esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Determination of time-weighted average (TWA) concentrations of volatile organic compounds (VOCs) in air using solid-phase microextraction (SPME) is advantageous over other sampling techniques, but is often characterized by insufficient accuracies, particularly at longer sampling times. Experimental investigation of this issue and disclosing the origin of the problem is problematic and often not practically feasible due to high uncertainties. This research is aimed at developing the model of the TWA extraction process and optimization of TWA air sampling by SPME using finite element analysis software (COMSOL Multiphysics, Burlington, MA, USA). It was established that sampling by porous SPME coatings with high affinity to analytes is affected by slow diffusion of analytes inside the coating, an increase of their concentrations in the air near the fiber tip due to equilibration, and eventual lower sampling rate. The increase of a fiber retraction depth (\(Z\)) resulted in better recoveries. Sampling of studied VOCs using 23 ga Carboxen/polydimethylsiloxane (Car/PDMS) assembly at maximum possible \(Z\) (40 mm) was proven to provide more accurate results. Alternative sampling configuration based on 78.5 \(\times\) 0.75 mm internal diameter SPME liner was proven to provide similar accuracy at improved detection limits. Its modification with the decreased internal diameter from the sampling side should provide even better recoveries. The results obtained can be used to develop a more accurate analytical method for determination of TWA concentrations of VOCs in air using SPME. The developed model can be used to simulate sampling of other environments (process gases, water) by retracted SPME fibers.  
Keywords:solid-phase microextraction; air sampling; air analysis; volatile organic compounds; COMSOL; time-weighted average +
Footnote †: journal: molecules
# Snapshot-Based Multispectral Imaging for Heat Stress Detection in Southern-Type Garlic
## 5 Conclusions
For example, they can implement site-specific irrigation or shading techniques to alleviate heat stress in specific areas, leading to more efficient resource allocation and improved crop yield. In summary, the use of snapshot-based multispectral imaging in the context of heat stress detection and garlic crop management offers several practical benefits for farmers. It enables non-destructive and rapid assessment, facilitates spatial mapping of stress indicators, supports precision agriculture practices, and empowers data-driven decision making. By leveraging this technology, farmers can detect heat stress early, implement targeted interventions, optimize resource allocation, and ultimately achieve improved crop health, yield, and economic outcomes. Despite the significant contributions made by this study, certain limitations and areas for future research have been identified. One limitation is the reliance on a specific range of wavelengths for analysis. Further investigations into the optimal wavelength range and the inclusion of additional spectral bands could provide more comprehensive insights into heat stress detection in garlic crops. Furthermore, the study focused on a specific geographical region and a limited number of garlic cultivars. Extending the research to different regions and incorporating a wider range of cultivars would enhance the generalizability and applicability of the findings. Additionally, the study primarily focused on heat stress detection in garlic crops. Exploring the application of snapshot-based multispectral imaging in the detection and management of other stresses, such as water stress or nutrient deficiencies, would expand the scope of research in this field. In conclusion, this study significantly contributes to the understanding of heat stress detection in garlic crops through the utilization of snapshot-based multispectral imaging and advanced modeling techniques. The findings highlight the superiority of the LS-SVM model and the potential of this imaging technology for precision agriculture applications. The identified limitations and areas for future research pave the way for further advance ments in the field, facilitating the development of more robust and comprehensive tools for stress detection and crop management in garlic cultivation. [PERSON], [PERSON] and [PERSON] contributed to the conception and design of the study. [PERSON], [PERSON] and [PERSON] performed data collection and preprocessing. [PERSON] and [PERSON] developed the machine learning and deep learning models. [PERSON], [PERSON] and [PERSON] conducted the analysis and interpretation of the results and critically reviewed and revised the manuscript. All authors have read and agreed to the published version of the manuscript. This work was carried out with the support of the \"Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ01501904)\" Rural Development Administration, Republic of Korea. Not applicable. The authors declare no conflict of interest.
## 2 Literature Review
### Feature Selection  
[PERSON] et al. introduced boosted correlation-based feature selection (ECFS), a method that uses feature-feature and feature-class correlations to effectively excerpt relevant feature subsets from multiclass gene manifestation data and machine learning datasets. ECFS has demonstrated high satisfactory classification accuracies across several benchmark datasets, outperforming KNN, Random Forest, and decision tree classifiers [19]. For accurate data models and effective data reduction, feature selection (FS) techniques are applied in data preprocessing. They are habitually engaged in tasks concerning regression, clustering, and classification. Standard filter, wrapper, embedded, hybrid, and advanced topics are covered in this study. To classify gene expression data, a study by [PERSON] et al. uses ERGS (Effective Range-based Gene Selection), an effective feature selection method. Features that discriminate between classes are given more weight in this method, which uses statistically defined effective ranges of features for each class. Results from experiments demonstrate the efficacy of this method, which can rank genes and pinpoint pertinent genes that cause conditions such as prostate cancer, lung cancer, DLBCL, leukemia, and colon tumors [20].  
A study by [PERSON] et al. compares the effectiveness of 6 common filter-based processes with 11 novel threshold-based feature selection strategies for bioinformaticsapplications. The methods display different behaviors when compared with conventional filter-based techniques. The new methods perform better than conventional filters, which makes them valuable for bioinformatics data analysis, based on experiments conducted on 17 datasets using the Naive Bayes and Support Vector Machine algorithms [21]. [PERSON] et al. propose a synthetic aperture radar target recognition method based on convolutional neural networks (CNNs). The method uses [PERSON]'s rank correlation to gauge the resemblance between the unique SAR image and the feature map. High-similarity feature maps are represented and chosen using joint sparse representation (JSR). Inner correlations between feature maps, the test sample, and the training class are also taken into account. For ten ground target classes, experiments demonstrate an average recognition rate of 99.32% under typical operating conditions and exceptional robustness under extended operating conditions [22]. A study by [PERSON] et al. focuses on feature selection in data mining and optimization for classification. Although efficient, the RFE-SVM algorithm is greedy. An alternate strategy uses local search operators in conjunction with the RFE-SVM algorithm to get around this. Promising results from experiments using datasets from the UCI Machine Learning Repository demonstrate the role of local search in classification. The quality of the final classifier is increased by reusing features that were eliminated during the RFE-SVM process [23].
## 6 Verification of the Proposed Protocols
### Formal Analysis of DDSec Protocol
#### 6.3.1 The Attack on DDSec Protocol  
The attack on a protocol can be explained using the attack derivation (abstracts) or attack trace (semantics). In ProVerif, the derivation explains the actions made by an attacker to break the security properties of a protocol, using abbreviations of internal representation of names or terms. It is a numbered list of steps each corresponding to a process or the attacker's action. While the attack trace represents the real attack as an executable trace of the considered process. A trace is a sequence of input and outputs on the public channel and of events in relation with the process. The input, output or event are followed by their location in the process at \(\{n\}\), referring to the program point at the beginning of the process. As mentioned above, when ProVerif is given query attacker (M) where M is the message transmitted on the channel c, it intends to prove that a state in which a property is unreachable by showing not attacker (M), the RESULT not attacker (M) is true, whereby the attacker does not have some term (M) i.e., message M. query x1 : t1,.., xn : tn ; event (e(M1,..,Mj)) ==> event (e'(N1,..,Nk)) tests the relationship between events. The correspondence asserts that, for each occurrence of the event, there is a distinct earlier occurrence of another event and if it does not then it is false.  
Three attacks were found: the attacker \(I\) starts by eavesdropping on the communication between entities, impersonates \(U\!E_{A}\) continuing the protocol with \(U\!E_{B}\), which completes the protocol with the attacker instead of \(U\!E_{A}\) as below.  
UEA -> gNB: {AdvMsg}, {KgNBa} UEB -> gNB: {IntMsg}, {KgNBb} gNB -> UEA: {DiscMsg}, {KgNBa} gNB -> UEA: {LinkUpMsg}, {KgNBb} UEA -> I_UEB: {PublMsg}, {PKI} I_UEA -> UEB: {PublMsg, {PKUEB} UEB -> I_UEA: {LookUp}, {PKI} I_UEB -> UEA: {LookUp}, {PKUEA} UEA -> I_UEB: {SenData}, {PKI} I_UEA -> UEB: {SenData}, {PKUEB}  
Figure 6: DDSec protocol attack results.
## 3 Results and Discussion
### Texture Analysis
#### 3.3.4 Practical Assessment  
Since the transferability of these findings to actual mouthfeel and chewing experience is limited, these TPA values were put in perspective by comparison with TPA values typically found for traditional dry fermented sausages based on a broad case study with similar analysis settings [61]. To this end, all values were adjusted to standardized values (N/cm\({}^{2}\)) to ensure comparability. Hardness values of traditional samples ranged from 31.26 N/cm\({}^{2}\) to 87.89 N/cm\({}^{2}\), with an average value of 41.87 N/cm\({}^{2}\), compared to our plant-based matrices with values of 6.85 N/cm\({}^{2}\)-79.34 N/cm\({}^{2}\). The springiness results of traditional samples were in the range of 4 mm-6.7 mm, averaging 4.7 mm, whereas the plant-based matrices produced here ranged from 3.7 to 5.4 mm. The obtained cohesiveness values ranged from 0.32 to 0.45 for meat salamis, averaging 0.40, whereas the plant-based matrices ranged from 0.29 to 0.40. Finally, the tensile strength ranged from 0.7 N/cm\({}^{2}\) to 20.6 N/cm\({}^{2}\), averaging 7.9 N/cm\({}^{2}\), among all tested meat products. The plant-based matrices, however, ranged between 0.2 and 1.3 N/cm\({}^{2}\).  
Thus, the tensile strength values of the plant-based matrices only reached the lower edge of the range for comparable meat products. Yet all four tested characteristics were in comparable regions, especially samples with higher dry matter, gluten and pectin contents. It should be noted, though, that texture profile analysis only allows for a limited comparison in terms of actual texture perception. These results only give a broad estimate of the model matrices' qualitative suitability to act as raw fermented sausage analogs. Further evaluations, such as sensorial analysis and oral processing, are required to understand which food structure causes what kind of change in the chewing process [76].  
The influence of the embedded particles (TVP + fat gel) on the textural properties of the whole system should also not be neglected. They both may act as active fillers, and their individual properties, as well as their interplay with the continuous matrix, are essential for the textural characteristics of the composite product [77; 78]. Additionally, textural properties of different hydrocolloids vary widely, as shown in numerous studies [79; 80]. When applied combined, e.g., alginate/pectin mixed gels, further synergistic effects have been reported [81]. The use of different starter cultures can also have a distinct influence on the textural attributes of fermented products [82; 83]. Therefore, with the large variety of LM pectins, as well as other potential hydrocolloids, TVPs, fat replacers and starter cultures, in commercial markets, a large toolbox of potential levers for texture and flavor adjustments is becoming available.
## 3 Results and Discussion
### XRD Results  
The XRD patterns (Figure 4) almost confirmed the indications obtained from both the DT analyses and bound water results; therefore, concerning the hydration products, ye'elimite and calcium sulfate reacted quickly and the 3 CaO\(\cdot\)Al\({}_{2}\)O\({}_{3}\cdot\)3 CaSO\({}_{4}\cdot\)32H\({}_{2}\)O concentration grew for up to 28 days of aging. The aluminium hydroxide main peak was also found in all the hydrated systems.  
Furthermore, belite was virtually not involved in the hydration process, owing to both the higher reaction kinetics of the calcium sulfoaluminate and the related rapid water consumption. Quartz signals, already present in the TTRSBT samples, were detected in  
Figure 4: XRD patterns of the C_R (**A**), C_15 (**B**), C_25 (**C**), and C_35 (**D**) hydrated for 4 (down) hours and for 2 (middle) and 28 (up) days. A, anhydrite; E, ettrinigite; G, aluminium hydroxide; Q, quartz; Y, ye’elimite.  
Figure 3: Chemically bound water calculated by TG until 56 days of aging (g/100 g of anhydrous cement) for the C_R, C_15, C_25, and C_35 cement pastes vs. aging time.  
all the investigated CSA-blended cements. Figure 5 shows the evolution of the ettrinigite vs. the curing time. Elevated quantities of ettrinigite had already formed after 4 h of hydration in all the systems; after 2 days, the 3 CaO\(\cdot\)Al\({}_{2}\)O\({}_{3}\cdot\)3 CaSO\({}_{4}\cdot\)32H\({}_{2}\)O concentration almost reached its maximum value, and after that, it slightly rose until 28 days of curing.
## 3 Analysis of Test Data
### Analysis of Surface Deformation Results
The second group of the test excavation program involves constructing the left and right side sections first and subsequently excavating the middle section after the upper and lower support structures form a closed loop. Settlement values at monitoring points 1-6 are, respectively, 4.62%, 18.64%, -1.70%, 13.49%, 6.96%, and 58.70% smaller than those of the first group of tests, indicating that the timely formation of the closed loop of the support structure has a certain control effect on surface settlement.
# Overview of an Experimental Program for Development of Yield Surfaces Tracing Method
## 2 Materials and Methods
### Apparatus  
An axial-torsional Instron 8852 universal hydraulic testing machine (UTM), equipped with 100 kN axial and 1 kNm torsional fatigue rated load cells and an Epsilon Tech 3550 biaxial extensometer comprise the core experimental apparatus. The UTM can exert loading sequences at arbitrary combination of the axial load and torque to a TS clamped in hydraulic grips. The UTM is driven by a powerful external hydraulic power unit that can induce maximum plastic strain in the TSs. The Dynacell load transducer has 0.5 class precision load measurements rating. The experiments were controlled, and test input parameters specified using a proprietary software, WaveMatrix 2, installed on a desktop computer interfaced with the UTM controller. Advanced algorithms for the YST test control, the evaluation of elastic moduli, elastic and plastic components of both axial and shear strains, as well as the evaluation of equivalent plastic strain, are written in C# language.  
The Epsilon Tech 3550 axial/torsional extensometer was used for the contact measurement of both axial and shear strain in the test specimen reduced section. The EXTM gage length of \(L_{\text{g}}\) = 25 mm covers \(>\)80% of the reduced TS length (\(L_{\text{c}}\)) of 30 mm. Measurement range is 5% for axial strain and 3 deg for torsional shear strain angle corresponding to a 5.2% shear strain. EXTM compatibility with the testing machine enables feedback control of the test, i.e., the possibility of performance of strain-controlled loading on the basis of the EXTM output.
## 3 System Model
### Packet Arrival at the UAV  
The bandwidth is denoted by \(\mathcal{B}_{j}\), and \(P_{i}^{t}\) indicates the power consumed by the UAV while communicating with the \(i\)th vehicle. The term \(\sigma^{2}\) is the noise power density at the receiver of the vehicle. The channel gains of the UAV at time slot \(t\) are denoted as \(h_{i,j}[t]\). The communication links can be LoS or NLoS with probabilistic path loss depending on the UAV position, obstacle height, number of obstacles, and the UAV height (\(\mathcal{H}\)). The instantaneous transmission rate for the \(i\)th vehicle in a time slot is given by Equation (2) as  
\[s_{j}^{i}(b_{j}^{i},x_{i},y_{i})=b_{j}^{i}\log_{2}(1+\Gamma_{j,i}) \tag{2}\]  
where \(b_{j}^{i}\) is the instantaneous bandwidth utilized by the \(i\)th vehicle in the \(j\)th transmission window, and \(\Gamma\) is the SNR. The packets comprising processed local models arrive at the \(M/k\) queue where the inter-arrival time (\(\lambda_{t}\)) between successive packets is exponentially distributed, and in each TTI, an arrival is independent of the previous arrivals. The local models (\(\Psi_{j}\)) considered for federated averaging, similar to the approach proposed in [26],  
Figure 2: System Model: Delay is accumulated as vehicles in different clusters generate and transmit local models to the UAV. The UAV transmits the global model to the vehicles. Note, each vehicle captures a different kind of data packet, leading to non-i.i.d. and heterogeneous data.  
are the summation of local models from all vehicles (\(\forall i\in\mathcal{V}\)) transmitted in the previous TTIs as given by Equation (3):  
\[\Psi_{j}(b_{j}^{i},x_{i},y_{i})=\lambda_{t}\frac{\mathcal{T}}{t=1}\psi_{j}^{i}, \quad\forall i\in\mathcal{V} \tag{3}\]  
The local models arrive at the queue, which accumulates at a rate specified by Equation (4). For uplink transmission, the data transmitted from a vehicle at time \(t\) are restricted by the available bandwidth (\(B_{i}^{l}\)) and uplink transmission window (\(\tau_{u}\)):  
\[\psi_{j}^{i}=\begin{cases}s_{j}^{i}(b_{j^{\prime}}^{i},x_{i},y_{i}),&\text{if }\tau_{i}\leq i\leq\lambda_{i}\\ \min(B_{i}^{l},s_{i}^{l}\times\tau_{u}),&\text{otherwise}\end{cases} \tag{4}\]
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 7 Fault Location Finding Methods
### FIS Based Algorithm for FL  
Self-learning and fault-tolerant abilities of FIS algorithms let them refine pre-set fuzzy rules, which then employed for fault location finding [140; 141]. Mother wavelet, Db4, with ANFIS is used to locate faults. Efficiency is validated by Monte Carlo simulation and error found to be 5% [140]. Norm entropy of harmonic coefficients (62.5-500 Hz), main frequency coefficients (0-62.5 Hz) and transient coefficients (500-4000 Hz) achieved by the 6-level DWT using Db4 mother wavelet. And treated as an input for ten ANFIS regression algorithms trained by BP gradient descent technique along with the LLS method [141].
## 3 Results and Discussion
### The Effect of Recycling Water  
The low-temperature water is directly discharged at the outlet of the air refrigeration cycle, and the cooling capacity is also wasted. If the cooling capacity of the water that separated from the separator can be recycled, the COP of the system will improve. For an open-loop low-pressure cycle, the water separated from the supplied side can be mixed with the air at the expander inlet without bringing additional wet load to the supplied side, which can reduce the temperature of the expander inlet. The water can be atomized and injected as much as possible into the inlet of the expander to cool the humid air of the expander inlet to a saturated state. In order to avoid problems such as the corrosion of parts caused by liquid at the turbo-expander inlet, the mass flow of the recirculating water should not be so large that the moist air of the expander inlet is supersaturated. The power consumption of the atomized water is ignored in the calculations. When the mass flow of the condensed water is greater than what the expander inlet requires, the remaining water can be atomized and injected into the compressor inlet, which makes a wet compression in the compressor. During the compression, the water evaporates and absorbs the compression heat, reducing the power consumption. The reduced theoretical compression work is equal to the latent heat of the water, and the effect of the increased mass flow is also calculated.  
Figure 9 shows the variations in COP with the expansion ratio in open-loop low-pressure cycles with recycling water. Without wet compression, the optimal expansion ratio occurs when the mass flow of the recycling water just makes the wet air of the expander inlet reach a saturated state. When the expansion ratio is larger than the optimal expansion ratio, the temperature of the expander inlet cannot continue to decrease, reducing the effect of the recycling water on the cooling capacity of the system. The maximum COP of the TC, TC-C and C-TC cycles are 1.310, 1.297 and 1.268, respectively. Compared to the COP before recycling the water, the maximum COP of the TC, TC-C and C-TC cycles have increased by 34.5%, 30.1% and 29.5%, respectively. The optimal expansion ratio of the TC, TC-C and C-TC cycles is 1.78. At the same time, the supplied-air temperature of the system is higher than 283.15 K. When the remaining water is used for wet compression, the optimal expansion ratios of the TC, TC-C and C-TC cycles are 2.12, 2.32 and 2.35, respectively, and the corresponding maximum COP are 1.410, 1.483 and 1.453, respectively. Compared to  
Figure 8: (**a**) Comparison of COP of open-loop high pressure cycle under different indoor wet bulb temperature conditions; (**b**) p-H diagrams for each cycle at the initial optimal COP.  
the COPs before recycling the water, the maximum COPs of the TC, TC-C and C-TC cycles have increased by 44.7%, 48.8% and 48.4%, respectively.
# Measurement and Prediction of Wind Fields at an Offshore Site by Scanning Doppler LiDAR and WRF
In the current study, a variation of this approach is employed where the velocity field is computed from the combination of LiDAR-measured LOS velocities and wind directions measured by a wind vane mounted on a meteorological tower (or a turbine nacelle). It should be noted that multiple studies have employed dual and triple LiDARs to collect more accurate wind speed data [15; 16]. However, deploying two or three LiDARs can significantly increase the cost. The objective of this study was to evaluate the performance of scanning Doppler LiDAR technology in the measurement of wind profiles and to propose a method for the reconstruction of wind fields from LiDAR-measured LOS velocity data. The LiDAR measurement data was further used to observe the near-shore marine atmospheric boundary layer (ABL) by comparing the wind blowing from the land and that from the sea and by considering the effect of coastal terrain. Furthermore, LiDAR measurement data was used to validate numerical simulations conducted using a mesoscale model. Finally, the flow field around the wind turbine was measured and analyzed to investigate the wake characteristics. The measurement data was also used to validate several wake models.