esa-sceva/satcom-chunk-collection huggingface dataset:
Chromotography_**2015**, \(2\), 66-78; doi:10.3390/chromotography2010066  
_chromotography_ ISSN 2227-9075  
www.mdpi.com/journal/chromotography  
Determination of Trichloroethylene in Water by Liquid-Liquid Microextraction Assisted Solid Phase Microextraction  
**[PERSON] and [PERSON]. [PERSON] ***  
Center for Intelligent Chemical Instrumentation, Clippinger Laboratories, Department of Chemistry and Biochemistry, Ohio University, Athens, OH 45701, USA  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-740-994-0265; Fax: +1-740-593-0148.  
Academic Editor: [PERSON]  
_Received: 12 December 2014 / Accepted: 3 February 2015 / Published: 9 February 2015_  
**Abstract:** A method for the determination of trichloroethylene (TCE) in water using portable gas chromatography/mass spectrometry (GC/MS) was developed. A novel sample preparation method, liquid-liquid microextraction assisted solid phase microextraction (LLME-SPME), is introduced. In this method, 20 \(\mu\)L of hexane was added to 10 mL of TCE contaminated aqueous samples to assist headspace SPME. The extraction efficiency of SPME was significantly improved with the addition of minute amounts of organic solvents (_i.e._, 20 \(\mu\)L hexane). The absolute recoveries of TCE at different concentrations were increased from 11%-17% for the samples extracted by SPME to 29%-41% for the samples extracted by LLME-SPME. The method was demonstrated to be linear from 10 to 1000 ng mL\({}^{-1}\) for TCE in water. The improvements on extraction efficiencies were also observed for toluene and 1, 2, 4-trichlorobenzene in water by using LLME-SPME method. The LLME-SPME method was optimized by using response surface modeling (RSM).  
**Keywords:** liquid-liquid microextraction assisted solid phase microextraction; trichloroethylene; water contaminants
# Improvements in Rock Mass Description for Stope Design by Geophysical and Geochemical Methods
## 6 Photogrammetric Scanning for Rapid Geometrical Characterization
### Results and Discussion
Despite these challenges, advancements in drone technology, such as multi-camera systems, improved battery life, and enhanced navigation capabilities, suggest a promising future for UAV-based photogrammetry in underground excavations, particularly in the context of ore and rock mass characterization for stope design. The integration of multi-camera systems in drones could enhance both the quality and the speed of data capture, leading to more detailed and comprehensive 3D models. Improvements in battery technology could extend drones' operational duration, making them more practical for large-scale underground mappings. Also, advancements in navigation systems, particularly for GPS-denied environments like underground excavations, could increase the precision and reliability of data acquisition. Developing drones that are more resilient to harsh underground conditions would also be a significant step forward. Additionally, streamlined and automated data-processing software could make drone photogrammetry more accessible and reduce dependence on highly specialized technical expertise. By incorporating these advancements, drone technology holds considerable promise for revolutionizing rock mass characterization in underground excavations, particularly in the context of stope design. The combination of rapid data acquisition, safety enhancement,and comprehensive geological analysis positions drone photogrammetry as a valuable tool in the future of geotechnical engineering and underground space utilization.
## 4 Discussion
### Metallurgy  
Conventional Ni-Ti instruments produced higher torque or force compared with M-wire, gold wire, blue heat-treated and shape memory alloy instruments [20; 43]. Similarly, non-heat-treated glide-path instruments produced higher screw-in forces compared with those produced by heat-treated instruments [14]. However, conventionally machined instruments generated lower torque but higher forces compared with those of their electropolished counterparts [56].  
Several studies compared torque and forces of various heat-treated instruments. M-wire instruments (ProTaper NEXT; Dentsply Sirona) generated a higher torque and force than those of postproduction heat-treated Ni-Ti instruments (TruNatomy; Dentsply Sirona) [44]. Other M-wire instruments ((Reciproc; VDW), (WaveOne; Dentsply Sirona) and ProTaper NEXT) had higher torque or force values than blue heat-treated instruments (Reciproc Blue; VDW) [57], gold-wire instruments (WaveOne Gold; Dentsply Sirona) [58], and FireWire instruments (Edge Endo; Albuquerque, NM, U.S.) [21]. One rotary instrument made of stainless steel (Gentlefle, MedicNRG, Kibbutz Afikim, Israel) exhibited a lower force than that exhibited by Ni-Ti rotary instruments ((ProTaper NEXT) and (Revo-S; Micro-Mega, Besancon, France)) [59].  
Conventional Ni-Ti alloy instruments exhibited higher torque or apical forces than instruments made of heat-treated alloys in most cases. Instruments made of different alloys with different heat treatments exhibited different torque and forces, and instrument size, design, and kinematics should be standardized to test the abstruse metallurgical properties of different alloys.  
The limitation of this review is that there was no uniform standardization of experimental designs, including root canal models and testing devices, which caused methodological heterogeneity among the included studies.
## 4 Authentication, Tracing and Fraud Detection  
Traceability, i.e., defining the geographical origin of a food, is important for different reasons; for instance, as properly described by [PERSON] and [PERSON] in their review [9], in situations where high quality species are replaced by less valuable ones. This practice frequently occurs for seafood, whose origin identification is a complex issue. In their review paper, the authors provide a complete overview of recent applications of vibrational spectroscopy coupled with chemometrics for solving this kind of issues.  
Additionally, traceability is a key concept also for agro-food; in particular, for those foodstuffs presenting peculiar organoleptic characteristics ascribable to the growing area or the know-how of local farmers. As a consequence, the verification of the origin of high-valued food products is of crucial interest. Tracing, authentication and characterization of agro-foods are complex problems, commonly accomplished by classification methods. The main chemometric tools adopted to solve these kind of issues have been briefly described by [PERSON] and co-authors in a review included into the special issue [10]. The same approaches are also used for the detection of frauds in foodstuff, such as sophistications or adulterations.  
The remaining research articles of the special issue belong to this field, and they are based on the combination of chemometric classifiers and spectroscopic techniques.  
An example of this is the paper published by [PERSON] and collaborators [11], where three potato (_Solanum tuberosum_ L) grown in the area of Majella National Park (Abruzzo, Italy) and five commercial varieties cultivated in the same area were characterized by means of morphological descriptors and microsatellite (SSR) DNA markers. Eventually, samples were also analyzed by infrared spectroscopy and partial least squares discriminant analysis (PLS-DA) [12] was used to classify them according to their origin. The developedmethodology achieved high accuracy, leading to 97% and 80% correct classification, for training and test samples, respectively.  
Similarly, the paper published by [PERSON] and co-authors [13] is focused on predicting the geographical origin of a typical agro-food, the \"walnuts of Sorrento\", grownin Southern Italy. In order to hit this goal, walnuts were analyzed by near infrared spectroscopy and then classified by different approaches. Data was collected either on the shell, or on the kernel of the fruits. The two sets of spectra were separately analyzed by PLS-DA, and by multi-block strategies. The most accurate results were obtained when data was handled by SO-PLS-LDA; in this case, the classification model led to a total error rate of \(\sim\)1% in external prediction.  
This research received no external funding.  
Does not apply.  
The author declares no conflict of interest.
## 4 Applications of Integrated Photonics
### All-Optical Modulators  
All-optical modulators based on PICs represent a pivotal advancement in optical communication and signal processing [93; 94]. These modulators leverage the inherent advantages of PICs to manipulate light signals directly without converting them to electrical signals, thereby reducing latency and power consumption [95]. Utilizing materials such as Si, InP, or LN, all-optical modulators can achieve high-speed modulation rates necessary for modern data transmission [96]. They operate by altering the properties of the optical signal--such as its phase, amplitude, or polarization--using mechanisms like the EO effect, TO effect, or carrier injection. The integration of these modulators within a photonic circuit allows for compact, scalable, and energy-efficient designs, which are crucial for the development of advanced optical networks, efficient computing, and emerging technologies like quantum communication. The seamless integration of all-optical modulators with other photonic components on a single chip further enhances their potential, paving the way for ultrafast, high-capacity, and resilient communication systems.  
The potential for graphene as a material in ultrafast optoelectronics has been demonstrated in applications such as all-optical modulators. The atomic thinness of graphene is a significant factor in its suitability for this purpose. However, its limited light absorption capacity results in graphene-based modulators often having low modulation efficiencies or requiring high switching energies. Plasmonic enhancement offers a potential solution to these challenges, although the insertion loss (IL) associated with plasmon-enhanced devices can be significant. [PERSON] et al. proposed a plasmon-enhanced graphene all-optical modulator designed for integration into the silicon-on-insulator (SOI) platform [97]. The schematic of the proposed device and the electric field distribution are shown in Figures 3g and 3h, respectively. The device's performance metrics included switching energy, ER, IL, and operational speed. Theoretically, it achieved ultrafast switching (<120 fs) with low energy consumption (<0.6 pJ) and operated at an ultra-high bandwidth exceeding 100 GHz. Simulation results demonstrated a high ER of 3.5 dB over a 12 \(\upmu\)m length, corresponding to a modulation efficiency of approximately 0.28 dB/\(\upmu\)m. Furthermore,the modulator exhibited a record-low IL of \(\sim\)6.2 dB among plasmon-enhanced graphene all-optical modulators reported to date [97].
# Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
## 3 Results
### Landslide Susceptibility
The study was conducted using high-resolution data for Northern Uttarakhand region. [PERSON] et al. [36] applied the weight of evidence model to determine the susceptibility using a semi-automatic derived landslide inventories. [PERSON] et al. [93] was the first to introduce landslide intensity as a parameter for hazard determination, which was applied for Garhwal Himalayas. The intensity was determined by analyzing the volume and velocity, which depends on the landslide area, debris thickness and the types of failures. However, lately, the rise has been in the use of computational techniques such as Machine Learning (ML), Support Vector machine (SVM), and Artificial Neural Network (ANN), which have  
Figure 9: Tree map of the factors used for determining landslide hazard and susceptibility. proved to outperform traditional approaches [13; 94; 95; 96]. However, the studies have largely focused on landslides in Uttarakhand Himalayas. [PERSON] et al. [97] used backpropagation neural network and showed prediction capability of 80%. [PERSON] et al. [98] used various ML ensemble models using Multilayer Perceptron (MLP) Neural Networks and six different ensemble techniques, thereby depicting better capability using ensemble framework. [PERSON] et al. [99] analyzed susceptibility for an area of 323 km\({}^{2}\) in Uttarakhand using various ML models (SMO-SVM, VFI, and LR), FT, MLP-neural networks, and NB. In the first case, SMO-SVM performed best, whereas, in the latter case, MLP neural networks and FT models provided similar accuracy compared to NB. [PERSON] et al. [13] used four hybrid machine learning models for Uttarakhand Himalayas. [PERSON] et al. [13; 95; 100] extensively analyzed the Pittorgarh region (242 km\({}^{2}\)) in Uttarakhand using various hybrid ML models (Table 2). [PERSON] et al. [107] compared susceptibility map for Darjeeling Himalayas using traditional, ANN, fuzzy, and combined neural fuzzy weighing schemes and found the combined neural fuzzy model to provide more accurate results. Following this, two different models (combined neural certainty and fuzzy) were compared with the neural fuzzy model for the same region and the latter was found to be better [108]. [PERSON] et al. [109] used Genetic Programming (GP) and Particle Swarm Optimization (PSO) SVM model for the Darjeeling Himalayan region and found GP method to perform better. [PERSON] et al. [110] conducted susceptibility analysis using a hybrid spatial multiciteria
## 4 Summary of the Characteristics of RL2-RSF and FMSF  
Figure 9 summarizes the characteristics of RL2-RSF and FMSF concerning outliers (spike and step). The horizontal axis is the magnification of the width of the outliers (spike and step) with reference to \(\lambda\)c, and the vertical axis is the magnification of the height of the outliers (spike and step) with reference to the maximum roughness component. When the height value of the outlier is less than twice the height value of the maximum roughness component, this outlier becomes impossible for the human eye to distinguish between outlier and roughness. Therefore, in Figure 9, the area corresponding to the height value less than twice is written as \"Cannot distinguish from roughness\".  
From Figure 9a, RL2-RSF removes spikes whose height values of the spike are more than twice the height values of the maximum roughness component. The width of the outlier depends on the primary profile. However, the widths of the outlier narrow to about 0.5\(\lambda\)c and are removed as spikes. When the width of the outlier is wider than 0.5\(\lambda\)c, RL2-RSF tries to extract the shape component as a step; however, this causes problems because the area near the step boundary cannot extract an accurate step.  
From Figure 9b, the robustness of the FMSF to the height of the spike is the same as that of RL2-RSF. The FMSF removes the effect as a spike when the width of the outlier is less than 0.2\(\lambda\)c and extracts the shape component as a step when the width of the outlier is wider than 0.5\(\lambda\)c. Therefore, the FMSF may cause problems if the width of the outlier is between 0.2\(\lambda\)c and less than 0.5\(\lambda\)c.  
Based on the above results, guidelines for the usage of RL2-RSF and FMSF are shown. When it is known that the step component is included in the measurement data or when a visual inspection is performed before the measurement, and it is assumed that dust or dirt wider than 0.2\(\lambda\)c is eliminated, using the FMSF is preferable; because the FMSF is capable of extracting steps and is compatible with the SF when no outlier exist. On the other hand, the situations in which it is desirable to select RL2-RSF are as follows. Note that full compatibility with SF in the case where no outliers are included in the primary profile is not required as a precondition. When it is known that the primary profile does not include the step component and there is a possibility that it may contain large outliers due to negligent dust removal. In this case, RL2-RSF, which removes even larger width outliers as spikes, is suitable.  
Figure 10 presents a flowchart demonstrating these results.  
Figure 9: Characteristics chart of RL2-RSF and FMSF for filter usage: (a) RL2-RSF; (b) FMSF.
# Ag-Cellulose Hybrid Filler for Boosting the Power Output of a Triboelectric Nanogenerator
## 2 Materials and Methods
### Material Characterizations  
The morphologies and crystal structure of the CF and NR-CF@Ag composite films were studied using a scanning electron microscope (SEM) (Helios Nanolab, FEI, Waltham, MA, USA) and X-ray diffraction (XRD) analysis (PANalytical EMPYREAN, Malvern, UK). Fourier-transform infrared spectroscopy (FTIR TENSOR27, Bangkok, Thailand) was employed to probe the chemical structure of the composite films. The dielectric properties of the fabricated NR composites were probed using a Keysight E4990 A impedance analyzer (Colorado Springs, CO, USA) at a frequency ranging from \(10^{2}-10^{6}\) Hz.
## 4 Wideband Energy-Management Circuit  
### Energy Management for EM Converter  
The energy-management circuit of the EM converter serves primarily to provide the necessary output voltage for WSN's operation. In particular, it produces the voltage needed to power the WSN at different vibration levels. Different energy-management architectures can be proposed with different main operational elements. One interesting architecture among all is the use of a rectifier stage. In this structure, a rectifier stage is used as Voltage Multiplier (VM) configuration connected directly to a super capacitor and a WSN [12]. However, in this case, the input signal can be lower or even higher than the optimal voltage level required for charging the super capacitor, which can cause the node to malfunction or even damage to the storage element. Consequently, a voltage regulator can be added to the previous architecture to protect the super capacitor from being over-charged [24]. However, the voltage regulator can dramatically reduce the system efficiency and requires a minimum input voltage to function properly, which is not always guaranteed. Alternatively, a DC-DC converter can be used instead of the voltage regulator to amplify or reduce the input voltage to a predefined super capacitor voltage level. This architecture allows reaching a wide working range, as well as the protection of the super capacitor in the case of high input voltage levels.  
In contrast, the energy-management circuit can be based on discrete components [20], MEMS circuits [25; 26], or commercial converters in a chip [27]. The circuit with discrete components was built using basic power electronic components, including diodes, switches, and comparators, which increase the circuit size and complexity. Integrated circuits, such as MEMS circuits, are based on transistors and require a high fabrication cost within a long period of investigation and fabrication for the design of a suitable solution. Due to their efficiency, ease of integration, small size, and low cost, commercial on-chip converters are becoming more popular for energy-harvesting systems. Besides, they can integrate sophisticated features, such as Maximum Power Point Tracking (MPPT), rectifier stages, and cold-start circuits [28].
# Application of a Local Dynamic Model of Large Eddy Simulation to a Marine Propeller Wake
## 3 Verification and Comparison
### Compare with PIV Data  
The comparison between LES-LDKSGS simulation results and PIV measurement values ([PERSON] & [PERSON], 2016, reproduce from [3]) of the phase average axial velocity profile at streamwise position \(x/D=0.06\), \(0.08\), \(0.1\) and radial location \(r/R=0.7\), \(0.8\), \(0.9\), is shown in Figure 7. The values are normalized with free stream velocity. It can be seen that at these locations in the near field, the two results are basically consistent, except that the flow downstream of the blade tip region is smoothed faster than the measured results of PIV.
## 6 Security Analysis and Results
### Additional Security Discussions
#### 6.5.2 Need for Multiple Verifiers  
In this paragraph, we discuss the importance of using multiple verifiers in **Disccard**, _i.e._, \(v>1\). We point out the obvious fact that our scheme's robustness against the adversary increases with \(v\), naturally with increasing communication cost. Recall that the verifier nodes are contacted by those nodes that are deployed outside their home zone, and this happens only once for each such out zone node. To understand the important role played by these verifiers, let us assume that the adversary has compromised \(p\) fraction of nodes in the home zone. When the verifier nodes are chosen at random, based on the seed broadcast by the base station, it is likely that some of these \(v\) verifiers might be under the control of the adversary. The probability of all of the \(v\) verifiers to be compromised by the adversary is clearly \(p^{v}\). With probability \(1-p^{v}\), our scheme will be able to detect and one of the uncompromised verifier node will respond with a \(Sig_{p}(duplicate)\) signature to the sender and take necessary revocation steps. However, if \(p\) is high, the adversary has compromised and has control over a large fraction of nodes in the home zone, then it becomes increasingly expensive, in terms of communication, to increase \(v\) to thwart such an adversary. One possible way to offset this problem is to potentially disseminate the duplicate node information of a territory across to nodes in the neighboring territories, as well. Then, the verifier nodes can be chosen from not just the home zone, but also from neighboring nodes. A second option is for the sensor network architect to increase the number of nodes in the home zone. A final complication that arises when the adversary has control over a large fraction of nodes in the home zone is this: the replicated nodes may choose to wait for a few time cycles accumulating false genuine signatures from these compromised nodes, thereby defeating **Disccard** (unless they happen to accidentally contact an uncompromised node in the meantime). We raise these questions to motivate further work in this area and delegate these tasks to future work.