esa-sceva/satcom-chunk-collection huggingface dataset:
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 5 Simulation Results and Analysis
### Results Analysis  
#### 5.5.1 PSNR Performance  
Figure 4 provides a detailed depiction of the PSNR performance of the proposed CCB model across various SNR constraints in SR fading channels with differing parameters. The model demonstrates strong adaptability to varying signal-to-noise ratios and channel conditions, with performance that is equivalent to or even surpasses that of JSCC and WITT. Particularly, in heavy environments, where channel noise significantly impacts the system, the challenge to decode information semantics becomes greater. This may be attributed to the utilization of neural networks for encoding and decoding, avoiding the introduction of additional redundancies that undermine other schemes. Due to the targeted training of the channel parameters specific to the scene, our scheme exhibits a performance advantage of more than 10 dB across all SNRs when compared to other models. The results indicate that, as a versatile model, our CCB achieves satisfactory SNR adaptation in complex satellite-ground channel environments, with negligible performance loss.  
\begin{table}
\begin{tabular}{l l l} \hline
**Model** & **Type** & **Layers** \\ \hline Deep JSCC & Encoder & (Conv + PReLU) \(\times\) 5 \\  & Decoder & (Transconv + PReLU) \(\times\) 5 \\  & & Transconv + sigmoid \\ \hline ASCN & Encoder & (Conv + ReLU) \(\times\) 3 \\  & & (ResNet + SNR Adaptive) \(\times\) 2 \\  & & SR Channel ModNet \\  & Decoder & SR Channel ModNet \\  & & (ResNet + SNR Adaptive) \(\times\) 2 \\  & & (Transconv + ReLU) \(\times\) 2 \\  & & Conv + sigmoid \\ \hline WITT & Encoder & Patch Embedding + Swin Transformer Block \\  & & (Patch Merging + Swin Transformer Block) \(\times\) 3 \\  & & Channel ModNet \\  & Decoder & Channel ModNet \\  & & (Patch Division + Swin Transformer Block) \(\times\) 4 \\ \hline \end{tabular}
\end{table}
Table 2: Baseline model structure.  
Figure 4: (**a**) PSNR performance over the light fading channel. (**b**) PSNR performance over the heavy fading channel. (**c**) PSNR performance over the average fading channel.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Type/Parameter** & \(\mathbf{b_{0}}\) & \(\mathbf{m}\) & \(\mathbf{\Omega}\) \\ \hline Light & 0.158 & 19.4 & 1.29 \\ Heavy & 0.063 & 0.739 & \(8.79\times 10^{-4}\) \\ Average & 0.126 & 10.1 & 0.835 \\ \hline \end{tabular}
\end{table}
Table 3: SR channel parameters
## 4 Results and Discussion
### Dependence of Released Capacity on Cell Temperature  
Consider the temperature effect to the released capacity of the lithium-ion cells. The cells were cycled in the training mode inside of the climatic chamber Binder MK240 (BINDER GmbH, Germany) at various temperatures. The cell temperature was determined using LM35 temperature sensor attached to the cell. The obtained experimental results are given in Figure 1. In the experiments, batteries were used with LiMn\({}_{2}\)O\({}_{4}\) (IMR) and LiNiMnCoO\({}_{2}\) (INR) cathodes.  
According to the authors of [19], the dependence of the released by lithium-ion cells capacity on temperature is described by the equation  
\[C=C_{mref}K\frac{\left(\frac{T-T_{L}}{T_{ref}-T_{L}}\right)^{\beta}}{(K-1)+ \left(\frac{T-T_{L}}{T_{ref}-T_{L}}\right)^{\beta}} \tag{6}\]  
where \(T_{ref}\) is a reference temperature of cell, \(C_{mref}\) is top capacity released at temperature \(T_{re}\) and \(T_{L}\) is a temperature at which \(C=0\), i.e., temperature, at which all the electrochemical processes stop.  
In the temperature range from \(T_{ref}\) to infinity, the capacity \(C\) in Equation (6) changes from \(C_{mref}\) to \(C_{mref}\)K, thus parameter \(K\) shows how many times (theoretically) the capacity \(C\) can be increased with the increase in cell temperature.  
Figure 1: Dependence of cell released capacity on temperature of studied cells at discharge current 0.2 C\({}_{n}\) (C\({}_{n}\) is cell nominal capacity): (**a**) cell with cathode LiMn\({}_{2}\)O\({}_{4}\) (IMR); and (**b**) cell with cathode LiNiMnCoO\({}_{2}\) (INR).  
Based on the obtained experimental data (Figure 1), the optimal parameters for Equation (6) were found by the least square method and the Levenberg-Marquardt optimization procedure. The optimal parameters are listed in Table 1.  
According to the conducted research (Figure 1), in the temperature range 25-55 \({}^{\circ}\)C, the relative deviation of capacity from its average value is less than 1%. Thus, in this temperature range, it is possible to neglect the influence of temperature when studying the dependence of the cell released capacity on the discharge current. This statement is correct for all cells studied by us (Tables 2-4).
# Encapsulation of \(\alpha\)-Lipoic Acid in Halloysite Nanotubes
## 2 Materials and Methods
### Loading Halloysite Nanotubes  
In this work, the vacuum method is used as the most widely used method of loading nanotubes with the substance under study. Previously, two methods were tested in the work, in the first of which loading occurs from a solution where the active pharmaceutical ingredient (API) is in excess, according to literature [35], and in the second, \(\alpha\)-lipoic acid and HNTs were mixed in a mass ratio of 2:1.  
According to Method 1, crushed, sieved, and dried HNTs were added to the solution with an excess of API. A vacuum was applied to the formed suspension and, after the appearance of bubbles on the surface of the suspension,  
The use of vacuum to improve the loading efficacy of the halloysite nanotubes is well established experimentally, but its interpretation has been reported recently [36; 37]. Of course, due to capillarity, the nanotubes are quickly filled, and there is no need for vacuum to remove air from inside. This process was repeated at least two to three times to completely fill the halloysite tubes with the \(\alpha\)-lipoic acid solution. This filling protocol ensures a slow release of the active molecules from the composite [37]. After completion of the vacuum cycle, the mixture was centrifuged, decanted, and dried under vacuum. According to the second method, a mixture of solution and halloysite was obtained, which was a thick paste. Further processing in a vacuum with periodic returns to atmospheric pressure was carried out three times according to the first method. Testing both methods with \(\alpha\)-lipoic acid showed that the second method is more rational and accurate because the amount of drug added to HNTs can be determined directly without analyzing the nanocomposite, which makes it possible to more accurately establish the concentration of \(\alpha\)-lipoic acid in the solution [38]. Therefore, all further studies on the loading of \(\alpha\)-lipoic acid were carried out according to the second method.
# Research on Output Voltage Stability of Non-Contact Excitation Motor
## 5 Simulated Analysis
### Voltage and Current Output by the Full-Bridge Inverter Circuit  
In electromagnetic resonance, alternating current needs to be used to transmit energy, so it is necessary to add a full-bridge inverter circuit after direct current to convert direct current into alternating current.  
As shown in Figure 7, it is a voltage simulation diagram output by the resonant circuit. After stabilization, the output voltage amplitude is 245 V, and the output voltage and current are both AC. Then, as seen in Figure 7b, the output current is approximately sinusoidal alternating current, and the amplitude of the wind is 800 A.
# Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering
## 2 Signal Model and Performance Metrics
### Performance Metrics  
Some major performance metrics of waveforms, i.e., PAPR and throughput, are discussed in the following.  
Figure 1: Time & frequency transformation of multi-carrier waveform.
## 3 Current Measurement Techniques
### Other Techniques Applicable to Both Soil Moisture and Snow Property Determination
#### 3.3.4 Gamma Logger  
Gamma loggers record natural background gamma radiation from the Earth, which is attenuated by snow pack or soil moisture. The measurements have a footprint of several to hundreds of square meters, depending on the height above ground of the instrument, which can, e.g., be airborne or placed on a mast [84, 85]. The method is dependent on naturally occurring radioactive minerals and therefore is not equally applicable in all areas. Alternatively, a source of gamma radiation can be inserted, to measure attenuation across a specific volume [86]. Use of several sources at different energies can simplify calibration and simultaneous determination of soil density [87]. Accuracy is in the order of 1% [86]. By collimating the beam using slits, soil moisture at a pinpointed position can be determined [88], but this is mostly suitable for laboratory experiments on soil samples. The disadvantage of approaches using an artificial source is the risks associated with the use of radioactive materials in the environment.
# Image Transmission over Cognitive Radio Networks for Smart Grid Applications
###### Abstract  
Today, Smart Grids (SGs), as the goal of the next-generation power grid system, span extremely wide aspects from power generation to end-user utilities. In smart grids, Energy and Information flows are mutually dependent and performance degradation of one side may have a high impact on the other side. In this work, we introduce our architecture for monitoring of Wind Turbine (WT) farms in smart grids. In our proposed system an industrial camera is embedded on a Wireless Cognitive Radio node for each WT to capture appropriate images and stream videos to the cognitive coordinator. Any packet loss in transmission between an embedded cognitive node and the coordinator can degrade peak signal-to-noise ratio (PSNR) of the received images. The image streaming is a delay sensitive transmission which should be done in harsh environments in SGs. To tackle these challenging issues, we introduce our efficient model, called JOPSS, for joint optimization of both packet size and Number of Spectrum Sensing Iterations (NSSI) during image transmission in time-restricted conditions. We define our proposed objective function as the quotient of the Overhead Time and the Effective Transmission Time (ETT). In addition, we introduce our methods based on the Minimum of Overhead Time Channel Selection (MOTS) for the efficient channel selection along with Dynamic Parameter Updating Procedure (DPUP) to benefit different strategies in Mandatory and Proactive Handoffs (MHO/PHO). The obtained results show that noticeable improvements in both PSNR and feature-similarity (FSIM) can be achieved on our models JOPSS and JOPSS-SAFE, respectively.  
cognitive radio networks; resource allocation; video streaming; PSNR; Smart Grids; Wind Turbine conditional monitoring
# Wideband Millimeter-Wave Perforated Hemispherical Dielectric Resonator Antenna
## 2 Antenna Configuration
### HDRA of Two Solid Dielectric Layers  
To enhance the impedance and AR bandwidths, the single-layer hemispherical DRA has been coated by a lower permittivity dielectric layer as illustrated in Figure 1. The coating layer's dielectric constant needs to be less than that of the DRA so that the coat acts as a transition layer between the DRA and free space. With optimized coat thickness, the wave reflections at the DRA surface can be minimized, which results in an enhanced bandwidth. However, the choice of the outer layer's dielectric constant is limited by the  
Figure 1: The configuration of the proposed layered HDRA.  
Figure 2: A single-arm feeding spiral slot.  
availability of commercial materials. Therefore, a polyimide layer has been utilized with a dielectric constant of \(\varepsilon_{r2}\) = 3.5. On the other hand, the outer layer thickness of \(\delta a=a_{2}-a_{1}\) was increased from 0.9 to 2.4 mm. A comparison of the effects of different outer layer thicknesses on the reflection coefficient is presented in Figure 4. From these results, it can be noted that the widest impedance bandwidth was observed when the outer layer thickness is 1.9 mm and the narrowest bandwidth appeared when it was 1.4 mm. Figure 5 illustrates that the widest AR bandwidth is also achieved when an outer layer thickness is 1.9 mm. On the other hand, an outer layer thickness of 1.4 mm yielded the narrowest AR bandwidth. Based on Figures 4-6, it was found that using a dielectric coat thickness of 1.9 mm results in significant improvements in impedance and axial ratio bandwidths. This is because with such a coat, the DRA excites the following resonance modes: TE\({}_{311}\), TE\({}_{112}\), TE\({}_{511}\), and TE\({}_{312}\) at 19 GHz, 24.6 GHz, 28.6 GHz, and 33.4 GHz, respectively, with an overall bandwidth of 57% spanning from 18.6 to 33.5 GHz. Therefore, a thickness of 1.9 mm, which corresponds to \(a_{2}\) = 5.7 mm, has been used for the outer layer. Furthermore, Figure 7 illustrates the variation in S\({}_{11}\) bandwidth as a function of the coating thickness with the maximum bandwidth achieved using the same outer layer thickness of 1.9 mm.  
Figure 4: Simulated return losses with various radii of the outer layer.  
Figure 3: Simulated return losses of a single layer HDRA.
## 3 Results
### Effect of **TMC** on \(Ntera\)-2 Spheroids  
One of the key features of CSCs in tumors is adaptive immune resistance. This unique characteristic helps CSCs escape destruction by immune cells such as lympho T and NK cells or macrophages, resulting in tumor progression and metastasis. This phenomenon is thought to be related to programmed cell death ligand 1 (PD-L1). PD-L1 is expressed on tumors and binds to programmed cell death 1 (PD1) on immune cells, leading to the inhibition of tumor-infiltrating lymphocytes (TILs) [13]. Therefore, PD-L1 blocking decreases the growth of tumors. In this study, we measured tumor growth inhibition due to the activity of **TMC** using the research model, which was 3D NTERA-2 spheroids co-cultured with macrophages. **TMC** showed the ability to inhibit the growth of 3D tumor spheroids (Figure 8). As a result of **TMC** application, the areas of the 3D spheroids were reduced by \(58.50\%\pm 1.60\%\), which is a significant reduction in comparison with the negative control after three days of treatment (\(p<0.05\)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{2}{c}{**\% Proliferation**} \\ \cline{2-3}  & **NTERA-2** & **CCD-18 Co** \\ \hline TM (TbPO4\(\cdot\)H2O @silica-NH2) & \(92.23\pm 3.68\) & \(93.41\pm 2.19\) \\ \hline
**TMC** (TbPO4\(\cdot\)H2O @silica -NH2 -mAb*CD133-mAb*PD-L1) & \(85.88\pm 5.76\) & \(89.67\pm 4.36\) \\ \hline CD133-FITC (ThermoFisher) & \(87.63\pm 7.08\) & \(90.33\pm 2.41\) \\ \hline Negative control & \(100\) & \(100\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The effects of TMC on the proliferation of NTERA-2 and CCD-18 Co cells  
Figure 7: Flow cytometry analysis of CCD-18 Co cells incubated with (**a**) TbPO4\(\cdot\)H2O@silica-NH2; (**b**) **TMC**, (**c**) CD133-FITC; and (**d**) negative control using a Novocyte flow cytometry system and NovoExpress software (ACEA Bioscience Inc.).
## 2 The Detection and Isolation Method of the Parity Space Method
### The SVD-Based Method and Improvement
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Types** & **Anomaly Vector** & **Duration** \\ \hline False Alarm Point & \(\backslash\) & 1 \\ Isolated Outliers & \(\mathbf{b}_{\rm e}=\mathbf{d}_{io}+\mathbf{\varepsilon}\) & 1 \\ Outlier Patches & \(\backslash\) & \(<\)\(N\) \\ Transient Fault & \(\backslash\) & \(<\)\(N\) \\ Noise Fault & \(\mathbf{b}_{\rm e}=\mathbf{e}+\mathbf{\varepsilon}\) & \(>\)\(N\) \\ Constant Drift Fault & \(\mathbf{b}_{\rm e}=\mathbf{d}+\mathbf{\varepsilon}\) & \(>\)\(N\) \\ Multiplicative Fault & \(\mathbf{b}_{\rm e}=\mathbf{K}_{e}\mathbf{H}\mathbf{x}+\mathbf{\varepsilon}\) & \(>\)\(N\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The anomaly vectors of several typical anomalies. In [22], it is suggested that the \(F_{D}\) obeys the \(\chi^{2}\) distribution, so the threshold \(T_{D}\) can be determined with a given false alarm probability \(\alpha\). If \(H_{0}\), then the system can be considered fault-free; if \(H_{1}\), then proceed to the isolation step. The fault isolation function of each sensor is calculated out. Next, the largest isolation function \(F_{1}^{k}\) is found and the \(k\)-th sensor is isolated.
# A Multiple Access Protocol Based on Gray Forecast for Satellite Network
## 7 Conclusions  
This paper focuses on the multiple access protocol for a satellite network. To address the limitations of the current work, we propose a multiple access protocol based on gray forecast for a satellite network. A series of simulations and comparison experiments verify the superiority of the algorithm in stacking and average communication delay of user queue packets in the context of access source model forecast. However, in actual network applications, multiple sources may exist. As such, future studies should consider how to make reasonable decisions about different types of source predictions.  
[PERSON] mainly came up with the research idea and wrote the paper. [PERSON] and [PERSON] designed and performed the experiments. [PERSON] analyzed the data. [PERSON] supervised the work. All the authors have read and approved the final manuscript.  
This work is partly supported by the National Natural Science Foundation of China under Grant Nos. 61572261, 61702284 and 61662039, the Natural Science Foundation of Jiangsu Province under Grant No. BK20150868, the Postdoctoral Found of Jiangsu Province under Grant No. 1701165C and the NUPTSF under Grant No. NY214013.  
The authors declare no conflict of interest.
# Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis
###### Abstract  
Climate change threats make it difficult to perform reliable and quick predictions on floods forecasting. This gives rise to the need of having advanced methods, e.g., computational intelligence tools, to improve upon the results from flooding events simulations and, in turn, design best practices for riverbed maintenance. In this context, being able to accurately estimate the roughness coefficient, also known as Manning's \(n\) coefficient, plays an important role when computational models are employed. In this piece of research, we propose an optimal approach for the estimation of '\(n\)'. First, an objective function is designed for measuring the quality of 'candidate' Manning's coefficients relative to specif cross-sections of a river. Second, such function is optimised to return coefficients having the highest quality as possible. Five well-known meta-heuristic algorithms are employed to achieve this goal, these being a classic Evolution Strategy, a Differential Evolution algorithm, the popular Covariance Matrix Adaptation Evolution Strategy, a classic Particle Swarm Optimisation and a Bayesian Optimisation framework. We report results on two real-world case studies based on the Italian rivers 'Paglia' and 'Aniene'. A comparative analysis between the employed optimisation algorithms is performed and discussed both empirically and statistically. From the hydrodynamic point of view, the experimental results are satisfactory and produced within significantly less computational time in comparison to classic methods. This shows the suitability of the proposed approach for optimal estimation of the roughness coefficient and, in turn, for designing optimised hydrological models.  
meta-heuristics; river flow analysis; manning's coefficient +
Footnote †: journal: Applied Sciences
# Classification of Valvular Regurgitation Using Echocardiography
## 3 Methodology  
### Existing Methodologies Used in Proposed Methodology  
#### 3.1.1 Gray Level Cooccurrence Matrix (GLCM) and Haralick Texture Features  
GLCM is a statistical method of extracting texture features that considers the spatial relationship of pixels in an image. A square matrix of size \(N\times N\) defines gray level values of pixels at a different position. For the image, \(I(i,j)\), the intensity features and texture features that were extracted are Energy, Correlation, Entropy, Contrast, Homogeneity, Mean, Standard Deviation, Root Mean Square (RMS), Skewness, Variance, Smoothness, Kurtosis, shade, prominence and Inverse Difference Normalized, Inverse Difference Moment Normalized (IDM) [30,31]. Haralick texture features are also texture descriptors of an image that specify the spatial relationship among the neighboring pixels in an image [32]. Haralick features are calculated using GLCM and widely used due to their simplicity and perceptive interpretations [33]. The different extracted Haralick features are angular second moment, sum of squares, sum average, sum variance, sum entropy, difference variance, difference entropy, Info measure of correlation 1, and info measure of correlation 2 [34]. Texture features are selected because they provide information about the intensities of an image. Such features partition images into the region of interest and classify them.
# Evaluation of Forward Models for GNSS Radio Occultation Data Processing and Assimilation
## 2 Experiments
### Data, Collocation, and Quality Control  
#### 2.1.1 Data  
We used the FY3D observation in September 2019 as the 'true value' which is available for download at [[http://www.nsmc.org.cn](http://www.nsmc.org.cn)]([http://www.nsmc.org.cn](http://www.nsmc.org.cn)) (accessed on 8 June 2021). The daily number of occultation soundings is approximately 500, and the vertical resolution is approximately 150-300 m. The quality control of the FY3D data is assessed by the China Meteorological Administration [29], and the result has been qualified by ECMWF [5]. The quality control result is displayed in near real-time (NRT) monitoring at the ROM SAF website (Available at: [[https://www.romsaf.org/monitoring/index.php](https://www.romsaf.org/monitoring/index.php)]([https://www.romsaf.org/monitoring/index.php](https://www.romsaf.org/monitoring/index.php)) (accessed on 8 June 2021)). As shown in Figure A2 in Appendix B, the quality of FY3D (The mean relative difference (RD) between ECMWF forecast and FY3D is less than 2%) in September 2019 established a good condition for experiment 1.  
We also used ECMWF operational analyses in experiment 1, which can be accessed at [[https://www.ecmwf.int/en/forecasts/dataset/operational-archive](https://www.ecmwf.int/en/forecasts/dataset/operational-archive)]([https://www.ecmwf.int/en/forecasts/dataset/operational-archive](https://www.ecmwf.int/en/forecasts/dataset/operational-archive)) (accessed on 8 June 2021). Such data are produced daily by ECMWF's direct 4 Dvar atmospheric model. Hereafter we call this data EC4 Dvar. These data are in 128 \(\times\) 64 grids; hence, the resolution is approximately 2.8\({}^{\circ}\)\(\times\) 2.8\({}^{\circ}\); They have 137 levels and are output daily at four hours: 00, 06, 12, and 18 UTC. Since the resolution of EC4 Dvar is very coarse, we used strict collocation and quality control.  
In experiment 1, the refractivity profiles were the experimental values; Therefore, we first calculated refractivity from temperature, water vapor partial pressure, and pressure of EC4 Dvar via Equation (1) according to their own level grids.  
In experiment 2, we used FY3D to simulate analyses such as ECMWF, ERA5, and FNL. The level of them are 137, 37, and 31 respectively. The height and levels interval can be found Figure A3 in Appendix B.