esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### PCA Analysis  
PCA is a multivariate technique that is used to reduce dimensionality of the data set thereby assisting in data visualisation. Once the principal component scores were plotted, this technique allowed for similarities and differences between samples on each experimental day to be visualised, producing groupings of days that were most similar. Loadings were used to give a representation of the chemical classes providing the most discrimination for each experimental day or grouping of days on the biplot.  
The results of the PCA analysis (Figures 2 and 3) show the distribution of experimental soil samples on each day based on the sum of the normalised peak area for each chemical class. Control sites grouped very closely due to low variation of background compounds between sites. Distribution of experimental sites between days was more dispersed because of increased variation between these samples. In order to improve clarity the data points for controls were removed and a dashed circle on the plot indicates the area they were present. Data points found outside of this circle represent days that the decomposition VOC profile can be discriminated from the control soils. For both SPME (Figure 2) and sorbent tubes (Figure 3) the experimental soils at the beginning and end of the field trial were similar to control soils, likely due to reduced VOC production at these times.  
The use of sorbent tubes distinguished the experimental soils from control soils for a longer duration when compared to SPME. The loadings demonstrate that a larger number of chemical classes contributed to the dispersion of the experimental soil data points using sorbent tubes. Ketones, aromatics, and esters were the classes detected in common between the two techniques and were responsible for discrimination. The loadings of the PCA biplot for sorbent tube sampling indicated that this technique recovered additional nitrogen-containing compounds, alcohols, hydrocarbons, and sulfides that were specific to experimental soils and important in showing discrimination between experimental days. Figure 3 demonstrates that the suite of compounds for sorbent tubes more effectively discriminated experimental soil from control soil over the entirety of the trial and therefore sorbent tubes were more successful at collecting a valuable range of decomposition VOCs.  
Figure 2: Principal component analysis (PCA) biplot of the calculated PCA scores and loadings using SPME data. Each point for scores represents the average of the four replicate carcasses on each experimental day (_i.e._, D6 = day 6). The average abundance of each compound in the carcasses were summed for each chemical class on each day and input for PCA analysis. A circle indicates the range of control soils from corresponding experimental days.
# Carbonyl Composition and Electrophilicity in Vaping Emissions of Flavored and Unflavored E-Liquids
## 3 Results and Discussions
Propionaldehyde, acrolein, and/or dimethylglyoxal, on the other hand, were not detected in the vaping emissions of benzyl alcohol- and l-(-)-menthol-flavored e-liquids. One possible explanation is that benzyl alcohol and l-(-)-menthol have antioxidative properties [47; 48]. For benzyl alcohol, the oxidation reaction was most likely directed toward the formation of benzaldehyde. Several carbonyls (formaldehyde, acetaldehyde, acetone, propionaldehyde, acrolein, glyoxal, methylglyoxal, and benzaldehyde) identified in this study are consistent to those reported previously [10; 49; 50; 51; 52]. While formaldehyde, acetaldehyde, and acetone are common products of PG and VG degradation processes, productions of benzaldehyde and _trans_-2-hexenal are very likely linked to transformation of flavoring chemicals in e-liquids. For instance, [PERSON] et al. [52] detected benzaldehyde in 108 out of 145 flavored e-liquids' vaping aerosols, and the highest benzaldehyde level (141.2 ug/30 puffs) was found in emissions of cherry-flavored e-liquids [52]. Interestingly, _trans_-2-hexenal was detected in vaping emissions of _trans_-2-hexenol (a C6 alcohol), l-(-)-menthol (a C10 monoterpene), and linalool (a C10 monoterenoid). It is well known that primary alcohols can oxidize to yield aldehydes. Thus, it is reasonable to detect _trans_-2-hexenal in _trans_-2-hexenol emissions. The formation of _trans_-2-hexenal in menthol- and linalool-flavored e-liquid vaping emissions could be attributed to oxidation and thermal degradation processes, as described in previous literature [53; 54; 55]. However, future studies are needed to investigate the detailed pathway of formation of _trans_-2-hexenal. Additionally, the presence of methylbutenal and methylpentenal compounds in the emissions of some flavored e-liquids may indicate that they were formed through oxidation and degradation of parent flavoring chemicals. E-cigarette emission products are influenced by different parameters, including e-cigarette devices, battery voltage, vaping topography, and e-liquid constituents. [PERSON] et al. [51] demonstrated acetol and 2-propenol were primarily produced by PG degradation, and acrolein was mostly produced by VG degradation. [PERSON] et al. [56] reported 4-200 times higher formaldehyde, acetaldehyde, and acetone levels as battery voltage increased from 3.2 to 4.8 V. A combination of factors such as lower battery voltage output, PG/VG ratio, and e-liquid constituents may explain why hypothesized production of menthone and linalool-8-aldehyde were not detected in the vaping emissions of e-liquids flavored with l-(-)-menthol and linalool, respectively.
## 5 Discussion
### Implications for Real-World Applications  
The findings from this research have significant implications for real-world IoV applications, particularly in enhancing network security and robustness.  
* Improved DDoS Detection: Research has shown that the use of newer machine learning modules together with feature selection techniques is beneficial in handling DDoS detection and mitigation tasks. This upholds the reliability and security of Internet of Vehicles systems, which are important for autonomous vehicles and smart transport systems.
* Complexity and Reduction: The application of feature selection through the Gini Index is not only instrumental in increasing the detection accuracy, but it also helps cut down on the computation burden that is placed on the models. This is critical as adopting an approach that narrows down features, which are most relevant to the processing of IoV devices and are usually resource-constrained, can be lightened. Such simplification of models is important in the real-time detection of DDoS attacks as it increases the response time and helps in the better allocation of resources in the IoV setup.
* Privacy-Preserving Solutions: Federated Learning offers a viable solution for IoV networks to leverage collaborative learning while maintaining data privacy. This is especially relevant for applications involving sensitive information, such as autonomous vehicles and smart transportation systems. By ensuring data privacy, Federated Learning can increase user trust and acceptance of IoV technologies.
* Scalability and Adaptability: The scalability of Federated Learning makes it suitable for widespread IoV deployment, accommodating a large number of devices with varying capabilities. This adaptability ensures that security solutions remain effective as the IoV ecosystem evolves. Furthermore, Federated Learning can adapt to new and emerging threats, providing a robust defense mechanism for future IoV networks. The practical effectiveness of our method is evidenced by its ability to accurately detect different types of DDoS attacks. Notably, our models achieved detection rates of 28.65% for DrDoS_DNS, 28.94% for DrDoS_SNMP, 9.20% for DrDoS_UDP, and 20.61% for NetBIOS attacks. These findings illustrate the robustness of our approach in real-world scenarios where a variety of attack types are encountered.  
In light of challenges in IoV environments, namely, data privacy and scalability issues, we employ Federated Learning. Most of the conventional DDoS detection methods, including threshold-based methods, though efficient, result in poor privacy and poor scalability problems when applied to decentralized IoV environments. Our approach combines federated learning with Gini index-based feature selection in an attempt to address such issues by opening up a way for efficient privacy-preserving decentralized detection at reduced computational overhead.
# Dynamic Simulation Model and Performance Optimization of a Pressurized Pulsed Water Jet Device
## 3 Simulation of Dynamic Characteristics of the PPWJ Device
### Simulation Model  
Based on the operational principle and structure of the device, suitable sub-models are selected from the model library of the AMESim software to build a simulation model of the system. As shown in Figure 2, the main elements of the system are the hydraulic pump, PPWJ device, check valve, relief valve, and throttle valve. The relief valve is used to control the pulse pressure by regulating \(q_{\rm sin}\), and the throttle valve is used to control the pulse frequency by regulating \(q_{\rm frin}\). The simulation setup time is 2 s with a step size of 0.01 s.
## 3 Results and Discussion
### Core-Flooding Effluent Analysis  
#### 3.3.1 Steady Shear Rheology  
At low shear rates (Figure 9), the steady shear viscosity evaluations of core flood's effluent showed significant reductions. Despite the core length (6 cm), a significant loss in shear viscosity at the sand face around the well bore is observed. Viscosity loss due to mechanical degradation can be clearly seen at all shear rates (low to high), as shown in Figure 9.  
Figure 8: Core pressure drop versus increasing interstitial velocity for an HPAM solution. (**Left**) Pressure data plotted are for the Flopaam 6035 S 3000 ppm at 22 \({}^{\circ}\)C in synthetic seawater and (**right**) Hengfloc 63026, 3000 ppm at 55 \({}^{\circ}\)C in reservoir brine. Elong. (Elongational viscosity) was measured via e-VROC. Calculated pressures were obtained by using [PERSON]’s equation and the Carreau–Yasuda flow curve fitted to rheometer data.  
Effluents results in Figure 10 help to conclude that Hengfloc 63026 solution depicted higher viscosity degradation compared to Flopaam 6035 S solutions. Moreover, Flopaam 6035 S was resistant to semi-harsh conditions of salinity, thermal as well as high shear rates (around injection and production well scenarios) compared to Hengfloc 63026. Note that percentages of viscosity degradation shown in Figure 10 are calculated based on a comparison of the steady shear viscosity before and after core flooding using Equation (4) [83].  
\[\textit{Degradation Rate}\left(DR\right)=\frac{\eta_{o}-\eta_{e}}{\eta_{o}} \times 100 \tag{4}\]  
where, \(\eta_{o}\) = viscosity of the solution before core flood, and \(\eta_{e}\) = viscosity of the solution after core flood.  
Figure 10: Viscosity degradation of polymer solutions while flowing through core plugs over wide range in increasing injection rates.  
Figure 9: Steady shear viscosity diluted polymer solutions before core flood (B) and after core flood (A), at 22 and 55 \({}^{\circ}\)C, prepared with reservoir brine and synthetic seawater.
# A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM
## 4 Particle Swarm Optimization-Support Vector Machine (PSO-SVM)
### Process of Fire Image Detection Based on the PSO-SVM Model  
Combining the abovementioned contents, the flow diagram of fire image detection based on the PSO-SVM model is shown in Figure 3.  
The process is described as follows. First, collect a series of candidate images using a video camera and divide the collected images into the training sample set and testing sample set. Then, calculate the Tchebichef moment invariants \(\widetilde{T}_{pq}\) of every candidate image and select some of them to construct its feature vector \(\mathbf{V_{i}}\). Next, train the SVM with the feature vectors of the training sample set and optimize the SVM parameters by using PSO. Afterwards, the training sample set is input into the SVM with optimal parameters once again, contributing to the PSO-SVM model. Finally, the feature vectors of the testing sample set are input into the PSO-SVM model to predict their belonging labels, achieving the fire image detection.  
To fully examine the performance of the PSO-SVM model, the detection rate \(Acc\) is used as the evaluation criteria, as shown in the following expression:  
\[Acc=\frac{TP+TN}{TP+FP+TN+FN}\times 100\% \tag{36}\]  
where \(TP\) is the amount of the correctly predicted fire images; \(TN\) is the amount of the correctly predicted non-fire images; \(FP\) is the amount of fire images that are incorrectly predicted as non-fire images; and \(FN\) is the amount of non-fire images that are incorrectly predicted as fire images.  
Figure 2: The flow diagram of particle swarm optimization-support vector machine (PSO-SVM) model construction.
# From Mirrors to Free-Space Optical Communication--Historical Aspects in Data Transmission
###### Abstract  
Fast communication is of high importance. Recently, increased data demand and crowded radio frequency spectrum have become crucial issues. Free-Space Optical Communication (FSOC) has diametrically changed the way people exchange information. As an alternative to wire communication systems, it allows efficient voice, video, and data transmission using a medium like air. Due to its large bandwidth, FSOC can be used in various applications and has therefore become an important part of our everyday life. The main advantages of FSOC are a high speed, cost savings, compact structures, low power, energy efficiency, a maximal transfer capacity, and applicability. The rapid development of the high-speed connection technology allows one to reduce the repair downtime and gives the ability to quickly establish a backup network in an emergency. Unfortunately, FSOC is susceptible to disruption due to atmospheric conditions or direct sunlight. Here, we briefly discuss Free-Space Optical Communication from mirrors and optical telegraphs to modern wireless systems and outline the future development directions of optical communication.  
Free-Space Optical communication; telecommunications; wireless communication; data transfer history; communication networks +
Footnote †: journal:  
_Future Internet_
## 3 Positioning System Experimental Platform
### WinBus
#### 3.1.3 Simultaneous Localization and Mapping (SLAM)  
SLAM is another positioning system used by the WinBus. Mapping and matching are the two parts of the SLAM method. The quality of the mapping determines whether this method is accurate because the positioning data source is from the feature of the map. The intricacy of the environment and the number of dynamic objects are two factors that influence the SLAM's outcome. The clarity of the mapping will be affected if the characteristics of the environment are ambiguous or the base map environment changes too frequently. This kind of application has some risks for autonomous vehicle applications. In the drawing part, for scenes where one want to position oneself, first, create a map. The lidar mounted on the car will record the scene point cloud at a slow speed. The recorded point cloud is created by overlaying the map, and the completed map is the base map at the end of the comparison. Then, the scene environment is instantly scanned during the comparison. SLAM compares the current point cloud with the map point cloud. Additionally, it finds feature locations that match the current point cloud. The output coordinates are unified three-dimensional coordinates for the WinBus application. The WinBus uses SLAM to support the positioning system. Increasing the positioning system's reliability is the goal.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 2 Locata Core Technologies
### VRay Antenna  
VRay is an antenna that is designed for indoor and outdoor industrial applications, which mitigates multipath using a Correlator Beam-Forming (CBF) technique [26]. To mitigate multipath, the beams that formed at the receiver must be pointed to the directions of the LocataLite transmitters. The receiver initially sweeps the beams in a search pattern so as to maximise the signal power from each LocataLite. Once the optimal beam settings are determined, the corresponding angle-of-arrival measurements  
Figure 2: Typical LocataLite setup [29].  
are used to estimate both the approximate position of the Locata Rover receiver and the antenna orientation [30]. Figure 3 shows the evolution in the development of the VRay antenna.  
As shown in Figure 3, the first VRay prototype was developed in 2007, and it was a one-dimensional 12 cm diameter board with eight elements. The second prototype developed (2009) was a two-dimensional 30 cm diameter device with 64 elements. The last prototype (2011) had a three-dimensional form with 80 elements. The commercial version (2014) is a basketball-sized spherical array that consists of 20 panels with 80 elements. Each panel comprises four elements arranged with one central element and the three other elements forming a triangle. The element spacing between each panel is half the signal wavelength [26]. The results from both static and kinematic experiments have confirmed that the positioning accuracies are at the centimeter-level, and the antenna orientation solutions are accurate to a few degrees [30; 31; 32].
## 2 Network Model
### NGFI Network  
We study the NGFI network which operates with both double-split and a single-split deployment scenarios defined in [7]. In double-split, the baseband functions are split and realized in a distributed way in RU, DU, CU entities, whereas in single-split, DU is co-located with either RU or CU. In the single-split scenario considered in this paper, the DU entity is co-located with CU. The RUs are located close to the antenna site, and the CU and DU are placed at PP nodes, which are spread over different sites of the network.  
As defined by the 3 GPP organization [28], several options have been distinguished for performing the split of baseband processing functions. According to the indications presented in [7, 29], in this work we assume that the functional split between RU and DU implements Option 7.2, and the function split between DU and CU applies Option 2.  
We assume that subsets of RUs may be clustered to enable joint processing for the purpose of multi-cell coordination [30]. Accordingly, the DUs associated with the RUs belonging to a cluster must be placed in the same PP node to enable joint processing.
## 3 Results and Discussion
### Propidium Iodide Fluorescence Assay of Phytophthorahora Nicotianae Mycelia  
Compared to the control, 450 uL/L DADS-treated _P. nicotianae_ mycelia were highly stained (Figure 4), indicating that DADS could destroy the cell membrane of _P. nicotiana_ mycelia and induce apoptosis of mycelial cells.  
Our studies were similar to others showing that the essential oil of _Chrysanthemum indicum_ L (EOC) significantly increased the EC values of _P. nicotianae_ mycelia, and the integrity of mycelial cell membranes was destroyed by EOC [20]. Another study also confirmed that the cytoplasmic membrane damage and subsequent leakage of _Candida_ species were caused by _Coriandrum sativum_ essential oil [35].  
Prevention of Phytophthorahora Nicotianae by Garlic Essential Oil and Diallyl Disulfide in Pot Experiment  
As shown in Table 2, root irrigation and fumigation with GEO and DADS had a certain control effect against tobacco black shank (19.4-59.7%). The control effect of DADS was higher than that of the same dosage of GEO, and the control effects were concentration-dependent. Consistent with the results of antifungal tests in vitro, fumigation treatment showed better disease control than root irrigation. These results indicated that DADS can serve as a fumigant for tobacco black control.  
Figure 4: Effect of DADS on cell death of _P. nicotianae_. (**a-1**) Mycelia treated with 450 uL/L DADS under the laser field; (**a-2**) mycelia treated with 450 uL/L DADS under ordinary light field; (**b-1**) control under the laser field; (**b-2**) control under ordinary light field.
# Evaluation of Modern Internet Transport Protocols over GEO Satellite Links
## 2 Related Work
The experiments were performed on an emulated satellite platform, where they tested HTTP/2 over TCP and HTTP/3 over QUIC with two web page sizes (11 KB and 5 MB). They found that QUIC with the default congestion control (Cubic) degrades the performance when web page size is increased because of the lack of PEP proxy supports in QUIC. However, they did not test TCP without PEP proxy to be compared with QUIC to ensure fairness in comparison. Finally, the authors of [14] evaluated the performance of TCP-PEP and Google QUIC (GQUIC) with Cubic congestion control over satellite networks. They found that TCP-PEP outperforms GQUIC in terms of web page load time due to the splitting feature on TCP PEP proxies. However, they did not mention their network architecture and evaluation methodology. Also, the feature of multiplexing on HTTP is absent here since their web page profiles consisted of one object only (one stream). On different, but related, developments, micro satellites with Small Optical Transponder (SoTA) have been developed and tested to provide low-orbit optical communication link to a ground station [15]. Optical LEO satellite links are considered a potential solution to overcome the limited spectrum and data rates of RF satellite links. Preliminary experimental results have been reported in [16] while a comprehensive simulation platform for LEO optical communication was developed in [17] which also reports the effect of many system configurations and parameters on link performance. However, as the technology is being developed most of the focus is currently on perfecting and evaluating the optical link itself and, hence, higher layer protocols such as HTTP are not yet considered. All of the papers discussed above did not evaluate the performance of HTTP traffic with different web page structures over QUIC and TCP transport protocol with different congestion control algorithms. In this paper, we will evaluate the performance of HTTP/3 over QUIC, and HTTP/1.1 and HTTP/2 over TCP with recent congestion algorithms and various web page structures. Also, we will provide important user-level performance metrics which are not considered in the above papers. Moreover, we will analyze the behavior of HTTP traffic based on objects' size distribution within the web page.