esa-sceva/satcom-chunk-collection huggingface dataset:
# Off-Axis Tension Behaviour of Unidirectional PEEK/AS4 Thermoplastic Composites
## 2 Materials and Methods  
The composite system used in this study is PEEK/AS4 thermoplastic epoxy/carbon Cetex(r) TC1200 prepreg produced by Toray Advanced Composites, Nottingham, UK. The nominal thickness of the prepreg is 0.14 mm. The AS4 carbon fibre has a diameter of 7 \(\upmu\)m. The polyetheretherketone (PEEK) matrix is a high-performance engineering thermoplastic  
Figure 1: Illustration of on-axis tension and off-axis tension tests: (**a**) On-axis tension test; (**b**) off-axis tension test.  
material. The density of the prepreg is 1.46 g/cm\({}^{3}\) and the resin content by weight is 34% in the lamina [29]. The basic properties of the PEEK/AS4 prepreg are summarized in Table 1.  
A 400 \(\times\) 700 mm\({}^{2}\) unidirectional laminate was manually stacked and fabricated by hot pressing. The laminate was consolidated in the hot press compression chamber at 390 \({}^{\circ}\)C for 20 min and then cooled down at 4 \({}^{\circ}\)C/min until room temperature.  
Off-axis tension specimens were cut at different orientations from the laminate by waterjet. Six off-axis angles \(\theta\) = 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\) and 90\({}^{\circ}\) (transverse compression) were selected to cover different combined stress states. The sketch of the off-axis tension specimen is shown in Figure 2. Nominal dimensions of the specimens were 120 mm (H) \(\times\) 10 mm (W) \(\times\) 3 mm (T). A slender specimen design is chosen with an aspect ratio of 12 to weaken the stress undulation across the width direction.
## 4 Results
### Simulation Results and Analysis
However, the inclusion of an RIS significantly boosts the achieved rate compared to the scenario without an RIS, particularly as the source power rises. The impact of adding more RIS elements is also evident: the higher the number of RIS elements, the higher the achieved rate. Specifically,  
Figure 7: The harvested power by the IoT device versus the distance between the source and the device for different RIS phase-shift optimization approaches (\(N=50\)). with \(N=100\), the rate reaches approximately 14 bps/Hz at \(P_{S}=25\) dBm, which is the highest among all scenarios. Without an RIS, the achieved rate is significantly lower, especially at lower source power levels, where it remains below 6 bps/Hz at \(P_{S}=25\) dBm. Thus, as the source power increases, the gap between the achieved rate with an RIS and without an RIS widens, demonstrating the effectiveness of the RIS in enhancing the data rate in IoT systems. The performance gain becomes more pronounced as the number of RIS elements increases, reinforcing the importance of the RIS in leveraging reflection properties to maximize system throughput. Moreover, without an RIS, the rate improvement as the source power increases is more gradual, indicating that traditional systems without an RIS struggle to efficiently utilize increasing power, unlike systems enhanced with an RIS, which show more dramatic improvements in the data rate with increased power. Figure 8: The harvested power by the IoT device versus the distance between the source and the device for different numbers of RIS elements (6 DChOA-DRL approach). Figure 9: The achieved rate by the IoT receiver versus the source power for different numbers of RIS elements by using the 6 DChOA-DRL-based optimization approach. The convergence curves shown in Figure 10 illustrate the performance of various algorithms across different epochs (180, 240, and 300). In all three subfigures (a, b, and c), the 6 DChOA-DRL algorithm consistently exhibits the fastest convergence, reaching the lowest RMSE values compared to the other methods. At epoch 180, the 6 DChOA-DRL algorithm achieves an RMSE of nearly 5, significantly outperforming other methods like PSO, the ChOA, and a DNN, which still show high error rates above 15 at the same point. As the number of epochs increases, other methods gradually reduce their RMSE, but 6 DChOA-DRL maintains a clear advantage, stabilizing at an RMSE close to zero by epoch 240, while other methods (such as RL and CNN) continue to show higher error levels. The key reason behind the superior performance of 6 DChOA-DRL lies in the combination of RL, a DNN, and the 6 DChOA. By efficiently navigating the solution space and quickly identifying optimal RIS phase shifts, 6 DChOA-DRL demonstrates remarkable robustness and efficiency, especially as the number of epochs increases. This combination allows for more accurate learning and faster adaptation, giving 6 DChOA-DRL a significant edge in achieving lower RMSE values with fewer iterations.
# Dark-Field Hyperspectral Microscopy for Carbon Nanotubes Bioimaging
## 4 In Vivo Studies of CNTs
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**System** & **Animal Model** & **Type of CNTs** & **Size of listed CNTs** & **Treatment Condition** & **Sample Preparation** & **Results** & **Reference** \\ \hline \multirow{6}{*}{Cystic/N/N enhanced dark-fall microscopy} & \multirow{2}{*}{Cystic/N/N} & \multirow{2}{*}{L-5 \(\mu\)m, D-1-2 arm} & 0, 11, and 45 \(\mu\)m/mouse & Lung were fixed with N/N/N & Microscopy revealed that CNTs generally \\  & & & by physiological regulation & embedded, sectional, and & observed within the functional tissue but also in \\  & & & 1, \"per-pressure\" & standard with [PERSON] & & \\  & & & & 1, \"per-pressure\" & standard with [PERSON] & & \\ \cline{2-6}  & & & & & 1, \"per-pressure\" & & \\ \cline  
Despite some difficulties raised in recognising the particles using HSI in histologically processed samples, enhanced dark-field microscopy itself provides the possibility to quantitatively assess the distribution of CNTs in tissues by standard morphometric grid point counting methods. [PERSON] et al. have implemented this technique to measure the lung burden distribution of CNTs, determining that the majority of nanotubes (68%) are within or penetrating alveolar macrophages [93]. In a later study, the research group managed to analyse changes in the distribution of MWCNTs in the lungs over 336 days post-exposure and the clearance rate of aggregated and singular particles (Figure 4B) [94]. The same approach with murine histology samples from extrapulmonary organs confirmed translocation and accumulation of CNTs in other tissues with time (Figure 4C) [95; 96]. In addition, it was found that total CNT content in the lungs of transgenic mice overexpressing SPLUNC1 (Scgb1a1-hSPLUNC1 TG), a protein involved in the innate immune system response in the respiratory tract region, was lower than in their wild-type (WT) littermates [97]. Moreover, the clearance rate of oxidised CNTs by macrophages in NADPH-oxidase-deficient mice measured using the point-counting technique was also decreased compared to the control group [98].
# Acute Effects of Static Stretching on Submaximal Force Control of the Ankle
## 1 Introduction
One approach to evaluating force control in dynamic conditions is the use of a force-tracking task, in which participants are required to continuously match their force output to a changing target trajectory in real time. This method allows for the assessment of not only the overall accuracy of force production but also the ability to modulate force precisely in both timing and magnitude, providing a comprehensive assessment of neuromuscular control [21; 22]. Specifically, the task demands temporal precision, adjusting the force output at the correct moment to follow the target waveform, and magnitude accuracy, which involves generating the appropriate level of force to match the target amplitude. In contrast, previous studies evaluating the effects of SS on force control have primarily focused on force steadiness measured under isometric conditions with constant target torques [19; 20]. This approach primarily reflects magnitude accuracy, that is, how consistently one can maintain a specified force level without accounting for the temporal aspects of force modulation. Given that SS has been shown to impair force steadiness, it is plausible that similar neuromuscular alterations may extend to dynamic tasks that require continuous adjustments in both the timing and magnitude of force. Moreover, SS has been reported to reduce the rate of force development (RFD) [23; 24], which may hinder the rapid modulation of the force output required in the time-sensitive phases of dynamic tasks. A decline in RFD may compromise temporal precision, especially during the force generation or release phases that demand fast and accurate transitions in force levels. To date, no studies have investigated the effects of SS on dynamic force control during force-tracking tasks, which require continuous and precise adjustment of submaximal force. Therefore, this study aimed to investigate the acute effects of SS of ankle plantar flexors on force control using a time-varying force-tracking task. It was hypothesized that SS would lead to a decrease in force-tracking performance, particularly in tasks that require precise modulation of the submaximal force.
## 4 Simulation of the Proposed Control
### Simulation Result  
Four different scenarios to ride through an on-shore fault are shown in Figures 7-18. In each case, on-shore and off-shore AC voltages and DC voltages were measured. Moreover, off-shore frequency was measured at the GSC of DFIG, and rotor speed was expressed. Finally, power from the on-shore side and off-shore side was measured in each case.  
800 kV, which exceeded 1.2 pu. Therefore, the 500-MW DC chopper seemed to be small to ride through severe fault on-shore.  
Figures 8 and 9 show the difference between the simulation Cases B and C in Table 3. The proposed control used droop control between on-shore voltage and off-shore frequency, so time delay between power responses existed. For every case, response time for a 50-ms and a 10-ms time delay between converter communication and 20-ms GSC frequency change pickup time was applied. Because on-shore AC voltage reached about 0 pu, on-shore power showed nearly 0 MW in Scenario A. The off-shore power showed the difference between each control strategy. In Figure 9a, the proposed control strategy showed about 100 MW of power still transferred during the fault at the off-shore side. The result of this power is expressed in Figure 7c. The DC voltage in proposed control case steadily increased during the fault. However, the amount was limited, and it showed that the DC voltage did not exceed 1.2 pu. In Figure 9b, turbine deloading with fast communication is shown. Low voltage detection to turbine deloading only had a 10-ms latency, which was relatively faster than the proposed control. Because the turbine reduced the power generation rapidly, relatively stable DC voltage was observed compare to other control strategies. However, the disadvantage of the strategy was the fast turbine speed. Compared to the proposed control strategy, the turbine deloading strategy showed nearly about 0.05 pu faster turbine speed.  
Figure 7: Simulation results of the on-shore fault scenario with 0.001\(\Omega\) fault resistance. (**a**) On-shore AC voltage for each simulation case. (**b**) Off-shore AC voltage for each simulation case. (**c**) Rectifier-side DC voltage for each simulation case.
## 2 Materials and Methods
### Four Time Function Models and Parametric Analysis
#### 2.2.1 Usher Time Function Model  
The Usher model, introduced in 1980 by [PERSON], is a theoretical model used to describe the temporal variation of growth information. This model has found widespread applications in predicting oil and gas field production rates [46], roadbed settlement forecasts [47], and more recently, in predicting surface subsidence in goat areas. The expression for dynamic surface subsidence prediction in conjunction with the Usher model is as follows:  
\[W(t)=\frac{W_{m}}{\left(1+ae^{-bt}\right)^{c}} \tag{1}\]  
where \(a\), \(b\) are settling parameters and \(c\) is curve shape parameter.  
To more accurately describe the influence of different model parameters on the shapes of the three curves and the three phases of surface subsidence, we discussed parameters \(a\), \(b\) and \(c\) separately.  
When analyzing the impact of parameter \(a\) on the prediction model, we take \(W_{m}=-3560\) mm, \(b=0.05\), and \(c=4\). From Figure 2, it can be observed that parameter \(a\) has minimal influence on the changes in subsidence curves, subsidence velocity and acceleration trends, as well as their peak values. The curve shape remains nearly unchanged. As parameter \(a\) decreases, the time to reach maximum subsidence and stabilize is advanced, the time required to reach the maximum subsidence velocity and acceleration decreases, and the initial phase interval shortens, while the active and decline phases remain largely unaffected.  
When analyzing the impact of parameter \(b\), we use \(W_{m}=-3560\) mm, \(a=10\), and \(c=4\). From Figure 3, it is evident that parameter \(b\) has a more pronounced impact on the subsidence curve, subsidence velocity and acceleration curves. As the parameter \(b\) is reduced, the time taken for the three curves to reach their maximum values increases, and the curves become more gradual. The  
subsidence velocity and acceleration decrease, and the initial phase of surface deformation shortens, while the active phase and decline phase slightly increase.
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 8 Behavioral Anomaly and Change Detection  
Behavioral anomalies are rare events that deviate from the probability distribution of the \"normal\" data [52]. In comparison to other learning tasks, anomaly detection in human-centric systems is typically more challenging due to the following reasons: first, people's behaviors vary from person to person. Therefore, it is essential to build personalized detection models. Secondly, people's behaviors evolve over time, either in cyclic patterns or due to external and internal factors (e.g., changing of living environment and style, or adjusting of social, professional and family roles). Thus, it is crucial to train models that actively adapt to new behavior patterns. Additionally, an occasional activity change does not always imply a health-related concern.  
Most of the current ambient assistant living systems are focused on detecting sudden behavioral anomalies (point anomalies), which could indicate serious emergencies, e.g., fall detection. On the other hand, long-term behavioral changes may happen in a more subtle way, such as gradually reduced daily mobility, or slightly lower sleep quality over successive days. Such long-term changes may be more subtle compared to short-term emergencies. However, over time, the change might evolve into a significant impairment on a medical scale. Proactive detection of such changes can help geriatricians to discover  
Figure 13: Detected visiting events of one patient.  
health-related problems at an early stage, guiding them towards initiating personalized medical interventions.
# Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset +
###### Abstract
They lack traffic diversity, do not cover different types of attacks, and do not reflect current network traffic trends [10]. This paper offers the following contributions:  
* It presents a newly generated IDS dataset called GTCS (Game Theory and Cyber Security) that overcomes most shortcomings of existing datasets and covers most of the necessary criteria for common updated attacks, such as botnet, brute force, distributed denial of service (DDoS), and infiltration attacks. The generated dataset is completely labeled, and about 84 network traffic features have been extracted and calculated for all benign and intrusive flows. * It analyzes the GTCS dataset using Weka--an open source software which provides tools for data preprocessing and the implementation of several ML algorithms--to select the best feature sets to detect different attacks and provides a comprehensive analysis of six of the most well-known ML classifiers to identify intrusions in network traffic. Specifically, it analyzes the classifiers in terms of accuracy, true positive rates, and false positive rates. * It proposes an adaptive ensemble learning model that integrates the advantages of different ML classifiers for different types of attacks to achieve optimal results through ensemble learning. The advantage of ensemble learning is its ability to combine the predictions of several base estimators to improve generalizability and robustness over a single estimator.
## 5 Materials, Methods, and Experiment Setup
### Parameter Optimization Methodology  
This is the first experiment we conducted. We investigated the relationship between the parameter \(K_{p}\) of Equation (8) and the resulting edge map. To compare the edge maps dependent on parameter \(K_{p}\), we filtered each image in the dataset, setting various values for \(K_{p}\), and then we observed whether parameter \(K_{p}\) influences the resulting edge map.  
Subsequently, we undertook a second experiment to ascertain a value for \(K_{p}\) that maximizes the edge preservation metrics considered in this study. To achieve this goal,we measured the corresponding edge-preservation metrics to determine which value of parameter \(K_{p}\) maximizes each metric.
## 4 Results and Discussion
### Parameter Optimization  
#### 4.2.1 Window Size Selection  
The larger the window size, the more information contained in the image texture, but there is also a greater information redundancy. To investigate the optimal window size for this model, five window sizes (5, 7, 9, 11, and 13) were used in the experiment. The results are presented in Figure 11. Figure 11 showed that, as the window size increased from left to right, both the model's OA and training time values showed an increasing trend, but with different growth rates. However, when the window size was 11, the growth in both values was the smallest. The OA value was higher at 99.143% and the training time was 428 s. Therefore, for practicality, a window size of 11 was selected as the model input.
# Satellite Edge Computing for the Internet of Things in Aerospace
Moreover, the on-board resource is designed for specific applications, rather than the universal computing that supports multiple applications. It is challenging to share local resources with others. Therefore, edge computing cannot be directly applied to traditional satellites. Combined with the mobile edge computing and the characteristics of the satellite itself, this paper proposes an intelligent satellite (iSat) suitable for satellite edge computing. iSat is a class of multi-purpose satellite with a powerful standardized hardware platform and a fault-tolerant expandable satellite operation system. It can load different apps and share on-board resources with other satellites on-demand, providing a more robust and flexible personalized space service. The main contributions of this article are twofold. First, we design hardware structure and software architecture of iSat to transform satellites from traditional relay nodes into space edge computing nodes. It can dynamically load software in orbit, flexibly share on-board resources, and provide services coordinated with the cloud. Second, based on the mobile edge computing, we combine the mobile edge computing and satellite orbit to establish a system model of the application scenario and analyze the execution time and energy consumption of the task. Simulation experiment results show that the space edge computing system takes less time and consumes less energy than the traditional satellite constellation. The remainder of this paper is organized as follows: First, a rapid overview of relevant related work is presented in Section 2. Then, the architecture and application scenarios of iSat are proposed in Section 3. Next, the system model of the space edge computing system is analyzed in Section 4, which consists of the computation model, communication model, and satellite execution model. In Section 5, the simulation results are presented and discussed. Finally, Section 6 concludes with an exploration of future works.
## 4 Results and Discussion
### Lossless System  
Figure 3 depicts the BER vs. SNR curves, where various values of SCS, NRB, and BW are considered. In Figure 3a, a BW of 10 MHz, SCS values of {15, 30, 60} kHz, and NRB values of {52, 24, 11} are taken into account. Upon analyzing the outcomes, it is evident that at a probability of error of \(10^{-3}\), the SNR value for SCS = 15 kHz is \(-15.79\) dB, for SCS = 30 kHz it is \(-14.62\) dB, and for the SCS = 60 kHz curve, it is \(-10.07\) dB. In Figure 3b, the outcomes are presented for a BW of 20 MHz, SCS values of {15, 30, 60} kHz, and NRB values of {106, 51, 24}. For a probability of \(10^{-3}\), the SNR value for SCS = 15 kHz is \(-18.85\) dB, for SCS = 30 kHz it is \(-15.76\) dB, and for SCS = 60 kHz it is \(-14.69\) dB. Consequently, the curve that exhibits the best performance for a BW of 20 MHz is with SCS = 15 kHz and NRB = 10. It is evident that, with a larger BW there is a greater NRB, which translates to higher performance of the PDCCH channel.  
Figure 4 shows the BER vs. SNR curves for various values of BW, SCS, and NRB. In Figure 4a, a fixed SCS of 15 kHz is considered, with BW values of {5, 10, 20, 30} MHz and NRB values of {25, 52, 106, 160}. Upon analyzing the results, it is evident that, for a probability of error of \(10^{-3}\), the SNR value for BW = 5 MHz is \(-14.72\) dB, for BW = 10 MHz it is \(-15.79\) dB, for BW = 20 MHz it is \(-18.85\) dB, and for BW = 30  
Figure 3: BER vs. SNR for BW = {10, 20} MHz and several values of SCS and NRB. (**a**) \(BW=10\) MHz and (**b**) \(BW=20\) MHz.  
Figure 4b, a fixed NRB value of 51 is considered, with BW values of {15, 30, 50} MHz and SCS values of {15, 30, 60} kHz. Upon analyzing the results, it is evident that, at a probability of error of \(10^{-3}\), the SNR value for the curve with BW = 15 MHz and SCS = 15 kHz is \(-15.73\) dB, for the curve with BW = 30 MHz and SCS = 30 kHz it is \(-15.76\) dB, and for the curve with BW = 50 MHz and SCS = 60 kHz it is \(-15.78\) dB.
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 5 Evaluation Results
### Discussion  
The purpose of establishing the navigation constellation space-based integrated atomic time is to obtain a stable and real-time space-based time benchmark. It has great value in improving the timekeeping accuracy of the navigation constellation and reducing its dependence on the ground.  
The ALGOS algorithm adopted in this paper can provide higher stability. It assigns different weights to atomic clocks with different performance, where the weights can be estimated by the frequency stability of atomic clocks. However, this algorithm has only one stable weight for each clock; thus, it cannot take both short-term stability and long-term stability into consideration. The output time scale stability can only be optimized at a specified smoothing time point. The ALGOS algorithm is also a non-real-time algorithm and cannot generate a time reference in real time.  
Conversely, the Kalman filter is based on a certain dynamic model, which is real-time and can predict the state of the system. For different signal models, different Kalman filters will be obtained. A Kalman filter is a vector signal processor that can handle all measurements in a clock group, including the time difference, frequency difference, and frequency drift of atomic clocks. To design a reasonable Kalman filter, the key lies in modeling the noise of the atomic clock reasonably and accurately estimating the noise variance in its driving noise covariance matrix, so as to eliminate the influence of colored noise on the Kalman filter.  
In the future, our research on the space-based comprehensive time system will continue based on the Kalman filter algorithm and the ALGOS algorithm. Different atomic clocks have different noise models, and their corresponding covariance matrix calculation methods should also be adaptively improved, where we expect to obtain real-time, highly reliable, highly accurate space-based time.