esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 6 Experimental Results
### Comparison of Algorithms  
Table 5 below shows the results obtained by the algorithms applied outdoors as RSSI reported many outliers indoors. The position of the target is calculated as an average of five measurements, and the Euclidean distance error from the real point is provided in the last column of Table 3.  
The algorithm that performs best in this case is trilateration. This can be observed from the distance error, which is lower for almost every set of coordinates of the target node.  
It was not easy to predict that it would be the most accurate algorithm because it is true that it is the most complex, and in some cases, it performs operations that approximate the values in the best way (such as the proportional increase in the radii), but at the same time, it is also the one with the highest computational cost, so the estimation error can propagate and increase for each operation performed, compromising the localization.  
The least accurate algorithm is triangulation; it has quite high estimation errors. This is due to the fact it uses only two reference nodes to calculate the position compared to the other algorithms that make use of three nodes. In general, it can be said that the Min-Max algorithm and the trilateration algorithm provide relatively good results by calculating sets of coordinates that reflect the position of the nodes that are reliable enough.  
Note that the estimated coordinates from triangulation are null in the case where the target's coordinates are (2,0). This is true for any coordinate where the target node is on a straight line connecting the two reference nodes. This results from the absence of a triangle, making it unable to use the trigonometric formulas shown in Section 4.
## 2 Materials and Methods
### Estimation of Wood-Decay Fungi Presence  
In each tree, we conducted a thorough visual examination for signs of wood-decay fungi. We initially inspected the trunk, branches, leaves, and exposed roots for the occurrence of externally visible indications of decay, such as fungal fruiting bodies, conks, or discoloration [84]. In any case, we identified the predominant fungus on the trees' trunks, using an expert's judgment opinion, while we categorized this information into one binary variable (fungal presence = 1, fungal absence = 0). In _R. pseudoacacia_ trees, we observed the presence of a wood-decay fungus commonly associated with white pocket rot in the heartwood, resulting in the deterioration and degradation of the affected wood. It often manifests as large, shelf-like fruiting bodies (conks) on the trunk or branches of infected black locust trees. For _M. alba_ trees, we identified the presence of a fungus that causes powdery mildew disease, characterized by a white or grayish powdery growth on the leaves and stems leading to leaf distortion and reduced vigor. In _M. azedarach_ trees, we found the presence of _Diplodia spp._ as the predominant fungus. This fungal pathogen causes leaf blight and dieback, resulting in a decline in the health and vitality of the tree [85]. Regarding olive trees, we observed the occurrence of olive leaf spot or Cycloconium leaf spot, caused by the fungus _Spilocaea oleagina_ ([PERSON] [PERSON]. This disease manifests as small round to irregularly shaped spots on the leaves. Initially, these spots appear yellow or light green and progressively turn brown or dark brown, leading to defoliation and reduced vigor [86]. Finally, in the case of pine trees, we detected the presence of _Porodaadalea pini_ Brot., commonly known as red ring rot or red heart rot. This fungal pathogen primarily infects the heartwood of pine trees, resulting in decay and weakening of the wood. We visually identified its presence based on the formation of red or reddish-brown decay pockets or zones within the heartwood of the infected pine trees.
## 4 Simulation and Experimental Results  
Following the design procedure of the proposed circuit, the performance was evaluated by simulations and experiments. The experimental platform was built as shown in Figure 10. Note that animal tissues are employed as a substitute for human tissues in the experimental testing process.
## 2 The Proposed Methodology
### Image Stitching  
Image stitching was performed with an OpenCV image-processing library for a stepwise process of correcting optical distortion, cropping the circular TM field of view, and estimating affine transforms between neighboring fields using Speeded Up Robust Features (SURF) [21] key points matching. These transforms describe the translation, rotation, and skew of each peripheral field relative to the central field. Finally, a full-resolution mosaic is generated using the estimated transforms, and overlapping regions are linearly blended.  
To save time on developing a customized image stitching algorithm and to focus on the clinical impact of the proposed framework, we used Image Composite Editor (ICE) 2.0 (v2.0.3, Microsoft, Redmond, WA, USA, 2015) [22] software package, created by the Microsoft Research Interactive Visual Media Group, which generates seamlessly combined composite images. Microsoft ICE is distributed as a freeware for non-commercial use.  
Microsoft ICE does not have the capability of stitching images from totally different scenes. Therefore, reducing TM-irrelevant frames, as proposed in this study, was needed to utilize this tool. Our solution provides a feasible and practical alternative to manual selection. The proposed framework, SelectStitch, consists of two main processes, i.e., semantic segmentation and image stitching, as depicted in Figure 4.  
Figure 4: Proposed framework for composite images generation from otoscope video clips.  
Figure 5 demonstrates the image stitching process for the three of selected frames. In this example, the image stitching helped to obtain a single image that contains a more comprehensive view of the pathology.
Sensors_**2013**, _13_, 5649-5670; doi:10.3390/s130505649  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Fast Nearly ML Estimation of Doppler Frequency in GNSS**  
**Signal Acquisition Process**  
**[PERSON] \({}^{1,\ast}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) NavSAS Group, Department of Electronics and Telecommunications, Politecnico di Torino,  
Corso Duca degli Abruzzi 24, Torino 10129, Italy; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) NavSAS Group, Istituto Superiore Mario Boella, Via Pier Carlo Boggio 61, Torino 10138, Italy;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +39-327-979-3717; Fax: +39-011-227-6299.  
_Received: 14 March 2013; in revised form: 17 April 2013 / Accepted: 22 April 2013 / Published: 29 April 2013_  
**Abstract:** It is known that signal acquisition in Global Navigation Satellite System (GNSS) field provides a rough maximum-likelihood (ML) estimate based on a peak search in a two-dimensional grid. In this paper, the theoretical mathematical expression of the cross-ambiguity function (CAF) is exploited to analyze the grid and improve the accuracy of the frequency estimate. Based on the simple equation derived from this mathematical expression of the CAF, a family of novel algorithms is proposed to refine the Doppler frequency estimate with respect to that provided by a conventional acquisition method. In an ideal scenario where there is no noise and other nuisances, the frequency estimation error can be theoretically reduced to zero. On the other hand, in the presence of noise, the new algorithm almost reaches the Cramer-Rao Lower Bound (CRLB) which is derived as benchmark. For comparison, a least-square (LS) method is proposed. It is shown that the proposed solution achieves the same performance of LS, but requires a dramatically reduced computational burden. An averaging method is proposed to mitigate the influence of noise, especially when signal-to-noise ratio (SNR) is low. Finally, the influence of the grid resolution in the search space is analyzed in both time and frequency domains.  
**Keywords:** acquisition; CAF; refinement; CRLB; least square; averaging method
## 4 Preparation of Single-Cell Protein (SCP) for Use in Food
### Reducing the Nucleic Acid Content  
Microbial protein must be purified primarily from nucleic acids. The Protein Advisory Group recommends that the daily intake of nucleic acids from SCP should not exceed 2 g. The metabolism of pyrimidines produces ammonia, water, and carbon dioxide, and purines produce uric acid. Excess uric acid in the human body leads to the development of gout or arthritis, and may contribute to the formation of kidney stones. This is due to the inability of humans to synthesize uricase [109]. Therefore, the nucleic acid content of SCPs must be reduced by enzymatic, chemical, or thermal methods [110].  
A simple thermal method to reduce the amount of RNA was developed in QuornTM. In this process, _Fusarium_ biomass is rapidly heated to 64 \({}^{\circ}\)C for 20-30 min to inhibit growth, destroy ribosomes, and activate endogenous RNAases. After this process, mycoprotein contains about 1% of nucleic acids, but there are also significant biomass losses (33-38%) [111]. For this reason, this method was improved by increasing the temperature to 72-74 \({}^{\circ}\)C and extending the incubation to 30-45 min (biomass losses of 30-33%) [110]. The heat shock method can also be successfully used to reduce the amount of nucleic acids in yeast biomass. [PERSON] et al. [112] developed a three-step method that resulted in a reduction in the nucleic acid content in the biomass of the yeast _C. utilis_ from 7 to 1.0-1.5%. The first stage of this process is a few seconds of thermal shock at a temperature of 54-70 \({}^{\circ}\)C, then cooling to 45 \({}^{\circ}\)C, and finally a 1 h incubation at 55 \({}^{\circ}\)C.  
A significant reduction in the amount of nucleic acids can be achieved using alkali treatment. [PERSON] and [PERSON] (1988) [113] used a 4.5% NH\({}_{4}\)OH solution and a temperature of 65 \({}^{\circ}\)C. After 30 min of incubation of the yeast biomass, the nucleic acid content in the _K. marxianus_ biomass decreased from 12.08 to 1.2%, and in the case of the yeast _S. cerevisiae_, decreased from 7 to 1.4%. [PERSON] and [PERSON] (1975) [114] developed a method for reducing nucleic acids in the SCP of the yeast _Rhodotorula glutinis_ using acidic treatment. After optimizing the parameters, it was found that the most efficient reduction in the nucleic acid content (from 6.5 to 1.2%) occurs after acidifying the medium to pH 2.0 and incubation at 90 \({}^{\circ}\)C.
# Study of Standing-Wave Thermoacoustic Electricity Generators for Low-Power Applications
## 3 Acoustic Impedance Matching
Therefore, it contributes in determining the resonance frequency of the integral acousto-mechanical system and influences the structure of the acoustic field near the stack. So, an improper maximization of \(\eta_{ae}\) and \(W_{a}\) could produce the detrimental effect of degrading the \(\eta_{ha}\) performance. Proper impedance matching requires instead that the working conditions of the coupled system be optimal both for the TA engine in converting heat to acoustic power and the LA in converting acoustic power to electrical energy. Therefore, once a range of optimal working conditions for the LA alternator are selected, the resulting \(\omega\) and \(Z_{a}\) values should be used as inputs in the optimization procedure of the TA engine for realizing impedance matching at the location where the  
Figure 1: The operation frequencies of the loudspeaker giving rise to an electrical output power values of 50 and 100 W, and the acoustic-to-electric conversion efficiency as a function of the load resistance. LA is installed. In general, the above requirements cannot be met simultaneously and compromises should be made among them in the design phase when coupling the LA to the TA engine. In the next section the procedure is applied in the case of standing wave TAEGs for three different coupling typologies of the LA.
## 5 Computational Complexity  
In the proposed soft decision recognition algorithm, the most complex computation is the calculation of \(p_{i}\). The major computational consumption appears in Equation (23), which includes the \(n_{e}^{th}\) root calculations, productions and \(tanh\) function in the real number field. While, the hard decision algorithm only has production and addition calculations over \(\mathrm{GF}(2)\). However, our proposed algorithm utilizes the soft decision outputs of the channel, which can provide more information about the reliability decision bits, so require very lower calculation times of \(p_{j,i}\) than the hard decision one, which can also reduce the total computational complexity in the recognition procedure.
## 3 Results
### Mapping Using ML Approach
#### 3.5.3 Performance of the ML Model Against Simulated Measurements
The Radon transform \(R\{f\}(\rho,\theta)\) of a function \(f(x,y)\) is given by the following integral:  
\[R\{f\}(\rho,\theta)=\int_{-\infty}^{\infty}f(x,y)\,\delta(x\cos\theta+y\sin \theta-\rho)\,dx\,dy\]  
where  
Figure 16: ECDF of (**a**) \(\Delta\theta\) for the trained model with loss function that has \(\lambda_{1}=1\), \(\lambda_{2}=0\), (**b**) \(\Delta\theta\) for the trained model with loss function that has \(\lambda_{1}=0\), \(\lambda_{2}=1\). Figure 15: Empirical cumulative distribution function (ECDF) of (**a**) \(\Delta r\) for the trained model with loss function that has \(\lambda_{1}=1\), \(\lambda_{2}=0\), (**b**) \(\Delta r\) for the trained model with loss function that has \(\lambda_{1}=0\), \(\lambda_{2}=1\). \(f(x,y)\) is the original image function;
* \(\rho\) is the perpendicular distance from the origin to the line of projection;
* \(\theta\) is the angle of projection relative to the x-axis;
* \(\delta(x\cos\theta+y\sin\theta-\rho)\) is the Dirac delta function that constrains the projection to a particular line at a distance \(\rho\). The Radon transform accumulates the projection of the intensity values along the lines at different angles \(\theta\). It then produces a heat map in which the local maximum corresponds to the main parameters of the ellipse, in particular, the angle \(\theta\) that we are looking for. In Figure 16a, we use the median of \(\Delta\theta\) to calculate the orientation error, taking into account the maximum possible rotation, which is 360\({}^{\circ}\), we obtain an error of approximately 13%. In Figure 16b, the error is approximately 2.5%. We believe that these errors, both localization and orientation, can be further reduced by using more sophisticated models, such as the 'pix2 pix' method [37], or advanced ML techniques such as the Super Resolution Generative Adversarial Network (SRGAN) [38].
# Drone Arc Routing Problems and Metaheuristic Solution Approach
## 4 Proposed Solution Approach
### Solution Representation and Initial Solution  
The solution representation is constructed as a two-dimensional list based on the required edges in the graph. However, when using the neighbor search operators, the deadheading edges is also included in the solution representation. Therefore, the solution representation to be used in the structure of the algorithm is dynamic. The proposed solution representation is explained using the graph presented in Figure 1. \(E_{R}=\{(1-2),(1-3),(3-2),(11-12),(6-4),(6-5),(5-4),(7-8),(8-10),(10-9),(9-7), (13-14),(14-15),(15-16),(16-17),(17-18),(18-13)\}\) is the set of the required edges to be visited on the graph. Assume that the \(4\) drones' routes serving the required edges are as shown in Figure 4.  
In Figure 4, each drone route is represented by a different color. The dashed lines indicate the deadheading edges. Therefore, the resulting drone routes can be expressed as follows (d indicates the deadheading on the relevant edge):  
_First drone route (blue-colored route):_\(\mathrm{d(depot-10)-(10-9)-(9-7)-(7-8)-d(8-2)}\)\(-\)\((2-3)-\)d(\(3-\)depo).  
_Second drone route (red-colored route):_\(\mathrm{d(depot-4)-(4-5)-(5-6)-d(6-12)-(12-11)-d(11-depot)}\).  
_Third drone route (green-colored route):_\(\mathrm{d(depot-10)-(10-8)-d(8-16)-(16-15)-(15-14)-(14-13)-(13-18)-(18-17)-(17-16)-d(16-depot)}\).  
_Forth drone route (orange-colored route):_\(\mathrm{d(depot-2)-(2-1)-(1-3)-d(3-6)-(6-4)-d(4-depot)}\).  
It is seen that all 17 required edges are serviced. The shortest paths on the route are expressed as d, which stands for deadheading. Since the drone routes are included as a two-dimensional list based on the required edges in the solution representation, the solution representation corresponding to the obtained drone routes is as follows:  
\(\{((10-9)-(9-7)-(7-8)-(2-3)),((4-5)-(5-6)-(12-11)),((10-8)-(16-15)-(15-14)-(14-1 3)-(13-18)-(18-17)-(17-16)),((2-1)-(1-3)-(6-4))\}\)  
We can define the edges in the solution representation as \(Route_{ij}\): the \(jth\) order edge of the \(ith\) drone route. For example, edge \((12-11)\) is the third-order edge of drone route 2 and can be described as \(Route_{23}\). On the other hand, the solution representation does not have to contain only the required edges. This is because the information about the non-required edges in the route is also used in the generating neighbor solution phase. In this case, it is also necessary to include the deadheading edges in the solution representation. The length of the solution representation could increase or decrease. Thus, the length of the solution representation is dynamic.  
The initial solution was randomly generated with the proposed solution representation. No problem-specific heuristic algorithm in order to construct initial solution was implemented.
## 3 Results  
### Spectral Signature of Hybrids  
The six sorghum hybrids evaluated showed different reflectance behaviors in the visible range, especially from the 660 nm wavelength onwards, where the difference in reflectance between the hybrids becomes more evident. The difference in reflectance between the hybrids is even more marked from 735 nm onwards, where the reflectance from the H5 and H6 hybrids was similar and always lower than the others. The H4 and H2 hybrids had low reflectance up to just above 735 nm, standing out from the others from this wavelength onwards, especially the first one (Figure 3). Therefore, it is possible to observe distinct spectral signatures for each hybrid evaluated.  
At wavelength B_475, the highest reflectance was observed for hybrid H1 and the lowest for hybrids H2 and H4 (Figure 3A). At wavelengths G_550 and R_660 (Figure 3B,C), the highest reflectances were observed by hybrids H1 and H4, while the other hybrids reflected less, but with no difference between them. At RE_735 (Figure 3D), hybrids H1, H2, H3, and H4 had the highest reflectances, which did not differ from each other, while hybrids H5 and H6 had the lowest reflectances. At wavelength NIR_790, hybrids H2 and H4 had the highest reflectances (Figure 3E), while the lowest reflectances were observed for H5 and H6.  
Figure 4 shows the means of the vegetation indices, in which for all of them, except LAI, the highest means were achieved by the H4 hybrid, indicating a higher chlorophyll activity by this genotype. The lowest means for VIs were observed for hybrids H1, H5, and H6, while for LAI these hybrids had the highest means.
# Broadband Spectral Domain Interferometry for Optical Characterization of Nematic Liquid Crystals
## 3 Chromatic Index Variation of \(Mlc2132\)  
### Principle  
A first experimental set of measurements is performed at a constant room temperature of 20 \({}^{\circ}\)C. The thickest cells are employed. The two arms of the interferometer are balanced in dispersion so that the output spectral phase difference only originates from the LC layer, \(\varphi_{o,e}(\omega)\).  
A convenient way to write \(\varphi_{o,e}(\omega)\) consists of a polynomial development, with \(\omega_{0}\) the central angular frequency :  
\[\varphi_{o,e}(\omega)=\varphi_{o,e}^{0}+\varphi_{o,e}^{1}(\omega-\omega_{0})+ \frac{\varphi_{o,e}^{2}}{2}(\omega-\omega_{0})^{2}+\frac{\varphi_{o,e}^{3}}{6 }(\omega-\omega_{0})^{3}+... \tag{5}\]  
where \(\varphi_{o,e}^{1}=(\frac{d\varphi_{o,e}}{d\omega})_{\omega=\omega_{0}}\) corresponds to a linear group delay term (GD), \(\varphi_{o,e}^{2}\) is the Group Delay Dispersion (GDD) and \(\varphi_{o,e}^{3}\) the third order dispersion (TOD).  
We have seen that the output of the FTSI technique gives the sample phase with the first linear order subtracted Equation (2):  
\[\varphi_{o,e}^{ FTSI}(\omega)=\frac{\varphi_{o,e}^{2}}{2}(\omega-\omega_{0})^{2}+ \frac{\varphi_{o,e}^{3}}{6}(\omega-\omega_{0})^{3}+... \tag{6}\]Therefore, considering Equations (4)-(6), it comes :  
\[\varphi_{o,e}^{FTSI}(\omega)=n_{o,e}(\omega)\frac{\omega}{c}L-\varphi_{o,e}^{0}- \varphi_{o,e}^{1}(\omega-\omega_{0}) \tag{7}\]  
Thus, with an iterative method, \(n_{o,e}(\omega)\) and its first order derivative are the fitting parameters to retrieve the spectral phase. The latter is measured over two different wavelength ranges: from 450 nm to 700 nm and from 650 nm to 880 nm. However, this method is able to measure only relative dispersion relations, e.g., it is necessary to know the refractive index at one specific wavelength \(n_{o,e}(\lambda_{ref})\) to completely get \(n_{o,e}(\omega)\).
# SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
## 1 Introduction
The discovered information in that study is invaluable for an MCS study since it can be used in user recruitment to meet the two objectives that were mentioned above. In this article, we aim to cope with the battery drains of the participants in an MCS system. To this end, we propose a novel methodology for participant recruitment for participatory data acquisition in MCS systems by introducing a multi-context user recruitment policy into user incentives, which is based on two indicators: (i) user sociability, which denotes an indication of willingness for participation in crowd-sensing campaigns, and (ii) the energy, which denotes the residual battery level of mobile devices. The proposed scheme is called Sociability-Oriented and Battery-Efficient Recruitment for Mobile Crowd-Sensing (SOBER-MCS). The sociability-awareness in participant recruitment enforces the recruitment of active users, whereas energy-awareness enables prioritizing personal use of the participant devices in lieu of sharing built-in sensors when the residual battery level drops below a certain threshold. We run extensive simulations for the performance evaluation of SOBER-MCS and show that the multi-context user recruitment can improve battery consumption up to 18.5% while improving the user and platform utilities up to 12% and 20%, respectively. The article is organized as follows. In Section 2, we present the related work and background on the subject. Section 3 presents the proposed recruitment model in detail. The performance evaluation of the proposed solution is presented in Section 4. Finally, Section 5 concludes the article and provides future directions.