esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 SPME-GC/MS Methods
### Volatile Phenols in Wine  
4-Ethylphenol (4-EP) and 4-ethylguaiacol (4-EG) are associated with wine defects that can form during winemaking or, more commonly, wine aging. These compounds are characterized by sensorial characteristics described as \"stable\", \"animal\" and \"phenolic\" and their presence is particularly detrimental for the product [74; 75; 76]. They are produced by winery contaminants, such as _Brettanomyces_ and _Dekkera_ yeasts, through processes of decarboxylation and reduction of ferulic and _p_-coumaric acids present in the grape [75]. Sensory thresholds of 4-EP and 4-EG in wine are 440 \(\upmu\)g/L and 33 \(\upmu\)g/L, respectively [77].  
For their determination several sensitive SPME-GC/MS methods were proposed [20; 78; 79]. [PERSON] _et al._, proposed the use of two PDMS 100 \(\upmu\)m fibers: for 4-EP the method had a limit of detection (LOD) and of quantification (LOQ) of 2 \(\upmu\)g/L and 5 \(\upmu\)g/L, respectively, for 4-EG 1 \(\upmu\)g/L and 5 \(\upmu\)g/L, respectively [80]. An optimized method for analysis of ethylphenols and vinylphenols in white and red wines was developed by the use of StableFlex Carbowax/DVB (CW/DVB 70 \(\upmu\)m) and polyacrilate (PA 85 \(\upmu\)m) fibers [79].  
4-EG and 4-EP in wine were also analyzed by a multiple-headspace SPME method. By performing three consecutive extractions of the sample with a CW/DVB fiber, the possible matrix effects were minimized by providing a LOD of 0.06 \(\upmu\)g/L for both 4-EG and 4-EP [81].
## 3 Results
### Temporal and Spatial Dynamics of NEP/WUE/CUE  
#### 3.2.1 Temporal Variability in NEP/WUE/CUE  
During the study period, the mean value of the NEP in the MRYR was 221.49 gC m\({}^{-2}\), which varied between 77.64 gC m\({}^{-2}\) in 2001 and 321.38 gC m\({}^{-2}\) in 2018 (Figure 6). Overall, the MRYR showed a stable upward trend. Due to the structure, morphology, and function of the vegetation itself, the highest multi-year mean NEP was for the forests, followed by the shrubs, croplands, and grasslands (Figure 6). The NEP growth rates were the opposite, with the grassland ecosystems showing the largest increase of 8.05 gCm\({}^{-2}\)a\({}^{-1}\), and the forest ecosystems showing the smallest change of 5.87 gC m\({}^{-2}\) a\({}^{-1}\).  
The WUE and CUE fluctuated upward from 2001 to 2022 (\(p<0.001\)) (Figure 6). The linear rate of increase in the WUE was 0.012 gC m\({}^{-2}\) mm\({}^{-1}\) a\({}^{-1}\), and its mean value was 0.52 gC m\({}^{-2}\) mm\({}^{-1}\), which suggest that in the MRYR, about 0.52 gC is fixed when one millimeter of water evaporates. The linear rate of increase in the CUE was 0.009 a\({}^{-1}\), with a mean value of 0.31 (Figure 6). Regarding the vegetation types, excluding shrubs, the trend for the WUE was greater than 0 for all vegetation types (the forests tended to be closer to 0); among them, the grassland ecosystems showed the greatest increasing trend. The significant increase in the NEP with a flatter ETa caused a significant increase in the WUE across the land cover types. The mean CUE values for shrubs, forests, croplands, and grasslands were 0.44, 0.42, 0.32, and 0.26, respectively. Similarly, the grasslands had the highest linear growth rate (0.013a\({}^{-1}\)), followed by croplands (0.007a\({}^{-1}\)); the CUE for the shrubs and forests increased, but none of them were significant (\(p>0.05\)). The trends of the NEP, WUE, and CUE show that the environmental quality of the MRYR has recovered significantly.  
Figure 5: DSI for dry and wet years. (**a**) DSI 2001 (driest year); (**b**) DSI 2021 (wettest year).
# Fast Spectral Approximation of Structured Graphs with Applications to Graph Filtering
## 7 Approximation of Graph Filtering
### Results for Approximate Graph Filtering  
In the previous discussion, it was shown that the approximation quality for graph filters is \(\mathcal{O}(\epsilon)\), where \(\epsilon\) is related to the difference between the matrix representation of the graphs. For the graph family \(\mathcal{S}^{l}_{1/2}\), this parameter has an order given by \(\mathcal{O}(\sqrt{n})\). Hence, it is expected that the error, for different filter orders, increases following a square-root law. In the following numerical experiments, we illustrate this behavior for approximating a heat kernel filter, i.e., \(h(\lambda)=e^{-\tau\lambda/\lambda_{\text{max}}}\), where \(\tau=10\) and \(\lambda_{\text{max}}\) is the maximum eigenvalue of the matrix representation of the graph. All the filters evaluated are implemented and designed using the GSP Toolbox [38]. For these experiments, we evaluate the performance of the approximation in terms of the normalized error \(e_{\text{n}}=\|\mathbf{H}\mathbf{x}-\tilde{\mathbf{H}}\mathbf{x}\|/\|\mathbf{H}\mathbf{x}\|\), where \(\tilde{\mathbf{H}}\) is the approximate graph filter as described before. Here, we have considered four types of inputs: (i) uniform distributed signals with entries in the range \([0,1]\) (positive/uniform), (ii) zero-mean normalized Gaussian distributed signals (Gaussian), (iii) binary signals (Binary) and (iv) bipolar signals with entries in \(\{-1,1\}\) (Bipolar). In Figure 8, the results for these comparisons are shown. From Figure 8a, we can observe the square-root law for the increase on the approximation error as described in the theory. Here, we performed the comparison using a filter order \(K=30\) for both the true and approximate filter. The increasing trend of the error is only reflected in the Gaussian and Bipolar signals as those kinds of signals are zero mean asymptotically. For the Positive/Uniform and Binary signals, the error decays sublinearly as the size of the graph increases. This is due to the fact that the mean term of the signal dominates the expression of the error. In Figure 8b,c, a comparison for different filter orders is shown. Here, it can be seen that the error remains constant across different filter orders. This result is due to the fact that the tested filter is sufficiently smooth for obtaining good approximations with low-order Chebyshev polynomials. The trend observed in Figure 8a is again observed for the different filter orders.
## 3 Analysis and Reliability Verification of an Engineering Case
### Analysis of Theoretical Calculation Results  
#### 3.2.1 Reliability Verification of the Proposed Method  
Figure 6 compares the compensated level of horizontal displacement of the tunnel calculated by the method developed herein with the measured data and the finite element data [2]. First, the curve of the horizontal displacement of the tunnel determined by the derived calculation method showed the characteristics of a large displacement in the middle and small displacements at both ends, which was just like the distribution of the measured data and the finite element data distribution; thus, the calculated distribution of the horizontal displacement of the tunnel well matched the measured data and the finite element data distribution. Second, the method proposed herein calculated the maximum horizontal displacement in the center of the tunnel at 3.22 mm, but the measured maximum horizontal displacement and the maximum horizontal displacement of the finite element method in the center of the tunnel were 3.01 and 3.2 mm, respectively. Hence, the measured maximum horizontal displacement and the maximum horizontal displacement of the finite element method of the tunnel were slightly smaller than the theoretically calculated value; however, the difference was negligible and satisfied the calculation accuracy requirements. Third, the range of the influence of grouting on rectification of the horizontal displacement of the tunnel calculated by the method developed in this paper was roughly the same as that reflected by the measured data; both presented an influence range of \(-\)30 to 30 m.  
Figure 5: Layout of rectification of tunnel displacement by the sleeve valve pipe technique: (**a**) front view; (**b**) floor plan.  
In summary, the results of the developed method were in good agreement with the measured data, which proves that the method derived in this work is reliable. Moreover, the proposed method has a certain degree of accuracy in calculating the correction of the horizontal displacement of existing tunnels caused by grouting. In order to accurately rectify the horizontal deformation of a tunnel, the grouting distance, the grouting depth, and the grouting volume should be designed and controlled in advance. Moreover, this method can help verify whether the impact of these parameters is reasonable and thus can be a reliable guide of actual engineering design. The parameters can be adjusted through repeated substitutions and calculations.
## 2 RF Communication Devices  
### Bluetooth and Wi-Fi  
Bluetooth is a well-established technology that has been applied in networks by researchers and industry with many algorithms and implementations. It has a communication protocol that utilizes low powered networking technology developed by Ericsson, with the aim to be used as an alternate to cable communication in computer peripherals, such as mouse, keyboards, printers, and other connected devices. Bluetooth has been proposed for the deployment in smart cities due to the ease of connection with smartphones for all users. Bluetooth uses the Bluetooth Protocol Stack function for the following mains components: Bluetooth radio, baseband, link manager protocol, logical link control of adaptation protocol, and service discovery protocol [19; 20]. Bluetooth range is 10 m in the most common class 2 form, and now is widely used in smartphones and laptops to transmit and receive information. Although the initial goal was to implement Bluetooth as a replacement to cords, it is now becoming the standard for ad hoc networks due to its low power and characteristics. Bluetooth is capable of transmitting 1 Mb/s while using 1 tenth of the power required by Wi-Fi, thus making it very suitable for ad hoc networks. [21; 22].  
Another popular wireless technology is Wi-Fi. Wi-Fi is the most widely used technology for wireless high speed internet communications for computers and smartphones. Wi-Fi is an industry standard codenamed \"802.11b\" by the Institute of Electrical and Electronics Engineers (IEEE), and is a class of certified wireless networking products [23; 24]. The crucial turning point of Wireless Local Area Network (WLAN) technologies was the year 1985, because this year IEEE started to standardize the Wi-Fi technology with the IEEE \"802.11\". Prior to this date, many companies and research laboratories around the world have researched and implemented Wi-Fi-like technologies. In 1980, IBM's Rueschlikon Laboratory [25; 26] researched infrared (IR) technology and WLANs manufacturing floors. Around the same time period, HP's Palo Alto Laboratory designed a WLAN prototyped with a direct sequence spread spectrum. Aloha Academic's research was not far behind them when they implemented the ALOHA wireless system from the University of Hawaii. The ALOHA system had a 9600 bps wireless bandwidth and was the predecessor of today's 4G/5G, since the Aloha System inspired Ericsson to make the Mobitex, the first early cellular wireless system [27; 28]. Since that era, the Wi-Fi has been an essential technology for our daily lives. It has also transformed the technology around cellular networks, such as 4G/5G, and many other wireless communication protocols. Wi-Fi and its associated technologies have revolutionized our lives and changed them forever, and it has had a transformative effect that has encouraged the rapid expansion of computer and smartphone technology.
## 2 Materials and Methods
### Procedure for CPE  
For the pre-concentration procedure, aliquots of 50 mL of either the standard (containing 40 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro g}\)/L Pb\({}^{2+}\)) or sample solution (six replicate samples of 10 mL of digested acid) were transferred into centrifuge tubes with a glass stopper (25 mL in capacity); the pH of each sample solution was adjusted to pH 6. To this solution, 2.0 mL of (0.08%-0.8% \(\
icefrac{{v}}{{v}}\)) Triton X-114 solution and 1.0 mL of ligand (DLNL) (50-100 \(\mathrm{\SIUnitSymbolMicro m}\)) were added; the pH range (2-10) was adjusted by the addition of 0.1 M HCl/NaOH solution in acetate buffer. The mixture was then introduced to the thermostated water bath and heated at 30-60 \({}^{\circ}\)C for 5-20 min. After different time intervals, centrifugation for 10 min at 4000 rpm was done and separation of the two phases was obtained. The contents of tubes were cooled in an ice bath, the surfactant-rich phase became viscous, and the upper aqueous phase was carefully removed by a pipette. To decrease the viscosity of extracts (i.e., the remaining micellar phase (500 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro L}\))), 0.5 mL acidic ethyl alcohol (0.1 M HNO\({}_{3}\)) was added to lower its viscosity and make the sample handling easier. The extracted solutions were introduced into the flame by conventional aspiration. The duplicate blanks of both complexing reagents were prepared simultaneously without addition of samples and standards. The recovery is calculated as:  
\[\mathrm{Recovery}\;(\%)\;=\;\frac{\mathrm{C_{S}V_{S}}}{\mathrm{C_{O}V_{O}}} \times 100=\frac{\mathrm{C_{O}V_{O}-C_{W}V_{W}}}{\mathrm{C_{O}V_{O}}}\times 100,\]  
where \(\mathrm{C_{O}}\) represents the lead content in the sample-surfactant mixture of volume \(\mathrm{V_{O}}\), \(\mathrm{C_{W}}\) represents the lead content in the aqueous phase of volume \(\mathrm{V_{W}}\), and \(\mathrm{C_{S}}\) represents the lead content in the surfactant phase of volume \(\mathrm{V_{S}}\).
Sensors_**2012**, _12_, 4715-4729; doi:10.3390/s120404715  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**On the Selection of Transmission Range in Underwater Acoustic Sensor Networks**  
**[PERSON], [PERSON] * and [PERSON]**  
School of Computer Engineering, Nanyang Technological University, 639798, Singapore;  
E-Mails: [EMAIL_ADDRESS] (M.G.); [EMAIL_ADDRESS] (J.C.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +65-6790-4319; Fax: +65-6792-6559.  
_Received: 2 March 2012; in revised form: 5 April 2012 / Accepted: 5 April 2012 / Published: 11 April 2012_  
**Abstract:** Transmission range plays an important role in the deployment of a practical underwater acoustic sensor network (UWSN), where sensor nodes equipping with only basic functions are deployed at random locations with no particular geometrical arrangements. The selection of the transmission range directly influences the energy efficiency and the network connectivity of such a random network. In this paper, we seek analytical modeling to investigate the tradeoff between the energy efficiency and the network connectivity through the selection of the transmission range. Our formulation offers a design guideline for energy-efficient packet transmission operation given a certain network connectivity requirement.  
**Keywords:** underwater acoustic sensor networks; transmission range selection; energy efficiency; connectivity; random networks
# A Vision-Based Micro-Manipulation System
## 2 Related Work
### Image Enhancement  
In the process of capturing microbial cell images, the quality of these images can be influenced by various factors, including fluctuations in illumination [19]. Moreover, microbial images may also be susceptible to distortions arising from camera lenses and exposure time settings. To address the illumination issue, image enhancement techniques are employed as a preprocessing step. Numerous methods for enhancing illumination and contrast have been explored in the literature [20; 21; 22]. Contrast enhancement is a technique used to improve image quality and reveal subtle details in low-contrast areas. Various intensity adjustments, often determined by users, are used to enhance visual contrast. However, the effectiveness of these adjustments depends on user-defined function coefficients, and different transformations can produce distinct patterns.  
In the realm of biomedical image enhancement, various techniques have been explored. [PERSON] et al. harnessed Wiener filtering and Curvelet transforms to enhance red blood cell images and reduce noise [23]. [PERSON] et al. [24] introduced Contrast-Limited Adaptive Histogram Equalization (CLAHE) for detecting cell nuclei boundaries in Pap smear images, optimizing image backgrounds and regions of interest through CLAHE and global thresholding in preprocessing. [PERSON] et al. [25] opted for histogram equalization in the preprocessing stage, aiming to enrich contrast in leukemia microscopic images to facilitate cell segmentation and cancer detection. [PERSON] et al. [26] turned to histogram equalization for image enhancement, striving to classify normal RBC and poikilocyte cells using Artificial Neural Networks, fueled by a dataset of 100 images from diverse blood samples. [PERSON] et al. [27] uncovered the potential of Gamma Equalization (GE) to enhance images. Additionally, [PERSON] [28] highlighted the versatile use of the Retinex filter in GIMP for enhancing both panoramic radiographic and microscopy images, offering valuable applications in medical, biological, and other scientific imaging. [PERSON] et al. [29] proposed an enhanced Multi-scale Retinex (MSR) technique with chromaticity preservation (MSRCP) for enhancing bacterial microscopy images, leading to improved contrast and visibility of bacterial cells, as confirmed by Image Quality Assessment (IQA) parameters.  
[PERSON] et al. [5] presented a Fuzzy Automatic Contrast Enhancement (FACE) method that utilizes fuzzy clustering to improve image contrast automatically, avoiding visual artifacts and retaining original colors.
# Rapid Online Solid-State Battery Diagnostics with Optically Pumped Magnetometers
## 3 Discussion  
As the results show, our battery scanner could rapidly measure that the magnetization becomes weaker or stronger, depending on whether the cells are either degaussed or magnetized. In particular, these measurements allow one to immediately conclude that a cell has been overheated--for example, something that is of concern for chip-based devices.  
MPMS is an ideal tool to study magnetic properties of materials, and is widely applied to battery prototype research [24]. It can evaluate the charge-state of the battery precisely by monitoring the transition metal's magnetic property during the charge or discharge period [21; 28]. In this work, MPMS could provide a high background magnetic field (\(-4\) to \(4\) T), giving access to the complete m-H curve [29], while our piercing solenoid system could only provide a stable background magnetic field from \(-80\) to \(80\) \(\upmu\)T. However, the superconducting coil of the MPMS suffers from residual magnetic field due to trapped flux, which is absent in our normal conducting coils that also allow for a much higher resolution of the background field. Therefore, our system is better suited for measurements at low fields in order to identify ferromagnetic contributions influenced by temperature [30] (see Figure 2b).  
Furthermore, the atomic magnetometer setup could produce a diagnostic map of the battery cells' induced magnetic field within \(20\) s, providing potential resolution of the distribution of magnetic particles. A high-speed motor or a magnetometer array instead of a single sensor could even be used for these noncontact diagnostics of batteries on a sub-second time scale. These measurements can be performed in situ and operando with differently sized cells and under factory conditions. We show that batteries damaged by heating can be identified; this is particularly important for surface-mounted devices.  
The magnetic field detection sensitivity of our setup is better than \(400\) fT\(/\sqrt{\text{Hz}}\) from \(1\) to \(150\) Hz, which could help to detect a microgram difference in nickel content between cells. The dynamic range of our atomic magnetometer is approximately \(5\) nT, which also puts an upper limit on the nickel concentration. Recent developments in Earth-field atomic magnetometers with larger dynamic ranges [31] could solve this problem. These direct magnetic field measurements with atomic magnetometers provide hitherto inaccessible opportunities for cell characterization, classification, and monitoring in research and industry. Apart from battery diagnostics, the technique can be applied to general magnetic-susceptibility-based diagnostics, as well as for residual magnetization measurements--for example, in rock magnetism studies and magnetic materials testing [32].
# Land Suitability Mapping Using Geochemical and Spatial Analysis Methods
###### Abstract  
Assessing the suitability of urban and agricultural land is essential for planning sustainable urban and agricultural systems. The main objective of this study is to evaluate the suitability of land in Ioannina plain (western Greece) concerning the soil contents of two potentially toxic elements, cadmium (Cd) and cobalt (Co). Geochemical and spatial analysis methods were applied to assess the distribution of Cd and Co in the soil of the Ioannina plain and identify their origin. The primary anthropogenic sources of Cd and Co in the topsoil of the study area can be attributed to traffic emissions, aircraft operations, vehicle crushing and dismantling activities. Element content is compared to international guidelines and screening values. Cadmium and Co concentration in the soil of the study area is well above the European topsoil mean. Thus, the urban and agricultural lands cover the vast majority (92%) of the total area. Cadmium concentration in soil of the study area with a mean (mg kg\({}^{-1}\)) 1.7 and 2.0 was observed in agricultural and urban land use, respectively. Cobalt content in soil of the area studied with a mean (mg kg\({}^{-1}\)) 30.8 and 37.1 was recorded in agricultural and urban land use, respectively. Land evaluation suitability by adopting criteria provided from the international literature is discussed.  
cadmium; cobalt; soil; GIS; Ioannina plain +
Footnote †: journal: Applied sciences  
1  
Dept. Sci.2021115404[[https://doi.org/10.3390/app11125404](https://doi.org/10.3390/app11125404)]([https://doi.org/10.3390/app11125404](https://doi.org/10.3390/app11125404))