esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup  
### Double-Pulse Excitation of Water Flow  
Figure 1a shows the experimental setup. The water flow nozzle system with two pipette tips and a reservoir, which has been introduced previously [46], was used for creating a thin water flow. The flow thickness was measured at 17 \(\pm\) 2 \(\upmu\)m with a laser-based thickness measurement system (0.25 \(\upmu\)m resolution, SI-T80, Keyence, Osaka, Japan). The width of the flow is 8 mm at most, which is much wider than the laser focus size. More than 120 mL of water was being circulated by a pump (PMD-211, SANSO, Hyogo, Japan) with the flow rate at 70 mL/min during experiments. The estimated flow velocity at the laser focus is 5.8 m/s, which means that the water target moves about 1-cm in every laser irradiation and refreshes its surface. The flow angle and its position were controlled mechanically. The water flow position was monitored by a laser-based displacement sensor (0.2 \(\upmu\)m resolution, LK-G80, Keyence, Osaka, Japan).  
A pulsed femtosecond laser (\(>\)35 fs/transform-limited, 800 nm, TEM\({}_{00}\), M\({}^{2}\) \(<\) 1.3, the contrast ratio \(>\) 1000:1 to the pre-pulse and \(>\) 100:1 to the post pulse, Mantis, Legend Elite HE USP, Coherent, Inc., Santa Clara, CA, USA) was used. The laser beam was split into the pre-pulse (vertically/s- pol., 0.1 mJ/pulse) and the main pulse (horizontally/p- pol.). The remaining main pulse was split into the probe for THz time-domain spectroscopy (THz-TDS) and the main pulse (0.4 mJ/pulse) with a series of half-wave plates and polarization beam splitters (65-906, 47-048, Edmund Optics, Barrington, NJ, USA). The time delay between the pre-pulse and the main pulse was determined to be from 0 ns up to 15 ns using tunable optical delays, and these two pulses were combined co-linearly for the double-pulse excitation using a cubic beam splitter (47-048, Edmund Optics, Barrington, NJ, USA). This combined beam passed through an optical chopper (3502, New Focus, CA, USA) which reduced the repetition rate from 1 kHz to 0.5 kHz for the lock-in measurements. The combined beams were tightly focused with a 1-inch off-axis parabolic mirror (OAPM, effective focal length \(f\) = 50.8 mm, 47-097, Edmund Optics, Barrington, NJ, USA) onto the thin water film with the incident angle of 60 degrees to the flow normal.
## 2 Materials and Methods
### Method
#### 2.3.4 Model Evaluation  
Model performance was evaluated using the coefficient of determination (R2) and root mean square error (RMSE). R2, which ranges from 0 to 1, indicates how well the model explains the variance of the target variable. RMSE measures the average prediction error. The final performance metrics were based on the best of five iterations for each model.
# StereoVO: Learning Stereo Visual Odometry Approach Based on Optical Flow and Depth Information
## 4 Experimental Results
### Implementation Details  
For our experiments, we employed the PyTorch framework for implementing our work and trained it using a single Nvidia RTX2080S GPU. The parameters of Flow-Net remained fixed during training. To optimize our approach, we utilized the Adam optimizer with a batch size of 100 and a learning rate of \(10^{-3}\) for 1000 epochs. Subsequently, we adjusted the learning rate to \(10^{-4}\) for 200 epochs, followed by another 50 epochs at a learning rate of \(10^{-5}\). We eventually fine-tuned our network via a batch size of 10 and a learning rate of \(10^{-5}\).
## 3 The Methodology
### Simulation Study
#### 4.1.1 Impact of Throughput  
Throughput is defined as the average number of packets successfully received at the destination node. Figure 6 represents the impact of throughput on the number of nodes. Applying four different deep neural networks for processing the inputs decreases the throughput, and it is implemented in BDCS-IoMT. Likewise, EIR-CIoT uses interoperability and energy-aware routing for throughput optimization. The RSS-based CH is elected for information transfer. RSS with beacon message transmission increases the communication  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Parameter** & **Value** \\ \hline Imitation zone & \(1000\times 1000\) m \\ \hline Quantity of radar node & 100 \\ \hline Deployment & Random \\ \hline MAC layer & IEEE 802.15.4 \\ \hline Control message & 20 bits \\ \hline Original oomph of node & 750 J \\ \hline Packet amount & 400 \\ \hline Retransmission amount & 7 (Max) \\ \hline Size of packet & 12 KB \\ \hline Interval of packet & 10 \(\mu\)S \\ \hline Communication range in sensor & 200 m \\ \hline Rate of data & 88 Mbps (Max) \\ \hline Slots amount & 16 \\ \hline Slot period & 10 \(\mu\)S \\ \hline \multirow{4}{*}{SRO} & \(SE\) & 0.05 \\ \cline{2-3}  & \(MUI\) & 70 D \\ \cline{2-3}  & R & 5 \\ \cline{2-3}  & Number of iterations & 100 \\ \hline Number of rounds & 100 \\ \hline Simulation time & 100 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
and the computational overhead; hence, the performance of throughput decreases as the number of nodes increases.  
However, uncertainty in data forwarding increases this overhead and reduces the throughput level. This challenge is focused on employing the optimum route using the SRO algorithm. As the objective of routing is lower communication overhead and energy-balanced information transfer, SRO determines the global optimum solution. Further, the elimination of unauthorized nodes in the network and employing authentication decrease the overhead and increase the chance of throughput. For instance, EIRCiToT obtains 190 Kbps throughput for 40 nodes, whereas BDCS-IoMT obtains 230 Kbps throughput for 40 nodes, and 280 Kbps and 320 Kbps for throughput, respectively.
# A 0.18 um CMOS Millimeter Wave Antenna-on-Chip with Artificial Magnetic Conductor Design
## 5 Conclusions
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# Alternative Design of Binary Phase Diffractive Optical Element with Non-\(\pi\) Phase Difference
## 3 Simulation
Figure 5: Simulated reconstructed intensity distribution of the traditional 0-\(\pi\) DOE for 1 \(\times\) 7 beam splitting with etching depth of (**a**) 562 nm; (**b**) 712 nm; (**c**) 762 nm. Figure 3: Calculated phase distributions and the reconstructed patterns: (**a**) proposed non 0-\(\pi\) design result, (**b**) conventional 0-\(\pi\) design result. In Figure 3, we find that as the number of diffraction orders contained in the target pattern increased, the optimal phase difference moved toward \(\pi\) gradually. In order to verify this speculation, we chose the square pattern consisting of N \(\times\) N orders as the target light field, as shown in the inset of Figure 6, and performed the non 0-\(\pi\) DOE design. The obtained optimal phase difference under different numbers of diffraction orders N is plotted in Figure 6. It can be seen that as N increased from 3 to 11, the optimal phase difference increased rapidly from 0.8\(\pi\) to 0.955\(\pi\). Thereafter, with the increase of N, the optimal phase difference increased slowly. When N was equal to 81, the optimal phase difference reached 0.995\(\pi\). From the variation law of the curve, we can conclude that when a target light field contains a small number of diffraction orders, a non 0-\(\pi\) DOE design can be considered to improve the diffraction efficiency. Conversely, when the target light field contains a large number of diffraction orders, it is recommended to design the traditional 0-\(\pi\) DOE.
## 4 Tabu Search
### The Framework of the Algorithm  
The pseudocode of our tabu search algorithm is provided in Algorithm 1. The initial solution is generated by the procedure described in Section 4.2. Starting from the initial solution, in each iteration, the search process identifies the best allowable neighbor of \(S_{current}\) and then replaces \(S_{current}\) with this neighboring solution. Subsequently, it tries to update the current best solution \(S_{best}\). If the new best solution is encountered, then \(\xi\) is reset to zero; otherwise, \(\xi\) is increased by one. The iteration ends after updating the tabu list. Once no new best solution is found after _maxIter_consecutive iterations, the search process terminates and outputs \(S_{best}\) as the final solution. The flow chart of the whole solution is shown in Figure 9.  
```
1:The current solution \(S_{current}\leftarrow\) an initial solution, the best solution \(S_{best}\gets S_{current}\), the tabu iteration index \(\tau\gets 1\), the tabu list \(TL\leftarrow\emptyset\) and the number of consecutive iterations without improving solutions \(\xi\gets 0\);
2:while\(\xi\leq\)maxIterdo
3:\(S_{current}\leftarrow\) the best allowable solution in the neighborhood of \(S_{current}\);
4:if\(C(S_{current})<C(S_{best})\)then
5:\(S_{best}\gets S_{current}\);
6:\(\xi\gets 0\);
7:else
8:\(\xi\leftarrow\xi+1\);
9:endif
10: Update \(TL\);
11:endwhile
12:return\(S_{best}\).
```  
**Algorithm 1** The tabu search algorithm.
# Speech Emotion Recognition through Hybrid Features and Convolutional Neural Network
## 3 Materials and Methods
### Datasets  
EMO-DB: The proposed methodology begins by collecting publicly available datasets. For this purpose, EMO-DB [35] was chosen as one of the datasets due to its widespread usage in the area of SER. EMO-DB is a German database that contains 535 audios of varying duration, recorded by 5 male and 5 female professional speakers. These audios are categorized into seven emotions: anger, boredom, disgust, fear, happiness, neutrality, and sadness. To ensure consistent speech quality, all audio files were recorded at a 16 kHz sampling rate, saved in wav format, with mono-channel and 16 bits per sample. The waveforms of the seven emotions are illustrated in Figure 3.  
RAVDESS: RAVDESS [36] is the second dataset used in the experiments due to its greater accessibility. This dataset includes 1440 audio files, recorded by 12 male and 12 female actors speaking English scripts with 8 different emotions: anger, calmness, disgust, fear, neutrality, happiness, sadness, and surprise. All recorded audio files have a 48 kHz sample rate and a 16-bit resolution.  
SAVEE: This study also utilized the SAVEE [37] dataset, which was recorded by 4 male researchers and students at the Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey. Each speaker was asked to speak 120 phonetically balanced English sentences in 7 emotional categories: anger, disgust, fear, happy, neutrality, sad, and surprise. This resulted in a total of 480 utterances.
# Mathematics-Driven Analysis of Offshore Green Hydrogen Stations
## 2 Materials and Methods
### PHASE V: Integration of All Components into a Cohesive System
Based on this relationship, using equation (Equation (43)) and considering that the molecular mass of water (\(m_{h20}\)) is equal to 18 \(\times\) 10\({}^{-3}\)\(\frac{\mathrm{kg}}{\mathrm{mol}}\) and that the density of seawater (\(\rho_{h20}\)) is approximately 1.02819 \(\frac{\mathrm{kg}}{\mathrm{T}}\), the estimation of the water flow required at the station's inlet (\(\frac{\mathrm{1}}{\mathrm{sec}}\)), for the production of a given number of \(\frac{\mathrm{mol}}{\mathrm{s}}\) of hydrogen, and as a function of the available photovoltaic panel surface area (m\({}^{2}\)), can be calculated as follows:  
\[\frac{1}{\mathrm{s}\times\mathrm{m}^{2}}=\frac{\left(\frac{\mathrm{mol}}{ \mathrm{s}\times\mathrm{m}^{2}}\right)\times m_{h20}}{\rho_{h20}} \tag{45}\]By isolating the photovoltaic panel surface area in equation (Equation (43)), the required number of square meters (m\({}^{2}\)) needed for the production of 1 \(\frac{\text{mol}}{\text{s}}\) of hydrogen is obtained:  
\[\text{m}^{2}=(1\ \frac{\text{mol}}{\text{s}})\times\frac{\frac{HV}{K_{ electronlayer,i}}*1000+\left|\frac{R\ \times(T_{\text{{\it out}}}-T_{\text{{\it in}}})}{K_{compressor}\times(1-Y)} \right|}{I_{n\_total}\times K_{panels}} \tag{46}\]  
Similarly, by isolating the photovoltaic panel surface area in equation (Equation (44)), the required number of square meters (m\({}^{2}\)) needed to produce 1 \(\frac{\text{kg}}{\text{s}}\) of hydrogen is obtained:  
\[\text{m}^{2}=(1\ \frac{\text{kg}}{\text{s}})\times\frac{1}{\left(\frac{\text{mol }}{\text{s}\times\text{m}^{2}}\right)\times m_{h2}} \tag{47}\]  
Likewise, by isolating the photovoltaic panel surface area in equation (Equation (45)), the required number of square meters (m\({}^{2}\)) needed for the consumption of 1 \(\frac{1}{\text{s}}\) of H\({}_{2}\)O is obtained:  
\[\text{m}^{2}=(1\ \frac{1}{\text{s}})\times\frac{\rho_{h20}}{\left(\frac{\text{mol }}{\text{s}\times\text{m}^{2}}\right)\times m_{h20}} \tag{48}\]  
Expressions (Equations (46)-(48)) can be used as comparative elements for different locations, green hydrogen production, and compression technologies. Thus, for the same amount of hydrogen produced (\(\frac{\text{mol}}{\text{s}}\), \(\frac{\text{kg}}{\text{s}}\)) and stored at the same output pressure (\(P_{out}\)), the most efficient location and green hydrogen station will be the one that achieves these values using the least photovoltaic panel surface area (m\({}^{2}\)). In other words, the station that requires lower energy consumption will be more efficient. The higher the efficiency of the green hydrogen station, the lower the associated costs related to installation, commissioning, and maintenance throughout its operational lifetime.
# Effects and Mechanism of Plasma-Activated Medium on Angiogenesis of Vascular Endothelial Cells
## 4 Discussion
However, PAM produced by long-term LTP treatment inhibited the secretion of VEGF and bFGF, and VEGF-VEGF-2 and MEK-ERK signaling pathways, thereby inhibiting the angiogenesis of cells. Under normal circumstances, the RONS level of organisms is in a relatively stable dynamic balance, but long-term or high-concentration of external RONS can produce a strong cytotoxic effect on cells [39, 40]. Studies have shown that exogenous H\({}_{2}\)O\({}_{2}\) can inhibit angiogenesis induced by bFGF or VEGF [41]. High concentration of exogenous NOitself has cytotoxicity and strong oxidation, which can make cell lipid peroxidation, inhibit cell activity and inhibit angiogenesis. The effects of RONS on different types of cells vary depending on the type of cells, the environment they are in, and the response and tolerance of cells to RONS [42; 43]. RONS are important components of LTP to exert the biological activity. According to the results in Figures 8 and 9, as the LTP treatment time extended, the RONS concentration in PAM and cells gradually increased. The results of this study also confirm that LTP has the characteristics of \"short-term treatment promoting cell activity and angiogenesis, while long-term treatment inhibiting cell activity and angiogenesis,\" mainly through LTP-generated RONS. The possible mechanism of PAM affecting HUVECs angiogenesis is shown in Figure 10. PAM obtained by LTP treatment for 15-30 s can promote the proliferation and angiogenesis of HUVECs, which suggests that we can promote angiogenesis by controlling and quantizing ROS level in cells through precise LTP production conditions. PAM obtained by long-term LTP treatment can inhibit cell proliferation, migration, adhesion and angiogenesis, which provides a new idea for the application of PAM in the treatment of tumors.
# Kashif: A Chrome Extension for Classifying Arabic Content on Web Pages Using Machine Learning
## 3 Methodology  
This study uses machine learning (ML) to classify Arabic websites as benign, suspicious, or phishing. As depicted in Figure 1, we followed two main phases to satisfy the study goal.  
In the first phase, the dataset was collected based on the keywords of Google Trends to find the maximum number of phishing and suspicious websites. Google Trends [19] is a service provided by Google that provides public discovery about the trends of people's search behavior within Google Search. After collecting the keywords, Helium Scraper tool [20] was utilized to collect the URLs. The Helium Scraper tool is manufactured by Helium 10 in Los Angeles, California, United States. The dataset was then cleaned by handling the missing values and the noisy data in the pre-processing step.  
The dataset was preprocessed in three steps. First, the data were cleaned by filling in the missing values. Second, noisy data are handled, such as removing non-Arabic web pages, not-found pages, or pages with an internal server error. Third, alphabets were  
Figure 1: Methodology.  
represented as numerical values in the dataset. For instance, we count the lengthy popular keywords instead of using the keyword itself.  
The URLs were then classified and labeled as benign, suspicious, or phishing using VirusTotal [21]. This was followed by the feature engineering step, which includes feature extraction and selection. The URL's lexical, content-based, and network-based features were extracted from the dataset. The most suitable features that enhance the accuracy were selected using the following methods: correlation, hi-square, and ANOVA. Then, we applied four ML classification algorithms: RF, XGBoost, SVM, and DT. Those algorithms were selected based on our previous survey of applying ML techniques to detect malicious URLs [8]. We evaluated the models using four evaluation metrics: accuracy, recall, F1 score, and precision.  
The best-performing model was then used to create the website extension. Lastly, we tested the functionality by loading the website extension on the Chrome browser and determining whether a given URL would be correctly classified in a short time.
## 4 Conclusions  
In summary, we implemented a collinear scheme to generate few-cycle DUV and XUV pulses in a gas, separating the resulting pulses spectrally and spatially by a perforated dichroic beam separator, forming a pump-probe experimental configuration. Additionally, we have demonstrated that by utilizing the [PERSON]'s phase stabilization scheme in a DLI, the timing jitter between the pump and probe pulses can be stabilized down to 12.9 \(\pm\) 0.8 as RMS of short-term jitter (0.5 Hz-41.5 kHz) and ~80 for long-term peak-to-peak drift over ~2 h. This allows continuous pump-probe delay scans over a range of 200 fs with attosecond timing precision. In addition, we demonstrated exceptional sub-20 as passive stability of our system, only requiring active stabilization to compensate for long-term thermal drift on >40 min time scales. A DLI setup solves the challenge of using a visible-light reference laser for optical phase stabilization and phase-locked delay scanning with a DUV and XUV pump-probe configuration. This excellent optical phase stability, combined with the ultrashort DUV/XUV pulses, enables the investigation of femtosecond and attosecond electron dynamics [28] with the highest possible timing precision, which is faster than the molecule's vibrational response.  
Conceptualization, D.-E.K.; methodology, T.K. and A.G.; software, A.G.; validation, T.K., A.G. and J.H.; formal analysis, T.K., A.G. and J.-H.M. investigation, T.K. and J.-H.M.; resources, Y.L.; data curation, T.K. and A.G.; writing--original draft preparation, T.K. and A.G.; writing--review and editing, T.K., A.G. and D.-E.K.; visualization, T.K. and Y.L.; supervision, D.-E.K.; project administration, D.-E.K.; funding acquisition, D.-E.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Max Planck POSTECH/KOREA Research Initiative Program, grant number 2016K1A4A4A01922028, through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning, and in part by the National Research Foundation of Korea (NRF), grant numbers 2020R1C1C1012953 and 2021R11A1A01057547.  
Not applicable.  
Figure 4: Phase-locked delay scan and stability of the double-layer interferometer over an optical delay range of 200 fs. (**a**) Normalized fringe amplitude in the lower-layer interferometer as a function of optical delay. (**b**) Timing jitter calculated from the fluctuations in the recorded interference signal within 2 s of measurement time for each delay step, yielding an average of 12.9 as RMS. Data points within 10% of the interference extrema have been removed due to unreliable conversion between amplitude and timing there (see text).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical restrictions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Fractional Frequency Reuse Optimal SINR Threshold Selection Using NIR and ISODATA
## 2 Literature Review  
### Cellular Network Model  
The base station (BS) positioned at the center of each hexagon represents the area of coverage, as depicted in Figure 1 below. A circle with identical area can be utilized to estimate every cell coverage area. The circle radius obtained by \(r=(\sqrt{3\sqrt{3}/2\pi})H_{s}\), \(H_{s}\) is the side of the hexagon. The resulting area of coverage total is \(A_{T}^{c}=\pi\big{(}r^{2}-r_{0}^{2}\big{)}\). The shortest distance between the nearest mobile station (MS) to the serving base station is \(r_{0}\).  
Figure 1: Two-layer FFR network layout.