esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization
## 4 Intelligent Integration Model of Wi-Fi/BLE/QR/Mems Sensors
### Accuracy Estimation of Landmark Detection and Trajectory Evaluation
The overall trajectory error larger than the set threshold was not considered in the procedure of the final crowdsourced navigation database generation. To evaluate the accuracy of Wi-Fi fingerprinting based on \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Landmarks** & **Accuracy** & **Time** & **Cost** \\ \hline Wi-Fi Station & 0.25 m (75\%) & Quick & High \\ BLE Node & 0.52 m (75\%) & Quick & Medium \\ QR Code & 0.76 m (75\%) & Slow & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of landmark detection approaches. Figure 12: Accuracy of MLP error prediction. the generated navigation database, the basic k-nearest-neighbors algorithm (KNN) and the support vector machine (SVM) were applied for Wi-Fi fingerprinting classification. In this case, we also compared the performance of two kinds of navigation database: the proposed MLP-enhanced database (CED) and quality-evaluation-aimed database (QED) [9]. A comparison of the final localization accuracy using KNN and SVM is shown in Figure 13.  
Figure 13 shows that the proposed MLP-based database construction algorithm effectively improved the robustness of final generated database and obtained much better positioning performance than other algorithms; the estimated positioning error was within 5.22 m in 75% using different database matching algorithms.
## 3 Stationary Localization Algorithms
### Summary  
As shown in Table 1, in this section, we compare the stationary localization algorithms from following aspects:  
* Anchor type: Most stationary localization algorithms use anchor nodes to localize unknown nodes except NDLP.
* Ranging method: ALS and MLSL are range-free localization algorithms. HLS, UPS and LSLS use TDoA to obtain distances between anchor and unknown nodes. LSHL and RBBL measure distance by one-way ToA and two-way ToA, respectively. ARTL provides the asymmetrical round-trip strategy to calculate distance.
* Message communication: Unknown nodes participate in the message communication in most algorithms except in USP and USP, where they stay \"silent\".  
* Time synchronization: Generally speaking, accurate time synchronization introduces more energy consumption and hardware cost. Therefore, time synchronization-free are more suitable. Only HLS and LSHL require time synchronization.
* Localization coverage: Localization coverage of RLA, UPS, USP and RBBL is small. Only the unknown nodes within the communication range of anchor nodes can be localized. However, localization coverage can be appropriately broadened by adjusting the transmitting power (ALS) and using hyperbola-based scheme instead of circle-based one (HLS and PL). Also, some algorithms have been proposed for large scale UWSNs by adding more anchor or seed nodes (ML, ARTL and NDLP) or using hierarchical localization approach (LSHL and LLS).
* Localization time: In general, large scale localization (LSHL and LLS) need longer time. ARTL and NDLP requires long time to complete the asymmetrical round-trip strategy and select seed nodes, separately.
* Localization accuracy: LSHL, RLA and LLSLS are hierarchical localization introducing inevitable accumulated error. ALS provides a coarse location estimation of an unknown node within a certain area. NDLP is an anchor-free scheme. Therefore, their localization accuracy is low.
* Computational complexity: Most centralized algorithms are computationally light. In contrast with centralized algorithms, distributed algorithms' computational complexity are often higher.
* Energy consumption: Unknown nodes' energy consumptions in most stationary localization algorithms are relatively high. Unknown nodes in centralized localization algorithms consume high energy to send information to a central node or sink node, and in distributed localization algorithms they consume high energy to localize themselves.
# Sparse Multiuser Receiver Design in Large Scale Array System
## 6 Conclusions  
In this paper, we have developed a receiver designing scheme applied to scenarios where the number of RF chains is less than that of users using the mmWave and THz massive hybrid array. An analog phase shifter network design method is proposed according to the minimax criterion. Furthermore, we have developed a receiver algorithm based on the GAMP method to recover signals from multiple users through less observation information. The simulation results indicate that, compared with the traditional DFT matrix-based structure, the proposed hybrid structure can utilize limited RF chains to form more beams and serve more users, avoiding the ubiquity of null in the DFT matrix-based structure. Furthermore, our proposed GAMP algorithm can recover the modulation information and complete the demodulation of BPSK data transmitted by multiple users with fewer observed signals.  
Conceptualization, X.D. and J.C.; methodology, X.D.; software, X.D. and D.L.; validation, X.D. and X.B.; formal analysis, D.L. and B.L.; writing--original draft preparation, X.D. and J.C.; writing--review and editing, X.B. and B.L.; supervision, X.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Prediction of River Sediment Transport Based on Wavelet Transform and Neural Network Model
## 2 Methods  
### Data  
Mengsheng hydrological station, located in Lincang City, Yunnan Province (99\({}^{\circ}\)25E, 23\({}^{\circ}\)22N), is the main stream hydrological station of Nanbi River in the Lancang Water System, with a catchment area of 1766 km\({}^{2}\), inter-annual average precipitation of 1687.9 mm, inter-annual average runoff of 161,766,000 m\({}^{3}\) and inter-annual average flow of 51.3 m\({}^{3}\)/s, in which water resources of 334,269,000 m\({}^{3}\) are available in Lincang city area.  
The hydrological data contains a total of 54 years of observed data for rainfall, runoff and sediment from 1964 to 2017 at Mengsheng hydrological station in the Lancang River system. Before studying the relationship between rainfall, runoff and sediment, the hydrological data will be preprocessed [6]:  
\[y(i)=\frac{x(i)-min_{x(i)}}{max_{x(i)}-min_{x(i)}},\ i=1,2,\cdots,a \tag{1}\]  
where \(max_{x(i)}\), \(min_{x(i)}\) are the maximum and minimum values of data \(x(i)\), respectively; \(a\) is the number of the data.
## Ambient Intelligence Context-Based Cross-Layer Design in Wireless Sensor Networks  
**[PERSON], [PERSON] * and [PERSON]**  
Department of Electrical & Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand; E-Mails: [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (A.A.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +64-09-921-9999; Fax: +64-09-921-9973.  
External Editor: [PERSON]  
Received: 15 July 2014; in revised form: 29 September 2014 / Accepted: 30 September 2014 /  
Published: 14 October 2014  
**Abstract:** By exchanging information directly between non-adjacent protocol layers, cross-layer (CL) interaction can significantly improve and optimize network performances such as energy efficiency and delay. This is particularly important for wireless sensor networks (WSNs) where sensor devices are energy-constrained and deployed for real-time monitoring applications. Existing CL schemes mainly exploit information exchange between physical, medium access control (MAC), and routing layers, with only a handful involving application layer. For the first time, we proposed a framework for CL optimization based on _user context_ of ambient intelligence (AmI) application and an ontology-based context modeling and reasoning mechanism. We applied the proposed framework to jointly optimize MAC and network (NET) layer protocols for WSNs. Extensive evaluations show that the resulting optimization through context awareness and CL interaction for both MAC and NET layer protocols can yield substantial improvements in terms of throughput, packet delivery, delay, and energy performances.  
**Keywords:** wireless sensor networks; cross-layer optimization; context; ambient intelligence
###### Abstract  
The high-precision dynamic positioning of highway vehicles is the foundation and prerequisite for achieving intelligent connected transportation. To address the shortcomings of the GNSS/INS combination and GNSS/INS/OD combination in tunnel vehicle positioning, this paper proposes a tunnel vehicle positioning method for the GNSS/INS/OD combination based on lateral distance measurements and lane constraints. Firstly, a lateral distance measurement of vehicles inside the tunnel is conducted based on laser radar point cloud data. Secondly, map matching positioning is performed based on lateral distance measurements, odometer, and lane markings. Experimental results demonstrate that, for a 4.6 km tunnel, the average absolute error in the lateral positioning is 0.294 m, and the longitudinal positioning error is no more than 0.6 m, which can effectively meet practical operational requirements.  
GNSS/INS combination; GNSS/INS/OD combination; laser radar; lateral distance measurement; lane line constraint +
Footnote †: journal: Applied  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Mechanical Performance of Cement Bound Granular Mixtures Using Recycled Aggregate and Coconut Fiber
## 1 Introduction
The use of CF enhanced the mechanical performance of the bound material, producing gains in indirect tensile strength (ITS), flexural strength (FLS), toughness, impact resistance, and restraining crack development [33; 34; 35; 36; 37; 38; 39; 40; 41]. However, incorporating high dosages of CF can increase voids and decrease density, which may affect performance [36; 37; 42]. The use of CF, particularly at high dosages, affects workability, and in the case of cement concrete, decreases the slump value [43; 42]. In the case of CBGM, the CF dosage should be optimized to ensure the integrity of the material, i.e., maximize durability. Currently, there is a gap in the knowledge regarding the mechanical performance of CBGM incorporating CF, as no literature was found on this matter. Large amounts of CDW and coconut husk are produced, both of which lack significant large-scale applications. In this regard, the construction of road pavement is a possible application, as it is an activity that consumes large amounts of aggregate. In this study, to explore the use of fibers, these materials were included in bound layers, particularly sub-base and base layers. The consumption of RA and CF, which are obtained from waste materials, would enable savings in the consumption of natural resources and reduce the volumes of such materials in landfills. This work promotes the use of recycled materials, RA and CF, in CBGM by presenting the results of an experimental program designed to understand their effects in the mechanical performance of CBGM. Following the materials characterization, the mechanical performance of the CBGM was assessed according to the current European specifications framework. The mechanical performance was evaluated in the laboratory through the immediate bearing index (IBI), unconfined compressive strength (UCS), ITS, and FLS tests.
# Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies
## 4 Conclusions  
This study set out to assess rice quality using chemical and aroma fingerprinting obtained from a handheld NIR spectrometer and low-cost e-nose sensors combined with machine learning modeling. The study has shown that the high overall accuracy of the machine learning models is robust in assessing rice quality traits. The findings suggest that the method presented in the study may allow qualitative and quantitative rice quality monitoring at a lower cost than the conventional method. The results add to the expanding application of artificial intelligence among rice producers and the government authority to monitor and assess rice quality at a reasonable price and rapidly. Further research could explore the potential application of chemical and aroma fingerprinting to predict consumer preferences concerning the various qualities of rice available in the market.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods11091181/s1](https://www.mdpi.com/article/10.3390/foods11091181/s1)]([https://www.mdpi.com/article/10.3390/foods11091181/s1](https://www.mdpi.com/article/10.3390/foods11091181/s1)), Figure S1: Set up of the electronic nose measuring a cooked rice sample.  
Conceptualization, A.A., C.G.V. and S.F.; methodology, A.A., A.P., C.G.V. and S.F.; software, C.G.V. and S.F.; validation, C.G.V., A.P. and S.F.; formal analysis, A.A.; investigation, A.A., C.G.V. and S.F.; resources, C.G.V., A.P. and S.F.; data curation, A.A., C.G.V. and S.F.; writing--original draft preparation, A.A.; writing--review and editing, C.G.V., A.P. and S.F.; visualization, A.A., C.G.V., A.P. and S.F.; supervision, S.F. and A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData and intellectual property belong to the University of Melbourne; any sharing needs to be evaluated and approved by the university.  
The authors would like to acknowledge the support from the Digital Agriculture, Food, and Wine Group from the Faculty of Veterinary and Agricultural Sciences (FVAS), The University of Melbourne.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Optimization of Preparation Conditions for Adsorption Substrates of La-EPRC Particles
#### 3.1.2 Optimal Lanthanum Nitrate Solution Impregnation Time  
The experimental results are depicted in Figures 4 and 5. With the extension of the impregnation time of lanthanum nitrate solution, the adsorption and phosphorus removal capacity of the modified material gradually improves. The adsorption capacity of modified steel slag steadily increases within the 4-12 h impregnation time range, reaching equilibrium after 12 h. In contrast, modified fly ash essentially reaches equilibrium after 8 h of impregnation. Extending the impregnation time allows sufficient time for lanthanum ions to reach the surface of steel slag and fly ash, becoming stably immobilized on the material's surface. The loading of lanthanum on the material's surface essentially saturates in the later stages, and further extending the impregnation time does not significantly impact the adsorption effect. According to the experimental results, the impregnation time for modified steel slag was set at 12 h. Since the adsorption capacity of modified fly ash did not show substantial differences after 8 h of impregnation, the impregnation time for modified fly ash was also set at 12 h for the convenience of subsequent experiments.  
Figure 4: Effect of impregnation time of modified steel slag on the effect of phosphorus removal by adsorption.  
Figure 5: Effect of impregnation time of modified fly ash on the effect of adsorption for phosphorus removal.
# Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review
## 3 Wavelet Transformation for Non-Stationary Time-Series: State-of-the-Art
### Application of WT in Other Fields  
In some few works, the wavelet decomposition was also applied in other fields. For example, [PERSON] et al. [67] introduced the CWT to analyze the ecological time-series. The authors also applied the Monte Carlo assessment for statistical significance test of WT and compared the Morlet and Mexican hat wavelets using simulated and real ecological data. In addition, [PERSON] et al. [68] also used the CWT in order to analyze ecological TS for the red grouse population in northeast Scotland. They discussed the choice of the Morlet mother wavelet and drew a comparison between EMD and DWT in order to prove the effectiveness of the CWT in analysing ecological non-stationary TS. As for [PERSON] et al. [69], they used a wavelet-based spectrum in order to analyze the relation between the Lemming cycles and the climate change. A Lemming distribution and a climate data from 1970 to 2005 were used. [PERSON] et al. [70] have introduced a stationary WT for forecasting a solar photovoltaic (PV) system power. The proposed approach has been divided into two steps: training and prediction. A hybrid method based on a wavelet and random forest technique has been also presented. Finally, to minimize the prediction error, a bias compensation technique has been proposed. [PERSON] and [PERSON] [72] used the Morlet CWT for the nineteenth century French GNP series analysis. The wavelet-based spectrum was used in order to add some new details to measure the accuracy. In fact, they separated the accuracy of a series into amplitude and time variations. [PERSON] et al. [71] applied the WT for forecasting TS in the Roman Empire and the European Union. First, the TS were decomposed using Haar and Morlet wavelet. Then, the sign change of the spectrum of Lyapunov exponents was analyzed. These steps allow the analysis and detection of the instability of a state during was and crises. Finally, the artificial neural network was used to quantitatively determine and forecast the stability of a state. Some other works used wavelets in chemistry. For example, [PERSON] et al. [73] investigated the corrosion of steel in crude oil using a wavelet analysis of electrochemical noise. The Daubechies WT and energy distribution were applied to separate the contributions of two types of corrosions in oil samples with different compositions and to analyze the effect of those variables on the changes that occurred during 55 days of immersion in the experiment. This approach was adopted for different oil samples: the corrosion process of AISI 1020 in crude oil, naphthenic acid (500 and 3000 ppm), and H2S (50 and 1000 ppm).
# Wireless May Benefit Blockchain +
###### Abstract  
Wireless technologies now take every part of one's everyday life. As such, it will be no longer a surprise if a blockchain system is composed of wirelessly connected nodes. However, wireless communication is known for its inherent unreliability caused by noise, interference, limited bandwidth, etc. Motivated by this fundamental problem, this paper investigates the impact of wireless communications on the performance of three representative consensus mechanisms, viz., proof of work (PoW), proof of stake (PoS), and proof of coverage (PoC). It features a comprehensive analytical framework that mathematically derives metrics quantifying the scalability and the level of decentralization of the three consensus mechanisms, constituting a key contribution of this work. The paper then proceeds to present extensive simulation results as a means to confirm the underpinning theoretical findings. Overall, we emphasize that the framework's holisticity will allow it to be applied to diverse consensus mechanisms.  
blockchain; consensus; BFT; scalability; decentralization 2020 ac aclcopyright: 2  
(UASs), blockchain enhances airspace management for drones, addressing the complexities of high mobility and dynamic wireless conditions [11]. Finally, in healthcare applications, blockchain enables the secure and seamless sharing of sensitive patient data from wearable devices, ensuring integrity and privacy even under varying network conditions [12]. These use cases illustrate the profound versatility of blockchain as a foundational technology that, when paired with wireless connectivity, optimizes diverse systems by ensuring data security, transparency, and decentralized coordination.  
Nevertheless, one should take notice of a key challenge here: the reliability of wireless communications is generally lower in comparison to wired communications (e.g., ethernet), attributed to various random factors such as noise, interference, and limited bandwidth [13]. One should also note that this lower reliability affects the performance of consensus in blockchain [14; 15].  
All open public blockchains are based on the idea that they should be able to reach a consensus across a distributed network, even when there are conflicts, without putting control in one place [16]. Nonetheless, the additional dynamicity that is brought by wireless networking has not been thoroughly discussed in the literature as of yet. Therefore, it will be a valuable academic attempt to formally analyze the performance of a blockchain system established on a wireless network.
# FPGA Implementation of a BPSK 1D-CNN Demodulator
## 4 Experiment and Results
### Structure Parameters of the Network  
In order to obtain the best network structure, several networks were designed by changing the two most important structure parameters: the length of the input vector and the number of neurons in the hidden layer. The modulated signals of different SNR were used to judge the performance of the network. As a representative case, the bit error rate (BER) results when SNR was 6 dB are shown in Figures 12 and 13.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **Range** & **Variance Rate** \\ \hline SNR & 0–15 (dB) & — \\ Carrier frequency offset & 10 \(\pm\) 0.1 (MHz) & 10\% \\ Symbol rate offset & 5 \(\pm\) 0.01 (MHz) & 2\% \\ Sampling frequency offset & 80 \(\pm\) 0.1 (MHz) & 1.25\% \\ Initial phase & 0–2\(\pi\) & — \\ \hline \hline \end{tabular}
\end{table}
Table 1: The range of the variables.  
Figure 11: The curve of training loss.  
The result shown in Figure 12 indicates that the network cannot converge when the length of the input vector is too short (less than 4). The performance of the network was kept at an acceptable and stable level when the length of the input vector was slightly larger than the sampling number of each carrier period. In the experiment, the sampling number of each carrier period was 8, and the length of the input vector was set as 9.  
The result shown in Figure 13 indicated that with the increase of the number of neurons in the hidden layer, the network performance gets better and better. However, change is not obvious when the number is more than 20. Considering that too many neurons may bring a greater amount of computation, the number was set as 20 in the experiment.