esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Outage Probabilities
### Scenario 1: Single Transmission though a Relay  
In the first scenario, as shown in Figure 3, the source transmits data to the relay and the relay passes the data to the receivers in the group if the relay receives the data from the source successfully. Hence, the data reception at a receiver fails if the relay does not receive from the source successfully or the receiver does not receive from the relay successfully. With given source and relay positions \((x_{S},y_{S})\) and \((x_{R},y_{R})\), the outage probability for a receiver at location \((x_{D},y_{D})\), denoted as \(P_{Outage(1)}(x_{D},y_{D})\), can be written as:  
\[\begin{array}{l}P_{Outage(1)}(x_{D},y_{D})=1-\left\{\left(1-P_{SR}^{Outage} \right)\left(1-P_{RD}^{Outage}\right)\right\}\\ =P_{SR}^{Outage}+P_{RD}^{Outage}-P_{SR}^{Outage}P_{RD}^{Outage}\end{array} \tag{25}\]  
where \(P_{RD}^{Outage}\) is a function of \((x_{D},y_{D})\). Hence, the outage probability for the group considering all receivers in the group can be expressed as:  
\[\begin{array}{l}P_{Outage(1)}^{Group}=\frac{1}{N_{D}}\iint_{Group}P_{Outage( 1)}(x_{D},y_{D})f_{D}(x_{D},y_{D})dx_{D}dy_{D}\\ =\frac{1}{N_{D}}\iint_{Group}(P_{SR}^{Outage}+P_{RD}^{Outage}-P_{SR}^{Outage}P _{RD}^{Outage})f_{D}(x_{D},y_{D})dx_{D}dy_{D}\end{array} \tag{26}\]  
where \(Group\) is the region of the group, \(N_{D}\) is the number of destinations (receivers) in the group, and \(f_{D}(x_{D},y_{D})\) is the destination density satisfying:\[N_{D}=\iint\limits_{Gorup}f_{D}(x_{D},y_{D})dx_{D}dy_{D}. \tag{27}\]  
If the destinations are uniformly distributed over \(Group\), then the destination density \(f_{D}(x_{D},y_{D})=D_{D}^{Uniform}\) satisfies:  
\[\iint\limits_{Group}D_{D}^{Uniform}dx_{D}dy_{D}=N_{D}, \tag{28}\]  
and Equation (26) can be rewritten as:  
\[P_{Outage(1)}^{Group}=\frac{1}{A_{Group}}\iint\limits_{Group}(P_{SR}^{Outage}+P _{RD}^{Outage}-P_{SR}^{Outage}P_{RD}^{Outage})f_{D}(x_{D},y_{D})dx_{D}dy_{D}, \tag{29}\]  
where \(A_{Group}=\iint_{Group}1\,dx_{D}dy_{D}\).
# Evaluation of Keratin/Bacterial Cellulose Based Scaffolds as Potential Burned Wound Dressing
## 2 Materials and Methods
### Obtaining the Keratin Hydrogel  
The keratin hydrogel was prepared according to the [PERSON] et al. [45], with slightly modifications. Carefully selected undyed hair (origin from men, around 30 years old, provided by a hairdressing unit) was harvested, by cutting into segments of 2-5 cm length. The hair was grounded using a ball mill (Retsch PM 100, Retsch GmbH, Haan, Germany) at 100 rpm, for 30 min and transformed in pieces smaller than 1 mm; then, the samples were cleaned in a mixture CHCl\({}_{3}\)-CH\({}_{3}\)OH (2:1; \(v/v\)) for 24 h at boiling point, using a Soxhlet extraction. The hair segments were then dried at 40 \({}^{\circ}\)C, for 24 h. Then, the hair was placed in distilled water, inside a stainless-steel sealed capsule, at a solid/liquid ratio = 2:35 (_w/v_). The capsule was maintained 2 h at 150 \({}^{\circ}\)C. After thermal treatment and cooling, the compound was centrifuged 10 min. at 10.000 rot/min. Two phases were separated: one solid, insoluble in water, considered a \(\beta\)-keratose, and one soluble in water, \(\gamma\)-keratose [46; 47].  
To prepare the \(\beta\)-keratose hydrogel, 2 g of solid phase were oxidized for 3 h, by boiling in a 3% H\({}_{2}\)O\({}_{2}\) solution, with a liquor ratio of 1:11 (_w/v_). The solid compound obtained was cleaned in ethanol 96% and drained, by pressing with a filtering paper. The resulted solid was treated with 0.7% NaOH solution (in 70% ethanol), at a temperature of 70 \({}^{\circ}\)C. To keep the protein in a swollen state along the treatment, the pH was adjusted to 8. The compound was boiled for 3 h and then, maintained under stirring at 20 \({}^{\circ}\)C for 5 h. The product was filtered and washed with 100 mL distilled water; the purification was repeated 5 times.  
The resultant \(\beta\)-keratose and \(\gamma\)-keratose were characterized by conductometric titration on the device pH/ion-center sensION MM 374 (Hach, Loveland, CO, USA).
# Adaptive Network Routing Technology for Near-Moon Space Cross-Domain Transmission
## 4 Simulation and Verification
### Link Stability Comparison  
Figure 3 shows the comparison of link stability between this design and other literature methods under the same routing requirements of \(\Delta_{\rm max}\leq 30\) s, \(\Phi_{\rm max}\leq 10\) hops, and \(F_{\rm min}\geq 1\)\(Kbps\). It can be seen that due to the unified consideration of communication optimization under mixed topology conditions, the stability of this design does not sharply decrease when the routing request is larger than \(10^{5}\) compared to other algorithms. This is mainly because the comparative ORPT only considers the unified search of communication node routing and does not separately consider local areas under different rate and topology conditions, resulting in the routing process often being directly ignored when a node's speed is low, ignoring the time delay caused by multiple jumps that may be required for other transmission paths; the DFWA, on the other hand, excessively focuses on minimizing data loss rather than considering the highest overall network transmission efficiency, resulting in a sharp decrease in stability when routing requests are large. At the same time, its minimum loss rate strategy results in difficulty in convergence of the search process and recovery of interrupted networks when topology changes. Therefore, the overall performance of cross-domain network communication applications in near-lunar space is limited; the SDN-WSN constrains the transmission strategy based on service quality indicators, making it difficult to transmit low-quality critical information when the number of applications is large, resulting in a decrease in the stability of data transmission throughout the entire network.  
Figure 3: Comparison of link stability with different route application times.  
Figure 4 shows the average stability of our algorithm for the same number of routing applications (105 times) and different maximum routing delay times (5-30 s). It can be seen that our design is superior to other methods in the literature. When the delay time is relaxed to 30 s, the link stability approaches 0.8; when the link stability drops sharply below 7 s, it is difficult to satisfy in orbit applications like other methods, indicating that effective routing is difficult to complete under strict routing delay time constraints. Compared with this design, the SDN-WSN has a certain degree of monotonicity in terms of stability and time delay, as it adopts a strategy based on quality of service, which tends to improve communication service quality when delay time constraints increase. The DFWA adopts a minimum data loss strategy, which leads to an increase in the number of information routes as the routing time delay increases, exceeding the maximum hop limit and resulting in routing functionality failure. The ORPT conducts a unified, exhaustive search because it barely considers the characteristics of different domains, and its stability is not significantly related to the routing delay time constraint.
# Compact 15 mJ Fiber-Solid Hybrid Hundred-Picosecond Laser Source for Laser Ablation on Copper
## 3 Experimental Results and Discussion
### The Spectral Characteristics of Yb-Doped Fiber Master Oscillator
It shows that, with the continuous increase in pump power, the 3 dB bandwidth is only slightly broadened to 0.78 nm, and the pulse energy is concentrated at the 1064.1 nm without multipeak structures. This is due to the combined effect of dispersion and SPM on pulse evolution, in which dispersion length (\(L_{D}\)) and nonlinear length (\(L_{NL}\)) provide very important length scales. They can be calculated by \(L_{D}=T_{0}{}^{2}/\,\beta_{2}\,\)1 and \(L_{NL}=1/\gamma P_{0}\), where \(T_{0}\) is the pulse width before stretching [31]. As a result, the dispersion length and the nonlinear length are about 80 km and 0.025 km, respectively. Obviously, when the length of single-mode fiber stretcher is 1.35 km (\(L\)), \(L_{NL}\leq L\ll L_{D}\). Therefore, SPM plays a major role in the evolution of optical pulses, which leads to the changes in the pulse spectrum. Moreover, the single-mode fiber stretcher provides positive dispersion at 1064 nm. The laser pulse propagating in the normal dispersion regime can accelerate the pulse broadening via SPM effect. Furthermore, the peak power of the pulses decreases with the broadening of the pulse width; thus, the SPM-induced nonlinear phase shift is reduced. Therefore, Figure 2c shows that the broadening and distortion of the spectrum has been effectively suppressed with the use of the pulse stretcher. We inject the laser pulses from Figures 2c and 3b into the QCW laser diode side-pumped Nd: YAG regenerative amplifier, and a pulse energy of 4 mJ at a repetition rate of 500 Hz is obtained. The main energy is concentrated at the central wavelength to match the gain bandwidth of the Nd:YAG, which is one of the main factors for achieving high-energy.
## 2 Proposed EPP-Based Beam Scanning Approaches
### Proposed SLL/EPP-BSA
#### 2.2.2 Simulation Results of the Proposed SLL/EPP-BSA for Five Test Case  
**Test case (1): \(\mathbf{R=1}\)**  
In this case, the cosine wave half period is chosen to be equal to the array length, \(H=L_{h}=4.935\,\lambda\), hence \(R=1\). Accordingly, the EPPs of the synthesized array elements for \(A_{c}\) changing from positive to negative are shown Figure 29. The relation between the amplitude changes of the cosine waveform and the scanning angle, HPBW, SLL, and maximum scanning range for the eight-element ULAA at a compression ratio \(R=1\) are listed in Table 9. When the cosine wave amplitude is changing from positive to negative and its magnitude is changing from \(A_{c}=0\) to \(A_{c}=\lambda\), the array main beam tilted from a broadside direction of \(\theta^{\circ}=90^{\circ}\) to \(\theta^{\circ}=62.36^{\circ}\) with minimal changes in the HPBW that is increased by \(0.12^{\circ}\) at \(\theta^{\circ}=62.36^{\circ}\) compared to the broadside direction, while the SLL increased from \(-29.79\) dB to \(-10.53\) dB as the main beam is titled away from the broadside direction to \(\theta^{\circ}=62.36^{\circ}\). The achieved maximum scanning range is \(-27.64^{\circ}\leq\theta_{r}\leq 27.64^{\circ}\) around the broadside direction. The polar plot of the synthesized radiation pattern using the SLL/EPP-BSA approach at compression ratio \(R=1\) and \(A_{c}=\lambda\) is shown in Figure 30. This technique
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 3 Spatiotemporal Feature Enhancement Methods  
Typically, such models for lip-reading that are grounded in deep learning leverage the power of 3D CNNs, 2D CNNs, or a hybrid of both to distill spatiotemporal features from sequences of lip movements. These mechanisms often struggle to fully capture and represent the temporal and spatial characteristics of video sequences. To address this issue, various spatiotemporal feature enhancement techniques have been proposed, including spatial feature enhancement, temporal convolution enhancement, attention-based enhancement, pulse feature enhancement, audio-visual auxiliary enhancement, and so on.
# Vehicle Rollover Warning and Control Based on Attitude Detection and Fuzzy PID
## 4 Co-Simulation Analysis and Result Verification  
To validate the proposed strategy, a joint CarSim/Simulink simulation platform was used. The overall simulation model included LTR value calculation module, rollover threshold judgment module, PID/fuzzy-PID controller, linear hydraulic brake module, target brake wheel judgment module, CarSim vehicle model. The simulation model of the control strategy can be completed by combining several modules above and linking  
Figure 13: General drawing of the brake-by-wire system.  
them with the already established CarSim vehicle model, as shown in Figure 14a for the final model of PID control and Figure 14b for the final model of fuzzy-PID control. When Simulink is linked, the s-function module in Simulink calls the CarSim vehicle model, obtains the output parameters (vertical load, transverse swing angular velocity, roll angle, roll angle velocity, etc.) set by the vehicle model, and returns the braking torque (or brake wheel cylinder pressure) of the target braking wheel obtained after the controller calculation to the CarSim vehicle model. Finally, the response of the model can be continuously adjusted using PID parameter adjustments to achieve a better control effect. The tuning reference results in a scale factor of 103, an integration factor of 5, and a differentiation factor of 0.5.  
Figure 14: Co-simulation model. (**a**) PID whole vehicle control model; (**b**) Fuzzy PID whole vehicle control model.
## 2 Materials and Methods
### Data Processing  
For tasks 2 (carrying a box) and 3 (walking without load), the gait cycles were considered as follows: A step of a gait cycle started with the right foot terminal contact and its subsequent terminal contact (toe off to toe off). The first and last step of a gait cycle were not considered. A total of eight steps per subject were included in the further analysis. The lifting cycles were divided into lifting and lowering, as described in Section 2.3.1. Five lifting and lowering cycles per subject were included in the analysis. For the bending task, the middle 50 s were selected and divided into 2 s segments. The first and last 2 s segments were selected for further analysis.  
The joint angles and muscle signals acquired during each measurement were normalized to a standardized time scale of 101 points. Subsequently, all time-normalized data was  
Figure 4: Sensor placement of sEMG and Xsens sensors.  
averaged per measurement day, comprising the eight gait cycles and five lifting/lowering cycles for each subject. This resulted in a single time-normalized dataset per measurement day for each individual, which was utilized for further comparative analysis using SPM. All data processing steps were programmed and executed in R (R Core Team) [30].  
To assess the reliability of the measurements, we employed statistical parametric mapping (SPM) to compare the parameters of each task between day one and day two. SPM offers a comprehensive statistical analysis framework that enables the identification of significant differences across the entire duration of the motor task. All SPM analyses were conducted in MATLAB Version: 9.13.0 (R2022b), using the open-source software package from spm1D 0.4 [31]. Prior to the analysis, all data were tested for normal distribution. Paired \(t\)-tests were then used to compare the paired conditions of day one and day two.  
The entire time series data representing the lifting, carrying, and walking cycles, as well as the static holding sections, were compared for both kinematic and sEMG data of day one and day two. At each time point, a scalar output statistic called SPM{t} was computed and evaluated by determining the critical threshold at \(\alpha\) = 0.05 significance level. If the trajectory of SPM{t} crosses this critical threshold at any time point, they were marked as statistically significant. For significant areas, the effect size Cohen's d (d) was also determined.
# Investigation of Using CAPTCHA Keystroke Dynamics to Enhance the Prevention of Phishing Attacks
## 8 Conclusions and Future Work  
Phishing is a constant and complex issue, with the capacity to do extensive damage to the targeted party and maximise gains for the attackers. It has already led to injurious losses in the business, government, and technology sectors. Nevertheless, to date, the mechanisms for preventing phishing attacks have proved insufficient, leaving practical challenges to be overcome. Therefore, this paper proposes an approach to preventing phishing attacks using CAPTCHA keystroke dynamics. In particular, a combination of three timing features was deployed to distinguish between samples of authenticated users and phishers: keystroke duration, di-graph duration, and latency. In total, 75 users participated in the experiment over a period of six weeks, and Euclidean distance was used to verify the user samples.  
The results offer sufficient evidence of the effectiveness of capturing keystroke dynamics to prevent phishing, indicating that this approach is worthy of further study. Moreover, the proposed approach outperforms all previous work in the literature in terms of speed, acceptance, cost-effectiveness, and ease of integration with other systems. Also demonstrated was the benefit of collecting adequate samples in the enrolment phase, where the users were identified. Additionally, the experiment proved that the performance of keystroke dynamics could be improved by determining the appropriate threshold. This will be the focus of the author's research going forward.  
In future work, experiments will be conducted in an online environment with a larger number of participants in order to evaluate the proposed system in a real-world environment and obtain more accurate results. In addition, it will also be interesting to investigate other attacks that may face the proposed system when applied in the real world, such as replay attacks. Moreover, further features will be included to improve the results, and additional classifiers and more advanced methodologies will be applied.  
Conceptualization, E.K.A., A.M.A. and S.A.A.; Data curation, E.K.A.; Formal analysis, E.K.A.; Funding acquisition, E.K.A.; Investigation, A.M.A. and S.A.A.; Methodology, E.K.A. and A.M.A.; Project administration, A.M.A. and S.A.A.; Software, E.K.A.; Supervision, A.M.A. and S.A.A.; Validation, E.K.A., A.M.A. and S.A.A.; Writing--original draft, E.K.A.; Writing--review & editing, A.M.A. and S.A.A. All authors
# A Novel Multi-Task Performance Prediction Model for Spark
## 3 Proposed Model
### Data Preprocessing
#### 3.2.1 One-Hot Feature Encoding and Feature Normalization  
Feature encoding and normalization are essential steps in data preprocessing. Spark log data contain some discrete features, such as some parameters in the Spark execution process. We use One-Hot encoding to treat these discrete features, enabling them to be accepted by the model.  
One-Hot encoding is a method to represent discrete variables as vectors, transforming variables with multiple possible values into numeric vectors suitable for machine learning algorithms. We denote the data requiring One-Hot encoding as \(data_{dis}\), specifically presented in Equations (1)-(3).  
\[data_{dis}=\{(x_{11},x_{12},\ldots,x_{1k}),\ldots,(x_{i1},x_{2},\ldots,x_{ik}),\ldots,(x_{n1},x_{n2},\ldots,x_{nk})\} \tag{1}\]  
\[data_{dis_{i}}=(x_{i1},x_{i2},\ldots,x_{ij},\ldots,x_{ik}) \tag{2}\]  
\[x_{j}=[1,2,\ldots,t] \tag{3}\]  
Here, \(i,j,k,n,t\in\mathbb{Z}^{+}\). The dataset \(data_{d}i\)s consists of \(n\) discrete data points. \(data_{dis_{i}}\) denotes any given data point in the discrete dataset, corresponding to the \(i\)-th entry and its entire set of discrete feature values. Each data point consists of \(k\) discrete features, and \(x_{j}\) stands for any one of these \(k\) features, which possesses \(t\) potential values.  
Given that all feature variables \(x_{j}\) in the dataset \(data_{dis}\) are discrete, we can employ One-Hot encoding to transform them into numeric vectors, making them compatible with neural network models.  
In conclusion, we successfully transformed discrete variables into numeric vectors using One-Hot encoding, optimizing them for machine learning algorithms. For continuous attributes, such as data scale, normalization was undertaken to eliminate scale disparities among features, ensuring that the model is not overly influenced by a particular feature. After feature encoding and normalization, the consolidated dataset is denoted as \(Data=\{data_{1},\ldots,data_{i},\ldots,data_{n}\}\), where \(i,n\in\mathbb{Z}^{+}\). Each \(data_{i}\) is represented by the feature set \(X_{i}\) and the predicted or actual value \(y_{i}\). Any given \(X_{i}\) comprises \(\{x_{1},x_{2},\ldots,x_{k},\ldots,x_{m}\}\), \(where\ k,\ m\in\mathbb{Z}^{+}\), and \(x_{k}\) represents any specific feature value.
## 3 Experimental Results
### Single Radiating Element  
The S-parameters of a single radiating element, which are shown in Figures 5 and 6, have been measured using a 40 GHz vector network analyzer. The radiating element achieves a 10 dB impedance bandwidth that is roughly 16.5 %, which is greater than the Ka-band requires, and it overcomes the bandwidth achieved in recently published works, such as [27; 28], 5%, and [29]. The isolation between ports is lower than \(-\)25 dB from 27.5 to 31 GHz. Simulation and measurement outcomes are quite similar, although there are slight differences owing to fabrication tolerances and measurement errors, as well as the uncertainty in the dielectric constant parameter of dielectric substrates at 30 GHz, whose value can be influenced by other characteristics such as the material anisotropy, the substrate thickness, or the copper roughness.  
In order to measure the circular polarization radiation pattern of the manufactured radiating element prototype, a beamformer chip is assembled to feed the orthogonal polarizations of each radiating element. This beamformer chip includes a 5-bit phase control and a 5-bit gain control for each of its eight channels, in a 6 mm \(\times\) 6 mm QFN package.  
Figure 5: Return loss and isolation results for a single radiating element.  
To generate the RHCP and LHCP radiation patterns, we feed the input ports with the same RF signal but with a phase shift between the vertical and horizontal polarization of \(90^{\circ}\) or \(-90^{\circ}\), respectively. The RHCP and LHCP normalized radiation patterns in the \(\varphi=0^{\circ}\) plane at 28 and 30 GHz are depicted in Figure 7. The measurement results show a ripple caused by the measurement system effect as well as the finite ground plane of the prototype that produces edge diffraction. For both polarizations, 28 and 30 GHz co-polar components show a maximum directivity of 6.5 dBi with a wide beam-width of about 95\({}^{\circ}\).