esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
### Lc-Esi-Qtof-Ms/Ms Analysis
#### 3.5.2 Putative Identification of Procyanidins  
Condensed tannins, known as proanthocyanidins, were noted in this study, specifically procyanidins (PAs), as the monomer units are solely (epi)catechins [71].  
The putative identification of procyanidin B was carried out, with the observation of procyanidin dimers, where the prominent molecular cation was [M + H]\({}^{+}\) at \(m/z\) 579.1502. The cleavage of interflavonoid bonds quinone methide (QM) allowed for the formation of the catechin monomer at \(m/z\) 289. Additional fragmentations were observed, producing the cation at \(m/z\) 427.1024, indicating the RDA cleavage of the flavonoid core (considered a standard fragmentation pattern for procyanidin B dimers), with subsequent loss of water (from ring C), resulting in the ion at \(m/z\) 409.0915 [71; 72].  
In this study, the \(m/z\) values of fragments obtained from positive ion electrospray ionization MS/MS were consistent with those reported in the literature. The most significant fragments were formed from the RDA reaction, QM cleavage, heterocyclic ring fission (HRF) and benzofuran formation (BFF) [71; 73].  
Similar to the literature [74], other \(m/z\) values of precursor ions for PA oligomers were noted, including \(m/z\) 867.2131 compatible with PA trimers, \(m/z\) 1157.2922 and \(m/z\) 1155.2775 compatible with PA tetramers, and \(m/z\) 1307.2887 compatible with gallic-acid-substituted PA tetramers. Additionally, pseudomolecular ions, common for large molecule fragmentations with low intensities, were observed throughout the analysis.  
It is known that the mixed stereochemistry of PAs and their various degrees of polymerization promote different biological activities [72]. Among Malpighiaceae, PAs have been reported in _B. argyrophylla, B. caapi, Byrsonina verbascifolia_ (L.) DC., _M. glabra_ L. (Syn. _Malpighia punicifolia_ L.), and _M. emarginata_ DC. [58], generally associated with antioxidant potential, including free radical scavenging and subsequent preventive effects against possible diseases, including neurodegenerative conditions, as well as antibacterial, antiviral, and anti-inflammatory effects [58; 71].
# Internet of Drones Intrusion Detection Using Deep Learning
## 5 Simulation Results
### Performance Analysis
#### 5.2.1 LSTM_RNN Performance over UNSW-NB15 Dataset  
Though the first set of experiments examined the accuracy of the LSTM_RNN with different sample sizes, this was done using these datasets due to a large number of records and fields. The dataset was adopted to fit the proposed framework for drones. Figure 11 shows the detection accuracy versus sample size. As shown in the figure, LSTM_RNN  
Figure 11: LSTM_RNN accuracy with the sample size.  
Figure 10: Base station Spark LSTM_RNN.  
accuracy increased with the increase of the sample size. It became more stable with the increase in the sample size. Moreover, the average accuracy was 95%, and in some cases, it reached 97%. This is a good indicator of the performance of the proposed methodology.  
Figure 12 shows the comparison between the LSTM_RNN, LR, and KNN in binary classification. In this experiment, 10% of the dataset was used for training. As shown in the figure, LSTM_RNN performance showed a much better performance than LR and KNN algorithms. The accuracy of the LSTM_RNN reached 97%, while the Precision, Recall, and F-score were 94%, 98%, and 96%, respectively. On the other hand, KNN performed well with, on average, 91% Accuracy, Recall, and F-score followed by LR with almost 89%.  
Figure 13 shows another classification performance based on multiclass attacks. As can be seen, the LSTM_RNN and KNN had relative performance in terms of Accuracy and Precision. However, the Recall of LSTM_RNN, LR was much better than that of KNN, where the true positive value was much better than KNN. On the other side, LR still has lower precision, accuracy, Recall, and F-score than LSTM_RNN.  
Here is another set of experiments targeting the three algorithms' performance based on the UNSW-NB15 detailed attacks. As shown in Figure 14, LSTM_RNN showed a great  
Figure 12: UNSW-NB15 Binary classification performance comparison.  
Figure 13: UNSW-NB15 Multiclass classification performance comparison.  
overall performance of the attacks presented in the UNSW-NB15 dataset. On average, LSTM_RNN outperformed KNN by 2% accuracy while it outperformed LR, on average, by 5% accuracy.
# Hardware Model Checking Algorithms and Techniques
## 2 Background and Notation
### Symbolic Representation of Transition Systems
#### 2.11.5 Symbolic Representation of Invariant Properties  
Given a transition system \(\mathcal{TS}=\langle S,S_{0},R,AP,L\rangle\) and an invariant property \(\psi\) with invariant condition \(\varphi\) over \(AP\), we define a symbolic representation of the invariant condition as follows.  
**Definition 75** (Symbolic Invariant Property).: _A symbolic invariant property encoding \(\varphi\) is the propositional formula \(P\) over \(V\) such that_  
\[P\overset{\text{def}}{=}\varphi[a_{1}\leftarrow\xi_{a_{1}},\ldots,a_{n} \leftarrow\xi_{a_{n}}] \tag{37}\]  
_for each \(a_{i}\in\operatorname{Vars}(\varphi)\), i.e., the formula obtained by substituting each propositional variable a of \(\varphi\) with its characteristic formula \(\xi_{a}\) over \(V\)._  
**Example 10**.: _Given the hardware design and its transition system described in Example 1, the invariant property \(\psi\) defined in Example 5 will have an invariant condition \(\varphi\) encoded as follows:_  
\[\varphi=\
eg a_{0}\vee\
eg a_{1} \tag{38}\]  
\[P=\
eg\xi_{a_{0}}\vee\
eg\xi_{a_{1}}=\
eg v_{0}\vee\
eg v_{1} \tag{39}\]
## 3 Methods and Tools
### Air Flow Study
The grid with the highest average quality index, but not lower than 0.85, was chosen as the optimal grid. Thus, the grids of object I consisted of 6678, 537,168  
Figure 1: Three types of principal model structures with (1) inlet cube channel, (2) inlet surface, (3) cylindrical tube imitating mycelium porous media idealization, (4) outlet surface and (5) outlet cube channel. (**a**) Structure with 25 cylinders; (**b**) Structure with 144 cylinders; (**c**) Structure with 625 cylinders. and 5,192,530 elements. The element size was chosen to be 5 \(\times\) 10\({}^{-4}\) m, 5 \(\times\) 10\({}^{-5}\) m and 5 \(\times\) 10\({}^{-6}\) m, respectively. For the min size, the mesh was defeatured, in particular defeaturing the size and capture curvature, specifically the min size curvature, and the min size was 5 \(\times\) 10\({}^{-5}\) m, 5 \(\times\) 10\({}^{-6}\) m and 5 \(\times\) 10\({}^{-7}\) m. In all cases, smoothing was set to medium. The first one, with an average quality index of 0.98, was chosen as the optimal one (an error of up to 2% is acceptable). Similarly, object II grids consisted of 66,370, 394,278 and 2,431,316 elements. The element size was chosen to be 2 \(\times\) 10\({}^{-4}\) m, 2 \(\times\) 10\({}^{-5}\) m, 1 \(\times\) 10\({}^{-5}\) m, respectively. For the min size, the mesh was defeatured, in particular defeaturing the size and capture curvature, specifically the min size curvature, and the min size was 2 \(\times\) 10\({}^{-5}\) m, 1 \(\times\) 10\({}^{-5}\) m and 5 \(\times\) 10\({}^{-6}\) m. In all cases, smoothing was set to medium. The first one, with an average quality index of 0.97, was chosen as the best one (permissible error up to 3%). Object III had 2,092,359, 2,113,120 and 2,114,437 elements, and the first one, with an average quality indicator of 0.95, was chosen as the optimal one (permissible error up to 5%). The element size was chosen to be 5 \(\times\) 10\({}^{-3}\) m, 5 \(\times\) 10\({}^{-4}\) m and 1 \(\times\) 10\({}^{-4}\) m, respectively. The mesh was defeatured, and in particular, the defeaturing size for all three mesh options was 5 \(\times\) 10\({}^{-8}\) m. For the min size, the capture curvature and, in particular, the min size curvature was 5 \(\times\) 10\({}^{-3}\) m, 5 \(\times\) 10\({}^{-4}\) m and 5 \(\times\) 10\({}^{-5}\) m. In all cases, smoothing was set to medium. Tetrahedra were chosen as the mesh extension parameters. To create the model, the laminar viscosity model was applied, and the stationary type was chosen. The main model parameters and explicit pseudotransitional relaxation factors are provided in Table 1.  
To analyze the model solution and obtain results for each case, calculations were performed after at least 1000 iterations until the residuals of all variables (continuity, x-, y- and z-velocity) did not reach a level of no more than 10\({}^{-5}\).
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 5 Hydrogen Applications in End-Use Sectors
### Decarbonizing the Heating Sector  
Green hydrogen can play an important role in decarbonizing the heating sector, not only for industrial processes, as previously seen, but also for meeting heating needs at the level of residential and commercial buildings. Hydrogen can be used to reduce the GHG emissions related to space heating, hot water production, and cooking in the building sector, where fossil fuels are currently the go-to option. To reduce the consumption of these fuels, especially natural gas, hydrogen can be blended with it, taking advantage of the already existing gas infrastructure and equipment, or can be used as a direct replacement for conventional natural gas [1].
# Mobility-Aware Hybrid Flow Rule Cache Scheme in Software-Defined Access Networks
## 6 Conclusions  
In SDN-based access networks, the proactive rule cache method that does not need the flow rule installation procedure has been considered for the delay requirement of the mobile flows. However, due to the location prediction error which can result in the waste of flow rule caches, an efficient flow rule cache strategy is required. To address this problem, this paper proposed a mobility-aware hybrid flow rule cache scheme considering the delay requirements of flows and the response delay of the controller with the flow table capacity constraint. Based on the hybrid usage of reactive and proactive cache methods, the proposed scheme can efficiently utilize the flow rule caches while satisfying the delay requirement. The proposed scheme was formulated with ILP and solved with the heuristic algorithm. The simulation results demonstrate that the proposed scheme outperformed the existing schemes in terms of the flow table utilization ratio, average response delay, and flow rules hit ratio considering the flow arrival rate and prediction errors. For future works, the proposed scheme will be applied to the OpenFlow-based physical SDN platform to show the practical performance.  
**Author Contributions:** Conceptualization, Y.K. ([PERSON]), J.P. and Y.K. ([PERSON]); methodology, Y.K. ([PERSON]); software, Y.K. ([PERSON]); validation, Y.K. ([PERSON]), J.P. and Y.K. ([PERSON]); formal analysis, investigation, Y.K. ([PERSON]); resources, data curation, J.P.; writing--original draft preparation, Y.K. ([PERSON]); writing--review and editing, Y.K. ([PERSON]); visualization, Y.K. ([PERSON]); supervision, Y.K. ([PERSON]); project administration, J.P.; funding acquisition, Y.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
Figure 5: Flow rules hit ratio according to the mobility prediction error.  
**Funding:** This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No.2020R1G1A1100493).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Weighted \(z\)-Distance-Based Clustering and Its Application to Time-Series Data
## 4 Experimental Results
### Time-Series Datasets
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & & **K-Means** & **TSKmeans** & **DSKmeans** & **FCM** & **Gmm** & **SCC-IW** \\ \hline \multirow{3}{*}{CBF} & DBI & 1.9847 & 2.2397 & 2.2115 & 1.5132 & 1.6953 & **1.2029** \\  & DI & 0.2980 & 0.2968 & 0.2695 & 0.2882 & 0.3198 & **0.3452** \\  & SI & 0.2862 & 0.2236 & 0.2294 & 0.2071 & 0.3135 & **0.3981** \\ \hline \multirow{3}{*}{Coffee} & DBI & 1.5204 & 1.4311 & 1.4311 & 1.7645 & 1.4311 & **1.1966** \\  & DI & 0.3008 & 0.2586 & 0.2586 & 0.2633 & 0.3045 & **0.5019** \\  & SI & 0.4353 & 0.4447 & 0.4447 & 0.2749 & 0.4447 & **0.6344** \\ \hline \multirow{3}{*}{ECG5} & DBI & 1.2276 & 1.2320 & 1.2318 & 1.2287 & 1.2168 & **1.0752** \\  & DI & 0.0433 & 0.0384 & 0.0386 & 0.0274 & **0.0574** & 0.0239 \\  & SI & 0.5446 & 0.5424 & 0.5161 & 0.5445 & 0.5445 & **0.5452** \\ \hline \multirow{3}{*}{Face4} & DBI & 1.4030 & 1.9185 & 1.8185 & 1.8166 & 1.3659 & **1.3381** \\  & DI & **0.3399** & 0.2176 & 0.2469 & 0.2742 & 0.3284 & 0.2735 \\  & SI & **0.4301** & 0.2547 & 0.2279 & 0.2475 & 0.4036 & 0.2523 \\ \hline \multirow{3}{*}{Light7} & DBI & 1.4136 & 1.8542 & 1.6855 & 1.4740 & 1.6682 & **1.2476** \\  & DI & **0.3324** & 0.2275 & 0.2871 & 0.2811 & 0.3151 & 0.2181 \\  & SI & **0.3493** & 0.1827 & 0.2921 & 0.2494 & 0.2914 & 0.2025 \\ \hline \multirow{3}{*}{Oil} & DBI & 1.0658 & 1.0559 & 1.5853 & 1.6679 & 1.2671 & **0.6984** \\  & DI & 0.3941 & 0.3000 & 0.1660 & 0.2279 & 0.3062 & **0.4959** \\  & SI & **0.5317** & 0.5261 & 0.2448 & 0.1893 & 0.4837 & 0.4494 \\ \hline \multirow{3}{*}{OSU\_leaf} & DBI & 2.0969 & 2.1381 & 2.3428 & 2.0592 & 2.2647 & **1.3514** \\  & DI & 0.2569 & **0.2712** & 0.2561 & 0.1837 & 0.2507 & 0.1365 \\  & SI & 0.2661 & 0.2529 & 0.2256 & 0.1241 & 0.2347 & **0.2755** \\ \hline \multirow{3}{*}{Sony\_Surf} & DBI & 2.0878 & 1.8717 & 1.9832 & 2.6212 & 2.0551 & **0.8246** \\  & DI & **0.2673** & 0.2246 & 0.2112 & 0.2314 & 0.2449 & 0.2196 \\  & SI & **0.2737** & 0.2612 & 0.2021 & 0.2296 & 0.2579 & 0.2267 \\ \hline \multirow{3}{*}{Synthetic} & DBI & 2.0156 & 2.2560 & 3.0803 & 3.3852 & **1.2028** & 1.2416 \\  & DI & 0.3088 & 0.2972 & 0.2694 & 0.2571 & 0.2518 & **0.6108** \\  & SI & **0.4830** & 0.4575 & 0.2430 & 0.1936 & 0.3557 & 0.4368 \\ \hline \multirow{3}{*}{Trace} & DBI & 0.7461 & 0.9415 & 1.0117 & 1.0132 & **0.6862** & 1.3145 \\  & DI & 0.1466 & 0.1029 & 0.0462 & 0.0662 & 0.1434 & **0.2315** \\ \cline{1-1}  & SI & **0.7013** & 0.4461 & 0.5390 & 0.6779 & 0.6926 & 0.6023 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Averaged ranking of 10 time-series datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SCC-IW vs** & **K-Means** & **TSKmeans** & **DSKmeans** & **FCM** & **Gmm** \\ \hline Fscore & 4.3834 & 4.3753 & 3.2550 & 2.2606 & 6.4330 \\ RI & 1.9429 & 1.9228 & 1.3644 & 1.7049 & 3.8533 \\ DBI & 2.5339 & 3.7467 & 3.5688 & 2.9619 & 2.0451 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparisons of DBI, DI, and SI on 10 time-series datasets.
## 3 System Model
### V2V Channel Model for Straight Road Scenario
#### 3.2.1 Modeling of the LOS Component  
According to Figure 1, \(D_{TR}^{LOS}\) represents the LOS propagation path between \(T_{X}\) and \(R_{X}\). Thus, the channel fading of the direct path \(h_{TR}^{LOS}\) can be expressed as follows:  
\[h_{TR}^{LOS}(t)=\sqrt{R/(R+1)}*e^{-2\pi f_{c}D_{TR}^{LOS}/c}*e^{2\pi t(f_{T}^{ LOS}+f_{R}^{LOS})}, \tag{2}\]  
where \(R\) is the Rician factor, representing the ratio of the average power of the LOS component to the total power of all NLOS components, used to characterize the strength of multipath components during signal transmission. \(c\) is the speed of the wave, which is approximately \(3\cdot 10^{8}\) m/s. \(f_{c}\) is the carrier frequency, while \(f_{T}^{LOS}\) and \(f_{R}^{LOS}\) are the Doppler shifts caused by the movement of the transmitting and receiving vehicles, respectively, affecting the LOS components. The specific formulas are given as follows:  
\[f_{R}^{LOS}=f_{c}v_{R_{x}}\cos(\alpha_{R_{0}}-\beta_{R})/c, \tag{3}\]  
\[f_{T}^{LOS}=f_{c}v_{T_{x}}\cos(\alpha_{T_{0}}-\beta_{T})/c. \tag{4}\]
## 5 So, How Can We Take It to the Skies?
### Designing a Base Plate for Proposed Support Structure and Full Assembly  
A base plate is designed for two linear guides to be attached, where the plate will be bolted to the helicopter floor accordingly for different helicopter models. A support block will be bolted onto the base plate for mounting the ball screw shaft, and two locking brackets are installed to the sides of the base plate for locking the entire CT scanner in a stowed position (Figure 9).  
The ball nut is fixed to the support block, then the ball screw shaft is inserted into the support structure, through the ball nut and support block. A motor is then attached to a gearbox, where the gearbox is mounted onto the support structure and attached to the ball screw shaft. The other end of the ball screw shaft will be supported with a ball bearing which is inserted into a hole located inside the support structure. A gearbox with a worm gear and worm wheel is selected to increase the output torque and redirect the transmitted torque perpendicularly; this is to reduce the overall length of the entire CT scanner model. Finally, the modified gantry is mounted onto the support structure via a dovetail joint, where the support structure is secured onto the sliders, which are mounted onto the linear guides (Figure 10).  
Figure 8: Support structure for the modified gantry.  
Figure 9: CAD model of base plate design.  
By comparing with the original CereTom model (Figure 4) and the newly modified CT scanner model (Figure 11), it can be seen that the lower compartment was removed in the CAD model, where the default caster wheel and caterpillar track mechanism were removed altogether. The battery units as well as the computers which were concealed within the lower compartment are repositioned away from the main structure. Furthermore, the circular outer frame along with the LCD control unit were removed and repositioned, respectively, while the lead curtains were modified to attach directly to the gantry.  
As the interior of air MSU will be exposed to dusty and harsh environments of regional Australia during operation, dust build-up from use over time may cause the default caterpillar tracks to slip during actuation and impede the performance of the CT scanner. Therefore, it was replaced with a highly dust-resistant ball screw design to tackle on this issue. Additionally, the support structure was designed to conceal the entire ball screw mechanism for additional dust protection. By comparing with the original CereTom model with the newly modified CT scanner model, the total height, width, and depth have been decreased by approximately 33 cm, 34 cm, and 29.4 cm, respectively. The current design allows the CT scanner to travel for up to 40 cm in the longitudinal direction (Figure 12).  
Figure 11: Fully assembled concept CT scanner model.  
Figure 10: Assembly of concept CT scanner model.
# Recent Advances in Optical Injection Locking for Visible Light Communication Applications
## 4 Applications of OIL in Visible Light Communication Systems
### The Applications of OIL in Fiber Optical Communication Systems  
Hybrid radio over fiber- and laser-based VLC systems could support the integration of fiber backbones and indoor networks to provide integrated broadband services, including Internet and telecommunication services.  
In 2014, a phase modulation-based bidirectional hybrid radio over fiber- and VCSEL-based fiber optical communication systems (680 nm/red) was proposed and demonstrated, which employed optical injection-locked VCSEL-based PM in intensity modulation (IM) converters and optical interleavers [31]. The key setup was the PM-IM converters, which consisted of an optical circulator and a VCSEL. As the VCSEL was self-injection-locked, the upper sideband (+1 sideband) of the phase-modulated optical signal was amplified, while the lower sideband (\(-\)1 sideband) stayed unchanged; therefore, the OIL enhanced the intensity of the upper sideband. Ultimately, the bit error rate and clear-eye diagram achieved good transmission performance over a 40 km single-mode fiber (SMF) transmission and a 12 m free-space laser-based VLC link with red LD.  
The following year, another research team successfully applied the two-stage OIL and optoelectronic feedback technique in a bidirectional lightwave transport system, which was based on fiber-visible laser light communication integration [32]. Light was successfully modulated directly for cable television, 16-quadrature amplitude modulation (QAM) and 16-QAM-orthogonal frequency division multiplexing signals. The system made use of optical injection locked DFB LDs within infrared regime (LD2 and LD3). The optical output of DFB LD2 was injected into DFB LD3 via an optical circulator. Not only was the channel capacity doubled, but good carrier-to-noise ratio, composite second-order distortion, composite triple beat distortion and qualified bit error rate values were also obtained over a 40 km SMF, a 1.43 km photonic crystal fiber and a 6 m free-space laser-based VLC transport system.  
The two aforementioned bidirectional lightwave transport systems show that fiber-laser-based VLC systems could have significant potential for providing integrated broadband services, such as cable television, Internet and telecommunication services, via optical fibers and indoor free-space networks.
# Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
## 3 Method and Algorithm
### Paillier Federated Network
#### 3.4.1 Paillier Algorithm  
As mentioned above, Paillier encryption is a partially homomorphic encryption satisfying additive homomorphism. It can be divided into three parts: key generation, encryption, and decryption.  
**Key generation**: First of all, select two primes \(p\) and \(q\) that are sufficiently large and equal length that also satisfy \(\gcd\left(p*q,(p-1)*(q-1)\right)=1\). Then, calculate \(n\) and \(\lambda\) as:  
\[n=p\cdot q \tag{7}\]  
\[\lambda=lcm(p-1,q-1) \tag{8}\]  
An integer \(g\) is selected randomly and satisfies \(g\in Z_{n^{2}}^{*}\), so that \(n\) can divide the order of \(g\). Then, define \(L(x)\) to calculate \(\mu\) as:  
\[L(x)=\frac{(x-1)}{n} \tag{9}\]  
\[\mu=(L(g^{\lambda}\ mod\ n^{2}))^{-1}\ mod\ n \tag{10}\]  
Thus far, we can get the public key as \((n,g)\) and the private key as \((\lambda,\mu)\).  
**Encryption**: Assuming the plaintext is \(m\), and the ciphertext is \(c\), the encryption process with the public key can be noted as:  
\[c=g^{m}\cdot r^{n}\ mod\ n^{2} \tag{11}\]  
**Decryption**: Accordingly, using a private key to decrypt the ciphertext \(c\) and the plaintext \(m\) is:  
\[m=L(c^{\lambda}\ mod\ n^{2})\cdot\mu\ mod\ n \tag{12}\]