esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 3 Intelligent Navigation Marker System
### Overall Structure of the Intelligent Navigation Marker System  
The overall architecture of the intelligent navigation marker system is illustrated in Figure 1. It is primarily divided into shore-based support modules, hardware device modules, functional implementation modules, and application scenarios.  
The intelligent navigation marker system is first equipped with various hardware devices, including a VHF module for voice communication between the markers and vessels, an automatic identification system (AIS) for the real-time collection of dynamic data such as vessel location, speed, and heading, a multi-camera module to capture video of the waterway to enable target detection and identification, and a hydrometeorological detection module to collect waterway hydrological data.  
The intelligent navigation marker system features scene perception capabilities and an augmented reality (AR) module. Scene perception leverages video object recognition and multi-source data fusion techniques to enable the precise monitoring of vessels and the surrounding environment, including target identification and integrated perception. The  
Figure 1: Overall architecture of the intelligent navigation marker system.  
AR module dynamically displays key information such as the vessel type, heading, speed, and traffic flow.  
The overall architecture facilitates efficient information transmission and processing through multi-layered technologies. The fleet of lightships, consisting of intelligent navigation marker nodes, is connected to the cloud data center via 4G/5G base stations. Data are stored and processed in the cloud, while the shore-based support provides monitoring interfaces and decision support functions. This architecture fully utilizes multi-source data fusion, edge computing, and cloud collaboration technologies, enabling the effective deployment of the intelligent navigation marker system in inland waterway transportation and thereby significantly enhancing waterway safety and service capabilities.
###### Abstract  
The uncertainty in parameter estimation arises from structural systems' input and output measured errors and from structural model errors. An experimental verification of the shuffled complex evolution metropolis algorithm (SCEM-UA) for identifying the optimal parameters of structural systems and estimating their uncertainty is presented. First, the estimation framework is theoretically developed. The SCEM-UA algorithm is employed to search through feasible parameters' space and to infer the posterior distribution of the parameters automatically. The resulting posterior parameter distribution then provides the most likely estimation of parameter sets that produces the best model performance. The algorithm is subsequently validated through both numerical simulation and shaking table experiment for estimating the parameters of structural systems considering the uncertainty of available information. Finally, the proposed algorithm is extended to identify the uncertain physical parameters of a nonlinear structural system with a particle mass tuned damper (PTMD). The results demonstrate that the proposed algorithm can effectively estimate parameters with uncertainty for nonlinear structural systems, and it has a stronger anti-noise capability. Notably, the SCEM-UA method not only shows better global optimization capability compared with other heuristic optimization methods, but it also has the ability to simultaneously estimate the uncertainties associated with the posterior distributions of the structural parameters within a single optimization run.  
Keywords:parameter identification; uncertainty estimation; Markov chain Monte Carlo; shuffled complex evolution metropolis algorithm; optimization algorithm +
Footnote †: journal: Applied Soft. 2019, 9, 4959; doi:10.3390/appr9224959
# Satellite-Assisted Disrupted Communications: IoT Case Study
## 7 Conclusions  
This paper investigated the application of LEO satellite constellations at an altitude of 600 km for the communication among smart devices in IoT scenarios. Our experiments validated the initial hypothesis, indicating the existence of an inversely proportional relationship between the number of satellites and the resulting RTT and ping loss. The introduction of inter-satellite links further contributed to the reduction of mean and range RTT, as well as ping loss. Nevertheless, there were instances where the placement of ground stations and satellites emerged as a dominant factor, leading to improved communication even with a lower number of satellites in the constellation. Additionally, cases were observed where an occupied communication channel resulted in a temporary and substantial increase in the RTT, affecting the overall performance of the communication. We plan to further explore the utilization of satellites, UAVs or VANETs in case studies where DTN solutions are applicable and identify trade-offs on IoT deployments.  
Conceptualization, G.K.; Methodology, G.K.; Software, G.K.; Validation, G.K.; Investigation, G.K.; Writing--original draft, G.K.; Writing--review & editing, G.K. and V.T.; Supervision, V.T.; Project administration, V.T.; Funding acquisition, V.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
The data utilized in Scenario 2 of this study were acquired from the Smart Santander testbed within the context of the ReWire project [48]. Although the focus of the present paper may differ from the primary objectives of the project, the availability of this data significantly contributed to our analysis. Researchers can find information on the related work of the ReWire project in [55; 56].  
The authors declare no conflict of interest.
## 5 Conclusions  
Although deep learning algorithms offer solid performance, particularly when dealing with spatial and temporal features, a lack of training data poses significant hurdles in forecasting models for solar energy. Therefore, the GAN-CSVR generative model has been proposed to expand datasets to improve predicting performance. The convolutional GAN has been developed by replacing the fully connected CNN layer with a simpler linear SVR layer to restrict the deviation of generated specimens. The proposed model was trained using a Multi-Objective loss function that combines MSE and BCE to ensure consistency between the input and generated samples. Two indexes are used to evaluate the quality of the data generated by the GAN-CSVR model (i.e., STD and CDF). According to the findings of simulations, GAN-CSVR can produce new and distinctive samples that accurately reflect the fundamental characteristics of the original data.  
Furthermore, the results of deep learning forecasting utilizing augmented data have been enhanced. In contrast to machine learning models, their efficiency remains constant. In conclusion, the proposed model offers a feasible solution to the problem of a small sample size during the training of the model. Balancing the augmented data according to the distribution of weather patterns and seasons will be explored in the future.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app132312768/s1](https://www.mdpi.com/article/10.3390/app132312768/s1)]([https://www.mdpi.com/article/10.3390/app132312768/s1](https://www.mdpi.com/article/10.3390/app132312768/s1)).  
Conceptualization, A.M.A. and H.H.; Data curation, H.N.A.H.; Formal analysis, F.A.G., M.E.D. and T.A.E.E.; Funding acquisition, M.E.D. and T.A.E.E.; Investigation, F.A.G., A.M.A. and T.A.E.E.; Methodology, A.M.A. and H.H.; Resources, M.E.D. and T.A.E.E.; Software, H.N.A.H.; Supervision, H.H.; Validation, F.A.G., H.H., H.N.A.H. and M.E.D.; Writing--original draft, A.M.A.; Writing--review and editing, A.M.A. and H.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deanship of Scientific Research at King Khalid University. This work is under grant number RGP2/52/44.  
Not applicable.  
The National Renewable Energy Laboratory (NREL) provides hourly global horizontal irradiance, and solar radiation data are freely available at [[http://www.nrel.gov/midc/srrl_bms](http://www.nrel.gov/midc/srrl_bms)]([http://www.nrel.gov/midc/srrl_bms](http://www.nrel.gov/midc/srrl_bms)) (accessed on 1 May 2021). Software License: Python 3.7 in environment Anaconda was used with the following libraries: Tensorflow, Keras, and sklearn libraries.  
The authors extend their gratitude to the Deanship of Scientific Research at King Khalid University for funding this work through Grant Number RGP2/52/44, and the first author would like to thank the Iraqi Ministry of Electricity, General Company for North Electricity Distribution.  
The authors declare no conflict of interest.
###### Abstract  
Discharging phosphate through wastewaters into waterways poses a danger to the natural environment due to the serious risks of eutrophication and health of aquatic organisms. However, this phosphate, if economically recovered, can partly overcome the anticipated future scarcity of phosphorus (P) resulting from exhaustion of natural phosphate rock reserves. An experiment was conducted to determine the efficiency of removing phosphate from a membrane bioreactor effluent (pH 7.0-7.5, 20, 35 mg phosphate/L) produced in a water reclamation plant by adsorption onto Dowex 21K XLT ion exchange resin and recover the phosphate as fertilisers. The data satisfactorily fitted to Langmuir adsorption isotherm with a maximum adsorption capacity of 38.6 mg\(\cdot\)P/g. The adsorbed phosphate was quantitatively desorbed by leaching the column with 0.1 M NaCl solution. The desorbed phosphate was recovered as struvie when ammonium and magnesium were added at the molar ratio of phosphate, ammonium and magnesium of 1:1:1 at pH 9.5. Phosphate was also recovered from the desorbed solution as hydroxyapatite precipitate by adding calcium hydroxide to the solution at a phosphate to calcium molar ratio of 1:2 at pH 7.0. The P contents of struvite and hydroxyapatite produced were close to those of the respective commercial phosphate fertilisers.  
phosphate; adsorption; struvie; hydroxyapatite; wastewater; fertiliser Article
## 2 Materials and Methods
### DRH, CRH, and Related Electric Effect Determination
At the same time, conductance measurements were obtained. Within the AEC, up to 6 samples can be housed at a time, in specifically designed PTFE filter holders. Each support is provided with a pair of electrodes that allows the measurement of the electrical resistance of the deposit at each RH step. The electrical measurements were performed with a multimeter (Hewlett-Packard-Agilent 3421A Data Acquisition Unit); data were sent to a PC and their processing was carried out using software created in LabView, which allowed the recording of resistance and conductivity measurements for each RH step established by the operator. During the humidification phase, the hygroscopic components of the deposit promote water uptake and a steep increase in the samples' conductance can be observed in correspondence to the deliquescence of water-soluble compounds, because of the formation of a conductive electrolytic solution. On the contrary, during dehumidification, when a strong decrease in conductance is measured, the crystallization of water-soluble compounds occurs [2]. The experiments were carried out at a fixed temperature (10, 22, 30 \({}^{\circ}\)C) for the entire duration of the cycle; no temperature fluctuations above 1 \({}^{\circ}\)C were recorded. Temperature and relative humidity were monitored by means of a thermo-hygrometric sensor (DMA572 and DMA672, LSI-LASTEM, with a resolution of 0.25% for RH and 0.035 \({}^{\circ}\)C for temperature) during the whole cycle. Figure 5 shows a picture and the scheme of the chamber. The deliquescence and crystallization regions were determined according to the gradient method [13; 19]. The variation of water mass' derivative as a function of RH was studied, identifying three key points: the beginning of the process (start: \"s\"), the maximum gradient of the derivative (gradient: \"g\"), and the end of the process (end: \"e\").
# Performance Analysis of Partitioned Step Particle Swarm Optimization in Function Evaluation
## 2 Partitioned Step Particle Swarm Optimization  
### Theory  
The PSPSO incorporates adaptive components and crossover on the basis of the current state of the particles. In [PERSON] et al.'s [21] concept of APSO, the adaptive component is based on the congestion and distribution of particles. For the PSPSO, these two conditions are reformulated as the \(\lambda\) and \(\gamma\) components, acting similar to congestion and distribution components, respectively. The \(\lambda\) component indicates the sparseness of the solution in the current iteration; the larger the \(\lambda\), the much closer are the fitness values. The \(\gamma\) component indicates how far is the fitness is the current best solution; the larger the value, the more near the particle is to the current best solution.  
\[\lambda=f(x)_{min}/f(x)_{max} \tag{2}\]  
\[\gamma=1-\frac{f(x)-f(gbest)}{f(x)_{max}-f(gbest)} \tag{3}\]  
\[\omega=\lambda\gamma\frac{(MaxIter-i)}{MaxIter} \tag{4}\]  
Equations (2)-(4) show the computation of the three components, \(\lambda\), \(\gamma\), and \(\omega\), respectively, where \(x\) is particle position, \(f(x)\) is the fitness function, _MaxIter_ is the maximum iteration, and \(i\) is the iteration number. At the beginning of the algorithm when the sparseness is still high, the particles will try to improve themselves by moving towards the known better solutions, _pbest_ and _gbest_. When the particles suddenly try to converge together, \(\omega\) will be high, which means that the algorithm will do more exploration. In other studies, the \(\omega\) is referred to as the \"mutation\" of PSO; when the \(\omega\) is high, the algorithm searches more the solution space. This means that the PSPSO will try to use the previous trajectory more to look for other solutions or to diversify.  
Traditional random walk for PSO includes the previous velocity, _pbest_, and _gbest_. Nonetheless, some research suggests that including _gbest_ makes the algorithm converge prematurely. To remedy this, the probability of using _gbest_ is decreased at the start of the iterations using the partition (\(p\)) (5). Even though the use of _gbest_ is reduced, the cooperative component will remain throughout the iteration through the use of a random particle's _pbest_ (\(pbest_{\text{rand}}\)). The velocity (6) would have two types of particles, the crossover of _pbest_ and _gbest_, and the crossover of _pbest_ and \(pbest_{\text{rand}}\). The vector representation for velocity (6) and particle movement update (7) is seen in Figure 0(a).  
\[p=rand<\left(\frac{i}{MaxIter}\right) \tag{5}\]  
\[vel_{new}=\omegavel_{old}+c_{1}rand(pbest-x)+c_{2}rand(p\;\text{gbest}+(1-p)\; \text{pbest}_{rnd}-x) \tag{6}\]  
\[part_{new}=part_{old}+vel_{new} \tag{7}\]  
Figure 0(b) shows the flowchart of the PSPSO, with the blue boxes indicating the modified equations for the PSPSO.
# Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features
## 3 Neural Network Architectures
### 1d-Cnn  
1D-CNNs have been effective in various fields, such as speech emotion recognition [33], music genre classification [34], and biomedical signal classification [35]. In this study, transposed speech data were used as input for the 1D-CNN model to capture the temporal patterns of frequency changes over timestamps.  
The structure of the model is illustrated in Figure 5. The architecture consists of five sequential convolutional blocks, each employing a kernel size of 3, with progressively increasing filter sizes: 32, 64, 128, 256, and 512. Each block comprises a Conv1D layer followed by batch normalization and ReLU activation. To prevent overfitting while maintaining the model's ability to learn time-dependent variations effectively, dropout layers (rate = 0.2) are strategically placed after selected convolutional blocks. The network culminates in a global max pooling layer followed by fully connected layers, with a final softmax activation layer for age group classification.
## 6 Convergence of NGN and FGI to Future Networks Concepts  
We can see also the start of discussions about what industry call Future Networks (FN). Generally, NGN was long term vision of the telco industry for managed networks with clear role of the service provider infrastructure specified in NGN or NGMN (Next Generation Mobile Networks). Many FN requirements are covered by NGN. However, some additional requirements have been identified that cannot be fulfilled with any actual NGN architecture.  
Figure 5: Complexity of protocol stack in NGN and Internet.  
Reuse and extension of existing NGN capabilities and services:  
* follow the real user demand for new services \(\geq\) define new service requirements
* independence of the service and transport, extend a mechanism for NG access/transport (e.g., FTTx)
* allow smooth and flexible service and platform evolution with new technologies
* improve scalability, reliability, robustness, security and privacy
* generalized mobility, openness, interoperability and interconnectivity
* extend end-to-end QoS mechanism for new content services
* utilize service creation flexibility and service orchestration  
New aspects that also have to be covered on FN:  
* virtualization and federation of resources (this can be partially achieved already now, depending on specific operator deployments)
* content centric services, content aware networks (improve multimedia services/capabilities, content selection, delivery and distribution, 3D and VR)
* social media, support of selected socio-economic aspects and energy awareness  
ITU-T (International Telecommunication Union--Telecommunication Standardization Sector) started a focus group in 2009, which analyzes requirements, principles and concepts for Future Networks from which we see that both NGN as well as FGI are valid candidates for Future Network.  
ITU-T Focus Group for Future Networks specified FN as following [27]:  
_\"Future Network (FN): A future network is a network which is able to provide revolutionary services, capabilities, and facilities that are hard to provide using existing network technologies\"_  
We believe [28, 29, 30] that converging NGN and FGI concepts can provide the best of both worlds to contribute to Future Networks.  
Definitely there exist also several other aspects that have to be taken into the account in future, like potential for effective migration from existing network, integration of \"legacy\" networks, business models and interconnection aspects, but also regulation or legislative framework for such future networks.
## 4 Experiments
### Qualitative Results
#### 4.3.2 Multi-Trajectory Prediction Performance  
Figure 6 showcases the SSA-GAN's multi-trajectory prediction performance on simulated multi-trajectory datasets. In the figure, the red lines represent the target pedestrian's trajectory obtained from the CARLA simulator, whereas the green lines represent manually annotated trajectories adhering to social norms. The purple area represents an envelope diagram containing the 25 predicted trajectories generated by the SSA-GAN, all of which  
Figure 5: Visualization of social attention weights for various pedestrian interactions: (**a**) Head-on walking; (**b**) head-on walking in denser crowds; (**c**) head-on walking with collision avoidance; (**d**) collision avoidance; (**e**) side-by-side walking with collision avoidance; (**f**) following and walking side by side. Each trajectory contains four positions (black dots) over four consecutive time steps, with arrows indicating the forward directions. Social attention weights for each time step are represented by purple, blue, orange, and red circles, respectively. The circle size corresponds to the social attention weight.  
fall within this area. The figure reveals that the SSA-GAN has a robust ability to generate diverse trajectories covering a range of plausible future trajectories across all four scenarios.
## 3 Numerical Simulations
### Results and Analysis
#### 3.3.2 Analysis of Heat Extraction Performance  
Figure 11 shows that the production temperature of the near-field zone decreased rapidly in the first 0.2 years due to the obvious change in the rock temperature around the tunnel. The production temperature curve stabilizes after 5 years, with a temperature drop of only 3.5 \({}^{\circ}\)C during this stabilization period. In addition, this figure indicates that the cumulative thermal energy presents a linear growth trend. At the same time, the share of thermal energy extracted in the stable region is approximately 67.5%.  
Figure 11: _Cont._  
Figure 10: The growth of the thermal influencing area with time: (**a**) Near-field zone; (**b**) Far-field zone.  
For the far-field zone, the temperature curve can be divided into three phases: stable region (0-0.8 years), decline region (0.8-3 years), and stable region (3-20 years). This phenomenon exhibits both the temperature variation characteristic of EGS and CLGSs. The production temperature of EGS often remains constant during the initial stage, while that of CLGSs often shows a sharp drop in this stage [41]. Due to the high production temperature in the first stage, thermal energy accumulates rapidly, accounting for about 39.5% of the total. Along with extracting time going by, both efficiency and production temperature attenuate mostly after running for 0.8 years because the scale of the high-permeability zone is limited in this model.  
As shown in Figure 12, the cumulative heat recovery of the far-field zone is about 8.3 times that of the near-field zone, which can make up for the small heat recovery of the near-field zone, and then this system can provide sufficient energy for utilization. Although the heat extraction capacity of the near-field zone is relatively low, its operation mode is controllable, and its performance parameters are highly predictable. Meanwhile, despite the poor controllability of the far-field zone, it has large heat production, simple construction, and low investment. Consequently, the EGS-E combines the characteristics of both in an organic way, allowing for scalable and controllable geothermal energy extraction.  
Figure 11: Production temperature and accumulated heat energy with time: (**a**) Near-field zone; (**b**) Far-field zone.  
Figure 12: Comparison of heat extraction between near-and far-field models.
## Abbreviations  
The following abbreviations are used in this manuscript:  
ASaverage shadowing AWGNadditive white Gaussian noise CDFcumulative distortion function CEschannel estimation errors CSIchannel state informationCSTRN cognitive satellite-terrestrial relay networks  
DF decode-and-forward  
EVM error vector magnitude  
FHS frequent heavy shadowing  
HIs hardware impairments  
HPA high power amplifier  
ILS infrequent light shadowing  
IQI in-phase quadrature-phase imbalance  
LOS line of sight  
MC Monte Carlo  
MMSE minimum mean square error  
OP outage probability  
PDF probability distribution function  
PN phase noise  
PUs primary users  
SER symbol error rate  
SNDER signal-to-noise-plus-distortion-and-error ratio  
SNR signal-to-noise-ratio  
SR shadowed Rician  
STRN satellite-terrestrial relay network