esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Aroma Profiling of L. siceraria and C. sativus Fruits Ulsing SPME Coupled to GC-MS  
SPME is well suited for the profiling of aroma for low-strength aroma food matrices, and also at low temperature for collection, compared to steam distillation, providing the true composition of volatile blends, [11] and this is the first time it has been reported in both species.  
In this study, three independent replicates were analyzed under the same conditions via SPME-GC/MS and represented by total ion chromatograms (Figure 3A). 93 volatile constituents were detected in _L. siceraria_ and _C. sativus_ fruits and categorized into alcohols (11), aldehydes (16), ketones (14), fatty acids (3), oxides/ethers (7), S-containing compounds (1), monoterpenes (3), sesquiterpenes (5), esters (22), furans (1), and aliphatic (7) and aromatic (3) hydrocarbons (Table S1). Both species showed comparable aroma composition, as seen in the relative percentile of each class in Figure 3B.  
In _L. siceraria_, alcohols and ethers were the major volatile classes, detected at 23.34% and 23.52%, respectively, whereas aldehydes were the most abundant in _C. sativus_ (25.63%), followed by ethers (23.93 %) (Figure 3B, Table S1). Sesquiterpenes were detected in both fruits at low levels, i.e., 1.05% and 1.07% in bottle gourd and cucumber, respectively. Linalool is the major volatile constituent detected in both fruits at 18.76 % and 17.70 %, followed by safrole (11.4%, 10.5%), anethol (9.43%, 11.2%), and octanol (9.18%, 11.1%) in bottle gourd and cucumber, respectively.  
Interestingly, hexenal, nonenal, and nonadienal (cucumber aldehyde) aldehydes that were reported to account for cucumber's pleasant aroma [46] are herein detected in bottle gourd as well for the first time, at comparable levels except for 2,6-nonadienal (peak 19), which is more abundant in cucumber (Table S1).
###### Abstract  
In this paper, we present a real and simulated study of a frequency up mixing employing an electro-optical sampling semiconductor optical amplifier Mach-Zehnder interferometer (SOA-MZI) along with the differential modulation schema. The sampling signal is generated by an optical pulse clock (OPC) at a frequency of \(f_{s}=19.5\) GHz. The quadratic phase shift keying (QPSK) signal at an intermediate frequency (IF) \(f_{IF}\) is shifted to high frequencies \(nf_{s}\,\pm\,f_{IF}\) at the SOA-MZI output. Using a simulator entitled Virtual Photonics Inc. (VPI), we generate sampled QPSK signals and analyze their merits during conversion gains and error vector magnitudes (EVMs). We conducted simulations of mixing in the SOA-MZI operating in a high-frequency band up to \(195.5\) GHz. The positive conversion gain is accomplished over the mixing frequencies. The EVM is used to evaluate the performance of the electro-optical sampling up-convertor. The EVM reaches \(14\%\) at a data rate of \(5\) Gbit/s at \(195.5\) GHz. During the experimental work, the results obtained in simulations are set side by side with the factual ones in the frequency range up to \(59\) GHz. Thus, the comparison between them confirms that they have approximately the same performance.  
electro-optical sampling SOA-MZI; frequency up mixing; quadratic phase shift keying [PERSON]; [PERSON].
## 2 Materials and Methods
### Live and Dead Cell Standards  
Live and dead cell standards consist of live or dead _Campylobacter_ cells (_C. coli_ or _C. jejuni_) in a specific concentration, and they were used to spike the samples. These cell standards were essential for the in-house validation (accuracy, trueness, and measurement uncertainty). Three standards that were provided by BfR were: _C. jejuni_ live-cell standard (NCTC 11168, 4.6 \(\times\) 10\({}^{6}\) CFU/mL), _C. jejuni_ dead-cell standard (DSM 4688, 1 \(\times\) 10\({}^{9}\) bacterial counts/mL), and _C. coli_ live-cell standard (WDCM 00004, 7 \(\times\) 10\({}^{5}\) CFU/mL). The _C. coli_ dead cell standard (ATCC 43478, 1 \(\times\) 10\({}^{9}\) bacterial counts/mL) was prepared at LGL according to the BfR protocol [33]. Live- and dead-cell standards were stored at \(-\)80 \({}^{\circ}\)C, thawed at room temperature for 15 min, and kept on ice for 30 min prior to the artificial spiking.
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 5 Fiber Optics
### Interferometric FOS  
When the light is emitted by a light source, it is divided through a prism into two beams with the same frequency, polarization direction, and initial phase. One beam is a signal light, and the other one is a reference light. When the two beams meet, interference occurs. The interferometric sensors use the interference image to infer the effect of external factors, such as strain, on the signal light. An interferometric FOS can be divided into four main categories: Michelson FOS, Mach-Zehnder FOS, Sagnac FOS, and Fabry-Perot (FP) FOS. Figure 8 shows the Fabry-Perot FOS [37].  
The principle of operation of interferometric sensors is based on the monitoring of the phase-shift of the propagated light in fiber optic under an applied strain relative to an unstrained reference fiber [3]. This light phase-shift is measured with the aid of an interferometric setup. The advantage of this method is high precision and immunity to fluctuation. In addition, the single-mode fibers used in this method are smaller in diameters compared to their multi-mode counterparts and can be embedded in a material, such as composite laminates, without resulting in discontinuity of the material. On the contrary, the installation of these single-mode fibers should be done very carefully in the field environment. The single-mode fibers should be successfully cleaved and coupled together to ensure minimal coupling losses [3].  
This type of sensors has also been used in Civil Engineering applications. [PERSON] et al. [55] used a fiber FP interferometer sensor to investigate the effects of the concrete hydration process. Two experiments were conducted: a calibration experiment and a concrete hydration experiment. Based on the results of the first experiment, the sensor showed a linear response (a direct relationship). The results of the second experiment showed that the sensor is suitable for reliable and stable monitoring of the concrete hydration heat. [PERSON] et al. [56] developed a partially multiplexed FP interferometric fiber sensor for in situ strain measurement of composite structures. The proposed sensor is compact, robust, low-cost and offers long-term stability with reasonable accuracy. Finally, [PERSON] et al. [57] used FP interferometric and FBG sensors to monitor the curing process of carbon FRP  
Figure 8: Fabry-Perot FOS [37].  
composite laminates. The results showed that both embedded FP interferometric and FBG sensors could be used to monitor the curing progress of composite materials and detect the occurred damages during the fabrication process of composite structures.
# Visual Navigation Systems for Maritime Smart Ships: A Survey
## 2 Smart Ships and Navigation Technology
### Smart Ships
#### 2.1.1 IMO Guidance and Industry Requirements for Smart Ships
Four IMO committees--the Maritime Safety Committee (MSC), the Legal Committee (LEG), the Facilitation Committee (FAL) and the Marine Environment Protection Committee--are involved in this task, which affects many areas. At the same time, the introduction of technologically advanced Information and Communications Technology (ICT) is expected to lead to steady growth of the maritime industry [7]. The MSC has taken note of the test standards for real ship experiments on smart ship development submitted by relevant countries, provided advice on the development of standards, and contributed to the safety of smart ships and the prevention of pollution of the environment. The MSC held its 101 st session in 2019, and approved interim guidelines on MASS testing, which include guidelines related to the safety, security and protection of the marine environment and list the mandatory instruments of compliance. These guidelines also cover the staffing and qualifications of participants in MASS experiments, human factors and infrastructure to ensure the safe conduction of experiments, management of cyber risks, etc.
# Detection of Azo Dyes in Curry Powder Using a 1064-nm Dispersive Point-Scan Raman System
## 4 Conclusions  
This study used a newly developed 1064 nm dispersive point-scan Raman system for detection of azo color dyes contamination in curry powder. Sudan-I and metanil yellow color dyes mixed separately with curry powder at 1%, 3%, 5%, 7%, and 10% concentration (\(w/w\)) were detected. One Raman spectral image of the mixture sample at each concentration was obtained covering the sample surface area of 25 mm \(\times\) 25 mm, using a step size of 0.25 mm along X and Y directions. The Raman spectral images of curry powder-Sudan I and curry powder-metanil yellow mixture samples were converted into binary detection images to detect Sudan-I and metanil yellow pixels. The number of detected pixels of chemical contaminants linearly correlated with the actual sample concentration (R\({}^{2}\) = 0.99). The Raman system was further used for simultaneous detection of Sudan-I and metanil yellow mixed together with curry powder (each contaminant at 5% concentration, \(w/w\)). Self-modeling mixture analysis (SMA) was used to decompose the mixed spectral information of the sample mixture, and extract pure component spectra of Sudan-I and metanil yellow. The spectral features of extracted  
Figure 11: Color-coded chemical image of Sudan-I and metanil yellow generated by contribution images. White pixels represent Sudan-I and red pixels represent metanil yellow.  
pure component spectra matched with that of the reference spectra of Sudan-I and metanil yellow. Spectral information divergence (SID) was used to identify the corresponding component for each retrieved pure component spectra. The binary detection images obtained from the contribution images could identify Sudan-I and metanil yellow pixels in the mixture sample. The results show that the method can be used to detect multiple chemical contaminants in a complex food matrix.  
Mention of specific products is for identification only and does not imply endorsement by the U.S. Department of Agriculture to the exclusion of other suitable products or suppliers.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data, with other co-authors [PERSON], [PERSON], [PERSON], and [PERSON] also participating in the discussion of the results; [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 1 Introduction
The DPDop model incorporates a range of input variables, including the wind fields (wind speed and wind direction), the wave parameters (such as significant wave height (SWH), wave direction, and wave number) or a full two-dimensional (2D) wave spectrum, and radar configuration settings (e.g., incidence angle, look direction, and polarization). It is capable of estimating the Doppler effects of wind-generated waves and swells, either separately or in combination. The validation of the DPDop model was conducted using HF radar measurements collected in the peripheral region of Hurricane Maria, under wind speeds below 28.7 m/s. The results showed a bias of 0.02 m/s and a root mean square error (RMSE) of 0.19 m/s [29]. However, we acknowledge that validation based on a single case study is insufficient to fully assess the model's robustness and applicability across diverse and complex sea state conditions. The objective of this study is to conduct a comprehensive evaluation of radial ocean surface current retrievals over the South China Sea (SCS) using the Doppler shift data derived from Sentinel-1 SAR, in combination with drifting buoy observations. Both case-based analyses and statistical assessments are employed to validate the accuracy of the retrieved radial current velocities. In addition, the Surface Merged Ocean Currents (SMOC) dataset is used as an independent reference to further evaluate the retrieval performance. The remainder of the paper is organized as follows: Section 2 describes the data and methodologies; Section 3 presents the experimental results; Section 4 provides an in-depth discussion; and Section 5 concludes the study.
## 1 Introduction
In the Yiluo River Basin, there are relatively few studies using remote sensing models to invert the actual evapotranspiration in the basin. [PERSON] and [PERSON] [13], [PERSON], [PERSON] [29] and [PERSON] and [PERSON] [30] applied the SEBS model to estimate the actual evapotranspiration in the Yiluo River Basin and explored the spatial distribution characteristics of evapotranspiration. However, the SEBS model requires too many parameters that are difficult to obtain directly, and it has high data requirements; moreover, these studies only simulated the actual evapotranspiration for a day or typical days, failing to explore the temporal variation characteristics of evapotranspiration over continuous time periods (e.g., multiple years). Therefore, in this paper, the actual evapotranspiration of the Yiluo River Basin from 2001 to 2020 was estimated based on the PT-JPL model, and the applicability of the PT-JPL model in the Yiluo River Basin was verified and analyzed by using meteorological, hydrological and remote sensing data. On this basis, the spatial and temporal characteristics of actual evapotranspiration in the Yiluo River Basin were analyzed, and the relationships between actual evapotranspiration and influencing factors such as precipitation, temperature, and vegetation were explored, with a view to providing scientific references for the rational development and utilization of water resources in the basin.
## 2 Background
### Overview of Advanced Persistent Threats (APTs)
#### 2.1.2 APT Attack Lifecycle and Its Impact  
As seen in Figure 3, the APT attack lifecycle includes the following eight stages:  
Stage 1: Conducting a survey is the initial step towards launching a targeted attack. The research and information regarding the targeted organization are acquired to break the organization's border security.  
Stage 2: Comprises employing various entry vectors to obtain the first footing within a network.  
Stage 3: On the server or endpoint, malicious code is run, granting complete control of the computer or system. This stage will develop a strong attempt to retain persistence beyond the first agreement.  
Stage 4: After gaining complete control of the compromised node, attackers will try to acquire additional access to the system and data by targeting privileged accounts.  
Stage 5: The attacker is attempting to gain recognition once again through the internal network. Various methods, such as searching for files and directories, are utilized to locate valuable targets.  
Stage 6: These techniques can be used by an intruder to gain unauthorized access and make modifications to internal network systems.  
Stage 7: The APT utilizes malware backdoors or a remote administration tool to maintain a foothold in the network's environment.  
Stage 8: Before data exfiltration, the attacker must retrieve private data from remote devices [11].  
As stated earlier, APT refers to an intrusion activity in which an attacker establishes an illegal, long-term presence on a network to capture sensitive data [8]. Cyber attacks using APTs are frequently targeted towards large organizations or government networks. Such intrusions have a plethora of consequences, including intellectual property theft, sensitive data breach, critical company infrastructure disruption, and entire site takeovers. Additionally, government-sponsored APT assaults are frequently utilized as cyber warfare weapons.
# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 3 WSN Architecture
### Routing Algorithms and Applications
#### 3.3.2 Routing in Non-Dense Forests  
In non-dense mountainous and flat forests, where vegetation and trees are sparsely distributed, the number of WSN tiers and nodes is optimized accordingly. The spacing between consecutive circles can exceed the maximum sensing radius, concentrating nodes in areas with vegetation. Axis nodes are always present, with consecutive ones separated by the effective sensing radius. In this setup, inter-tier routing is disabled--nodes cannot transmit across different tiers. The hybrid adaptive-semi-adaptive routing algorithm is ideal here. Nodes within each tier are divided into quadrants, and data transmission is limited to neighboring nodes within the same tier and quadrant, either clockwise or counterclockwise. Data is relayed within the circle until it reaches the appropriate axis node, which then forwards it along the axis path to the sink node.  
Figure 5 illustrates the application of the hybrid adaptive-semi-adaptive routing in forest fire scenarios. Inter-tier and inter-quadrant communication is restricted, allowing only clockwise and counterclockwise data flow within the same quadrant and tier. Fires are detected at nodes 0, 4, 6, 8, 9, 14, 15, 18, and 20. Blue arrows represent data originating from the node itself, while red arrows indicate forwarded data. Nodes 1 to 4 on the star-shaped circle send data directly to the sink. Node 15 routes its data to the nearest axis node 16, which forwards it along the axis to the sink. If node 7 receives simultaneous data from node 6, two-level dynamic arbitration is applied. Similarly, node 9, which has a fire and receives data from nodes 8 and 19, triggers two-level arbitration if the data arrives at once. In case of a fire at the sink (node 0) while receiving data from its four surrounding nodes, five-level dynamic arbitration ensures that GSM-based transmission to the control center proceeds smoothly.  
Figure 5: Example of hybrid adaptive–semi-adaptive routing for fire traffic.
# A New Low-Cost Compact Antenna for the 2.45 and 5.8 GHz ISM Bands
## 3 Parameters Study and Simulation Results
### Impact of Created Slots in Steps 3 and 4
This current distribution explains why the slots on the patch were designed to improve the antenna's performance. Another antenna performance parameter is its radiation efficiency. Simulated 2D electrical radiation patterns (in dB) for the two frequency bands in the E-plane (yz) and H-plane (xz) are shown in Figure 11. In both frequency bands, the co-polarized E-field is oriented along the y-axis (following the length of the feed line) and is maximal, reaching 20 dB at 2.45 GHz (Figure 11a) and 25 dB at 5.8 GHz (Figure 11c); also, it is (quasi)-omnidirectional. The cross-polarized E-field is oriented along the (xz) plane and is very weak, limited to \(-\)5 dB at 2.45 GHz (Figure 11b) and 0 dB at 5.8 GHz (Figure 11d). Figure 11: The 2D simulated electrical radiation patterns of the final antenna: at 2.45 GHz, (**a**) E-Field in co-polarization (yz-plane), (**b**) E-field in cross-polarization (xz-plane), and, at 5.8 GHz, (**c**) E-Field in co-polarization (yz-plane) and (**d**) E-field in cross-polarization(xz-plane). Figure 10: Surface current distribution on the antenna patch at 2.45 GHz for (**a**) step 1, (**b**) step 2, (**c**) step 3, and (**d**) the optimized design step. Figure 12 shows the antenna's radiation efficiency as a function of frequency. The dual-band antenna has a radiation efficiency of 90.3% and 96.2% at 2.45 GHz and 5.8 GHz, respectively. In addition, the antenna reaches its maximum radiation efficiency around 2.45 GHz.
## 2 Related Model and Methods
### Orbital Repetition Period of GPS Satellite  
In order to demonstrate the fluctuation in CNR for the same satellite, the orbital repetition period of GPS satellite should be determined first. By using [PERSON]'s third law,  
Figure 1: Geometric relationship between GPS satellite, receiver antenna, and surface of object for the multipath effect scenario.  
the orbital repetition period of GPS satellites can be estimated based on the broadcast ephemeris [25]. Firstly, the mean motion of a satellite can be calculated by:  
\[n=\left(GM/a^{3}\right)^{1/2}+\Delta n \tag{3}\]  
where \(n\) denotes the mean motion of a satellite; \(GM\) is the Earth's universal gravitational constant, which is 3.986005 \(\times\) 10\({}^{14}\) m\({}^{3}\)s\({}^{-2}\); \(a\) means the semi-major axis of the orbit ellipse; \(\Delta n\) is the correction to the mean motion; and both \(a\) and \(\Delta n\) can be directly obtained from the broadcast ephemeris. Secondly, considering that the GPS satellite runs two cycles around the earth every solar day, the orbital repetition period of GPS satellite is estimated by:  
\[T=2\times 2\pi/n \tag{4}\]  
where \(n\) is the mean motion which is obtained by Equation (3).  
Based on Equations (3) and (4), the orbital repetition period of the GPS satellite is obtained, which is approximately a sidereal day. Furthermore, the accurate shift time between two adjacent days can be calculated by 86,400-\(T\)[26]. Thus, to analyze the fluctuation in CNR during the flood, the shift time of the satellite should be considered.
## 4 Verification and Validation
### Validation of the Applicability of the Proposed Model Using Real Project Data  
#### 4.3.1 Interviews with Construction Experts  
A model in research can undergo validation through the utilization of an expert panel. This process entails engaging a group of experts to scrutinize the model's elements and assess its efficacy [47]. The usefulness, verifiability, and structure of the model are assessed by the experts to determine if the model meets its objectives [48]. The validation phase enhances practitioners' comprehension and utilization of the model within the field [48]. The assessment conducted by the experts can pinpoint any required adjustments to enhance the model's performance. In line with this, interviews were conducted with two experts working for a large-scale construction company to receive feedback about the usefulness of this study and show the applicability of the proposed model when real project data are used. Their company has been delivering significant landmark projects in the Middle East region, mainly in the United Arab Emirates (UAE), since it was founded in 1999. The interviewees included an IT expert and a construction innovation expert with more than ten years of experience. The interview had two sessions: (1) the authors presented the proposed game-theoretic model and its implementation for the hypothetical scenario provided in the previous subsections and (2) the experts were asked to give feedback on different elements of the presented model and provide information about two types of files in two of their projects. The experts' responses were collected through a structured, roundtable discussion during the second interview session. The discussion was guided by the prepared questions regarding the experts' opinions on the model and the characteristics of the file types and projects they preferred to share. This information was then used to show the proposed model's implementation using real project data, discussed in the following subsections. The implementation still had a hypothetical component, which was related to the malicious insider, since the interviewees could not provide information regarding this aspect. Therefore, the malicious insider scenario, including the insider's characteristics (see Table 5), job title, and suspicious activities, was kept the same. On the other hand, the characteristics of the files requested by the malicious insider and the construction projects were changed based on the experts' input. The experts shared the required level of information regarding their projects without disclosing the project names for confidentiality purposes.