esa-sceva/satcom-chunk-collection huggingface dataset:
# DIR-Net: Deep Residual Polar Decoding Network Based on Information Refinement
## 3 DIR-Net Method
### Attention-Based Decoding Subnetwork
The input is first squeezed by a fully connected layer to extract semantic features. After that, an expended fully connected layer is utilized to inflate the feature to its original shape. To limit the range of weights, the sigmoid function is used to normalize the feature value to [0, 1]. In the end, the normalized weights are multiplied by the original features to finish the refinement of the input feature. Through the units, this system can carry out adaptive processing on the input information according to importance, giving more weight to important information and less weight to unimportant or negative information. In the decode subnetwork, multiple AFC units are cascaded into a multilayer AFC network to decode the input data. The output of each AFC unit serves as the input of the next layer of the AFC unit. In the process of decoding each bit, the contribution of different bits is different. The attention mechanism in the AFC unit can impose an importance coefficient on each bit self-adaptively, thereby ensuring that important information can be fully utilized, while reducing the interference of other information to the decoding process. By this means, the features can be refined step-by-step. The detailed values of network parameters of the decoding subnetwork are listed in Table 2.  
Due to the inconsistent length of the codeword before and after decoding, a fully connected layer is added at the end of the decoding subnetwork. In this way, the features can be mapped to the original codewords to complete the entire decoding process. In the decoding network training phase, the decoding process is treated as a multilabel binary classification task. Accordingly, the loss function of the subnetwork is expressed as follows:  
\[Loss=-\Big{(}\mathbf{y}\cdot\log(\overleftarrow{\mathbf{y}})\Big{)}+(1-\mathbf{y})\cdot \log(1-\overleftarrow{\mathbf{y}})), \tag{15}\]  
where \(\mathbf{y}\) is the real decoded data, and \(\overleftarrow{\mathbf{y}}\) is the predicted decoded data. Figure 5: Structure of attention fully connected layer (AFC) module.
## 2 Methodology
### Presumptions for the Successful Integration of Green Hydrogen and Biomethane Production Processes
#### 2.3.1 Utilization Possibility of All Products from Electrolysis in WWTP
This study analyzed a final group of 34 sites out of the 186 WWTPs in the Valencia Region in Spain. The sites were selected based on technical criteria--the power \(p>100\) W and the head H \(>1\) m. For this group, electricity generation was estimated at 340,472 kWh/year. Technical modifications of the plants can increase the potential generation by 37.5% (to 468,434 kWh/year). However, this generation still accounts for only a small part of WWTP's electricity demand. In lowland regions, the installation of hydro turbines in urban wastewater networks is much more complex compared to regions with steep terrain or surrounded by mountains. Ultralow head is defined for sites with a hydraulic head of less than 2-3 m [87; 88]. Some studies even offered a feasibility threshold for such plants operating in urban areas--a minimum head of 4.5 m with a flow of 0.05 m\({}^{3}\)/s [89]. Paper [25] focuses on possible power generation by micro-hydro turbines integrated into lowland wastewater systems, which convert the potential energy of effluents in pipes into electric power. The case study was based on the Lithuanian WWTP system. Lithuania is a low-lying country; most WWTP sites have low or medium heads (up to 15 m). There are 58 WWTPs operating in the country. More than 20 potential sites for installing hydropower turbines were identified, with a preliminary power capacity estimated as less than 100 kW. However, only 7 of them have a potential power capacity of more than 20 kW but less than 70 kW. Another source of renewable electricity in WWTPs is cogenration plants, which burn biogas to produce heat and electricity. However, with the introduction of electrolyzers in WWTPs and the transition to the production of a more valuable product--biomethane--the combustion of biogas in cogeneration plants becomes impractical. In the EU, the possibility of using electricity for the production of green hydrogen from the electricity grid coming to WWTP only exists if the average CO\({}_{2}\) emissions from electricity production on the country or regional level do not exceed 18 g CO\({}_{2 eq}\)/kWh [9]. In 2023, only in Sweden, the greenhouse gas emission intensity of electricity generation (8 g CO\({}_{2 eq}\)/kWh) was below this level. Finland, France and Luxembourg had 40, 50 and 56 g CO\({}_{2 eq}\)/kWh, respectively [90], while EU-27 average was 210 g CO\({}_{2 eq}\)/kWh.
# Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory
## 5 CDS Implementation Case Study: OFDM Long-Haul Fiber Optic Communications
### Simulations Parameters  
We illustrate the modeling process by performing a numerical simulation of an OFDM system (see Figure 8). Signal propagation in an optical fiber is described by the nonlinear Schrodinger equation (NLSE), which is solved using the standard split-step Fourier scheme [34]. The output of the linear equalizer is passed to the perceptor (see Figures 4 and 8). \(X_{n}^{k}\) represents the transmitted symbols which takes on the values from the following set \(X_{n}^{k}\in\{\pm 3\pm 3j,\pm 1\pm 1j,\pm 1\pm 3j,\pm 3\pm 1j\}\) with equal probability and \(Y_{n}^{k}\) represents the corresponding received symbols after linear equalization.  
The simulation parameters of the fiber optic system are presented in Table 3. The CDS parameters are as follows: \(PF_{i=0}^{k}=2\), \(PF_{i=1}^{k}=5\), and 96% probability box. Also, the _Threshold\({}^{m=0}\)_ which is the FEC threshold in focus level zero is \(1.03\times 10^{-2}\) (with 14.3% OH for the HD) [41]. Typically, to guarantee reliable communication over a fiber optic link, ~20% SD-FEC overhead is used [25]. However, the CDS can replace the SD-FEC, and with this replacement, the data rate can be enhanced by ~20%. This is because, the CDS (as presented in Algorithm 1) can keep the BER under FEC threshold before HD.
# Numerical Voids Detection in Bonded Metal/Composite Assemblies Using Acousto-Ultrasonic Method
## 2 Materials and Experimental Setup
### Acousto-Ultrasonic Technique  
An ARB-1410 arbitrary waveform generator card and an AE PCI-2 receiving card, commercialized by the MISTRAS group, were used (Mistras Group, Princeton, NJ, USA). The signal applied to the piezoelectric sensor acting as the actuator corresponded to a continuous square signal with a frequency of 150 kHz. An S9204 sensor in emission mode with a bandwidth centered at 150 kHz and two R15 sensors as receivers with a 150 kHz resonance frequency (Mistras Group, Princeton, NJ, USA) were used. The sensors (transmitter and receivers) were placed on the composite plate so that they were aligned along the composite fiber direction. Sensor 2 (receiver) was located in the center of the composite plate. On the \(\beta\) specimens, this location corresponded to the location of the defect center. Sensor 1 (receiver) was located opposite to sensor T (the transmitter) with respect to the middle plane of the specimen. This arrangement is illustrated in Figure 2a. A low-melting-point crystal, phenyl salicylate, was used as the coupling between the sensors and the specimens. To acquire signals with a gain of 40 dB, we used an IL40S-HT preamplifier with a 32-1100 kHz band-pass filter. The recording threshold was set to 45 dB. For repeatable AU measurements, test conditions such as temperature, sensor location, spacing and type of coupling were kept constant. To simulate the effects of the mounting procedure and to consider the induced uncertainty, seven independent measurements were completed. Each series of measurements was composed of 10 shots. For the propagated signals recorded with the AE acquisition system, several features could be extracted from the signal, for instance, the amplitude or frequency centroids.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Longitudinal** & **Transversal** & **Longitudinal** & **Longitudinal** & **Transverse** & **Poisson’s** & **Density** \\  & **Young’s Modulus** & **Young’s Modulus** & **Poisson’s** & **Ratio** & **(GPa)** & **Ratio** & **(g/cm\({}^{3}\))** \\  & **(GPa)** & **(GPa)** & **Ratio** & **(GPa)** & **Ratio** & **Ratio** & **(g/cm\({}^{3}\))** \\ \hline Steel S355 & 210.00 & 210.00 & 0.24–0.30 & - & - & 7.85 \\ \hline Composite & 210.00 & 15.33 & 0.25 & 5.50 & 0.25 & 1.67 \\ \hline Adhesive joint & 12.80 & 12.80 & 0.29–0.34 & - & - & 1.95 at 20 \({}^{\circ}\)C \\ epoxy (Sikadur 30) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composite, steel and epoxy properties.  
Figure 1: Schematic representation of the composite/metal assemblies: (**a**) \(\alpha\)-type sample; (**b**) sample with \(\beta\)2-type defect (1/3 void); (**c**) sample with \(\beta\)1-type defect (centered void).
## Data and Methods  
### The Terrestrial Observation and Prediction System Model (TOPS)  
TOPS is a diagnostic ecosystem process model that simulates the fluxes of energy, carbon, and water through vegetation in response to climate and weather variability [16]. TOPS employs a Light Use Efficiency (LUE) model to calculate GPP [17], as follows:  
\[\textrm{GPP}=\textrm{PAR}\;\cdot\textrm{fPAR}\;\cdot\varepsilon \tag{1}\]  
where PAR is the Photosynthetically Active Radiation (W\(\cdot\)m\({}^{-2}\)) and fPAR is the fraction of Absorbed PAR. \(\varepsilon\) is the light use efficiency (g\(\cdot\)C\(\cdot\)J\({}^{-1}\)), calculated as:  
\[\varepsilon=\varepsilon\;\textrm{max}\;\cdot\textrm{min}\{\Psi_{\textrm{min}} (\textrm{Tmin}),\,\Psi_{\textrm{VPD}}(\textrm{VPD}),\,\Psi_{\textrm{SM}}( \textrm{SM})\} \tag{2}\]  
where emax is the maximum light use efficiency of a given biome, and \(\Psi_{min}(Tmin)\), \(\Psi_{VPD}(VPD)\), \(\Psi_{SM}(SM)\) are down-regulation functions of minimum temperature, VPD, and soil moisture, respectively. The down-regulation functions are parameterized depending on the land cover type.  
Soil moisture is simulated using a one-layer bucket model with predefined wilting point and field capacity. Precipitation and evapotranspiration dynamics largely control soil moisture. Evapotranspiration is simulated with a two-layer model that consists of soil evaporation and canopy evapotranspiration. The canopy evapotranspiration was simulated using the Penman-Monteith equation with a Jarvis-type stomatal conductance submodel [18]. Water cycle components in TOPS, very similar to those in BIOME-BGC [19], have been validated over the past 25 years, for example stream flow [16], snow cover [20], and water stress [21].  
Often less than average rainfall (hydrological drought) results in higher VPD inducing both physiological as well as meteorological drought conditions. Increased VPD triggers the closure of stomata resulting in a decrease in GPP. The stomatal responses to drought and their impact on canopy process are well observed in flux tower observations [22, 23].  
Because TOPS was developed from Biome-BGC, the GPP calculation in TOPS is similar to that of the MODIS 17 algorithm [24]. The main difference between the TOPS and MODIS 17 algorithms is that TOPS has a soil moisture routine and a soil moisture control on GPP, while the MODIS 17 algorithm is a VPD-only model. One of the reasons why MODIS 17 algorithm does not have soil moisture control is that MODIS 17 algorithm is developed for near real-time monitoring on a global scale. There are no satellite observations of soil moisture, and adding soil moisture sub-model is computationally expensive for an operational algorithm.
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 4 Results--Sea Surface Temperature Comparison
### SST Differences When Quality Level Changes  
Figure 9 shows the SST differences where SLSTR-A uses quality level 5 pixels and SLSTR-B uses quality level 3 pixels for all algorithm types and shows small mean differences (less than or of order 0.1 K) together with distinct non-Gaussian behaviour with extended tails with, for example, the D3 differences being very large. This implies that there is an extra source of error between the two quality levels which may be related to the presence of higher probability of cloud. This will be looked at in the next section.  
Figure 8: Distributions of the distance in pixels of the nearest SLSTR-A quality level 3 pixel from a quality level 5 pixel for locations where (**left-hand side**) the quality level doesn’t change between SLSTR-A and SLSTR-B and (**right-hand side**) for when the quality level changes from a 5 to a 3 in SLSTR-B.
# Damage Location Diagnosis of Frame Structure Based on a Novel Convolutional Neural Network
## 4 Experimental Results
### Comparison of Experimental Results under Noise Conditions  
Since wavelet decomposition can filter out part of the low-frequency characteristic information while filtering out low-frequency noise signals, selecting the appropriate level of wavelet decomposition and reconstruction will directly affect the accuracy of the whole fault diagnosis result. Therefore, in this paper, the original vibration signals were decomposed by five levels of wavelet decomposition and reconstruction, and the comparative  
Figure 9: Confusion matrix.  
experimental research was carried out in the case of each level of wavelet decomposition and reconstruction combined with the neural network, thus the most reasonable level of wavelet decomposition and reconstruction was obtained. In addition, in order to verify the advantages of the methods proposed in this paper, under the same conditions (different signal-to-noise ratio, the signal-to-noise ratio is \(-4\) dB, \(-2\) dB, \(0\) dB, \(2\) dB, and \(4\) dB, respectively, the definition of signal-to-noise ratio can be found in [37]), comparative experiments were conducted by using WDCNN, WDCNN + LSTM, CEEMDAN + WDCNN + LSTM, and the methods proposed in this paper. The accuracy and objective function values (Loss) of the fault diagnosis of the test set can be seen in Tables 3 and 4.  
From the experimental results in Tables 3 and 4, it can be seen that adding LSTM to WDCNN can extract the time characteristics of vibration signals, effectively linking the characteristics of front and rear vibration signals, and CEEMDAN can effectively highlight the characteristics of vibration signals, making the signal characteristics collected by the convolutional neural network more comprehensive. Compared with the method of the five level wavelet decomposition plus CEEMDAN + WDCNN + LSTM, it can be seen that the higher the level of wavelet decomposition and reorganization, the more serious the loss of characteristic information of the reconstructed signal. Therefore, adopting the 1-level wavelet decomposition and reorganization not only effectively filters the low-frequency noise signal, but also retains the characteristic information of the signal to the greatest extent.
## 4 Conclusions
Thus, the conducted studies have shown that using HIP treatment of fullerite C\({}_{60}\) powder at a temperature of 1270 K and a pressure of 0.1 GPa in an argon atmosphere, a chemical resistant material with high hardness and elasticity at a density lower than graphite consisting of closed graphene surfaces with a size of 2-5 nm connected by covalent bonds can be obtained. This material is promising for various applications and as a basis for the synthesis of new composite materials. Conceptualization, D.V.S. and O.V.P. ; research supervision, D.V.S., O.V.P., S.V.N. and V.N.S. ; writing--original draft, D.V.S., O.V.P., S.V.N., A.S.V. and V.N.S. ; fullerite C\({}_{60}\) HIP treatment, XRD measurement and its data interpretation, A.M.O. and B.S.K. ; Raman spectroscopy measurements, S.I.L; XPS and NEXAFS spectroscopy measurements and their data analysis, D.V.S., O.V.P., S.V.N., A.S.V., R.N.S., K.A.B. and V.N.S. All the authors discussed the results and commented on the manuscript during its preparation. All authors have read and agreed to the published version of the manuscript. The reported study was funded by RFBR, project number 19-32-60018, and the Komi Republic within the framework of research project No. 20-42-110002 r-a, the Grant of the President of the Russian Federation (MK-3796.2021.1.2), the bilateral program of the Russian-German laboratory at BESSY II and the state research target for the G.A. Razuvaev Institute of Organometallic Chemistry of RAS (theme No. 45.4). The study was supported by the Ministry of Science and Higher Education of Russia under Agreement N 075-15-2021-1351 in part of research on NEXAFS spectroscopy. The work was carried out using the equipment of the center for collective use \"Analytical Center of the IOMC RAS\" with the financial support of the grant \"Ensuring the development of the material and technical infrastructure of the centers for collective use of scientific equipment\" (Unique identifier RF--2296.61321X0017, Agreement Number 075-15-2021-670). Not applicable. **Acknowledgments:** Scientific research were performed at the Center for Studies in Surface Science of Research Park of St. Petersburg State University, and the Center for Collective Use at the Institute of Geology of Komi Science Centre of the Ural Branch of RAS. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Tourism Demand Forecasting Based on an LSTM Network and Its Variants
## 3 Data  
The forecast target is the number of tourists visiting Taiwan each month. The data obtained from the official website ([[https://stat.taiwan.net.tw/inboundSearch](https://stat.taiwan.net.tw/inboundSearch)]([https://stat.taiwan.net.tw/inboundSearch](https://stat.taiwan.net.tw/inboundSearch)) (accessed on 17 July 2021)) of Taiwan Tourism Bureau, Ministry of Transportation and Communications extend from January 1984 to May 2021. This study uses two series datasets to verify the feasibility and effectiveness of the proposed forecasting models. Series 1 is split into a training dataset, covering the period from January 1984 to August 1998, and testing dataset, for the period from September 1998 to September 2005, as shown in Figure 2. The training and testing ratio is a ratio of 70:30. Series 2 is divided into training dataset, covering theperiod from January 1984 to March 2010, and testing dataset, for the period from April 2010 to May 2021 (Please see in Table S1), as shown in Figure 3.  
The period of testing dataset of data series 1 covered the SARS outbreak, which had a great impact on Taiwan's TD [36]. The period of testing dataset of data series 2 covers the outbreak of COVID-19. The significance of this research lies in the model's ability to predict time series of catastrophic events, such as the SARS and COVID-19 outbreaks.
## 5 Emerging Trends in FSO Communication
### Quantum Cryptography  
Quantum cryptography represents a groundbreaking advancement in secure communication, utilizing the concepts of quantum mechanics to ensure the confidentiality and integrity of data. When integrated with FSO communication systems, quantum cryptography, particularly Quantum Key Distribution (QKD), offers unparalleled security, providing ultra-secure communication channels, particularly in scenarios requiring high levels of data protection. Also, integrating QKD into FSO involves transmitting qubits via the same optical channel, requiring precise alignment and stable atmospheric conditions to minimize decoherence and loss. So, AT, scattering, and absorption can impact the fidelity of quantum states. Advanced error correction and AO schemes can be used to alleviate these effects. Also, maintaining accurate alignment between the Tx and Rx is crucial. Automated tracking systems and fine-tuning mechanisms help maintain the integrity of the quantum channel [2; 453; 454; 455].  
In general, the integration of quantum cryptography with FSO communication systems holds immense potential for revolutionizing secure communications, particularly within the framework of 5G and future networks. By leveraging the principles of quantum mechanics, QKD provides a level of security that is fundamentally unattainable with classical cryptographic methods. As we advance towards more connected and data-driven environments, the role of QKD in ensuring ultra-secure communication channels will become increasingly vital, offering robust solutions across various sectors, from finance and healthcare to government and smart cities [453; 454; 456].
## 3 Methodology
### Network Structure  
The network structure is shown in Figure 5. During each iteration, we forward-propagated the initial model through the SGFD RNN operator to generate synthetic data and recorded the gradient information of the entire space-time field. We then filtered the seismic traces based on the first arrival constraints by comparing the synthetic data with the observed data. The selected traces were used to calculate the loss, which was then backpropagated to update the initial model. This process was repeated until most traces were selected for computation, the total first arrival time difference approached zero, and the loss function converged to a preset threshold.