esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Third-Order Korteweg-de Vries (KdV) Equation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(M=1,N=7\)} & \multicolumn{2}{c}{\(M=2,N=6\)} & \multicolumn{2}{c}{\(M=3,N=5\)} & \multicolumn{2}{c}{\(M=4,N=4\)} \\ \hline
**Grid** & **Error** & \(p\) & **Error** & \(p\) & **Error** & \(p\) & **Error** & \(p\) \\ \hline
33 & \(3.09\times 10^{-1}\) & - & \(8.54\times 10^{-1}\) & - & \(1.36\times 10^{0}\) & - & \(1.35\times 10^{0}\) & - \\
49 & \(3.64\times 10^{-2}\) & 5.41 & \(3.88\times 10^{-3}\) & 13.6 & \(1.50\times 10^{-3}\) & 17.2 & \(1.78\times 10^{-3}\) & 16.7 \\
65 & \(7.89\times 10^{-4}\) & 13.6 & \(6.33\times 10^{-5}\) & 14.6 & \(8.33\times 10^{-6}\) & 18.3 & \(1.20\times 10^{-5}\) & 17.6 \\
81 & \(3.97\times 10^{-5}\) & 13.6 & \(2.68\times 10^{-6}\) & 14.4 & \(1.87\times 10^{-7}\) & 17.2 & \(3.32\times 10^{-7}\) & 16.3 \\
97 & \(3.38\times 10^{-6}\) & 13.7 & \(2.06\times 10^{-7}\) & 14.2 & \(9.29\times 10^{-9}\) & 16.7 & \(2.00\times 10^{-8}\) & 15.5 \\
113 & \(4.14\times 10^{-7}\) & 13.7 & \(2.36\times 10^{-8}\) & 14.2 & \(8.40\times 10^{-10}\) & 15.7 & \(2.04\times 10^{-9}\) & 14.9 \\
129 & \(6.66\times 10^{-8}\) & 13.7 & \(3.57\times 10^{-9}\) & 14.3 & \(1.77\times 10^{-10}\) & 11.7 & \(3.60\times 10^{-10}\) & 13.1 \\
145 & \(1.32\times 10^{-8}\) & 13.7 & \(6.16\times 10^{-10}\) & 15.0 & \(1.03\times 10^{-10}\) & 4.64 & \(1.38\times 10^{-10}\) & 8.22 \\
161 & \(3.17\times 10^{-9}\) & 13.7 & \(7.24\times 10^{-11}\) & 20.4 & \(9.21\times 10^{-11}\) & 1.07 & \(1.00\times 10^{-10}\) & 3.07 \\
177 & \(9.17\times 10^{-10}\) & 13.1 & \(4.68\times 10^{-11}\) & 4.6 & \(9.01\times 10^{-11}\) & 0.23 & \(9.21\times 10^{-11}\) & 0.86 \\
193 & \(3.37\times 10^{-10}\) & 11.5 & \(7.69\times 10^{-11}\) & \(-5.73\) & \(8.97\times 10^{-11}\) & 0.05 & \(9.22\times 10^{-11}\) & 0.24 \\
209 & \(1.71\times 10^{-10}\) & 8.52 & \(8.54\times 10^{-11}\) & \(-1.32\) & \(8.96\times 10^{-11}\) & 0.02 & \(8.98\times 10^{-11}\) & 0.07 \\
225 & \(1.18\times 10^{-10}\) & 4.96 & \(8.81\times 10^{-11}\) & \(-0.41\) & \(8.95\times 10^{-11}\) & 0.00 & \(8.96\times 10^{-11}\) & 0.02 \\
241 & \(1.00\times 10^{-10}\) & 2.40 & \(8.90\times 10^{-11}\) & \(-0.14\) & \(8.95\times 10^{-11}\) & 0.00 & \(8.96\times 10^{-11}\) & 0.01 \\
257 & \(9.41\times 10^{-11}\) & 1.05 & \(8.93\times 10^{-11}\) & \(-0.05\) & \(8.95\times 10^{-11}\) & 0.00 & \(8.96\times 10^{-11}\) & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Calculated error values and orders (\(p\)) of the CD schemes with equal \((M+N)\). with the expected order of accuracy (e.g., 22 nd for \(M=3,\,N=9\)) and reducing the overall solution error to the values below \(\mathcal{O}(10^{-7})\).
# On the Needs and Requirements Arising from Connected and Automated Driving
## 4 Technical Requirements: from Automotive to Network Perspective
### Definition of KPIs
In such case, we can have zero packet drop probability and still violate the reliability requirement. We also note that SDUs that violate the deadline will probably be dropped by the application. However, with the chosen framework, we can separate between the application decision to drop packet due to excessive delays in the communication process and packet drops due to other reasons (noise, fading, interference, etc.). * **Availability** is defined as the probability that the requested service is declared as available. That is, at the time when the service is requested by the application, the communication system can either declare the service as available or unavailable. In the former case, the communication system will accept SDUs from the application and try to deliver them with the requested reliability and deadline. In the latter case, the application is blocked and it should initiate a fallback procedure to gracefully degrade the performance. The rationale for this KPI is that it might be very costly or even impossible to provide a service with very high reliability at all times and it is therefore necessary to occasionally declare the service as unavailable. Usually, there is a tradeoff between availability and reliability in the sense that we can make a system more reliable by reducing the availability, and vice versa. **Data rate** represents the number of bits sent per unit of time, typically measured in bits per second (bit/s). It is defined by the SDU rate and the SDU size, i.e., for a stream of SDUs that arrive with a rate \(R\) SDUs/s, the average data rate is \(R\times\) (SDU size). In case the deadline is less than the time between SDUs, i.e., \(\tau_{\text{dl}}<1/R\), the required data rate is (SDU size)/\(\tau_{\text{dl}}\). **Communication range** specifies the maximum distance between a transmitter and its intended receiver allowing for communication with a targeted SDU size, maximum latency (deadline), reliability, and for a given effective transmit power and receiver sensitivity, on average.
# Architectural Design for Digital Twin Networks
## 5 Design and Implementation Strategies
### Modeling
#### 5.2.2 ML/AI Approach  
These algorithms are key for enhancing DTNs, especially given their ability to manage complex and dynamic network conditions. These data-driven techniques utilize extensive network data to create detailed models that replicate and predict network behaviors, leading to more effective analysis and control.  
The strengths of AI/ML models in DTN modeling include their capacity to generalize from known data to new scenarios and their improving interpretability, making their outcomes more reliable. Future developments will likely focus on enhancing the real-time responsiveness and interactivity of these models to ensure they can swiftly adapt to network changes and support dynamic control within the DTN.  
GNNs, Deep Learning (DL), and Reinforcement Learning (RL) are among the AI methods frequently referenced. For example, while [18] discuss GNNs and DL, they do not detail their training processes. In contrast, ref. [19] effectively demonstrates the use of GNNs in DTN, showcasing how ML can be integrated to improve network performance and decision-making. Likewise, ref. [41] tackles the approach with RL, highlighting another AI technique's potential in optimizing network management and operations.  
This collection highlights the benefits of AI/ML for modeling smaller or specific network segments, where these methods can rapidly adapt and provide insights. As the field advances, AI/ML are expected to play an even greater role in the evolution of DTNs, driving them towards more intelligent and autonomous operations.
# Partial Discharge Wideband Full-Band High-Gain Resonant Cavity UHF Sensor Research
## 3 Resonant Cavity UHF Antenna Sensor Design
### Microstrip Monopole Patch Antenna Body Design
#### 3.1.2 Structural Optimization Based on Slot Dual Resonance  
The traditional slot double resonant structure is in the antenna surface of an artificially created rectangular groove: the groove will form a resonant cavity, so that the propagation of electromagnetic wave resonance thus increasing the antenna gain, but this practice will make the antenna bandwidth greatly reduced. Therefore, this paper uses the idea of slot dual resonant structure to form a slot dual resonant structure similar to the effect of a concave resonant cavity by adding a circle of peripheral patches around the microstrip monopole antenna as shown in Figure 6, while setting isosceles triangle structures on the top left and top right of the added peripheral patches and rectangular arm structures on the left and right sides [16; 25; 26; 27]. The improved slot dual resonance structure can concentrate the slot current to the center patch position and increase the center current density, which can also improve the gain of the antenna sensor.  
The geometric parameters of the slotted dual resonant structure of the antenna in Figure 6 are given in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value (mm)** \\ \hline Upper border length \(L_{\text{A}}\) & 5.0 \\ Isosceles triangle side length \(W_{\text{T}}\) & 25.0 \\ Left and right border width \(W_{\text{A}}\) & 5.0 \\ Large rectangle arm width \(W_{\text{A}3}\) & 40.6 \\ Small rectangle arm width \(W_{\text{A}2}\) & 20.0 \\ Lower border length \(L_{\text{B}}\) & 11.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table of geometrical parameters of slot double resonance structure.  
Figure 5: Curved flow monopole antenna gain direction diagram.  
Figure 6: Diagram of monopole antenna based on slot dual resonance structure.  
The VSWR and gain performance of the improved monopole antenna are simulated and analyzed. From the VSWR simulation plot in Figure 7 below, it can be seen that the VSWR fluctuation becomes larger compared with that before the improvement, but the VSWR is less than 5 in the 330 MHz-3 GHz band, which basically covers the frequency band of UHF signal and meets the design requirements of PD UHF sensor for power equipment. As can be seen from the gain direction diagram in Figure 8 below for the center frequency point of 1 GHz, the maximum gain is increased by 1.81 dBi to 3.08 dBi compared to that before the improvement, and the gain direction diagram is in the shape of \"8\" with good directionality, which is basically in line with the improvement direction to improve the gain performance.
###### Abstract  
Super-large-diameter caissons, serving as working wells for trenches pipe jacking technology, are being extensively constructed alongside the increasing adoption of trenchless technology in urban areas. However, being regarded merely as ancillary structures, the structural stability of a caisson during both construction and operation phases are often neglected. This study, centered on the super-large-diameter caissons within the Jinshui River flood control project in Zhengzhou, China, systematically monitored the mechanical behavior of caisson structures and surface settlement during construction and operational phases. Utilizing a validated FE method, the influence of groundwater fluctuations on the structural stability of caissons during operational phases was examined. Furthermore, potential occurrences of loose soil, voids, and caisson tilting were considered. Subsequently, the applicability of permeable polymer, foam polymer, and anchor rod reinforcement techniques were evaluated, followed by an analysis of the structural stability of the caissons post reinforcement during long-term operations. The findings demonstrate the minimal horizontal displacement of and stress variation in caissons under seasonal groundwater fluctuations, without significant structural alterations. Nevertheless, the presence of loose soil, voids, and caisson inclinations may decline the caisson's support force and bearing capacity. With the increase in non-compactness, void size, and inclination, the structural stability of caissons notably diminishes. Reinforcing loose soil with permeable polymers, filling voids with foam polymers, and utilizing anchor rods are all effective methods for strengthening caisson structures and enhancing their stability.  
super-large-diameter caissons; trenchless technology; groundwater fluctuations; structural stability; reinforcement +
Footnote †: journal: Applied Sciences
## 3 The ML Strategy within Network Slicing
### The Role of Machine Learning
#### 3.1.2 Unsupervised Learning  
Unsupervised learning is an ML paradigm in which the system solely receives input data without associated target outputs. In this approach, data instances are unlabelled, and the system endeavours to identify patterns or relationships among the variables and group the data without external guidance. When new data are introduced, the system utilises previously learned features to determine the data's group [84]. This technique is valuable  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Applications** & **Description** & **Use Cases** & **Strengths** & **Weaknesses** \\ \hline \multirow{3}{*}{\begin{tabular}{c} **Adaptive** \\ **Defences** \\ \end{tabular} } & \begin{tabular}{c} AI-driven adaptive \\ security measures \\ \end{tabular} & \begin{tabular}{c} AI-based Expert Systems can \\ automate defence and \\ mitigation decisions based on the \\ specific threat \\ \end{tabular} & Ability to adapt to \\  & & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} **Security** \\ **Orchestration** \\ \end{tabular} } & \begin{tabular}{c} Coordinating security measures \\ across different network slices and \\ components \\ \end{tabular} & \begin{tabular}{c} Creating frameworks based on ETSI \\ ZSM principles for security \\ management, like in the \\ 6G-OPENEC-SECURITY project. \\ \end{tabular} & \
\begin{tabular}{c} Effective \\ coordination of \\ security measures \\ \end{tabular} &
\begin{tabular}{c} Complexity in \\ integration and \\ management \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._for uncovering hidden structures, detecting patterns, and identifying relationships within datasets [83].  
Unsupervised learning is particularly advantageous in various security applications such as network traffic analysis, and clustering for identifying similar network behaviours and categorising threats [71]. Operating without the need for labelled data, unsupervised learning algorithms excel in uncovering hidden structures and detecting abnormalities within datasets. This capability allows for the efficient identification of unknown potential threats, unusual user behaviours, and emerging undefined attack patterns. Because it can deal better with unknown threats, unsupervised learning can create more robust anomaly detection systems [72]. Notably, unsupervised learning offers the flexibility to adapt to evolving threats and can uncover novel attack vectors that traditional security measures may not capture. However, challenges such as the interpretability of results, lower accuracy, and high computational complexity are among the considerations associated with this approach [79].
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 7 Current and Future Research Directions
### Central List of Future Research Directions  
Based on the works presented in Sections 3-6; we identified the following open challenges in data stream classification:  
* The lack of appropriate real-world benchmark datasets is a problem in data stream classification, especially in areas where privacy or other regulations are applied, e.g., in health care or when using social media data. A safe and secure way to share the datasets must be identified and developed.  
* Most preprocessing methods (e.g., discretization) still are in preliminary stages, as they have issues with efficiency in stream scenarios.
* Dealing with missing data is a challenging task, especially in evolving data streams.
* The combination of several data stream sources, as well as handling high-dimensional data, is in the early stages.
* Imbalanced data is a problem in data stream classification scenarios. This challenge touches on preprocessing tasks such as under- and oversampling and the actual classification and drift detection methods.
* Neural network-based approaches are often inferior in efficiency.
* A unified metric or concept for evaluation of stream classifiers is still missing. Due to the data's time dependency and possible verification latencies, current evaluation measures derived from the batch setting are not suitable for stream classification.
* More research regarding the detection of the type of concept drifts is needed.
* Promising potential lies in automated (hyper-)parameter configuration of classification and drift detection algorithms.
## 4 Results and Interpretation
### Macro-Scale Wettability Characteristics  
To minimize the potential influence of sample shape and size on experimental outcomes, we meticulously recorded the mass/weight changes of both the samples and the solution before and after the experiment. The volume of imbibed brine and crude oil was then calculated based on these measurements. To quantify the imbibition effect and facilitate a comparative analysis of sample wettability, we employed the Wetting Affinity Index, as defined by [PERSON] et al. (2016) [PERSON] et al. (2016) and [PERSON] et al. (2015) [PERSON] et al. (2015) [PERSON] et al. (2015):  
\[\text{Wetting Affinity Index of Brine}:\ (WI_{w})=\frac{I_{w}}{I_{w}+I_{o}} \tag{1}\]  
\[\text{Wetting Affinity Index of Oil}:\ (WI_{o})=\frac{I_{o}}{I_{w}+I_{o}} \tag{2}\]  
where \(I_{w}\) and \(I_{o}\) represent the volumes of brine and oil, respectively, imbibed into the samples.  
Analyzing the SI results for brine and crude oil (Figure 6), it is evident that the imbibition process can be broadly divided into two phases: an initial rapid imbibition phase followed by a subsequent stable phase. For crude oil imbibition, a stable equilibrium state is more readily achieved. For samples C1 (Figure 6a) and C2 (Figure 6b), the volumes of oil imbibed per unit volume were markedly higher than that of brine. Conversely, in samples C3 (Figure 6c) and C4 (Figure 6d), the imbibed volumes of brine exceeded that of oil. The calculated wettability indices for the four samples are depicted in Figure 6e, with brine wettability indices of 0.431, 0.302, 0.681, and 0.576 and crude oil-wet indices of 0.569, 0.698, 0.319, and 0.424, respectively. These results indicate that samples C1 and C2 are more oil-wet, while Sample C2 exhibits a stronger oil affinity. In contrast, samples C3 and C4 display a stronger tendency toward water-wet conditions.  
Figure 5: Contact angle results. (**a**) Box-plot of contact angles (CA) of four samples measured and (**b**) images of CA measurements.  
Figure 6: The SI results for brine and crude oil: (**a–d**) Unit volume imbibition curves for brine and oil of C1, C2, C3 and C4; (**e**) the distribution of wettability indices for hydrophilic and oleophilic samples.
# Planar UWB Monopole Antenna with Tri-Band Rejection Characteristics at 3.5/5.5/8 GHz
## 3 Parametric Study  
The parameters on the presented triple band-notched antenna were studied by Ansoft HFSS to investigate how they affected the performance of this antenna.
## 3 Results  
### Pearson Correlation Analysis  
After the Pearson correlation analysis, the correlation coefficients of each independent variable to the dependent variable are shown in the Figure 5 below (target is the dependent variable, taking the tunnel traffic conditions as an example). It can be seen from the figure that the accident type V8 has a high correlation with the road traffic conditions inside the tunnel, as the single-vehicle accidents are mainly collisions with the wall and abnormal parking, while the two-vehicle accidents are mainly rear-ending collisions and rubbing. This variable is not only important for predicting road traffic conditions but also for predicting the duration of accidents. In terms of time, the accident time V0 (day or night) has the highest correlation with whether the tunnel is in a high-traffic period during accident V1. The correlation between the accident date (whether it is a holiday), season, and tunnel traffic conditions during the accident is small. In terms of space, the accident location V6 (whether it is inside the tunnel) has the greatest influence, followed by whether it is in a deceleration zone, and the tunnel length and accident section have a relatively small impact. In terms of traffic volume, the ratio of traffic volume at the time of the accident to the average traffic flow during the same period of the month V7 and the mixing rate of large vehicles V9 have a greater impact on the traffic conditions. In addition, the weather V5 also has a significant impact on the tunnel traffic conditions after the accident.  
After analysis and comparison, the seven most relevant independent variables were selected to predict the tunnel traffic condition and accident duration during accidents. They are the involved accident types (V8), accident time (V0), accident location (V6), rate of large vehicles mixed in traffic (V9), the ratio of traffic volume during an accident to the average traffic volume in the same period of the month (V7), whether it is a traffic peak period (V1), and weather (V5) (See Figure 6).  
Figure 5: Correlation heatmap between independent and dependent variables.
# Proposed New Analytical Method of Tower Load in Large-Span Arch Bridge Cable Lifting Construction
## 3 Accuracy Verification of Calculation Method
### Numerical Model
#### 3.2.2 Loads in Analysis
Three load conditions are taken for verification of the calculation method's correctness, which are as follows: (1) the upstream main cable transports the rated load to mid-span while the remaining two groups of main cables are unloaded; (2) the middle main cable transports the rated load to mid-span while the remaining two groups of main cables are unloaded; and (3) the downstream main cable transports the rated load to mid-span while the remaining two groups of main cables are unloaded. The main loads are self-weight, wind cable initial tension, and cable system load. The self-weight is automatically realized by the numerical calculation software according to the material density. The forces acting on the tower tops obtained by the calculation method when transporting the rated load of a single main cable to the middle of the span are listed in Table 4.
## 2 Comprehensive Analysis of Space Debris
### Analysis of Space Debris Target Characteristics  
Targets in star maps usually exhibit point-like characteristics without structure and texture features. Star trackers detect the sunlight reflected from the space target, and the brightness of the resulting space target image varies to some extent due to the different reflectivity of different parts of the space target and the different observation phase angles at different observation moments. In general, the factors affecting the brightness of the detected target include the size of the target, the distance between the detector and the target, the target material properties, and the observation azimuth angle.  
According to theoretical analysis and experimental verification, the calculation formula of spatial target brightness characteristics is [23; 24]:  
\[M_{V}\ =\ -26.7-2.5\log(A\rho F(\varphi))+5.0\log(R) \tag{3}\]  
Figure 3: Observation diagram.  
Figure 2: The relationship between satellite observations and sunlight effect.  
where \(A\) represents the lateral area of the target, \(\rho\) denotes the reflection coefficient of the target, \(R\) denotes the distance between the target and the observer, \(\varphi\) represents the sun-target-observer phase angle, and \(F\) is the phase function, which is related to the current situation and direction of the target.  
Assuming the target reflectance \(\rho~{}=~{}0.1\) and the observed dislocation angle \(\varphi~{}=~{}0\), the calculation can be performed according to the typical spherical target brightness characteristics analysis shown in Table 1.  
The observation limit magnitude of a traditional star tracker is 5-6 magnitudes. Combining with the above table, we find that the star tracker can complete the observation of a target with a diameter over 0.5 m at 200 km, a target with a diameter over 0.2 m at 100 km, and a target with a diameter over 0.1 m at 50 km. In summary, it is clear that star trackers have the ability to observe space debris near its own orbit.
## 2 Proposed Methodology
### Tumor Diagnosis  
A patient with a progressive expanding mass on his left cheek was subjected to an incisional biopsy procedure, which showed a plexiform ameloblastoma. A GE (General Electric) light speed VCT XTe 64-row spiral CT scan was used, and the scanned images were then saved as a DICOM (digital imaging and communication in medicine) file. DICOM is a de facto international standard for sharing, exchanging, and storing digital medical images. The DICOM file contained 2D images of the anatomical structure.