esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Performance of the Supernet  
Here, The performance curve of supernet during the search process is illustrated in Figure 3, including the curve of loss functions, precision, recall, and mean average precision (mAP). Blue lines in Figure 3 is the averaged value among all 80 classes in COCO dataset. Three left columns report the tendency of bounding box loss, objectiveness loss, and classification loss. Two right figures on the top report the precision and recall of training set. Two right figures on the bottom report the mAP of validation set. Figure 3 shows that the supernet gradually converges during the search process, demonstrating the great convergence ability of our search method.  
The precision-recall, and F1-score curve of each classes are illustrated in Figure
## 5 Experimental Validation
### Experimental Results  
System identification of the SDOF shear building specimen was conducted prior to designing the LQR for the AMD. The identified natural frequency and damping ratio of the specimen were 0.905 Hz and 0.25%, respectively. The SOS metaheuristic optimization approach was applied to minimize the objective function as exposed in Equation (12). Since the specimen had only single degree-of-freedom, the Equation (12) became to minimize the peak absolute acceleration of the specimen. The procedure of design of LQR as well as training of MLP and ARX models for the experiments are illustrated in Figure 13. During the optimization of weighting matrices using SOS, a BLWM with a PGA of 0.8 m/s\({}^{2}\), a bandwidth from 0 to 15 Hz, and a duration of 140 s was used to excite the identified structural model with a time step of 0.005 sec. Accordingly, the weightings \(Q\) and \(R\) in the cost function shown in Equation (6) were optimized and the optimal value of \(Q\) and \(R\) were 1.8357 and 100, respectively in the experimental validation. Accordingly, the state feedback gain \(\mathbf{K_{g}}\) was obtained as [\(-\)0.2393, 0.1329]. Afterwards, the structural model was subjected to the band-limited white noise excitation again and the control force and structural acceleration were obtained. Then, MLP with 150 steps of acceleration (MLP-150) and the ARX controllers were trained offline using these data.  
Figure 12: Hardware and software layout for the validating tests.  
Eight of the fourteen earthquake ground acceleration records were selected as the excitation to the SDOF specimen. Note that the stroke of the AMD could limit the control force level applied to the specimen since the natural frequency of the specimen was 0.905 Hz. Therefore, the ground acceleration time histories were re-scaled to a PGA of 0.8 m/s\({}^{2}\) in the experimental validation. The LQR-SOS, MLP-150 and ARX were implemented in the experimental validation. A Kalman filter was designed based on the identified structural model in order to obtain the structural states for LQR state feedback control. Since both the MLP and ARX required the acceleration measured from the specimen to calculate the control force, the noise in the acceleration could result in high-frequency control force and enlarge the acceleration response. Therefore, a 4 th-order elliptic low-pass filter with a cutoff frequency of 15 Hz was implemented for the measured acceleration. Table 9 shows the experimental results of the three controllers with respect to the performance indices. Note that the specimen used in the validation was a SDOF shear building. Therefore, the performance indices \(J_{1}\) and \(J_{2}\) were identical. From Table 9, it
## 2 Proposed Methods
### Path-Optimized Search with EWMA Control
To ensure that the search path covers the curve's peak, the minimal fine-search interval \([l_{A},l_{B}]\) around \(P_{coarse}\) is first determined, where \(l_{A}=P_{coarse}+(\Delta+D)/2\), \(l_{A}=P_{coarse}-(\Delta+D)/2\). Then, scanning is driven to sample with a small step \(d\) (target accuracy) in this interval and obtain an image stack \(\mathcal{S}\). The focus position is calculated as follows: \(P_{fine}=l_{B}-\left\{\text{argmax}[F(\mathcal{S})]\right\}d\).
# CANPerFL: Improve In-Vehicle Intrusion Detection Performance by Sharing Knowledge
## 1 Introduction
Nevertheless, the majority of car manufacturers do not share their CAN bus dataset due to confidentiality. As a result, a federated learning scheme is suitable in this circumstance. To overcome this challenge, we propose a personalized federated learning training system called CANPerFL, which extracts a shared feature representation between different car models. In the CANPerFL, all car manufacturers will jointly train a global model which will be then treated as a pretrained model for individual transfer learning of each client. Because the global model learns diverse patterns of various CAN datasets, the latent features extracted from the model will help improve the performance of each client's model by training the frozen global model with a top classification layer on local data. To sum up, the main contributions of our research are as follows. 1. We propose a novel system called CANPerFL which utilizes federated learning to develop a robust in-vehicle IDS, in which different car manufacturers can collaborate in learning to detect attacks without violating data privacy. To the best of our knowledge, our study is the first in applying federated learning across different car manufacturers for learning the IDS model. 2. We collect real CAN datasets consisting of three different car models: Kia, Tesla, and BMW to test the proposed idea with several extensive experiments. 3. The proposed system improves the F1 score of the local models by 4% on average. In addition, we empirically prove that the personalized FL can automatically encourage each client to participate in the global training process, which provides the global learning process a better representation. The rest of this paper is structured as follows. Section 2 introduces fundamental knowledge about CAN bus as well as security threat models focused on in this study. Then, we provide a review of existing studies related to in-vehicle IDS in Section 3. Next, the details of the proposed system, i.e., personalized federated learning will be described in Section 4. In Section 5, we demonstrate various comprehensive experiments which show the effectiveness of our idea. Finally, the paper ends with Section 6, which summarizes our key findings and provides promising ideas for the future.
# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 3 Methodology
### Data Manipulation
#### 3.3.2 Data Pre-Processing  
The raw data from Tobii software provides a significant amount of information by default, while only some of which are necessary for creating a Markov Model. It originally came in JSON format, and Tobii Studio converted it into CSV files. We saved the raw data into a MongoDB table to get a quick overview of it. There were only six attributes in the MongoDB database that were relevant to our research: \"id eyetracker,\" \"participant name,\" \"local timestamp,\" \"GazePointX (ADCSpx),\" \"GazePointY (ADCSpx),\" and \"gaze event kind.\" Initially, there were 59 columns in the table. The six variables extracted are described below:  
* id eye-tracker-time sequential ordered list based on the timestamp where each recording was taken (i.e., the first recording is 1, the second is 2 etc.);
* participant name-14 participants (P001-P014);
* local timestamp-timestamp taken every 00:00:00.165 s (i.e., 10:07:46.441);
* GazePointX (ADCSpx)- the co-ordinates of the gaze-point in the X-direction;
* GazePointY (ADCSpx)-the co-ordinates of the gaze-point in the Y-direction;
* gaze event type-can be \"Fixation,\" \"Saccade,\" or \"Unclassified.\"  
Those variables are extracted for each participant, and all of the data is combined in one database using a JavaScript program before the coordinates can be defined as AOIs. As the participants were looking at different parts of the BT Player app during the experiment, it is only the times during which they were on the Purchase Flow and Content Discovery pages that are of interest; therefore, the recordings of each participant in the study were reviewed, and the timestamps based on when the participant was on relevant pages were recorded.  
However, some of the columns pertaining to the aforementioned key properties originally contained blank values. This is because the eye tracker continues to record throughout the whole trial, including the participants' eye movements when they glance away from the TV screen. The only valid region for the eye tracker in this experimental configuration is the TV screen. Once the gaze point falls outside of the barrier, the columns \"GazePointX (ADCSpx)\" and \"GazePointY (ADCSpx)L\" will have null values. A Java application is used to assign zero values (i.e., (0,0)) to certain types of records in order to maintain their status as objective data in this study. The application connects to the MongoDB database and replaces blank values in target columns with zeros in a recursive manner. By extracting information from these six important columns, irrelevant attributes from the original dataset are deleted. A MySQL database is created to store the data in order to maintain a well-structured data schema and conduct queries using rows and columns as opposed to JSON-formatted data (e.g., csv files). To do this, a Java program is written that can connect to both the MongoDB and MySQL databases.
## 4 Evaluation
### Scalability  
In parallel computing, scalability refers to a parallel system's ability to adjust its performance and costs according to changes in application and system requirements. The speedup [31] of an system is often used as a measure of its scalability. In this section, F3 BFS and Ceph are compared for their IO throughput speedups, which can be roughly calculated as the following equation:  
\[S=\frac{T_{n}}{T_{1}} \tag{1}\]  
where \(S\) is the I/O throughput speedup, \(T_{1}\) is the I/O throughput when there is only one data storage and \(T_{n}\) is the I/O throughput when the data storage node numbers increase to \(n\).  
It is theoretically possible for the speedup of the system to increase linearly with the number of nodes if it is designed very carefully, which is called linear speedup. For most parallel systems, their ultimate optimization goal is to make its speedup curve converge to the linear speedup curve.  
Figure 7 shows the sequential read/write throughput speedup of F3 BFS and Ceph, in which the dashed line represents the linear speedup curve. The feeding dataset is biology sequences, which is the same as the dataset used in the throughput evaluation. For both read and write throughput, F3 BFS generally performs better than Ceph. Especially noteworthy, as shown in Figure 7b, is that the speedup is converged to about 920 MB after the number of storage nodes reaches 11 because the read throughput reaches the upper limit of the network bandwidth (10 GB) at this point. At this time, the speedup is currently restricted by the network rather than F3 BFS itself.  
Figure 6: IOSPS evaluation for sequences IO.  
Figure 7: Data throughput speedups with different cluster nodes.
# Nanomechanical Characterization of Bacterial Polyhydroxyalkanoates Using Atomic Force Microscopy
## 4 Discussion
### Elastic Modulus Measurements in PHA  
The PHA produced with a substrate of 100:0 HAc:HPr shows the highest elastic modulus. The latter, indeed, differently from all the others, has a minimal amount of HV units (2 mol%). Therefore, it is reasonable to think that its mechanical properties are not far from those exhibited by poly-3-hydroxybutyrate homopolymer (PHB), which is reported in the literature to have Elastic modulus always above 1 GPa, usually between 1 and 4 GPa [41; 42]. In our case, P(3 HB-co-2% 3 HV) shows an elastic modulus of 1623 \(\pm\) 172 MPa.  
All the other PHA samples, having a higher amount of HV units than the case of the PHA produced with a substrate of 100:0 HAc:HPr, show lower and similar values of elastic modulus, without a specific trend. This result is in line with the study of [PERSON] et al. [43], who found that PHBV blends produced by mixed cultures, despite the compositional differences, had similar Elastic modulus, measured by tensile test.  
This apparently unexpected behavior can be understood by referring to the crystal phases of the extracted PHA. Despite the compositional variation in terms of HV fraction among the different batches, all PHA highlight three main faster-crystallizing phases,  
Figure 4: Elastic modulus from AFM force–distance curves of P(3 HB-co-88% 3 HV) one, three, and seven days after the deposition of the thin film.  
having similar melting temperatures tested by DSC, and therefore similar compositions. Since mechanical properties tend to be dominated by the properties of the crystalline phases, no large variation of elastic moduli from one batch to another is expected. The only exception is represented by the case of P(3 HB-co-2% 3 HV). This sample, indeed, has a content of HV low enough to exclude the formation of blends, and a single phase rich in HB units.  
The main variation of elastic moduli for the extracted PHA, excluding P(3 HB-co-2% 3 HB), can be noticed for the batches produced with 30:70 HA:HPr and 0:100 HA:HPr. They show average values of 528 and 885 MPa, respectively, with an average difference of 357 MPa. Such modulus is rather small. However, it must be considered that the crystallization kinetics of each sample is not identical and usually slow. Therefore, it is possible that tests were not performed on materials with a crystallinity fully developed.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 3 Metal Selenide Thin Films
### Cadmium Selenide Films  
The films were produced under various immersion cycles (30, 40, 50, and 60) by using cadmium chloride, sodium selenosulfate (Na\({}_{2}\)SeSO\({}_{3}\)), and ethylene diamine tetra acetic acid (EDTA) (complexing agent) [95]. Generally, homogeneous morphology and the hexagonal phase could be seen in all samples. The peak intensity (corresponding to (101) plane) absorbance value increased, but the band gap decreased with the increasing number of immersion cycles. Tartaric acid (C\({}_{4}\)H\({}_{6}\)O\({}_{6}\)) was used as a complexing agent during the formation of thin films at 27 \({}^{\circ}\)C under 45 cycles [96]. The reaction mechanisms are highlighted in Equations (28)-(31).  
\[\mathrm{Na_{2}SeSO_{3}+OH^{-}}\rightarrow\mathrm{HSe^{-}+Na_{2}SO_{4}} \tag{28}\]  
\[\mathrm{OH^{-}+HSe^{-}}\rightarrow\mathrm{H_{2}O+Se^{2-}} \tag{29}\]  
\[\mathrm{[Cd(tartaric acid)]^{2+}\rightarrow tartaric acid+Cd^{2+}} \tag{30}\]  
\[\mathrm{Cd^{2+}+Se^{2-}\to CdSe} \tag{31}\]  
The high-resolution transmission electron microscopy (HRTEM) displayed small particle sizes (30-40 A) while the EDX spectrum showed that the atomic percentage of Cd:Se was 55:45. Resistivity reduced when the temperature increased, indicating the semiconductor nature of the films. The films prepared onto commercial glass slides at room temperature showed a cubic phase, a band gap of 2.1 eV, and electrical resistivity of about 10\({}^{6}\)\(\Omega\cdot\)cm [97].
## 5 Precoder and Combiner Design Based on Reinforcement Learning
### Agent  
Since the dimensions of the precoding and combining matrices to be optimized do not coincide, two sets of agents with different action dimensions are set to form the part of solving the precoding and combining matrices. For the state of each agent, characterized as the L2-norm value of each row of the interactively generated precoding matrix and combine matrix, the set of state dimensions and the set of action dimensions of the agent are denoted as \(dim_{state}=\left\{d_{1}^{state},d_{2}^{state},\cdots,d_{2N_{s}}^{state}\right\}\) and \(dim_{action}=\left\{d_{1}^{action},d_{2}^{action},\cdots,d_{2N_{s}}^{action}\right\}\).
## 3 Results and Discussions
### Result of AI-Based Model
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Training**} & \multicolumn{3}{c}{**Testing**} \\ \cline{2-9}  & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline ANN & 0.775 & 0.880 & 0.744 & 0.761 & 0.785 & 0.886 & 0.723 & 0.749 \\ GPR & 0.237 & 0.487 & 0.411 & 0.938 & 0.316 & 0.562 & 0.451 & 0.895 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance evaluation matrices. Figure 15: Scatter plot between the experimental and predicted flexural strength for (**a**) ANN and (**b**) GPR. A Taylor diagram and box plot are presented in Figure 16 to compare the performance of the two models better. Taylor plots were used to show the prediction accuracy of the GPR and ANN models in a schematic approach. Three statistical parameters, including the correlation coefficient, RMSE, and standard deviations, were used to evaluate the degree of compliance among the actual and predicted flexural strength. The Taylor diagram (Figure 16a) shows that the GPR model has higher correlation and lower RMSE values than ANN, which confirms that the GPR model performs better than the ANN model. Figure 16b presents a box plot in which the distribution of relative error of the two models is presented for the two models. The value of the minimum, median, and maximum error, and first quartile and third quartile, were the features used to judge the two models performance. The lower quartile (Q\({}_{25}\)) value of the ANN model was \(-0.0456\), and the upper quartile (Q\({}_{75}\)) value was \(0.552\), which was higher when compared with the GPR model where (Q\({}_{25}\)) and (Q\({}_{75}\)) is \(-0.0358\) and \(0.0395\), respectively. The minimum and maximum error of the ANN was \(-0.163\) and \(0.175\), and the GPR model was \(-0.126\) and \(0.1400\), respectively, which shows the capacity of the GPR to predict the flexural strength compared to the ANN model.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 3 Theoretical Background and Considerations
### True Random Frequency Hopping with RPUs  
RPUs are hardware devices designed to produce true random numbers by harnessing unpredictable physical phenomena, such as quantum noise or thermal fluctuations. Thesenumbers are fundamentally unpredictable and cannot be reproduced, providing a higher level of security compared to pseudo-random sequences.  
Let \(\{R_{o}\}\) denote a sequence of true random numbers generated by an RPU. The corresponding frequency hopping sequence can be represented as:  
\[f_{o}=f_{\text{min}}+(f_{\text{max}}-f_{\text{min}})\frac{R_{o}}{R_{\text{max}}} \tag{3}\]  
where:  
* \(R_{o}\) is the true random number for the \(o\)-th hop,
* \(R_{\text{max}}\) is the maximum value that \(R_{o}\) can take. The entropy \(H\) of the true random sequence \(\{R_{o}\}\) is given by: \[H=-\sum_{o}P(R_{o})\log P(R_{o})\] (4) In the case of uniformly distributed random numbers, \(P(R_{o})=\frac{1}{R_{\text{max}}}\) for all \(o\), leading to: \[H=\log R_{\text{max}}\] (5) This maximum entropy ensures that the sequence is completely unpredictable.  
The process of generating and synchronizing the frequency hopping sequence is depicted in Figure 1. This flowchart indicates the steps from generating true random numbers to initiating FHSS communication.  
The primary security advantage of using RPUs lies in the true randomness of the generated sequences. Even if an adversary has access to the transmitted signal, they cannot predict future frequency hops without direct access to the RPU or the shared random sequence. This feature significantly enhances the robustness of the system against interception and jamming.
## 2 CFD Methodology and Numerical Modeling
### CFD Numerical Model
#### 2.1.3 Numerical Modeling: Transpiration Model  
In CFD numerical modeling, mathematical models of crop transpiration have been widely used and are applied to simulate and predict the growth environment of different crops [45; 46; 47]. In this research, the UDF (user-defined function) including heat exchange (W\(\cdot\)m\({}^{-2}\cdot\)K\({}^{-1}\)) and water vapor (kg\(\cdot\)m\({}^{-3}\cdot\)s\({}^{-1}\)) release between the crop canopy and the air within the indoor environment was established. The energy equations for radiation and sensible and latent heat exchange used in the mathematical model are as follows [45]:  
\[R_{n}-\frac{\rho c_{p}L_{ai}(T_{l}-T_{a})}{r_{a}}-L_{ai}\rho\lambda\frac{w_{f} -w_{a}}{r_{s}+r_{a}}\ =\ 0 \tag{8}\]  
where \(R_{n}\) is the net radiation (W\(\cdot\)m\({}^{-2}\)) and \(\rho\) is the air density of the indoor environment (kg\(\cdot\)m\({}^{-3}\)). \(c_{p}\) and \(L_{ai}\) are the specific heat of air (J\(\cdot\)kg\({}^{-1}\cdot\)k\({}^{-1}\)) and the leaf area index of the crop, respectively. \(T_{l}\) and \(T_{a}\) represent the surface temperature (K) and the air temperature around the crop (K). The latent heat of water evaporation for crops \(\lambda\) is set to 2257 Kj\(\cdot\)kg\({}^{-1}\cdot w_{f}\) and \(w_{a}\) are the absolute humidity of crop leaves and indoor ambient air (kg\(\cdot\)m\({}^{-3}\)). \(r_{a}\) and \(r_{s}\) are air boundary layer resistance and crop leaf stomatal resistance (s\(\cdot\)m\({}^{-1}\)), respectively. In this research, the value of net radiation in the crop canopy is 65 W\(\cdot\)m\({}^{-2}\), the leaf area index of the crop is set to 2 m\({}^{2}\) m\({}^{-2}\) and the characteristic length of the leaf of the crop is taken as 0.11 m [25].  
Considering that the air velocity could significantly affect the boundary layer resistance, it is necessary to consider the case of different air velocity for the calculation of aerodynamic boundary layer resistance. On the one hand, the boundary layer resistance equation for the leaf surface air velocity (V \(<\) 0.1 m\(\cdot\)s\({}^{-1}\)) is as follows [45; 48]:  
\[r_{a}\ =\ 840\bigg{(}\frac{d}{\mid T_{1}-T_{a}\mid}\bigg{)}^{0.25} \tag{9}\]  
On the other hand, the boundary layer resistance equation for the leaf surface air velocity (V \(\geq\) 0.1 m\(\cdot\)s\({}^{-1}\)) is as follows [45; 49]:  
\[r_{a}\ =\ 220\frac{d^{0.2}}{V^{0.8}} \tag{10}\]  
where \(d\) and \(V\) denote the characteristic length of the leaf (m) and the wind speed around the surface of the leaf (m\(\cdot\)s\({}^{-1}\)), respectively.  
Here, the stomatal resistance \(r_{s}\) of the crop is as follows [45; 49]:  
\[r_{s}\ =\ \frac{200(31+R_{n})\big{[}1+0.016\times\big{(}T_{a}-16.4\big{)}^{2} \big{]}}{(6.7+R_{n})} \tag{11}\]  
Table 1 briefly summarizes the research methods used in some related numerical simulation studies. \"y\" means that the relevant method or model was used in the study, and \"n\" means that it was not used.