esa-sceva/satcom-chunk-collection huggingface dataset:
# Blind Turbo Equalization of Short CPM Bursts for UAV-Aided Internet of Things
###### Abstract  
With the surge of Internet of Things (IoT) applications using unmanned aerial vehicles (UAVs), there is a huge demand for an excellent complexity/power efficiency trade-off and channel fading resistance at the physical layer. In this paper, we consider the blind equalization of short-continuous-phase-modulated (CPM) burst for UAV-aided IoT. To solve the problems of the high complexity and poor convergence of short-burst CPM blind equalization, a novel turbo blind equalization algorithm is proposed based on establishing a new expectation-maximization Viterbi (EMV) algorithm and turbo scheme. Firstly, a low complexity blind equalization algorithm is obtained by applying the soft-output Lazy Viterbi algorithm within the EM algorithm iteration. Furthermore, a set of initializers that achieves a high global convergence probability is designed by the blind channel-acquisition (BCA) method. Meanwhile, a soft information iterative process is used to improve the system performance. Finally, the convergence, bit error rate, and real-time performance of iterative detection can be further improved effectively by using improved exchange methods of extrinsic information and the stopping criterion. The analysis and simulation results show that the proposed algorithm achieves a good blind equalization performance and low complexity.  
blind equalization; continuous-phase modulation; EM algorithm; Lazy Viterbi algorithm Article Blind Turbo Equalization of Short CPM Bursts for Internet of Things  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Blind Turbo Equalization of Short CPM Bursts for UAV-Aided Internet of Things  
2022222226508[[https://doi.org/10.3390/s22176508](https://doi.org/10.3390/s22176508)]([https://doi.org/10.3390/s22176508](https://doi.org/10.3390/s22176508)) Academic Editor: [PERSON] Received: 30 July 2022  
Accepted: 25 August 2022  
Published: 29 August 2022  
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**Copyright**: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
## 2 Related Work  
The reported studies are mainly limited to the effects of system temperature on external stiffness and hydraulic damping properties, and the nonlinearities introduced by temperature and frequency variations are generally overlooked. [PERSON] et al. [24] investigated the enhanced effects of temperature on the total HPS force, at the range of 0 to 40 \({}^{\circ}\)C under the excitation frequency of 0.5 Hz. [PERSON] et al. [13] used an empirical model to consider the linear variation of fluid density. The pressure drop across the bleed orifice coupling different chambers was shown to be affected by the temperature in the range of 20 to 75 \({}^{\circ}\)C. [PERSON] and [PERSON] [15] implemented the Benedict-Webb-Rubin real gas state equation. A heat transfer model was developed using the lumped capacitance method while introducing a thermal time constant. The measured gas pressure after a step displacement input was used to identify the heat convection of gas. [PERSON] et al. [25] further compared different gas-temperature relationships using the first law of thermodynamics (or energy equation), considering the heat transfer of the gas chamber; however, the obtained results underestimated the hysteresis of the gas pressure during HPS operation.  
[PERSON] and [PERSON] [26] also implemented an adiabatic gas process for the excitation frequency above 1 Hz, but its effect on fluid pressure was not investigated. [PERSON] et al. [27; 28] proposed a lumped parameter thermodynamic model considering the thermal capacity of gas, oil, and strut body. The temperature was shown to increase at different rates at different sections of the HPS. [PERSON] et al. [29] proposed a statistical reliability model of a hydro-pneumatic suspension with separated gas and oil, considering the effect of temperature, which was mainly used to investigate the robustness under gas leakage and seal failure.  
It has also been reported that increased oil temperature could decrease the fluid density and thus decrease the maximum damping force [30]. The fluid bulk modulus and fluid viscosity would also decrease with increased temperature [31]. The hysteresis of pneumatic spring caused by heat transfer was reported to be more significant at lower excitation frequency [32; 33]. Dynamic hysteresis can be usually described using Bouc-Wen and phenomenological models. [PERSON] et al. [34] reported that variations in the velocity and frequency range of excitation could lead to complex asymmetric hysteresis behavior. Moreover, variations in the gas fraction and temperature of HPS systems contribute to significant variations in fluid bulk modulus, while [PERSON] et al. [35] revealed that the dissolution of the entrained gas in the oil could be neglected if the external excitation took place within a time period below a few minutes.
# A Novel Dictionary-Based Image Reconstruction for Photoacoustic Computed Tomography
## 2 Methods
### The Proposed Method
The number of the steps is selected based on the trade-off between the execution time and the performance of the final reconstructed image. In our algorithm, we set the number of steps to 50. By allocating appropriate weights to each basis function, coefficients corresponding to dominant features will receive higher gain and those corresponding to less critical features will receive a lower gain. It should be noted that the weighting factors are determined based on what diagnostically relevant features need to be emphasized in the image, i.e., edges, singular points, and homogeneous textures. A statistical texture analysis method, the Gray-Level Co-Occurrence Matrix (GLCM) technique,  
Figure 1: Block diagram of the proposed method. All the italic letters have been described in the text. has been used to measure the homogeneous texture components [38]. GLCM is used in 0, 45, 90, and 135 degree directions. Singular points are small local areas that the signal values in these areas are changed in two dimensions. The Harris operator, by providing an analytical autocorrelation, considering the local intensity changes in different directions, has been used to detect singular points [39]. We used the canny filter, a first-order image edge detector, to determine the weighting factor TV (which signifies the edges). It should be noted that the main justification for the proposed method is to utilize the advantages of the overlapping methods to improve the image quality in limited view PACT systems. As mentioned, using only one sparsifying function leads to information loss. By combining the three basis, all the information loss in one of them can be mitigated using another one, leading to a higher image quality, compared to using any one of them.
# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
## 3 System Model
### Testbed and Dataset  
We used the publicly available dataset in [23] for our experiments. The dataset used in this experiment is available [8]. In this experimental setup, the dataset seen in Figure 2 was created using the RSSI readings from a set of 13 iBeacons on the first floor of the Waldo Library at Western Michigan University. The data were collected using an iPhone 6S, Apple USA. A labeled dataset (1420 samples) and an unlabeled dataset (1420 samples) were included in the dataset (5191 samples). In the original dataset in [8] authors have numbered the experiment area from A to U as rows and 1-18 as columns. In the experiment each cell has a unique identification number i.e B3001 as in Figure 2. When it comes to our experiment, the total experiment area divided in to four zones as A, B, C and D for simplicity and the ML models developed to estimate the location as one of these zones as a categorical problem. The recording was completed within the library's regular operating hours. During the experiments, other wireless devices were switched off to reduce the disturbances from the different sources. More significant RSSI values showed a nearer proximity to a given iBeacon. For out-of-range iBeacons, the RSSI was shown by \(-200\). Figure 2 shows the layout of the plan of iBeacons in green color circles. For the experimentation, we used only the labeled dataset given by the authors.  
Figure 2: Arrangement of the sensor nodes in the testbed [8].
## 4 Experimental Results and Analysis  
### Experimental Setup  
#### 4.1.1 Data Preparation  
The road network dataset used in this paper is the road network data of Xi'an and Wuhan downloaded from the OSM ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)), (accessed on 10 September 2021)) platform (see Table 1 for details). The POI basic information data, user history check-in data and user history rating data were crawled from Ctrip ([[https://you.ctrip.com/](https://you.ctrip.com/)]([https://you.ctrip.com/](https://you.ctrip.com/)), (accessed on 5 October 2021)) and Mafengwo ([[https://www.mafengwo.cn/](https://www.mafengwo.cn/)]([https://www.mafengwo.cn/](https://www.mafengwo.cn/)) (accessed on 5 October 2021)). The basic POI information includes POI-type information, number of photos, number of favourable comments, rating, star rating, etc. Historical user check-in data and score data refer to the number of users' check-in times and POI scores. The crawled user data and POI data are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**City** & **Number of Nodes** & **Number of Edges** & **Number of POIs** \\ \hline Xi’an & 25,431 & 33,010 & 4138 \\ Wuhan & 28,153 & 34,512 & 4350 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Introduction to road network dataset.  
Figure 5: Schematic diagram of the gene replacement operation.
## 4 Numerical Simulation of Soliton Molecules
### Influence of Third-Order Dispersion  
It is well known that when two closely spaced solitons propagate in an optical fibre, they can attract or repel each other depending on their relative phase [62]. But still, people may ask why such a dissipative system allows solutions with only discrete (more precisely, integer times \(\pi/2\)) phase differences. What is the magic behind it and what is the possible reason? Actually, when we take higher-order dispersion into account, for instance, here we add third-order dispersion term to this system, we find that the bound solitons do not possess exactly integer times \(\pi/2\) phase differences any longer (see Figure 20). The numerical results indicate that the two nearly in-phase bound solitons exhibit a phase difference of 0.126 rad.  
Figure 19: Evolution of a bound state when gain is decreased.  
Figure 18: Formation of an in-phase bound state and its spectral and temporal properties. (**a**) Evolution process of pulses driven by initial noise; (**b**) Optical spectrum and (**c**) temporal profile of the soliton molecules.  
Most experiments have claimed approximately perfect integer times \(\pi/2\) phase differences in their cavities. One reason is that the phase relationship is usually reflected only by spectral shape, rather than direct measurement of phase by some means like interference, which lacks sufficient accuracy. The other reason is that in most cavities the second-order dispersion dominates, which makes higher-order dispersion neglectable. If a near-zero GVD dispersion laser cavity is investigated, then higher-order dispersion terms might need to be revisited and studied in detail.  
It is also worth noting that a fibre laser does not have to ensure complete solutions with all \(\pi,0\) and \(\pm\pi/2\) phase differences. More often, only \(\pi\) and 0 [63], or only \(\pm\pi/2\)[17; 44], or only anti-phase [64] solutions have been found in theoretical researches, probably due to limitation of available simulation variables in a single laser cavity. Based on more simulation results from refs. [22; 44], we believe that a sole dispersion sign of the cavity is not able to determine or predict the possible phase difference of the solutions. It is rather tricky to select proper parameters for a stable bound state since such an exact balance is reached only when all effects related to gain, loss, nonlinearity and dispersion compromise to a same attractor, which enables soliton molecules an intriguing form in dissipative systems.
# Engine Fault Detection by Sound Analysis and Machine Learning
## 3 Methodology  
### Overview  
This paper presents a framework comprising data acquisition, preprocessing, feature extraction, fine-tuning, and classification, as illustrated in Figure 3. Data acquisition details are explained in Section 2. In the preprocessing phase, each audio signal was normalized and trimmed to 5 s to ensure consistency. Feature extraction was performed using Mel-Frequency Cepstral Coefficients (MFCCs) and Discrete Wavelet Transform (DWT) techniques, capturing important acoustic characteristics for fault diagnosis. The fine-tuning process involves Grid Search optimization to find optimal hyperparameters for machine learning models, while Extreme Learning Machines (ELM) are utilized for efficient and accurate classification of vehicle fault diagnostic classes. Additionally, Fourier transform-based feature extraction and Relief-f feature selection algorithm are employed for frequency analysis of each fault type, aimed at uncovering crucial frequency components associated with different fault types.  
Figure 2: Example audio signals for each vehicle diagnostic class.
## 3 Results & Discussion
### PANI@CNT/CNC SPME MN-Coupled GC-MS Analysis  
The ability of the PANI@CNT/CNC SPME MN to adsorb caffeine was tested through coupling to GC-MS. For comparison, the ability of a commercial SPME fiber to adsorb caffeine was tested in tandem with the PANI@CNT/CNC SPME MN. Caffe is an alkaloid compound commonly found in coffee and tea, and it acts as a stimulant with potential antioxidant effects [32]. Overlapped extracted ion chromatograms (EICs) for caffeine acquired using the PANI@CNT/CNC SPME MN and commercial SPME fibers are shown in Figure 6a,b, respectively. Excellent linear regressions for PANI@CNT/CNC SPME MN and commercial SPME, were observed (Figure 6c) across caffeine standard, 5-400 mg/L, with a% RSD ranging from 1.4% to 5.0% across this concentration range, when each standard was analyzed in triplicate, indicative of excellent precision.  
The PANI@CNT/CNC SPME MN had improved caffeine adsorption capabilities, evidenced by a 67% increase in sensitivity compared to the commercial SPME fiber, as determined by the ratio of the calibration sensitivities between the two platforms (Figure 6c). The increased affinity for caffeine for the PANI@CNT/CNC SPME MN compared to the commercial SPME based on DVB/CAR/PDMS can be attributed to the inherent high surface area impacted by the synergy of PANI@CNT/CNC hybrid materials for the SPME MN platform. This is consistent with literature where some conductive polymers have found to have excellent sorption for polar analytes [2; 9]. The LOD of the PANI@CNT/CNC SPME MN was 26 mg/L, which is well below the typical concentration of caffeine in coffee and teas. Figure 6d shows the mass spectra of extraction ion EIC for \(m/z\) 194 (caffeine standard) and \(m/z\) 197 (caffeine isotopic label internal standard) peaks.  
Figure 5: Overlaid Nyquist plots for (i) PANI@CNT/CNC SPME MN; (ii) CNT/CNC coated MN; and (iii) silylated MN taken in 25 mM K\({}_{3}\)FeCN\({}_{6}\) (in 0.10 M KCl).
## 2 Experiment and Method
Transformed by Equation (1), the overlap factor can be written as [21]:  
\[O(z)=\frac{X(z)\Bigl{[}\beta_{a}\Bigl{(}z_{f}\Bigr{)}+\beta_{m}\Bigl{(}z_{f} \Bigr{)}\Bigr{]}}{X\Bigl{(}z_{f}\Bigr{)}\Bigl{[}\beta_{a}(z)+\beta_{m}(z) \Bigr{]}}\exp\biggl{\{}-2\int_{z}^{z_{f}}\big{[}\alpha_{a}\bigl{(}z^{\prime} \bigr{)}+\alpha_{m}\bigl{(}z^{\prime}\bigr{)}\big{]}dz^{\prime}\biggr{\}}, \tag{3}\]  
where \(z_{f}\) represents the place where the overlap factor is equal to 1. The aerosol backscatter coefficient is derived from the ratio of the elastic signal to the nitrogen Raman signal:  
\[\beta_{a}(\lambda_{L}z)=\frac{X(\lambda_{L}z)\cdot X(\lambda_{R}z_{c})\cdot N (z_{c})}{X(\lambda_{R}z)\cdot X(\lambda_{L}z_{c})\cdot N(z_{c})}\beta_{m}( \lambda_{L},z_{c})\frac{\exp\bigl{\{}-\int_{z_{c}}^{z_{c}}\alpha_{a}(\lambda_{ L}z^{\prime})+\alpha_{m}(\lambda_{L}z^{\prime})dz^{\prime}\bigr{\}}}{\exp \bigl{\{}-\int_{z_{c}}^{z_{c}}\alpha_{a}(\lambda_{L}z^{\prime})+\alpha_{m}( \lambda_{L}z^{\prime})dz^{\prime}\bigr{\}}}-\beta_{m}(\lambda_{L},z), \tag{4}\]  
where \(\lambda_{L}\) and \(\lambda_{R}\) represent the signal wavelengths of elastic the channel and Raman channel, respectively. \(z_{c}\) is the reference height without aerosol. \(N(z)\) is the molecular concentration of nitrogen. Since the overlap factors of the elastic channel and the Raman channel are almost the same, the aerosol backscatter coefficient is not affected by the overlap factor. The aerosol extinction coefficient is calculated by the gradient method of the Raman signal:  
\[\alpha_{a}(\lambda_{L},z)=\frac{\frac{d}{dz}\ln\Bigl{[}\frac{N(z)}{X(\lambda_{ R}z)}\Bigr{]}-\alpha_{m}(\lambda_{L},z)-\alpha_{m}(\lambda_{R},z)}{1+\Bigl{[} \frac{\lambda_{L}}{\lambda_{R}}\Bigr{]}^{k}}, \tag{5}\]  
where \(k\) is the Angstrom exponent, commonly assumed as one. The low altitude aerosol extinction coefficient affected by the overlap factor is derived from the aerosol backscatter coefficient. The overlap factor is obtained by combining the above formulas.
## 2 Materials and Methods
### Methods
#### 2.3.2 Performance Indicators  
Direct Authenticity Test  
The performance of remote sensing LST products in Hunan Province was assessed using the following error parameters: Pearson correlation coefficient (R), Bias, relative bias (\(\text{Bias}_{\text{r}}\)), root mean square error (RMSE), and unbiased RMSE (ubRMSE). The equations for these indexes are:  
\[R=\frac{\text{cov}(RST,IST)}{\sigma_{RST}\sigma_{IST}} \tag{2}\]  
\[Bias=\frac{1}{m}\underset{i=1}{\overset{m}{\sum}}\left(RST_{i}-IST_{i}\right) \tag{3}\]  
\[Bias_{\text{r}}=\frac{1}{m}\underset{i=1}{\overset{m}{\sum}}\frac{\left|RST_{i }-IST_{i}\right|}{IST_{i}} \tag{4}\]  
\[RMSE=\sqrt{\frac{1}{m}\underset{i=1}{\overset{m}{\sum}}\left(RST_{i}-IST_{i} \right)^{2}} \tag{5}\]  
\[ubRMSE=\sqrt{RMSE^{2}-Bias^{2}} \tag{6}\]  
where \(RST\) is the LST of each remote sensing-based product, \(IST\) is the in situ measured LST, cov() is the covariance, and \(\sigma\) is the standard deviation.  
Triple Collocation Analysis  
The following parameters were used as indicators of TCA: Sensitivity, standard error (Stderr), correlation coefficient (Rho), fractional RMSE (fRMSE), and signal-to-noise ratio in decibels (Snr_db). The equations used are:  
\[Sensitivity_{a}=\frac{\text{cov}(a,b)\times\text{cov}(a,c)}{\text{cov}(b,c)} \tag{7}\]  
\[Errvar_{a}=\text{cov}(a,a)-Sensitivity_{a} \tag{8}\]  
If \(Errvar_{a}\geq 0\), then \(Stderr_{a}=\sqrt{Errvar_{a}}\)  
\[Rho=\begin{bmatrix}\sqrt{\frac{\text{cov}(a,b)\times\text{cov}(a,c)}{\text{ cov}(a,a)\times\text{cov}(b,c)}}\\ sign(\text{cov}(a,c)\times\text{cov}(b,c))\times\sqrt{\frac{\text{cov}(a,b) \times\text{cov}(b,c)}{\text{cov}(b,b)\times\text{cov}(a,c)}}\\ sign(\text{cov}(a,b)\times\text{cov}(b,c))\times\sqrt{\frac{\text{cov}(a,c) \times\text{cov}(b,c)}{\text{cov}(c,c)\times\text{cov}(a,b)}}\end{bmatrix} \tag{10}\]  
\[fRMSE=\sqrt{1-Rho^{2}} \tag{11}\]  
\[Snr_{a}=\frac{\text{cov}(a,a)\times\text{cov}(b,c)}{\text{cov}(a,b)\times \text{cov}(a,c)}-1 \tag{12}\]  
If \(Snr_{a}\geq 0\), then \(Snr\_db_{a}=-10\times\log(Snr_{a})\)  
where \(a\), \(b\) and \(c\) are the triple-collocated LST products in each grid.  
Of the preceding parameters, greater values of R, Sensitivity and Rho, and lesser values of Bias, \(\text{Bias}_{\text{r}}\), RMSE, ubRMSE and Stderr, indicate better product performance. The value of fRMSE has a well-defined range from 0 (perfect estimates) to 1 (total noise, with no ground truth signal), with values \(>\)\(\sim\)0.7 indicating an error variance that exceeds the variance of the true time series [40]. A value of zero for Snr_db indicates that signal variance is equal to noise variance; +3 (+6) dB indicates that signal variance is twice (four times) noise variance; \(-3\) (\(-6\)) dB indicates that signal variance is half (one-fourth) noise variance, and so forth [20].
## 2 Reagents, Materials and Methods  
### Reagents and Materials  
The following reagents were used for the construction of the biosensor and validation of the analytical method: recombinant human VEGF-A (165) (Abcam, Cambridge, UK), monoclonal rabbit antibody against VEGF-A (165) (Abcam, Cambridge, UK), recombinant human FGF-2 (17 kDa) (R&D Systems, Minneapolis, MN, USA), monoclonal rabbit primary and secondary antibody against FGF-2 (17 kDa) (R&D Systems, Minneapolis, MN, USA), ELISA kit for VEGF-A analysis (165) (R&D Systems, Minneapolis, USA), ELISA kit for FGF-2 analysis (17 kDa) (R&D Systems, Minneapolis, MN, USA), cysteamine hydrochloride, EDC [N-ethyl-N'-(3-dimethylaminopropyl) carbodiimide], NHS [N-hydroxysuccinimide], absolute ethanol 99.8% (Sigma Aldrich, Saint Louis, Missouri, MO, USA), HBS-ES solution (pH = 7.40, 0.01 M HEPES, 0.15 M sodium chloride, 0.005% Tween-20, 3 mM EDTA) (all reagents from Sigma Aldrich, Saint Louis, Missouri, MO, USA), PBS (pH = 7.40, phosphate buffered saline) (BIOMED, Lublin, Poland), and L-glycine solution (pH = 2.40, L-glycine solution with HCl solution) (all reagents from Sigma Aldrich, Saint Louis, Missouri, MO, USA). The bases of the biosensors were glass plates with sputtered gold, 50 nm thick, purchased from SSeens, PR Enschede, The Netherlands (1-07-04-000, Lot. H418-063). For the construction of a biosensor with 12 active sites, the light-curing polymer Elpemer SD 2457 (Lackwerke Peters GmbH, Kempen, Germany) was used, which, after screen printing  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Biomarker** & **Description** & **Ref.** \\ \hline \multirow{8}{*}{VEGF-A} & Elevated levels in AML patients, worse overall prognosis. & [17] \\  & Secretion by all 12 cell lines representing the lymphoma, leukaemia, and multiple myeloma phenotypes. & [18] \\  & Increased expression in blast cells (20 out of 28 patients) with newly diagnosed AML. & [19] \\  & Secretion into the culture medium in leukaemia cell lines and cultured bone marrow cells from & [20] \\  & Elevated levels in haematological cancers, such as MM, NHL, CML, CLL, CMML, MDS, and AML. & [21; 22; 23; 24] \\  & Increased mRNA expression in half of the 12 cell lines tested representing the phenotypes of & \\  & lymphoma, leukaemia, and multiple myeloma. & [18] \\  & Elevated levels of FGF-2 in HCL correlated with elevated levels of fibronectin. & [25] \\  & Elevated levels of FGF-2 in CLL from B lymphocytes and increased levels of FGF-2 in CML. & [26] \\  & Increased FGF-2 levels in ALL and increased bone marrow microvessel density. & [22] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Abnormalities in levels of VEGF-A and FGF-2 in haematological tumours.  
on a properly cleaned gold chip, was dried at 65 \({}^{\circ}\)C for 1 h and then cured with UV light for 5 min. The layer of this photopolymer prevented mixing of the tested solutions on the surface of the biosensor.