fundamental functions:  
\[v_{vs}(v_{i},s_{j})=v_{d}(v_{i},s_{j})\cdot v_{\varphi}(v_{i},s_{j})\cdot v_{ \theta}(v_{i},s_{j}), \tag{1}\]  
where \(v_{d}\) represents the visibility based on the distance between the sensor and the observed voxel, while \(v_{\varphi}\) and \(v_{\theta}\) correspond to the visibilities determined by azimuth and inclination angles between the sensor and the voxel. These calculations vary depending on the sensor type.  
Visibility is only calculated if there is a line of sight between the voxel and the sensor. The distance \(d_{vs}\) between the voxel and the sensor, which is used in the distance visibility function \(v_{d}\), is calculated using the following Euclidean distance formula:  
\[d_{vs}(v_{i},s_{j})=\sqrt{(v_{i_{x}}-s_{j_{x}})^{2}+(v_{i_{y}}-s_{j_{y}})^{2}+ (v_{i_{z}}-s_{j_{z}})^{2}}. \tag{2}\]  
Both azimuth \(\varphi\) and inclination \(\theta\) are calculated as angles within the spherical coordinate system:  
\[\varphi_{vs}(v_{i},s_{j})=\arctan 2\left(v_{i_{y}}-s_{j_{y}},v_{i_{x}}-s_{j_{x}} \right), \tag{3}\]  
\[\theta_{vs}(v_{i},s_{j})=\arcsin\left(\frac{v_{i_{z}}-s_{j_{z}}}{d_{vs}(v_{i}, s_{j})}\right), \tag{4}\]  
and used in the calculation of the azimuth visibility \(v_{\varphi}\) and inclination visibility \(v_{\theta}\), respectively.  
In this study, a model of an isotropic radio beacon is used, where the visibility of the distance \(v_{d}\) is based on the Received Signal Strength Indicator (RSSI). The SNR is normalized by \(rss_{max}\), resulting in \(v_{d}\). The azimuth visibility \(v_{\varphi}\) and the inclination visibility \(v_{\theta}\) are equal to 1, as it is assumed that the isotropic sensor detects (or emits) the signal equally in all directions.
## 4 Implementation of the Proposed UniChain System
### Editing a Record by a University Node  
The university node sends the student's ID to its SRHC to retrieve the associated RC address. Upon receiving the RC address, the university node then sends the filename of the requested record and its Ethereum address to the RC. The RC forwards the request to the ACC to check whether that received Ethereum address has a permission (i.e., \"owner\" access level) on the requested record or not. If the university node has a permission, the AAC forwards the university's encrypted symmetric key \(E_{pk_{university}}(SMK)\) to the RC. The RC in turns forwards the received key to the university node.  
The Cipher/Decipher Manager in the university node first decrypts the received symmetric key using its private key and then decrypts the access link with that symmetric key. The DB Manager of the university node follows the related access link and then retrieves the encrypted EAR from the database for editing. Note that, when a record is modified, its hash value will also be changed [24]. Thus, the DB Manager, after modifying the record, calculates the new hash of the modified record \(h(EAR)\). The DB Manager sends the student's ID to its SRHC to retrieve the associated RC address. The new hash value will be sent to the RC for updating. Moreover, the DB Manager creates a log indicating the process of record editing, hashes the log and then forwards the log to the Cipher/Decipher Manager for encryption. The encrypted log will then be forwarded to the LC. The LC adds a new entry with the received encrypted log, a \"new log\" status to indicate that the new log has not been added to the blockchain yet, and a timestamp indicating the last status update. Additionally, when the editing the student's record, the university node notifies the NC to updates the associated degree of the university node. The NC informs the REM to reevaluate the value of the record and thus updating the node's degree. The REM performs the calculations and returns the new degree to the NC for updating.
# Axial Load Transfer Mechanism in Fully Grounded Rock Bolting System: A Systematic Review
## 3 Analysis and Results
### Research Hotspots in Axial Load Transfer Mechanism of FGRBSs  
Co-occurrence keywords in VOSviewers were used to identify the research hot spot in the topic. Indeed, this feature can compute the frequency with which a given keyword occurs in the articles. After analysing all keywords and their co-occurrences, we identified 381 unique terms. Setting the minimum number of occurrences of a keyword as three, 48 keywords met the thresholds. The repetitive words such as \"rock bolts\", \"rockbolts\", \"support\", \"load\", \"reinforcement\", \"design\", and \"fully grouted rock bolts\" were omitted from the analyses. Finally, seven keywords, including pull-out test, failure, bond strength, stress-distribution, analytical model, mechanical behaviour, and simulation, can be seen in Figure 6 to show the co-occurrence mapping network of research hot spots tied to axial load transfer in FGRBSs. In the following section, the failure mechanism and the most influential parameters that can affect the axial transfer mechanism of the fully grouted rock bolting system are discussed in detail.  
Pull-out test is carried out to analyse the mechanical behaviour of rock bolts [47]. To carry out the pull-out test, a rock bolt is initially inserted into a hole drilled in a rock or concrete sample and then grouted. Once the grout cures, the pull-out load is subjected to one side of the bolt which is called the loading end. Over the testing process, the load-displacement relationship is monitored utilising load cells and displacement sensors such as linear variable differential transformers (LVDTs).