esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Challenges and Opportunities in Marine Biotechnology
### Strain Optimisation and Genetic Engineering  
Strain selection and improvement are central to advancing marine microalgal biotechnology. Naturally occurring strains show variability in growth and metabolite profiles, making standardisation difficult. For instance, lipid content among microalgal strains can range from 20% to over 60% of dry cell weight, while EPA levels in _Nannochloropsis_ species vary between 2% and 10% of total fatty acids, depending on the strain and cultivation conditions. These fluctuations affect consistency in downstream processing and final product quality. Standardisation efforts, such as ISO 19673:2019, which outlines protocols for the characterisation of microalgal biomass, are being increasingly adopted to ensure quality control and reproducibility across production systems [132].  
Genetic engineering and adaptive evolution are being used to enhance traits like lipid production or stress tolerance. Importantly, engineered _Pavlova lutheri_ strains overexpressing \(\Delta\)5-desaturase genes achieved a 30-40% increase in EPA content under optimised conditions, compared to their wild-type counterparts. Similarly, _Nannochloropsis gaditana_ strains modified to overexpress elongase genes have shown DHA yield improvements exceeding 20%, demonstrating the impact of targeted metabolic interventions. While transformation efficiencies and GMO-related regulatory barriers remain a challenge, these case studies underscore the promise of trait enhancement through synthetic biology [132].  
From an economic standpoint, the high cost of microalgal biomass production remains a limiting factor, especially when compared to terrestrial plants or synthetic alternatives. However, co-product strategies, circular economy approaches (e.g., use of wastewater or flue gas as inputs), and government subsidies are helping to offset production costs. For example, subsidy programmes under the EU Horizon 2020 Blue Bioeconomy initiative and USDA-funded algae biofuel grants have supported pilot- and commercial-scale algae production by lowering capital and operational expenses. Furthermore, typical productioncosts range from approximately $2-5 per kg biomass in open pond systems and $5-15 per kg biomass in photobioreactors, depending on technology, scale, and location [133; 134; 135].
## 6 Isolated Empirical Studies on ML-based xApp Vulnerabilities  
Lab-based evaluations that demonstrate specific ML attack/defense scenarios in O-RAN without a holistic or layered threat model are presented in references [49; 52].  
* [PERSON] et al. [49] shows adversarial input attacks on an interference classifier xApp, analyzing the impact of database manipulation.
* [PERSON] et al. [52] evaluates a DRL-based xApp under environment spoofing attacks targeting resource allocation.  
The rapid evolution of Open Radio Access Networks (O-RANs) introduces significant security challenges due to their openness, programmability, and reliance on ML models. To systematically analyze these risks, we propose an enhanced threat model that categorizes vulnerabilities into distinct classes and explores their interdependencies. This model leverages adversarial ML taxonomies [12] and integrates mathematical formulations to quantify the impact of individual and correlated vulnerabilities.
## 3 Results  
### Demographic Status According to RHR  
Table 1 shows a comparison of the general and socioeconomic characteristics according to the RHR (\(\leq\)64, 68-71, 72-79, and \(\geq\)80 ppm). In the group with the highest RHR of \(\geq\)80 ppm, there was a higher proportion of women (56.7%) than of men (43.3%), and the age group with the highest proportion (31.2%) was \(\geq\)60 years. There was a higher proportion of participants who were not married (60%) than were married (40%) in the highest RHR group, and those with an education level of high school or less accounted for the most at 69.4%. Finally, those with a household income of \(\geq\)3.01 million KRW accounted for the most at 58.1%, but there was no significant association. All factors except household income showed statistically significant differences depending on the RHR.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{6}{c}{**Resting Heart Rate, \(n\) **(\%)**} & \multirow{2}{*}{\(p\) *} \\ \cline{2-2} \cline{4-7}  & **Total** & \(\leq\)**67 bpm** & **68-71 bpm** & **72-79 bpm** & \(\geq\)**80 bpm** \\ \hline Sex & & & & & \\ Male & 2727 (44.1) & 980 (48.2) & 492 (41.7) & 711 (41.4) & 544 (43.3) & \\ Female & 3461 (55.9) & 1053 (51.8) & 689 (58.3) & 1006 (58.6) & 713 (56.7) & \\ Age, years & & & & & & \\
20–29 & 658 (11.9) & 175 (9.0) & 125 (11.6) & 206 (13.5) & 152 (15.5) & \\
30–39 & 875 (15.8) & 241 (12.4) & 180 (16.6) & 262 (17.2) & 192 (19.6) & \\
40–49 & 1032 (18.7) & 377 (19.4) & 214 (19.8) & 266 (17.5) & 175 (17.9) & \(<\)0.001 \\
50–59 & 1047 (18.9) & 419 (21.6) & 196 (18.1) & 277 (18.2) & 155 (15.8) & \\ \(\geq\)60 & 1915 (34.6) & 732 (37.7) & 367 (33.9) & 510 (33.5) & 306 (31.2) & \\ Marital status & & & & & & \\ Not married & 4564 (73.8) & 1691 (83.2) & 890 (75.4) & 1230 (71.6) & 753 (60.0) & \\ Married & 1622 (26.2) & 341 (16.8) & 291 (24.6) & 487 (28.4) & 503 (40.0) & \(<\)0.001 \\ Educational level & & & & & & \\ High school or less & 3790 (64.2) & 1200 (62.1) & 691 (61.6) & 1055 (64.6) & 844 (69.4) & \\ College or higher & 2113 (35.8) & 733 (37.9) & 430 (38.4) & 578 (35.4) & 372 (30.6) & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: General characteristics according to resting heart rate.
# Investigation of Nanocutting Characteristics of Off-Axis 4H-SiC Substrate by Molecular Dynamics
## 3 Results and Discussion  
### Influence of Lattice Orientation  
#### 3.1.1 Structure  
Figure 3 shows the coordinate number of the 4H-SiC workpiece after cutting corresponding to Figure 2a. Cutting speed was 100 m/s, and cutting depth was 1 nm. They are the default cutting parameters unless otherwise stated. Atoms with a coordinate number larger than three were removed. It can be seen that the defect propagates into the workpiece at an angle, and most of the atoms with the coordinate number less than three are located on the surface. The atoms in green and blue on the surface not being fully bonded indicates that these atoms were in amorphous phase after cutting.  
Figure 4 shows the structure transformation of three 4H-SiC workpiece models after cutting. Figure 4a-c correspond to Figure 2a-c, respectively. There are seven kinds of structure types in the workpiece. The atoms with an unidentified structure type were classified as \"others\". Most of them were in the amorphous phase. The atoms with a cubic diamond structure and the ones with a hexagonal diamond structure were classified as \"cubic diamond\" and \"hexagonal diamond\", respectively. The atoms being a first neighbor of a \"cubic diamond\" atom were classified as \"cubic diamond (1 st neighbor)\". Other structure types were defined in a similar way. It can be seen that defects all propagated in the basal plane. The defect in Figure 4a propagated deeper than the rest, and the defect in Figure 4c stopped propagating on the surface. Figure 5 presents the structures transformation in the defect region of three models after cutting. Figure 5a-c correspond to Figure 2a-c, respectively. The dashed line is the slip plane, and the yellow stick is the displacement vector. It can be seen that the atoms in the on-axis SiC model above the slip plane moved downward due to the compression, except the carbon atoms near the slip plane. The stacking sequence of Si was changed from -ABAC- to -ABBA- due to shear stress, which indicates that the defect in the basal plane is a stacking fault. The Si-C bonds crossing the slip plane were broken, and Si-Si bonds were formed after cutting. The stacking fault in the basal plane and the bond reconstrution were also found in the off-axis models. The difference is that the stacking sequence in the off-axis models was changed from -ABCB- to -ABBA-, which means the atoms above the slip plane in the off-axis models moved twice as long than that in the on-axis model.  
Figure 3: Coordinate number of the workpiece after cutting.  
Figure 4: Structure transformation of three workpiece models after cutting: (**a**) 4\({}^{\circ}\) off-axis toward [1\(-\)100]; (**b**) on-axis; (**c**) 4\({}^{\circ}\) off-axis toward [\(-\)1100].  
Figure 5: Structure transformation in the defect region after cutting: (**a**) 4\({}^{\circ}\) off-axis toward [1\(-\)100]; (**b**) on-axis; (**c**) 4\({}^{\circ}\) off-axis toward [\(-\)1100].
# Hash-Chain-Based Cross-Regional Safety Authentication for Space-Air-Ground Integrated VANETs +
## 4 Cross-Regional Identity Authentication Model Based on the Hash Chain  
The cross-regional identity authentication model based on the hash-chain proposed in this paper uses the wireless signal RF fingerprint feature as the node identity to ensure the uniqueness and non-cloning. Its structure is shown in Figure 3.  
This framework of the SAGIN based on the hash chain is a decentralized distributed structure. The CA is only responsible for the registration of the vehicle nodes when it enters the SAGIN, which need not communicate with the regional SM node. In this paper, a drone with computing power is used as a SM node to generate blocks to ensure the data security of vehicle identity information. In fact, the SM node only needs to have a certain computing and storage capacity. In practical applications, we recommend the use of drones in the background of space-air-ground integrated. The low-altitude flight function of the drones can increase the coverage of the SM nodes, and fewer SMnodes contribute to the improvement of safety. At the same time, the high degree of flexibility of the drone helps to schedule resources during congestion to help reduce the overall delay of the network. Among them, the SM node of the safe region A adds the cross-regional vehicle identity materials to the blockchain, and the SM node of the safe region B synchronizes the ledger information to obtain the vehicle identity materials entering the area. After the introduction of space-air nodes, the transmission of information through air nodes can reduce the overall delay of the network.
## 4 Data Stream Management System in Haskell
### The Mapping Module  
Two sources of knowledge are exploited to reason on data collected by the sensors. On the one hand, one needs detailed information about sensors, measurements domain and units, or accuracy. On the other hand, domain specific axioms are exploited when reasoning on a specific scenario.  
A partial view of the sensor ontology is formalised in DL in Figure 3. Figure 4 graphically illustrates the _TBox_ and _ABox_ of the ontology. The sensor \(s_{1}\) is an instance of the class _ActiveRDF_ and it measures temperature with an accuracy of 0.5 \({}^{\circ}\)C. The current temperature is 6 \({}^{\circ}\)C, and the measurement frequency is six observations per minute (Figure 4). Noting that _Temperature_ is a _PhysicalQuality_ (axiom 9 in Figure 3), there is a role _measure_ between the sensor \(s_{1}\) and the temperature value \(6^{\circ}C\), as axiom 1 defines. The corresponding RDF stream for the sensor \(s_{1}\) looks like:  
\[[((s_{1},measures,4^{\circ}C),8.30^{\prime}10^{\prime\prime}),((s_{1},measures,5^{ \circ}C),8.30^{\prime}20^{\prime\prime}),((s_{1},measures,6^{\circ}C),8.30^{ \prime}30^{\prime\prime})]\]  
where at each 10 seconds the measured value increases with one degree Celsius, from 4 \({}^{\circ}\)C to 6 \({}^{\circ}\)C.  
The ontology is translated into strict rules based on the conceptual instrumentation introduced in Section 3.3, in order to reason only within plausible logic. The resulted strict rules and facts appear in Figure 5. Observe that the terminology is considered static, whilst assertions may vary in time in Figure 5.  
Rapid developments of the sensor technology raises the problem of continuously updating the sensor ontology. The system is able to handle this situation by treating the ontology as a stream of description logic axioms. When applying the high order function \(map\) on the transformation function \(\mathcal{T}\), each axiom in the description logic is converted to strict rules as soon as it appears:  
\[map\ \mathcal{T}\ [A\sqsubseteq B,C\sqsubseteq\forall r.D,...]\]  
outputs the infinite list:  
\[[r_{1}:A(X)\to B(X)),r_{2}:C(X),r(X,Y)\to D(Y),...]\]  
The main advantage consists in the possibility to dynamically include new background knowledge in the system.  
Figure 4: Graphical view of the sensor ontology.  
Figure 5: Translating the sensor ontology.  
Figure 3: Partial view of the sensor ontology.
# Acoustic Emission for Structural Monitoring of Historical Masonry: An In-Field Application
## 4 Case Study: Santa Maria Delle Grazie
### Experimental Setup
In order to correlate the data obtained from the different instruments, the AE sensors were arranged linearly in proximity to the FRCM strips applied at the intrados, but not directly above them, so as to avoid interference with surface wave propagation caused by the presence of the reinforcement.
## 4 Results and Analysis
### Performance Evaluation Metrics  
Based on the confusion matrix shown in Table 4, we calculated the accuracy, precision, recall, AUC and F-score metrics to evaluate the proposed model. To determine the models' overall performance, their accuracy, precision, recall, and F1 score have all been evaluated.  
Accuracy  
This has been the primary and most effective metric utilized in classification algorithms, and is calculated by dividing the number of correctly predicted fake news instances by the number of fake news instances detected. In some cases, it may be referred to as the following:  
\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN}\]  
Precision  
The precision measure is defined as the ratio of the number of accurately identified fake news instances to the total number of instances of predicted fake news. It is represented by the following:  
\[Precision=\frac{TP}{TP+FP}\]  
Recall  
The recall is a metric that counts the number of fake news instances that are correctly retrieved from the entire fake news dataset and is calculated below as follows:  
\[Recall=\frac{TP}{TP+FN}\]  
F-Score  
In the following formula, \(F\) is the harmonic mean of recall and precision:  
\[F-score=2\;*\;\left(\frac{(precision*recall)}{(precision+recall)}\right)\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted**} \\ \cline{3-4}  & & **Real News** & **Fake News** \\ \hline \multirow{2}{*}{**Actual**} & Real news & **TN** & **FP** \\ \cline{2-4}  & Fake news & **FN** & **TP** \\ \hline \hline \end{tabular}
**TN (True Negative)**—the actual text was real news, and the predicted text was real news; **FP (False Positive)**—the actual text was real news, and the predicted text was fake news; **FN (False Negative)**—the actual text was fake news, and the predicted text was real news. **FP (False Positive)**—the actual text was fake news, and the predicted text was fake news.  
\end{table}
Table 4: Confusion matrix.  
AUC curve for ROC  
In binary classification problems, the ROC (receiver operating curve) is used primarily. Using several thresholds for identifying signals in noise, it plots true positive news against false positive news. AUC (area under curve) is used to measure the classifier's ability under ROC. The TPR and FPR are measured with the following:  
\[TPR=\frac{True\ Positive\ news}{All\ Positive\ news}\]  
\[\text{Recall}=\frac{False\ Positive\ news}{All\ Negative\ news}\]
## 4 Experiment and Simulation  
In order to support individual daily UV dose through the proposed system, accurate measurement of UV exposure outdoors and provision of safe UVB dose through indoor UVB LED lighting is essential. Therefore, the measurement accuracy of the IoT UV measuring device constituting the proposed system was evaluated and experiments were conducted to confirm the radiation characteristics of UVB LED lighting. Furthermore, simulations were conducted to confirm that the proposed system could meet individual daily UV dose and support vitamin D synthesis.
# Evaluation of Accuracy of Traffic Flow Generation in SUMO
## 2 Simulation
### Traffic Information  
The simulation utilizing SUMO was carried out by using a road network and the route data prepared by the method described above. The road network was converted by NETCONVERT, which is an attachment program of SUMO. The resulting route network is compared against the original map of the Jianghan Zone in Figure 2.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
\begin{tabular}{l} Hubei Provincial Bureau of Statics \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information for generating traffic demand in ActivityGen.  
We used ActigvityGen to generate trip files in SUMO according to geographic population data mentioned above, and then it was necessary to construct a route file. In this study, the DUAROUTER, which is another attachment program of SUMO, was applied to decide the route of the vehicle. This program inspects the minimum travelling time path between origin and destination written by the trip file using Dijkstra [23] method. We assumed that people use an intelligent navigation system to find their quickest way to work and going home, the same as the Dijkstra method. To optimize the distribution of trips, we used dynamic user equilibrium recommended by SUMO. The dynamic user equilibrium is based on the principle that through multiple iterations, all the vehicles will find the most timesaving way. The route file generated from geographical population distribution was iterated with a python program, also a sub-program provided by SUMO. It generates a new route file after each step of iteration. Then, based on the new generated route file, another step of iteration is launched. After 100 time-iterations, we chose the best trip distribution as our route file. The flow chart of the process of building the simulation and the data type have been used are shown in Figure 3, where the data in blue boxes were obtained from the Internet.  
Figure 2: (**a**) Original Map and (**b**) abstracted road network of Jianghan Zone.
# Dynamic Characteristic Analysis of Permanent Magnet Brushless DC Motor System with Rolling Rotor
## 3 Dynamic Response of the BLDCM System
### Analysis of the Effects of the Eccentric Distance  
To investigate the effect of the eccentric distance of the rolling rotor on the dynamic characteristic of the motor system, the eccentric distance \(\rho\) is chosen as the control parameter. The other system parameters are set as follows: rotational speed \(\Omega\) = 100 rps, and bearing backlash \(b\) = 5 \(\times\) 10\({}^{-5}\) m. Then, the dynamic behaviors of the rotor and stator with \(\rho\) = 3 \(\times\) 10\({}^{-3}\) m and 5 \(\times\) 10\({}^{-3}\) m are illustrated in Figures 5 and 6, respectively.  
Figure 4: Dynamic responses of the rotor (blue curves) and stator (red curves) at \(\Omega\) = 150 rps: (**a**) time history of the rotor; (**b**) frequency plot of the rotor; (**c**) rotor axis locus of the rotor; (**d**) phase portrait of the rotor; (**e**) time history of the stator; (**f**) frequency plot of the stator; (**g**) rotor axis locus of the stator; and (**h**) phase portrait of the stator.  
Figure 5: Dynamic responses of the rotor (blue curves) and stator (red curves) at \(\rho\) = 3 \(\times\) 10\({}^{-3}\) m: (**a**) time history of the rotor; (**b**) frequency plot of the rotor; (**c**) rotor axis locus of the rotor; (**d**) phase portrait of the rotor; (**e**) time history of the stator; (**f**) frequency plot of the stator; (**g**) rotor axis locus of the stator; and (**h**) phase portrait of the stator.  
Comparing Figures 2a, 5a and 6a, it can be clearly seen that the vibration amplitude gradually increases with the increase in the eccentric distance. However, under the three eccentric distances, the motor system is always multiple periodic. This signifies that the increasing eccentric force deteriorates the dynamic characteristic of the motor system, which becomes the main source of system vibration. The frequency plot verifies this finding. In Figure 2b, 3\(f_{\rm r}\) is the main response, whereas the rotational frequency \(f_{\rm r}\) in Figures 5b and 6b occupies the main component. The increasing eccentric force also leads to simplification of the axis trajectory diagram of the rotor, but the corresponding trajectory ranges increase, which can be seen in Figures 2c, 5c and 6c. This phenomenon also appears in the stator. When the eccentric distance increases, the axis trajectory diagram of the stator gradually becomes a circular curve, as shown in Figures 2g, 5g and 6g. Therefore, the eccentric distance should be reduced as far as possible in the design processing of the rolling rotor, such as by increasing the balance block or reducing the amount of material on the eccentric side.  
Similarly, under different eccentric distances, the corresponding 3D dynamic response curves are shown in Appendix B.
## 3 Results and Discussion
### Optimization of SPME Method
DVB-CAR-PDMS fiber has shown the best sorption capacity for some compounds such as food packaging contaminants in alcoholic beverages. In our study, a DVB/PDMS/CAR fiber with 50-30 \(\upmu\)m thickness and a CAR-PDMS with 100 \(\upmu\)m thickness were tested. Peak areas from decanal, 2-oxepanone and diethylph-thalate, which were the most abundant peaks, were compared between both types of fibers, with the first one achieving the higher response. This fact confirms that DVB/PDMS/CAR fiber is more appropriate to separate volatile compounds with higher molecular weight.