esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Waveform Adaptability Based on Lunar Channels
## 5 Conclusions  
In this paper, our research focused on addressing the unique challenges posed by the lunar environment to communication systems. With the goal of ensuring the reliability and stability of communication systems for lunar missions, we investigated the applicability of OFDMA and SC-FDMA waveforms. Our study incorporated performance indicators such as PAPR and BER, essential for evaluating communication system effectiveness. We identified the optimal modulation scheme and cyclic prefix length tailored for the lunar environment. Notably, our findings reveal that the cyclic prefix length in our communication system surpasses the delay, indicating its ability to effectively handle signal attenuation and frequency-selective fading. Additionally, the incorporation of multi-carriers addresses the challenges posed by frequency-selective fading, further enhancing the robustness of our designed communication system.  
Moreover, we adopted a 4 QAM modulation method, and our simulation results underscore its excellent performance in the lunar surface communication environment. This choice contributes to the overall reliability and efficiency of the communication system in the face of extreme lunar conditions. The outcome of our research demonstrates that the transmission structure we designed meets the system-level performance requirements for lunar communication. These insights are invaluable for the future design and optimization of communication systems for lunar missions. By paving the way for the seamless integration of advanced ground technologies in extraterrestrial environments, our work contributes to the ongoing efforts in space research and exploration, particularly in facilitating the return of humans to the moon.  
Conceptualization, M.J. and C.Z.; methodology, J.L.; software, Z.W. and D.Y.; validation, W.S.; writing--original draft preparation, H.W.; writing--review and editing, D.L.; funding acquisition, M.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China No. 62231012.  
Data Availability StatementData is contained within the article.  
The authors declare no conflicts of interest.  
Figure 6: Comparison of BER performance for different modulation schemes. (**a**) Comparison of QAM and PSK modulation BER performance in 20 MHz bandwidth. (**b**) Comparison of BER performance for different modulation schemes in 20 MHz bandwidth. (**c**) Comparison of QAM and PSK modulation BER performance in 10 MHz bandwidth. (**d**) Comparison of QAM and PSK modulation BER performance in 10 MHz bandwidth.
# Augmentation Embedded Deep Convolutional Neural Network for Predominant Instrument Recognition
## 2 Related Works
### Neural Networks for Data Augmentation  
In addition to WaveGAN, there are various generative neural networks that can be employed for data augmentation in instrument recognition. Among these models, Generative Adversarial Networks (GANs) and their variants are widely used. However, GANs often suffer from training instability, which limits their integration into pattern recognition models. Diffusion Models [18] and Flow Models [19] are effective large-scale generative neural networks commonly used for generating high-quality images. However, these models come with high computational complexity. Therefore, incorporating them into instrument recognition models would result in significant computational costs. In contrast, Variational Autoencoders (VAEs) [20] offer a stable training process and flexible model structure, making them suitable as embedding sub-networks in instrument recognition models. VAEs can be constructed based on the specific requirements of the task. Additionally, Conditional Variational Autoencoders (CVAEs) [21], which are a variant of VAEs, can generate data associated with specified labels. To address the inconsistency issue in data augmentation-based instrument recognition models, a fully connected Adversarial Conditional Embedded Variational AutoEncoder (ACEVAE) is designed. This model proposes an adversarial learning method into a Conditional Variational Autoencoder to generate more necessary samples and leverage the stability and flexibility of CVAEs to generate data with specified labels. By incorporating ACEVAE into instrument recognition models, it becomes possible to enhance the performance and alleviate the inconsistency problem encountered in the two-step data augmentation approach.
## 2 Related Works
### Channel Mode  
A number of studies have been conducted to design a stochastic UWA channel model [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28] based on the assessment of the experimental acoustic data retrieved at certain specific locations. Authors have suggested Ricean fading [17; 18] or Rayleigh fading [19; 20; 21], however, in some works, log-normal distribution [22; 23] and the Ricean shadowed distribution [26] to enable better fit for measurement. The majority of these approaches apply experiment-specific characteristics, including location of the network deployment and the kind of signals to be applied to perform the test. The available research primarily considers small-scale topological phenomena and suggests that a statistical model is suitable for both small as well as large-scale UWA channel conditions [17; 24; 25; 26]. In [27], authors applied log-normal fading to derive a channel model from dealing with large-scale phenomena. The authors of [27; 28] used the Bellhop model to generate an ensemble of channel responses, which was applied to measure the statistical characteristics of the acoustic channel. For their dynamic channel model design, they considered parameters such as water temperature and salinity level. They applied the Bellhop channel simulator and found that it exhibits statistical characteristics equivalent to those retrieved through the experiment. However, it could not gain consensus due to insufficient information about the acoustic channel conditions and exclusion of the surface dynamism. In [29], the authors applied acoustic environment data with the help of a dynamic surface shape model. These data were then exported to the Bellhop beam tracer to form an associated acoustic field. However, varying wind speeds caused significant deviation from the experimental results, that might be caused due to ineptness towards surface generation such as disrupting waves occurring at relatively higher wind speeds, bubble generation and wave curvature formation [30]. The authors of [31] examined the impact of bubbles on the UWA channels at varying wind speeds.
# Digital Systems in Smart City and Infrastructure: Digital as a Service
## 2 Research Background
### Information and Communications Technology  
A smart city is defined as a city in which ICT is merged with classic infrastructures, coordinated and integrated using new digital technologies [17]. The smart vision is sketched by defining goals, adding research challenges and considering scenarios within project areas. The concept of smart and connected communities is based on the IoT, crowd sensing and cyber-physical cloud computing to provide a comprehensive network of connected devices [18]; in addition smart sensors and big data analytics to enable the move from IoT to real-time control. An urban IoT is designed to support the Smart City vision that exploits the most advanced communication technologies to enable added value services for the administration of the city and for the citizens [19]; the urban IoT optimizes enabling technologies, protocols, and architecture with best technical solutions and practice guidelines. The smart mobile phones with sensor enabled technology such as Global Positioning System (GPS), gyroscope, microphone, camera, accelerometer [20] also provide new innovative services within the Smart City architecture and framework. The concept of Sensing as a Service is explored in technological, economic and social perspectives [21]; the research also identifies the major open challenges and issues including the methods to interconnect it in current IoT infrastructure, platforms and software applications offered as services using cloud technologies. The recent vision of the Future Internet (FI), and its particular components, IoT and Internet of Services (IoS) can become building blocks to progress towards a unified urban-scale ICT platform transforming a Smart City into an open innovation platform [22]; the proposed generic implementation is based on an Ubiquitous Sensor Network (USN) model that meets the requirements of open, federated and trusted platforms. The main challenges that may prevent the IoT to substantially support sustainable development of future smart cities are the disparity between connected objects and the unreliable nature of associated services [23];a cognitive framework which dynamically changing real-world objects are represented in a virtualized environment, and where cognition and proximity are used to select the most relevant objects for the purpose of an application in an intelligent and autonomic way.
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 5 Performance Evaluation
### Results for the Oslo Dataset
#### 5.2.2 Performance under Limited Information
The optimal \(k\) determining the minimum average positioning error varies with \(N_{NPCI}\), as already observed for variations of \(P_{RP}\); no clear common trend to all strategies was, however, identified for this parameter. Interestingly, the positive impact of the number of NPCIs on positioning accuracy observed in this work was not detected in [9], where using a single NPCI led to the best performance. Figure 11: Optimal \(k\) as a function of \(P_{RP}\) for the proposed strategies and the approach proposed in [7] using SINR in all strategies but _Net_.
# Improved Smart Pillow for Remote Health Care System
## 1 Introduction
### Core Body Temperature Minimum (CBTM) from the Smart Pillow  
We introduced a Smart Pillow for health sensing by embedding temperature and humidity sensors in the pillow [17]. The diagram of the \"Smart Magic Pillow\" is shown in Figure 3. Three temperature sensors are evenly distributed on the head resting side of the pillow, with a humidity sensor integrated with the middle sensor.  
Data from the sensors go through a gateway equipped with Bluetooth (BT) and Wi-Fi, eventually connecting to the cloud. By analyzing the data, the environmental parameters in the bedroom, bedtime routine and sleeping habits can be studied, including body temperature when specific conditions are met.  
As the head does not rest with the temperature sensor directly, the pillowcase and hair acts as an insulator, an appropriate compensated factor for body temperature is determined by Fuzzy Logic algorithms. It is proved by experiments that the result is close to the real body temperature, but it cannot replace a thermometer.  
Figure 3: The diagram of the “Smart Magic Pillow”.  
Machine learning is applied in the paper [18] to find a better and more accurate body temperature, but the result is not ideal because of the lack of consistency and insufficient volume of the training data.  
In this paper, we adapt the smart pillow for the Remote Healthcare System, with a pressure sensor consisting of 4 \(\times\) 10 elements to detect the user's existence. A unique protocol not dependent on any third-party is designed for reliable communication between the cloud server and the pillow. A real-time proactive monitoring mode is added for emergency concerns.
## 1 Introduction
We sought to minimize distortions arising from spatial and spectral aliasing, as well as atmospheric and BRDF effects, by using a compilation of 64,000,000 high quality Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-ng) hyperspectral from a diversity of agricultural landscapes in California to quantify characteristics of six widely used broadband vegetation indices. Here we used spatially and spectrally oversampled reflectances, derived from a state-of-the-art imaging spectrometer and atmospherically corrected with a state-of-the-art radiative transfer model. Simulating multispectral data directly from these observations minimizes the biases inherent in using compilations of broadband data and allows simultaneous quantitative intercomparison of tens of millions of reflectance spectra from a diversity of substrates and vegetation. Here, we compiled 64,000,000 5 nm, 3-5 m, AVIRIS-ng reflectance spectra from 15 flight lines collected throughout California in 2020. We estimated the photosynthetic vegetation fraction (F\({}_{\text{v}}\)), as well as the substrate and shadow fractions, from each AVIRIS spectrum by inverting a three-endmember linear spectral mixture model. We then convolved each 425-band AVIRIS reflectance spectrum with the 8-band spectral response of the Planet SuperDove sensor and used these simulated SuperDove spectra to compute VIs for each spectrum. We quantified the relationship of each VI distribution relative to the hyperspectral \(\text{F}_{\text{v}}\) distribution for the full set of mixed spectra. In so doing, we addressed the following questions:  
1. _How do the linearity and dispersion of each VI distribution vary with photosynthetic vegetation fraction estimated by the spectral mixture model?_2. _How sensitive is each multispectral VI to substrate background from common agricultural materials like soil, rock, non-photosynthetic vegetation, or plastics?_
3. _How does a traditional parametric similarity metric (Pearson correlation, \(\rho\)) compare to a popular nonparametric metric (Mutual Information, MI) for such a comparison?_
## 4 Experimental Implementation and Performance Evaluation
### Performance Analysis with CCR set to 16 Hz  
As mentioned before, we have conducted our analysis also with CCR set to 16 Hz. The obtained results are trend-wise similar to those with CCR set to 8 Hz. In particular, quadratically increasing metrics, such as EN-SCR-RES (Figure 10) and T-SCR-RES (Figure 11), tend to linearize for increasing values of \(N\).  
As discussed at the end of Section 4.2, the experimental results with the CCR set to 16 Hz in the presence of a linear topology can be interpreted as lower bounds for the system performance of networks with more general topologies.  
Figure 11: Time required to complete the SCR-RES phase with CCR set to 16 Hz (metric T-SCR-RES).  
Figure 10: Energy spent by the overall network during the SCR-RES phase with CCR set to 16 Hz (metric EN-SCR-RES). Bars show the amount of energy spent by BR and Nodes.
# Optimal Disintegration Strategy in Multiplex Networks under Layer Node-Based Attack
## 3 Experimental Analysis
### Experiments in Synthetic Networks with Different Degree Correlation  
Firstly, we consider the synthetic networks obtained from the Erdos-Renyi (ER) model in which the \(N\) nodes are connected randomly with probability \(p\). Meanwhile, previous research has proven that the interlayer degree correlation has a great impact on the response to the attack [31]. Therefore, we consider different multiplex networks including two layers coupled with three correlated structure: maximally negative (MN), uncorrelated (UC), and maximally positive (MP). In the MN case, the degree of nodes in different layers are maximally anti-correlated in their degree order, while they are maximally correlated in the MP case. For better understanding, we give a schematic illustration in Figure 2.  
The results of the optimal disintegration strategy and the six existing strategies are given in Figure 3. For better demonstration, we use dotted lines and solid lines to represent the multiplex node-based strategies and layer node-based strategies, respectively. Meanwhile, strategies with the same principle (like HMD and HLD strategies) are in the same color.  
The results in Figure 3 show that for the strategies with the same principle, the layer node-based strategies generally have better performance than the multiplex node-based strategies. The advantage decreases as the degree of different layer nodes tend to be positively correlated. Meanwhile, the EMD strategy has poor performance compared to the general HMD strategy when there is no interdependent  
Figure 2: Schematic illustration of three kinds of correlated structure, maximally negative (**a**), uncorrelated (**b**) and maximally positive (**c**). The nodes and edges that are failure under HLD strategy are shown in orange.  
relation between different layers. In particular, we find that the effect of the HLD and CI strategy approaches that of optimal disintegration strategy when the network is on the brink of collapse.
# AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices
## 3 Algorithm Design
#### 3.1.2 Random Forest Classifier  
First, multiple sets of \(\mathcal{D}:=\{r^{s},d_{CN}^{s},s\in\mathcal{S}\}\) are randomly generated. These would specify the constraints of the problem (31). Then, the above DFS+pruning algorithm is used to generate the labels corresponding to the generated sets \(\mathcal{D}\). The labels represent whether the CN can successfully complete the VNF mapping. After that, the training set generated in the above way can be used to train a classifier to solve this binary classification problem. The classifier used in this paper is RF, which is a very powerful supervised learning algorithm.
# An Interactive System Based on the IASP91 Earth Model for Earthquake Data Processing
## 4 Case Application of the Earthquake Data Processing System
### Data Import and Pre-Processing  
In this paper, an earthquake record is used as an example for its application and analysis using this system. The magnitude 6.5 earthquake in Java, Indonesia occurred on 15 December 2017 at 16:47:58.23 GMT (UTC = 0) with a source depth of 90 km. Since the distance from Guangxi to Java, Indonesia is about 3000 km, the first-to-wave phase of this earthquake should be a P-wave and the traveltime of this phase is about 10 min; therefore, it can be selected to import. The starting moment of the data is 16:51:00 and the record length is 13 min (Figure 6).  
Figure 5: Map of stations.  
From the imported data (Figure 6), stations G1713 and GX1724 are bad channels and need to be removed; meanwhile, stations GX08 and GX06 also have some interference and need to be filtered. Based on the frequency characteristics of natural earthquakes, Filtering parameters is low = 0.1 Hz and high = 2 Hz. The pre-processed seismic data are shown in Figure 7.
## 4 Conclusions  
In the present study, a simple and efficient method for the synthesis of a \(\beta\)-cyclodextrinrin-modified hyper-crosslinked resin (CDM-HCP) was presented. It was used to study the removal of anilines from waste effluents. The optimum process, such as amount of \(\beta\)-CD in the CDM-HCP, pH, ionic strength, and temperature of the solution was investigated for the improvement of aniline adsorption. The maximum adsorption capacities of the CDM-HCP were nearly 148.97 mg:g\({}^{-1}\) for aniline, 198.45 mg:g\({}^{-1}\) for \(p\)-methylaniline, 293.71 mg:g\({}^{-1}\) for \(p\)-chloroaniline, and 622.91 mg:g\({}^{-1}\) for \(p\)-aminobenzoic acid, which are higher than some commercial polymeric adsorbents, such as XAD-4, PA66, and AB-8. Aniline adsorption followed the pseudo-second-order kinetic model better. The adsorption isotherms of \(p\)-MA and \(p\)-ABC were fitted to the Langmuir model better, whereas aniline and \(p\)-CA obeyed the Freundlich model better. Moreover, regeneration experiments indicated that the adsorption performance after five cycles nearly attained that of the virgin adsorbent. The interaction mechanism, which was due to the synergistic effects of the weak interactions such as hydrogen bonding, hydrophobic interactions, and \(\pi\)-\(\pi\) stacking attractions, was confirmed by FTIR, XPS, and the theoretical calculation. Briefly, the present adsorption material exhibited great potential for its application in water treatment.  
The following are available online at [[http://www.mdpi.com/2073-4360/12/7/1620/s1/](http://www.mdpi.com/2073-4360/12/7/1620/s1/)]([http://www.mdpi.com/2073-4360/12/7/1620/s1/](http://www.mdpi.com/2073-4360/12/7/1620/s1/)).  
Methodology, investigation, and writing, Z.Y.; original draft preparation, G.W.; data curation and validation, Q.L.; data curation, funding acquisition, writing--review and editing, H.A. and X.Y.; project administration, H.J. All authors have read and agreed to the published version of the manuscript.  
This work was preliminarily supported financially by the National Natural Science Foundation of China (2191101377, 21425627, 31700470), the National Natural Science Foundation of China-SINOPEC Joint fund (U1663220), the Natural Science Foundation of Guangdong Province (2017A030313057), and the Guangdong Technology Research Center for Synthesis and Separation of Thermosensitive Chemicals (2015B090903061).  
Figure 12: Schematic mechanism of the adsorption of anilines with the CDM-HCP.
## 5 In-Structure Response Spectra
### Verification of In-Structure Response Spectra and Primary Structure Response  
To verify the proposed method for calculating the coupled ISRS, an SDOF oscillator was added to the FE model of the auxiliary building. Using this augmented model, the maximum absolute acceleration response of the added mass in the direction of interest was calculated using time history analysis for the same seismic input. The response of the augmented model was compared with the coupled ISRS obtained by the substructure method for two-DOF analysis in the frequency domain. Verification was performed for the H2- and V-directions at equipment location 2 and the V-direction at equipment location 3. Figure 9 shows the SDOF oscillator located at equipment location 3.  
The mass of the SDOF oscillator corresponded to 1% of the entire building mass. Five frequencies were chosen for the frequency of the SDOF oscillator: 2.6, 4.9, 7.2, 9.5 and 11.8 Hz for equipment location 2 in the H2 direction so that the overall tendency of the coupled ISRS, including the frequency at the peak, could be outlined. Different material damping ratios of 7% and 5% were applied to the shell elements of the building and the spring of the oscillator, respectively. The resulting numerical model has the characteristic of non-classical damping; therefore, the time history analysis and harmonic analysis for this verification were performed using the direct integration method. The Newmark method with a constant acceleration assumption was applied as in Section 5.2.  
Figure 10 compares the coupled response and ISRS obtained using the substructure technique for different positions and directions. The response acceleration values calculated using the two methods were almost identical at each frequency. Therefore, it can be observed that the two-DOF system analysis using the substructure technique in the frequency domain has sufficient accuracy.  
Figure 9: SDOF oscillator located at equipment location 3 for validation of two-DOF analysis.  
The two-DOF system yields the displacement response of the primary structure in addition to the acceleration response of the secondary system. Among the conditions applied to Figure 10, an oscillator with a 1% mass ratio and 14.5 Hz frequency in the vertical DOF at equipment location 3 was chosen for validation. The displacement of the primary structure at equipment location 3 in the vertical direction was calculated by using the two-DOF system in the frequency domain as well as the full FE model with an SDOF oscillator. The displacement response histories obtained by the two models are almost identical as shown in Figure 11. The peak displacements of the time histories in Figure 11 are 0.08811 m and 0.08815 m, which are almost the same. So, the two-DOF system can yield the primary system response with sufficient accuracy.  
Figure 10: Acceleration response of the oscillator from the coupled analysis and ISRS based on substructure technique: (**a**) H2-direction at equipment location 1; (**b**) V-direction at equipment location 1; (**c**) V-direction at equipment location 3.