esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Phase Characterization  
As eutectic medium entropy alloy Ta\({}_{0.4}\) with fine lamellar structure was successfully designed. The calculation system (Figure 1) reveals that the phases in the alloy are \(\gamma\) and Laves, respectively.  
We used XRD to further confirm the crystal structure. The XRD patterns of as-cast CrCoNiTa\({}_{\text{x}}\) alloys are presented in Figure 3a. With the increasing Ta addition, more diffraction peaks of Laves phase are observed in the Ta\({}_{0.3}\), Ta\({}_{0.4}\), Ta\({}_{0.5}\), Ta\({}_{0.7}\) alloys. The crystal structure of Laves phase is identified as C14 HCP with the lattice constant of a = b = 0.4797 nm and c = 0.7827 nm [23; 24].  
Figure 3b shows the EBSD phase mapping of typical \(\gamma\) (FCC)/Laves (HCP) lamellar microstructures. Fine Laves lamellae (green color) were parallel to each other and distributed in the \(\gamma\) phase. In addition, some coarse Laves lamellae also appeared. The volume fraction of \(\gamma\) and Laves phases was about 66.39% and 33.61% respectively. Moreover, the two phases in eutectic have a special orientation relationship, which is related to the minimum phase interfacial energy. The corresponding EBSD inverse pole figures of Laves and \(\gamma\) phases are shown in Figure 3c,d respectively. The two phases achieve the minimum interfacial energy by adjusting the lamellar spacing, preferential growth orientation. In the eutectic alloy, the two phases have a certain preferred orientation, namely (hkl)\(\alpha\)// (hkl)\(\beta\), indicating the two-phase growth orientation is preferred in a certain crystallographic direction. This orientation is caused by the decrease in the total interfacial energy. Therefore, the two-phase orientation in CrCoNiTa\({}_{0.4}\) is (001)\(\gamma\)// (\(\overline{12}\)\(\overline{10}\))Laves.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Alloy** & **Region** & **Cr** & **Co** & **Ni** & **Ta** \\ \hline \multirow{2}{*}{Ta\({}_{0.1}\)} & Primary \(\gamma\) phase & 32.6 & 32.8 & 33.1 & 1.5 \\  & Eutectic & 29.1 & 27.6 & 28.8 & 14.5 \\ \hline \multirow{2}{*}{Ta\({}_{0.3}\)} & Primary \(\gamma\) phase & 30.3 & 33.3 & 30.5 & 5.9 \\ \cline{2-6}  & Eutectic & 28.4 & 29.1 & 27.8 & 13.7 \\ \hline \multirow{2}{*}{Ta\({}_{0.4}\)} & Eutectic & 29.4 & 30.1 & 28.9 & 11.6 \\ \cline{2-6}  & Eutectic & 30.3 & 29.3 & 29.7 & 10.7 \\ \cline{2-6}  & Primary Laves phase & 23.3 & 29.1 & 20.8 & 26.8 \\ \hline \multirow{2}{*}{Ta\({}_{0.7}\)} & Eutectic & 32.1 & 27.3 & 29.5 & 11.1 \\  & Primary Laves phase & 23.8 & 27.5 & 20.6 & 28.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of characteristic regions in CrCoNiTa alloys (in at.%).
# Speckle Noise Detection and Removal for Laser Speech Measurement Systems
## 5 Key Outcomes and Conclusions
### Conclusions  
In this paper, we highlighted the problem of speckle noise for the laser speech measurement system, which can limit the efficiency and accuracy of speech information acquisition. We then proposed a novel automatic speckle noise detection and removal method. This method first foregrounds noise using decorrelation, based on a linear prediction (LP) model that improves the signal-to-noise ratio of the measured signal. This allows detection of the position of noise through a combination of the average short-time energy and kurtosis. The method not only accurately locates small clicks with a duration of few samples, but also finds the location of longer bursts and scratches with a duration of up to hundred samples. The located samples can then be replaced by more appropriate samples whose coding is based on the LP model. The method is not only able to improve the speech quality of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measurement** & **Objective** & **Noisy** & **AR** & **KR** & **Proposed** \\
**Distance** & **Metrics** & **Speech** & **AR** & **KR** & **Method** \\ \hline \multirow{2}{*}{100 m} & SNR & 1.054 & 1.520 & 1.642 & 2.064 \\  & PESQ & 1.635 & 1.768 & 1.726 & 2.160 \\ \hline \multirow{2}{*}{200 m} & SNR & \(-\)0.835 & \(-\)0.718 & \(-\)0.606 & \(-\)0.062 \\  & PESQ & 1.036 & 1.225 & 1.054 & 1.900 \\ \hline \multirow{2}{*}{300 m} & SNR & \(-\)2.159 & \(-\)1.677 & \(-\)1.628 & \(-\)1.272 \\  & PESQ & 0.635 & 0.778 & 0.827 & 1.560 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SNR and PESQ-based objective evaluation results.  
laser speech measurement systems, but also provides reference for the noise removal for ordinary electronic microphones.
# Marine Microalgae for Potential Lutein Production
## 4 Engineering of Biosynthetic Pathways in Microalgae for Lutein Production
The findings from chemical mutagenesis and genetic engineering studies suggest that an altered lycopene \(\beta\)-cyclase with enhanced activity in \(\alpha\)-branch and reduced or no activity for \(\beta\)-branch, as well as an altered lycopene \(\varepsilon\)-cyclase with enhanced activity, might be a target for creating a genetically engineered strain with enhanced lutein productivity. Additionally, the cytosolic MVA pathway of microalgae can be genetically engineered to express a whole heterologous metabolic pathway for lutein biosynthesis. This approach was recently successfully demonstrated in tobacco (_Nicotiana tabacum_ L.) plant, where a viral vector was used to express three enzymes (GGPP synthase (_crt_E), phytoene synthase (_crt_B/_psy_), and phytoene desaturase (_crtl_)) from the soil bacteria _Pantoea ananatis_ to biosynthesis lycopene [40].
# Application Ranges of EPB Shield TBM in Weathered Granite Soil: A Laboratory Scale Study
## 4 Application Ranges of EPB Shield TBM in Weathered Granite Soil Ground  
Figure 1 shows the application ranges of the EPB shield TBM proposed by [1], composed of three zones. As shown in the figure, soil conditioning with foam is possible in Zone I; however, in Zones II and III where the particle sizes are larger, that is, the soil is coarser, it is necessary to add polymers and fines in addition to foam to increase fine particle content and induce plasticity. By taking advantage of the particle-crushing effect of weathered granite soil and considering the findings presented here, new application ranges are proposed that are directly applicable to weathered granite soil. These are shown in Figure 12; the range proposed by [1] is presented in the same figure for comparison purposes.  
First, the ranges are divided into two categories by water content: A and B when the content is within the allowable range and A\({}^{\prime}\) and B\({}^{\prime}\) when the water content is excessive. Zone A (yellow area) in Figure 12 represents the region in which only foam is added for soil conditioning because the water content is within the allowable range (preferably the optimal value) shown in Figure 5; Zone A\({}^{\prime}\) (yellow area) in Figure 12 represents the region in which water-absorbing polymer was needed along with the foam because of water content of excavated soil above the upper bound shown in Figure 5.  
Zone B (orange-colored area) represents the region in which polymer slurry is needed for soil conditioning in addition to foam when the water content is controlled at around 10%; Zone B\({}^{\prime}\) (orange-colored area) represents the region in which foam, polymer slurry, and thickening agent are needed for soil conditioning for digging out tunnels below the groundwater table (water contents larger than 10%).  
The application ranges proposed in Figure 12 can be used even if the water content of the excavated soil is less than the lower bound shown in Figure 5 in Zone A; however, it is important to add water to the chamber so that water content can reach the allowable range and preferably the optimum value shown on curve A in Figure 5. Similarly, in Zone B, if the ground is only partially saturated, water must be added to reach 10%.  
Figure 12: Application ranges of EPB shield TBM in weathered granite soil ground.  
Figure 12 clearly shows that both zones expand more toward the right (coarser soil) than do the zones proposed by [1]. The main reason for the expanded application range could be the particle-crushing effect of the weathered granite soil, which moves the particle-size gradation curve of the excavated soil to the left after soil conditioning.
# A Survey on Satellite Communication System Security
## 7 Future Research Directions
### Standardized Terrestrial-Space Network Integration Protocols  
SCSs utilize a variety of protocols, ranging from those traditionally used in terrestrial networks to those specifically designed for SCSs. Among the protocols currently recognized as standards are CCSDS [130], 3 GPP NTN [131] and DVB-S [132].  
The Consultative Committee for Space Data Systems (CCSDS) is a key organization in the development of communication and data system standards for spaceflight. The CCSDS has developed over 100 standards that have been adopted by space missions globally to ensure reliable and efficient space communication [130]. In parallel, the 3 GPP NTN (non-terrestrial network) protocol framework, designed by the Third Generation Partnership Project (3 GPP), aims to extend mobile network communication to satellite networks. The efforts of the 3 GPP in Releases 17 and 18 regarding non-terrestrial networks (NTNs) mark a major milestone in the integration of SCSs with the 5G ecosystem. Specifically, Release 17 emphasizes the 3 GPP's focus on enabling satellite access for new radio (NR) in the FR1 bands, which are targeted at handheld devices, and on supporting massive IoT use cases with NB-IoT and eMTC technology [131]. Complementing these efforts, GSE (generic stream encapsulation) is a protocol for encapsulating streams defined by the digital video broadcasting (DVB) group, designed to carry packet-oriented protocols like IP over unidirectional PHYs such as DVB-S2, DVB-T2, and DVB-C2. The support provided by GSE includes multi-protocol encapsulation, network layer function transparency, multiple addressing modes, payload frame fragmentation over base band frames, and hardware filtering [132].  
Despite the establishment of these standards, it is rare for only one protocol to be exclusively used. Some systems may employ proprietary protocols that differ from the established standards. Therefore, the development and application of integrated protocols are crucial for enabling efficient communication among diverse SCSs. This approach could also enhance interoperability between various SCSs.
Finally, the quantification of adulteration in wine was carried out by voltammetric analysis of mixtures of white wines, apple juices, and ciders in different proportions and submission of the voltammetric data to partial least squares (PLS) analysis. LOD and LOQ were calculated to be 3.5 and 7.5% (\(v/v\)), respectively. An appropriate test set were employed for the external validation of the model. ## References  
* European Commission (2019) European Commission. 2019 Annual Report. In _The EU food Fraud Network and the System for Administrative Assistance--Food Fraud_; European Commssion: Brussels, Belgium, 2019. * Solovyev et al. (2021) Solovyev, P.A. ; Fauhl-Hassek, C.; Riedl, J.; Esslinger, S.; Bontempo, L.; Camin, F. NMR spectroscopy in wine authentication: An official control perspective. _Compr. Rev. Food Sci. Food Saf._**2021**, _20_, 2040-2062. * Tsopelas et al. (2018) Tsopelas, F.; Konstantopoulos, D.; Tsantili-Kakoulidou, A. Voltammetric fingerprinting of oils and its combination with chemometrics for the detection of extra virgin olive oil adulteration. _Anal. Chim. Acta_**2018**, _1015_, 8-19. * Fast GC-MS Method for the Quantification of Polystyrene/Polyurethane Oligomers in Liquid Food Simulants
* Tsochatzis 1 2
* 1 Department of Food Science, Aarhus University, Agro Food Park 48, 8200 Aarhus, Denmark
* FoodOmicsGR Research Infrastructure, AUTh node, 57001 Thessaloniki, Greece
* Bionic AUTh, Center for Interdisciplinary Research and Innovation, 57001 Thessaloniki, Greece
* Aristotle University of Thessaloniki, 57001 Thessaloniki, Greece
* Correspondence: Emmanouil.Tsochatzis@food.au.dk  
The use of plastic packaging is growing significantly, which is correlated with an increasing concern about the potential harm caused to the environment and human health. Oligomers are considered as \"Non-intentionally Added Substances\" (NIAS) and can potentially migrate from the polymeric material to the food or food simulant. Styrene, the A liquid-liquid extraction (LLE) with dichloromethane, followed by gas chromatography coupled to mass spectrometry (GC-MS) was applied. Limits of detections ranged from 0.32 ng mL-1 (1,1-diphenyl-ethylene) to 14.8 ng mL-1 for 7,9-di-tert-butyl-1-oxaspiro[4.5] deca-6,9-diene-2,8-dione. Accuracy and precision results showed acceptable recoveries (80-110%) and RSDs (<16%), respectively, at the tested concentration levels. The method was applied to seven FCM, indicating significant amounts of styrene and oligomers (dimers and trimers) and polyester urethane-based oligomers (PU) migrating into food simulant. Exposure assessment estimation was performed using EFSA's approach (Figure 39).
## 4 Proposed Collision Warning System and V2V Communications
### Object Sensing  
Let us consider methods to obtain object type data. Recently, LiDAR-based autonomous vehicle systems have been investigated [31; 32; 61]. However, due to its high cost [62], most of commercial vehicles such as Tesla and Mercedes-Benz adopt the computer vision as primary object detection rather than LiDAR [45]. Therefore, in this paper, we mainly consider the vision-based object detection system [63; 64].  
In addition to object type data, trajectory data including location and velocity can be obtained through the radar [42]. However, to obtain precise measurements, the system requires a supplemental calibration process to compensate measurement errors caused by \"_cosine effect_\" [59; 60; 65]. The radar can measure relative speed of the object precisely if the target moves directly in a direction of radar signal. Otherwise, the measured value does not coincide with the actual velocity. Let \(\phi_{k}^{i}\) be the angle between the direction of vehicle \(i\)'s radar signal and moving direction of object \(k\). Note that information about object \(k\) can be obtained by utilizing historic trajectories. We denote the the measured and compensated velocities about object \(k\) for \(i\) as \(V_{M}^{ijk}\) and \(V_{C}^{ijk}\), respectively. Then, we can calculate the compensated value by \(V_{C}^{ijk}=\frac{V_{M}^{ijk}}{\cos\phi_{k}^{i}}\). In the following, we describe how to transmit these data.
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
###### Abstract
For this comparison, we considered Eve to be an energy-detecting eavesdropper that can distinguish the communication signal from noise  
Figure 1: Tactical wireless communication system under an electronic warfare scenario. using the maximum likelihood (ML) method. The detection capability of Eve increases as the signal-to-noise ratio (SNR) and false alarm probability increases. Our results demonstrate that the proposed system offers significantly better undetectability than the BPSK-DSSS scheme in such critical cases. Thus, the proposed method is superior when [PERSON] is the most threatening. The remainder of this paper is organized as follows. In Sections 2-4, we describe the proposed covert anti-jamming communication system as depicted in Figure 2. Specifically, we discuss the signal design required to achieve covertness and anti-jamming performance in Section 2. In Section 3, we describe the time-frequency modulation method. In Section 4, we present an analysis of the undetectability of the proposed system and compare it with that of a conventional binary system. Section 5 discusses issues related to implementation, such as modulation methods and computational complexity. In Section 6, we numerically evaluate the BER performance of the proposed system. Finally, Section 7 summarizes our results and concludes the paper.
## 8 Extraction  
Unless a specific high value molecule is produced by the microalgae, the most valuable compounds present are carbohydrates, proteins, pigments and oils (sterols, fatty acids). In a first step, the polar and non-polar compounds often need to be separated, most typically using organic solvents [87]. This will affect the quality of the proteins [88]. In a second step, components of the two fractions can be purified using HPLC methods.  
Drying the biomass prior to lipid extraction increases the yield [89] but adds an additional step to the process, thereby increasing the energy costs. This step can be avoided using amines or amidines as solvents and, by adding CO\({}_{2}\) into the solution, it is possible to switch the polarity of these helping also with the recovery of the solvent [90].  
In a biorefinery approach, the various components of the biomass need to be extracted while avoiding damage to other components. Some products are more delicate compared to others andcascade extractions are often necessary. Yield, stability, and value are key drivers for the cascade order; it is typical for polar components to be extracted first. For this type of application, aqueous two-phase systems [91] and ionic liquids [92] are under evaluation.  
In order to approach the biorefinery concept fully, biomass valorisation needs to be tackled as a whole process to be optimised instead of a multistep process, trying to couple together multiple steps where possible (e.g., cultivation/harvesting [93; 94], harvesting/disruption [95; 96], disruption/extraction [97; 98]) but individual operational steps still need to be improved, optimized, and tested, prior to application on a larger scale within an understood techno-economic model.  
The 'waste' biomass that remains after the extraction of the valuable compounds could still be used for production of gas, bio-oil, and biochar through the process of hydrothermal liquefaction (HTL) or pyrolysis. These two processes have been evaluated on multiple microalgae strains opting in favour of HTL [99]. HTL has also been tested on biomass derived from remediation of acid mine drainage (AMD). Results found that most of the contaminant metals were present within the solid waste and, as such, were easy to recover [10].
# A Dataset of Pulsed Thermography for Automated Defect Depth Estimation
## 4 Data Labeling
### Three-Dimensional CAD-Based Method  
To overcome the limitations of the manual labeling method and effectively utilize the 3D information from the CAD files, a semi-automated labeling method was introduced. The first step of this method involved transforming the 3D model of each component into a two-dimensional depth image. The transformation was executed by simulating a virtual camera positioned above the specimen, focusing vertically on the specimen's bottom surface.  
The next step involved aligning the transformed depth images with the PCT EOF images, as illustrated in Figure 5. To achieve this, four corresponding points were manually identified on both the depth image and PCT image to compute the perspective transform matrix. This matrix is described by the following equation:  
\[\begin{bmatrix}5x^{\prime}\\ 5y^{\prime}\\ 1\end{bmatrix}=\begin{bmatrix}a_{1}&a_{2}&b_{1}\\ a_{3}&a_{4}&b_{2}\\ c_{1}&c_{2}&1\end{bmatrix}\begin{bmatrix}x\\ y\\ 1\end{bmatrix} \tag{4}\]  
where \(x,y\) denote the pixel locations from the CAD-generated depth image. The parameters \(a_{1}\), \(a_{2}\), \(a_{3}\), and \(a_{4}\) control the scaling, rotation, and shear mapping. \(b_{1}\) and \(b_{2}\) represent the translation of the image. \(c_{1}\) and \(c_{2}\) are projection parameters. Using this matrix, the depth image can be precisely overlaid onto the PCT image, enabling concurrent determination of the defects' depth and surface location.  
The thermal contrast resulting from the boundaries of the subsurface defects extends into their neighboring areas, creating difficulty for manual labeling in accurately delineating the precise boundaries of these defects in PCT images. The semi-automated approach, which is based on a 3D-CAD file, was, therefore, anticipated to generate higher accuracy in defining defect boundaries compared to manual labeling.
# Spatio-Temporal Heterogeneous Ensemble Learning Method for Predicting Land Subsidence
## 2 Methods
### Model Evaluation  
To comprehensively evaluate the prediction performance of each model, this study selected root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (\(R^{2}\)) as the main evaluation indicators. RMSE measures prediction error by calculating the square root of the average squared deviations between the predicted value and actual observed value. RMSE is highly sensitive to large errors, making the performance of the model in extreme cases particularly important. MAE provides a more intuitive error assessment by calculating the average of the absolute deviations between the predicted value and actual value. The advantage of MAE is its simple calculation and ease of understanding, making it a direct indicator for evaluating the prediction accuracy. The \(R^{2}\) indicator measures the model's ability to explain variability in the data  
where \(y_{s}^{t}\) and \(y_{s}^{t}\) represent the observed and predicted subsidence values at monitoring station \(j\) at time step \(t\), respectively; \(N_{E}\) is the total number of subsidence monitoring point samples in the test set; \(N_{S}\) is the total number of subsidence monitoring points; and \(\bar{Y}\) is the overall average of the observed values.
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 2 Background on Neutral Host Concept and Enabling Technologies
### Our Contribution  
Some other relevant 5 GPP projects that also deal with expanding network slicing functionalities are 5G SESAME [40], 5G ESSENCE [41], and SLICENET [42]. In particular, 5G SESAME and 5G ESSENCE focused more on the integration of RAN virtualization and slicing with SDN and NFVO architectures, while SLICENET focused on a platform for E2E network slicing beyond 5G technology. Compared to the aforementioned approaches, our neutral host framework innovates in the integration and extension of 5G enablers developed by academia and industry to demonstrate network slicing in real city-wide deployments. This materializes with the introduction of novel functional entities to the proposed architecture ( cf. Section 3) (e.g., Multitier Orchestrator; Multi-RAT RAN Controller) that enhance the system capabilities to better handle multiple technologies for neutral host deployments that meet 5G services requirements.  
In addition to the complexity associated with the integration and demonstration of all the technologies mentioned above, implementing a neutral host solution becomes even more challenging when considering real-world city scenarios. In this regard, the infrastructure planning and deployment phases play a fundamental role in achieving the potential added values of the system in operational conditions. While the feasibility of city-driven neutral host deployments was acknowledged from the study of business model perspective in previous works [43; 44; 45], there is a lack of literature validating the practical implementation of such deployments. The main contributions of this work are precisely oriented to close this gap regarding the deployment and validation of 5G-enabled neutral host frameworks in the real world. To the best of our knowledge, this paper is the first to describe a comprehensive deployment and validation of a 5G-enabled neutral host framework in three different city-wide deployments.