esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
While there have been several recent studies on the use of the FFF for manufacturing 3D printed composites using advanced material systems, such as the thermoplastic Ultem 9085, conducted at the premier applied research institute (the National Institute for Aviation Research (NIAR)), the coupled flexural-electrical evaluation of 3D printed multifunctional composites at ambient temperature was not considered. ambient temperature was not addressed [18]. Ultem 9085 is a thermoplastic material that can be processed by FDM, has great good structural properties, flame retardancy and low smoke generation [19]. Both the thermal and mechanical properties of Ultem 9085 was conducted [19]. The flexural and tensile tests were conducted on Ultem 9085 resin test coupons manufactured using FDM, with varying building orientations [19]. While the test results show the great structural properties of Ultem 9085 built to XZ orientation and confirm that the building direction has a significant effect on tensile and flexural properties, this study did not address the multifunctional properties of the 3D printed continuous carbon fiber composites [19]. A recent study on electro-mechanical effects of the multifunctional carbon fiber reinforced polymer composite structures was investigated to evaluate the effects of electric current and dynamic mechanical properties of the multifunctional composites [20]. It was found that if carbon fibers were used as the multifunctional parameter for both the reinforcing and sensing functions, and a low current was applied (-100 mA), then the mechanical properties of the multifunctional carbon fiber composites were not changed [20]. The effects of electric current connected to multifunctional carbon fiber regarding the traditionally manufactured composites' dynamic flexural properties were conducted in [20]. Tests results concluded a current limit of 0.5 A due to heat generation issues and no fluctuations on flexural properties, with varying average and connection time of the current applied to test coupons [20]. Traditional methods of conducting separate flexural and electrical tests to support the certification tests of an aircraft do not address the new technologies like additively manufactured multifunctional composites. The multifunctional characterization of the multifunctional continuous carbon fiber solid laminate composites is not addressed. Traditional means of compliance and method of compliance from [21] are not understood to support the certification tests for the coupled multifunctional flexural and electrical properties of the AM multifunctional composites. Thus, in this work, the coupling effects of the flexural and electrical properties of the additively manufactured multifunctional continuous carbon fiber solid laminate composites on the same component are investigated using the flexural-electrical testing at ambient temperature with the hope of a weight reduction of aircraft, UAS and spacecraft. These tests are repeated for different test coupons at ambient temperature conditions to simulate the real situations of aircraft, UAS, and spacecraft.
## 4 Results and Discussion
### Impact of the Number of Users and the Pilot Reuse Factor on Energy Efficiency
The proposed estimator achieved energy efficiency EE = 87.67 Mbit/joule at N = 4 and M = 80, whereas for the existing estimator, the energy efficiency EE = 83.25 Mbit/joule at N = 4 and M = 80. The proposed estimator improves the performance by 8.94% at this point. At N = 3 and M = 40, the energy efficiency achieved by the existing and proposed estimators became 65.97 Mbit/joule and 68.45 Mbit/joule, respectively. At N = 2 and M = 20, the energy efficiency achieved by the existing and proposed estimators became 49.46 Mbit/joule and 50.03 Mbit/joule, respectively. The energy efficiencies attained by the proposed and existing estimators for N < 2 have nearly the same value. As the pilot reuse factor increased from w = 1 to w = 2, the energy efficiency decreased. When the number of APs increased with the increased pilot reuse factor, the area throughput increased up to some point and diminished after a certain point. The pilot length became 20 since K = 10, N = 4, and w = 2, which means that the pilot length decreased as the pilot reuse factor increased.
## 3 Parameter Design of Anti Disturbance Voltage Control System
### Parameter Design of Second-Order LADRC System
At frequency \(\omega_{\rm r}\), the slope  
Figure 4: Equivalent two-degree-of-freedom control block diagram for second-order LADRC. of the amplitude frequency characteristic curve +1 and the phase close to the maximum can be provided. At the same time, when the bandwidth ratio \(\gamma\) is fixed and only the controller bandwidth \(\omega_{\rm c}\) is changed, the amplitude frequency phase frequency curve of the compensator shifts to the left or right accordingly. The larger the bandwidth ratio \(\gamma\), the more obvious the hysteresis effect. To achieve better phase compensation effect, compensation is carried out at frequency \(\omega_{\rm r}\). Figure 5: Peer plots for feedback controller transfer functions \(C_{1}(s)\) and tracker transfer functions \(C_{2}(s)\). (**a**) \(C_{1}(s)\)\(w_{c}=1\), (**b**) \(C_{1}(s)\)\(\gamma=1\), (**c**) \(C_{2}(s)\)\(w_{c}=1\), (**d**) \(C_{2}(s)\)\(\gamma=1\). As shown in Figure 5c,d, the tracker transfer function \(C_{2}(s)\) has a unit gain in the low frequency range, while in the high frequency range it manifests as a first-order differential link with a slope of +1. Similarly, when the bandwidth ratio \(\gamma\) is fixed and only the controller bandwidth \(\omega_{\rm c}\) is changed, the amplitude frequency phase frequency curve of the compensator shifts to the left and right accordingly. For this converter, the system is required to pass through a frequency \(\omega_{\rm x}\) of \(1/10\) of the operating frequency after compensation, i.e., \(\omega_{\rm x}=5.969\times 10^{4}\) rad/s. If the bandwidth center \(r\) requires the system to compensate for the system phase margin \(\phi\geq 30^{\circ}\), then the feedback controller \(C_{1}(s)\) needs to provide the phase at the bandwidth center \(\omega_{\rm f}\): \(\angle C_{1}(j\omega_{\rm f})=-180^{\circ}+\phi-\angle P(s)=37.1^{\circ}\). At this point, there are two ways to determine the bandwidth ratio \(\gamma\). If \(\gamma>1\), then \(\gamma=1.8484\); If \(\gamma<1\) is taken, then \(\gamma=0.541\), and both have the same phase compensation angle. From Figure 5c, it can be seen that when \(\gamma<1\), the phase lag effect of \(C_{2}(s)\) is not significant, and the system response is faster than when \(\gamma>1\). Therefore, in this paper, \(\gamma=0.541\) is used, so \(\omega_{\rm c}=\omega_{\rm f}/\gamma=110,330\) rad/s, \(\omega_{0}=\omega_{\rm f}\gamma=32,293\) rad/s. By setting the bandwidth center \(\omega_{\rm f}=\omega_{\rm x}\) to adjust the parameter \(b_{0}\), that is, after compensation \(\omega_{\rm f}=\omega_{\rm x}\) is the system amplitude frequency crossing frequency, at which point \(|C_{\rm o}(j\omega_{\rm f})P(j\omega_{\rm f})|=1\), the control gain of \(b_{0}=1.3636\times 10^{13}\), can be obtained.
# Study on the Influence of Deep Soil Liquefaction on the Seismic Response of Subway Stations
## 6 Conclusions  
This study employs numerical simulations to investigate the seismic responses of subway stations situated in deep liquefiable soil layers. Comparative analysis with subway stations embedded in non-liquefiable soil strata has yielded the following principal conclusions:  
(1) The liquefaction of deep soil layers significantly influences the seismic response of the strata; an appreciable increase in acceleration and horizontal effective stress is observed in the soil layers on both sides of the station structure; beneath the station structure, a marked decrease in acceleration and horizontal effective stress is noted.  
(2) Although the liquefiable soil layer is located 20 m beneath the surface, seismic activity can still induce severe liquefaction of the soil beneath the base slab of the station structure, thus leading to the uplift of the station structure. This phenomenon suggests that the presence of the station structure reduces the vertical effective stress on the underlying soil, thereby increasing the likelihood of soil liquefaction.  
(3) The influence of deep soil layer liquefaction on the seismic response of station structures is more pronounced. It is primarily manifested in indicators such as inter-story displacement, the acceleration responses of central columns, and tensile stress in central columns.  
This study also found that the uncertainty of soil layer parameters has a significant impact on the simulation results, which highlights the importance of field investigations.  
Based on the conclusions drawn, it is recommended that both the design and construction of subway stations should integrate a comprehensive consideration of liquefaction risks and implement appropriate protective measures. Furthermore, to enhance the precision of seismic designs, future research should incorporate an expanded dataset from on-site testing and advanced simulation techniques to better simulate the impact of liquefaction on subway stations.  
Methodology, M.S. and L.T.; Validation, M.S. and L.T.; Data curation, M.S.; Writing--original draft, M.S.; Writing--review and editing, M.S. and Z.W.; Project administration, L.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [National Natural Science Foundation of China], grant number [42072308].  
Not applicable.  
Data Availability Statement: Data are contained within the present article.  
The authors declare no conflicts of interest.
## 5 Control System Module  
### Kinematic Bicycle Model  
The vehicle bicycle model which consists of a stiff body and non-deforming wheels is shown in Figure 3. Consider the vehicle moving on the surface without slipping, there is full rolling friction between tires and surface [47]. This model is employed in NMPC to predict the future state of the system.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Quantity** \\ \hline Right wheel radius (\(WR_{r}\)) & 0.27 m \\ Left wheel radius (\(WR_{l}\)) & 0.27 m \\ Distance between wheels (\(W_{dis}\)) & 0.975 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measured parameters of the vehicle.  
In the figure, the distance between the rear and front axles of the car is defined as L (m). Linear and angular velocities are shown with V (m/s) and \(\omega\) (rad/s) respectively. Here \(\psi\) (rad) is the heading angle and \(\delta\) (rad) is defined as the steering angle of the vehicle. They can be formulated using relations (34), (35), and (36):  
\[\text{R}=\text{L}/\tan(\delta) \tag{34}\]  
\[\omega=\text{V}/\text{R} \tag{35}\]  
\[\psi=\dot{\omega} \tag{36}\]  
The center of the rear axle is chosen as the desired point. If we consider the instantaneous center of rotation (ICR), the kinematics bicycle model states are formulated as follows:  
\[\dot{x}=v\cos(\psi) \tag{37}\]  
\[\dot{y}=v\sin(\psi) \tag{38}\]  
\[\dot{\psi}=\frac{v}{l}\tan(\delta) \tag{39}\]  
Here, system states (position, heading, and linear velocity with respect to inertial frame (\(O\), \(X\), \(Y\))) are represented as \(X=[x\ y\ \psi]^{T}\). Vector of control inputs is defined in \(u=[v\ \delta]^{T}\), where (\(v\)) and (\(\delta\)) are the linear velocity and steering angle, respectively.  
Since the NMPC algorithm employed in this study is not computed in continuous time, at the first step, the kinematic model must be discretized. Considering the sampling period time dt, a data-sampling instant (\(t\)), and using the Euler approximation on (37)-(39), the discrete-time model can be formulated as follows:  
\[x_{t+1}=x_{t}+v_{t}\cos(\psi_{t})dt \tag{40}\]  
\[y_{t+1}=y_{t}+v_{t}\sin(\psi_{t})dt \tag{41}\]  
\[\psi_{t+1}=\psi_{t}+\left(\frac{v_{t}}{l}\right)\tan(\delta_{t})dt \tag{42}\]  
where:  
\[\text{X}_{k+1}=fd(x(t),u(t)) \tag{43}\]
# A Survey of Game Theoretic Approaches to Modelling Decision-Making in Information Warfare Scenarios
## 2 Information Warfare
### Types of Information Warfare Operations
#### 2.4.3 Future Information Warfare Operations  
New kinds of information warfare actions can emerge rapidly. [PERSON]'s [25] final category of 'cyberwarfare' collected a variety of futuristic attacks to emphasise this, some of which have since come to pass. Along with new categories of cyber-crime and cyber-bullying discussed already, an example of an emerging topic in information warfare is the concept of space war [26]. Uncontrolled or malicious satellites or uncontrolled satellite debris have the potential to create chaos in the space environment. Our dependency on satellite technology heightens the threat posed in this respect. Another aspect of space war more in the domain of information warfare is that of incepted and altered satellite signalling, and misuse of anti-satellite weapons technologies.
## 2 Materials and Methods
### Object Detection Algorithm  
#### 2.2.1 YOLOv5 Model  
YOLO (You Only Look Once) is a single-stage, high-efficiency object detection algorithm that enables end-to-end training while ensuring real-time processing capabilities, along with maintaining a high level of average accuracy [26]. Various versions of YOLO have evolved, with YOLOv5 being a relatively mature version that has been extensively tested and validated within the community. Its stability and reliability are crucial for practical applications, and compared to subsequent iterations, YOLOv5 demands more moderate computational resources.  
The YOLOv5 network's operation can be categorized into three distinct parts: feature extraction, feature enhancement and fusion, and predicting objects corresponding to the prior boxes. Corresponding to the tasks accomplished by YOLOv5, its model architecture  
Figure 1: Workflow demonstration diagram of the integrated approach to high-speed pavement monitoring.  
can be divided into three major components: the Backbone for primary feature extraction, the Neck for enhanced feature extraction, and the Yolo Head. The structural design of the model is illustrated in Figure 2.  
The input image initially undergoes feature extraction within the Backbone component of YOLOv5, where the extracted features are referred to as feature layers. During this primary feature extraction stage, three effective feature layers are obtained. These layers are then fed into the Neck for feature fusion through up-sampling and down-sampling processes, which integrate features of varying scales.  
The Yolo Head module serves as both a classifier and a regressor in YOLOv5. It leverages the feature extraction capabilities of the Backbone and the feature fusion from the Neck to produce three enhanced effective feature layers. Each feature layer possesses dimensions in terms of width, height, and channel count, allowing the feature map to be viewed as a collection of feature points, each with its own set of channel features.  
The Yolo Head module is responsible for determining whether the prior boxes on the feature points correspond to any objects. Its classification and regression tasks are executed within a \(1\times 1\) convolution.
## 3 Recent Developments and Progress
### Tropospheric Sounding by Ground-Based GNSS  
The GNSS has established itself as a leading method for the detection of ZTD. The advent of new multi-GNSS frequencies, coupled with advancements in precise-point positioning ambiguity resolution (PPP-AR) technology, presents both significant opportunities and challenges in the high-precision retrieval of GNSS-ZTD. These developments are crucial, as they enhance the accuracy and reliability of ZTD measurements, thereby facilitating improved atmospheric modeling and numerous applications in meteorology and climatology. However, the complexity involved in the integration of multiple GNSS frequencies and optimizing the PPP-AR processes also requires ongoing research and innovation to fully harness the potential of these technologies.  
Tropospheric delay usually refers to the signal delay generated by the electromagnetic wave signal when passing through the ionized neutral atmosphere below the height of 50 km. The delay is influenced by temperature, pressure, and humidity, as well as the locations of the receiver and transmitter antennas. The ZTD has been estimated based on the GNSS PPP method using MGEX stations over a period exceeding one year (Figure 10), which was assessed by using standard IGS tropospheric products, alongside radiosonde data and the ERA5 reanalysis dataset [24]. The ZTD time series derived from GPS were more consistent and precise when compared to those generated exclusively from the Galileo, BDS, and GLONASS solutions.  
One study investigated the link between ZTD detected by ground-based GNSS and the La Nina phenomenon [48]. Firstly, the GNSS-ZTD retrieval features of all existing dual-frequency combinations was thoroughly examined. Then, taking the popular dual-frequency combination as an example, the influences of PPP-AR and system combination were analyzed. The results were not merely a reference for GNSS-ZTD retrieval, but also provided an in-depth understanding of the relationship between the occurrence of the La Nina phenomenon and the performance of GNSS-ZTD retrieval. The difference in the ZTD inversion between the checked IGS stations during the La Nina and non-La Nina times was computed and globally interpreted. The greater the RMSE difference was, the more dramatic the ZTD change was during La Nina. Compared with the non-La Nina period, the ZTD in the Pacific remained significantly stable, while the ZTD values in Antarctica, East Asia, North America and Oceania changed more dramatically (Figure 11).  
Figure 9: Ionospheric disturbance characteristics during Typhoon Chandu, from GPS PRN32 and GLONASS PRN1 (Reprinted with permission from Ref. [47]. 2024 [PERSON], [PERSON]).
# EUR Prediction for Shale Gas Wells Based on the ROA-CatBoost-AM Model
## 3 Analysis of Primary Production Control Factors and EUR Prediction Model
### Comparative Analysis of Model Training Results  
Figure 7 compares the actual and predicted values for the ROA-SVM-AM, ROA-LR-AM, ROA-RF-AM, and ROA-CatBoost-AM models across the entire dataset. In each subplot, the red dashed line represents the ideal scenario where the predicted values equal the actual values (the diagonal). The scatter distribution reflects the fit of the models.  
As shown in Figure 8, the predictions from the ROA-RF-AM and ROA-CatBoost-AM models are closer to the diagonal, demonstrating a superior fit to the data. In contrast, the scatter distribution for the ROA-AdaBoost-AM and ROA-RR-AM models is relatively dispersed, suggesting potential prediction biases.  
Figure 7: _Cont_.  
The training results of four models were compared (as shown in Figure 8). Among them, the ROA-CatBoost-AM model achieved the highest R\({}^{2}\) and the lowest MSE and MAE, indicating the best fit and making it the most suitable for EUR prediction. Therefore, the ROA-CatBoost-AM model was selected as the final EUR prediction model.  
The best model was selected for analyzing the importance of influencing factors, with the results shown in Figure 9. The factor with the greatest influence on EUR is true vertical depth, while among the engineering factors, the drilled length of high-quality reservoirs has the most significant impact. For production factors, FBR330 (the flowback rate on the 330 th day) is the most influential.  
By identifying key controlling factors such as true vertical depth and drilled length of high-quality reservoir, well placement can be optimized to ensure that horizontal well sections maximize penetration through high-quality reservoirs, thereby improving EUR. Additionally, by incorporating Proppant Volume and average pumping rate, hydraulic fracturing parameters can be optimized to enhance fracture conductivity and increase  
Figure 8: Comparison of evaluation indicators of the four models.  
Figure 7: Distribution of actual and predicted values for the four models.  
production. FBR330, as an indicator of reservoir utilization efficiency, can be used to adjust flowback strategies to minimize early production losses. Furthermore, based on EUR predictions, well network layouts can be optimized, production schedules can be adjusted, and cost-benefit analyses can be conducted to balance investment and production capacity, ultimately improving the economic feasibility and long-term development efficiency of shale gas fields.
# Spoofing Traction Strategy Based on the Generation of Traction Code
## 2 Materials and Methods
### Proposed Spoofing Traction Strategy
#### 2.2.2 Generation of Traction Code  
The generation of traction codes is the core innovation in the proposed traction strategy. The code embedded in the signal-generation software completes the generation of the spoofing code described above. Figure 7 presents the generation steps of the traction code, where \(\mathrm{T_{coh}}\) is the coherent integration time, which is equals to 1 ms.  
In S1, the selection of the traction speed \(\mathrm{v_{drag}}\) follows certain principles, which is discussed later. In S2, \(\mathrm{\tau_{al}}\) can be calculated using Equation (9). In S4, \(\mathrm{C_{s}(t)}\) can be calculated using Equation (11).  
Consider the BeiDou Navigation Satellite System No. 33 satellite as an example to generate a spoofed traction code. We generate data for 20 s, and traction was selected to start at 1 s and end at 16 s. Three time points are randomly selected to observe the changes in the pseudo-code layer in the initial, intermediate, and final stages of traction. Figure 8 shows the transition process of the traction code sequence. It can be observed that the traction code changes with the advancement of the traction process and has no fixed form; conventional PRN codes have values of \(\pm 1\), whereas traction codes have values of 0 and \(\pm 2\); hence, it is not a PRN code in the conventional sense.  
Figure 7: Process diagram of the generation of traction code.  
Figure 9 shows the transition process of the PRN code sequence received by the spoofing target. Under the assumption of the proposed strategy, the received code sequence is always in the form of an authentication PRN code, and there is a significant phase shift caused by the code Doppler change \(\mathrm{v_{r}}\) based on the real value.  
Figure 8: Transition process of traction code sequence. (**a**) Initial form of traction code; (**b**) Intermediate form of traction code; (**c**) Final form of traction code.  
Figure 9: _Cont._  
Figure 10 shows a diagram of the deception process of the proposed traction strategy. In the dark-blue area, there is a procedure for generating the spoofing traction code, which is also a key step that differs from the other traction processes.
# Anomaly Detection in 6G Networks Using Machine Learning Methods
## 5 Research Methodology
### Stages in Pre-Processing  
The preprocessing converts the unprocessed data, in three steps, into an appropriate format for analysis before applying it. Algorithm 1 explains these processes (i.e., filtration, transformation, and normalization).