esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Boundary Conditions and Time Step  
As shown in Figure 3, the computational domain is divided into two parts. One part is the annular space between the wellbore and the grouting pipe, named fluid 1. Since this part is empty, no resistance is set. The inlet is set as the pressure inlet, in which the pressure of pulsating grouting is a sinusoidal wave and the pressure of steady grouting is constant [11]. The grouting pipe is defined as the wall. The pressure outlet is set on the ground to recycle the circulating slurry. The other part is the soil, which is named as fluid 2. Fluid 2 is defined as a homogeneous porous medium, and the flow resistance is computed using the Ergun equation, expressed as follows [18]:  
\[\frac{|\Delta p|}{L}=\frac{150\mu}{D_{p}^{2}}\frac{(1-\varepsilon)^{2}}{ \varepsilon^{3}}v_{\infty}+\frac{1.75\rho}{D_{p}}\frac{(1-\varepsilon)}{ \varepsilon^{3}}v_{\infty}^{2} \tag{15}\]  
where \(\Delta p\) represents the pressure drop, \(\mu\) is the dynamic viscosity, \(\varepsilon\) is the porosity, \(v_{\infty}\) denotes the fluid velocity, \(D_{p}\) is the particle diameter, \(\rho\) stands for the fluid density, and \(L\) signifies the length of the porous region. The viscous and inertial loss coefficient in each component direction may be identified as  
\[\alpha=\frac{D_{p}^{2}}{150}\frac{\varepsilon^{3}}{(1-\varepsilon)^{2}}\text{ and }C_{2}=\frac{3.5}{D_{p}}\frac{(1-\varepsilon)}{\varepsilon^{3}} \tag{16}\]  
Simulating pressure fluctuations accurately typically requires around 50 time steps within one sinusoidal period. Considering the effects of flow resistance and slurry properties on convergence, the time step is set to 5 e-4 s. In this study, the highest frequency  
Figure 3: Boundary condition of computational domain.  
is 3.5 Hz, so there are 571 time steps within one cycle, which is sufficient to accurately describe pressure fluctuations.
# Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
## 3 AE Impact on Network Traffic Classifiers
### Using AdverNet to Attack Target Models  
As an adversary, the objective is to misclassify the output of target models. Adversarial attacks are generally classified into three categories: white box, black box, and gray box. White-box attacks involve complete knowledge of the target model and direct access to its parameters and architecture. Using this information, attackers can craft sophisticated attacks by leveraging gradient-descent search algorithms. Gray-box attacks assume partial knowledge of the target model. Although not as powerful as white-box attacks, gray-box attacks can still be effective by exploiting certain vulnerabilities or weaknesses in the model. An attacker may have access to limited information, such as the model's architecture, output probabilities, or a subset of its parameters. This partial knowledge can be used to  
Figure 3: An overview of ML-based network traffic classification.  
devise evasion strategies and generate AEs to deceive a model. Black-box attacks present the most challenging scenario, as the attacker has zero knowledge of the target model. In such cases, attackers often use techniques such as transferability, whereby they train substitute models or gather information from similar models to create surrogate models. These surrogate models act as proxies for the target model and are used to generate AEs. By exploiting the transferability of the AEs, an attacker fools the target model without direct knowledge of its internals. In our approach, we generate AEs for AdverNet using the JSMA technique. These AEs are then tested against the target models to assess their vulnerabilities and the potential impact of the attack.
# An Equivalent Non-Uniform Beam-Like Model for Dynamic Analysis of Multi-Storey Irregular Buildings
## 2 The Beam-Like Model
### The Equations of Motion of the Proposed Non-Uniform Beam-Like Model
\right], \tag{9}\]  
where the relevant elements are given as:  
\[\mathbf{K}_{j_{x}i_{x}} = \frac{1}{h}\sum\limits_{k=1}^{N_{f}}GA_{x,k}\int\limits_{\zeta_{k -1}}^{\zeta_{k}}\psi^{\prime}{}_{i}\psi^{\prime}{}_{j}d\zeta\] \[\mathbf{K}_{j_{y}i_{y}} = \frac{1}{h}\sum\limits_{k=1}^{N_{f}}GA_{y,k}\int\limits_{\zeta_{k -1}}^{\zeta_{k}}\psi^{\prime}{}_{i}\psi^{\prime}{}_{j}d\zeta\] \[\mathbf{K}_{j_{x}i_{\delta}} = \frac{1}{h}\sum\limits_{k=1}^{N_{f}}\left(GJ_{z,k}+e_{y}^{2}GA_{x,k}+e_{x}^{2}GA_{y,k}\right)\int\limits_{\zeta_{k-1}}^{\zeta_{k}}\psi^{\prime }{}_{i}\psi^{\prime}{}_{j}d\zeta\, \tag{10}\] \[\mathbf{K}_{j_{x}i_{\delta}} = \mathbf{K}_{j_{\delta}i_{x}}=\frac{1}{h}\sum\limits_{k=1}^{N_{f}}e _{y}GA_{x,k}\int\limits_{\zeta_{k-1}}^{\zeta_{k}}\psi^{\prime}{}_{i}\psi^{ \prime}{}_{j}d\zeta\] \[\mathbf{K}_{j_{y}i_{\delta}} = \mathbf{K}_{j_{\delta}i_{y}}=-\frac{1}{h}\sum\limits_{k=1}^{N_{f}} e_{x}GA_{y,k}\int\limits_{\zeta_{k-1}}^{\zeta_{k}}\psi^{\prime}{}_{i}\psi^{ \prime}{}_{j}d\zeta\] \[\mathbf{M}_{j_{x}i_{x}} = h\sum\limits_{k=1}^{N_{f}}m_{x,k}\int\limits_{\zeta_{k-1}}^{ \zeta_{k}}\psi_{i}\psi_{j}d\zeta+\sum\limits_{k=1}^{N_{f}}M_{x,k}\psi_{i}\psi_ {j,k}\] \[\mathbf{M}_{j_{y}i_{y}} = h\sum\limits_{k=1}^{N_{f}}m_{y,k}\int\limits_{\zeta_{k-1}}^{ \zeta_{k}}\psi_{i}\psi_{j}d\zeta+\sum\limits_{k=1}^{N_{f}}M_{y,k}\psi_{i}\psi_ {j,k}\,\] (11) \[\mathbf{M}_{j_{\delta}i_{\delta}} = h\sum\limits_{k=1}^{N_{f}}I_{0,k}\int\limits_{\zeta_{k-1}}^{ \zeta_{k}}\psi_{i}\psi_{j}d\zeta+\sum\limits_{k=1}^{N_{f}}\bar{I}_{0,k}\psi_{i,k}\psi_{j,k}\]\[ \begin{split}\mathsf{P}_{j_{x}}&=-\tilde{u}_{gx}h\sum \limits_{k=1}^{N_{f}}m_{x,k}\int\limits_{\zeta_{k-1}}^{\zeta_{k}}\psi_{j}d \zeta-\tilde{u}_{gx}\sum\limits_{k=1}^{N_{f}}M_{x,k}\psi_{j,k}\\ \mathsf{P}_{j_{y}}&=-\tilde{u}_{gy}h\sum\limits_{k=1 }^{N_{f}}m_{y,k}\int\limits_{\zeta_{k-1}}^{\zeta_{k}}\psi_{j}d\zeta-\tilde{u}_ {gy}\sum\limits_{k=1}^{N_{f}}M_{y,k}\psi_{j,k}\\ \mathsf{P}_{j_{y}}&=0\end{split} , \tag{12}\]  
where \(\psi_{i,k}=\psi_{i}(\zeta_{k})\) is the shape function evaluated at the floor level, \(N_{f}\) is the number of floors and \(GA_{x}(\zeta),GA_{y}(\zeta),GJ_{z}(\zeta)\) represent the along axis variable shear and torsional stiffness that can be substituted with a step-wise distribution, constant for each inter-storey \(k\), in accordance with the equivalent beam model adopted in Figure 2d, as follows:  
\[GA_{x,k}=S_{x,k}\cdot h_{k}\qquad\qquad GA_{y,k}=S_{y,k}\cdot h_{k}\qquad \qquad GJ_{z,k}=C_{k}\cdot h_{k}, \tag{13}\]  
Finally, the equations of motion of the proposed equivalent multi-stepped beam in the generalized space are derived by Equation (8) in matrix notation as follows:  
\[\mathsf{M}\bar{\mathbf{q}}+\mathbf{K}\mathbf{q}=\mathsf{P}, \tag{14}\]  
It is worth noting that the structural damping will be introduced, in terms of modal damping ratios, in the uncoupled equations of motions that will be derived in Section 2.6.
## 2 Experimental Setup
### Z-Pinching Capillary Discharge Plasma Source  
Figure 1 depicts the schematic of the source and the charging circuit. Plasma is generated by a current discharge through a 10 cm long, 3.2 mm inner diameter ceramic capillary (Al\({}_{2}\)O\({}_{3}\)) filled with nitrogen gas. A ceramic capacitor bank with a maximum capacity of 21 nF is pulse-charged by a March-Fitch generator up to 90 kV, and switched by a self-breakdown spark-gap. The discharge voltage is regulated with nitrogen pressure in the spark-gap and by March-Fitch generator charging voltage. Before the main discharge, a 35 A, 3 \(\upmu\)s long current pulse pre-ionizes the gas in the capillary and prepares a uniform conducting channel. The main current pulse has a damped sinus shape, with a half-period of 150 ns and a maximum amplitude of 30 kA. The capillary is filled with nitrogen gas through a hollow in the electrode on its grounded side. Radiation is also emitted through this hole. The system is enclosed in duralumin housing in order to reduce electromagnetic noise. The photon flux of this incoherent source is \(5.5\times 10^{13}\) photons/sr/pulse, the source size at full width half maximum (FWHM) is 360 \(\upmu\)m, and the beam divergence at FWHM is 30 mrad [20; 30]. More details about the source and its optimization and characterization can be found in [20; 30; 31; 32].
# Multiple Extended Target Tracking Algorithm Based on Spatio-Temporal Correlation
## 3 Partition Method Based on Spatio-Temporal Correlation
#### 3.1.1 The K-Means++ Clustering Algorithm  
In multiple extended target tracking, since the measurement set at each time contains measurements from different targets and clutter, it is necessary to use a clustering algorithm to cluster the measurement set, i.e., measurement set partition. The K-means clustering algorithm is a kind of commonly used clustering algorithm, wherein \(K\) denotes the number of clusters. Since the number of extended targets and clutter varies at different times, \(K\) is also time-varying. If the \(K\) traversal interval is \([K_{L},K_{U}]\) (where \(K_{L}=1\),\(K_{U}=|Z_{k}|\)), the number of measurement partitions will be quite large when the number of measurements is large, so the value range needs to be narrowed. In addition, the K-means clustering algorithm is unstable because it randomly selects the initial points.  
To solve the above problems, the K-means++ algorithm was proposed in the literature [15]. The specific algorithm steps are as follows:  
1. Randomly select a center from the dataset \(c_{1}\);
2. Select the next central point \(c_{i}\) with probability \(\sum\limits_{\begin{subarray}{c}x\in X\end{subarray}}^{D(x^{\prime})}\), \(x^{\prime}\in X\);
3. Repeat step 2 until there are \(k\) central points, \(\mathcal{C}=\{c_{1},c_{2},\cdots,c_{k}\}\);
4. For each \(i\in\{1,\cdots,k\}\), set the cluster \(C_{i}\) to be the point set in \(X\) that is closer to \(c_{i}\) than \(c_{j}\), where \(i\
eq j\);
5. For each \(i\in\{1,\cdots,k\}\), let \(c_{i}\) be the centroid of all points in \(C_{i}\), \(c_{i}=\frac{1}{|C_{i}|}\sum\limits_{\begin{subarray}{c}x\in C_{i}\end{subarray}}x\);
6. Repeat steps 4 to 5 until \(\mathcal{C}\) is no changes.
## 2 Verification of Simulation Methods
### Experimental Platform  
This study presents the design of a fast transient cavity-tube combined experimental platform to investigate the fast transient response process of a cavity-tube combined flow path with initial pressure under sudden change boundaries. The experimental platform primarily comprises five components: the gas supply system, the test section, the sudden change boundary simulation device, the experimental auxiliary tools, and the data acquisition system. The schematic diagram of the experimental platform and the testing procedure are illustrated in Figures 2 and 3. This platform is capable of conducting cavity-tube combined venting tests under sudden change boundary conditions. The experimental geometry and initial conditions are detailed in Table 1.  
Figure 1: Simplification of the secondary air system cavity–tube combined flow path.  
Figure 3: Flowchart of the main experimental process.  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **Parameters** & **Values** \\ \hline Cavity Length \(L\)/mm & 30 \\ Cavity Diameter \(D\)/mm & 200 \\ Cavity Volume \(V_{Vessel}\)/mm\({}^{3}\) & \(9.43\times 10^{5}\) \\ Inlet and Outlet Diameter \(d\)/mm & 40 \\ Tube Length \(l\)/mm & 430 \\ Tube Volume \(V_{Tube}\)/mm\({}^{3}\) & \(5.40\times 10^{5}\) \\ Initial Pressure of Flow Path/kPa & 150.24 \\ Initial Pressure of Atmospheric Computational Domain \(P_{\text{B}}\)/kPa & 100.34 \\ Initial Temperature/k & 300.15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of the model.  
Figure 2: Schematic diagram of the fast transient chamber-tube combined test platform.  
The sudden change boundary required for the experiment is supplied by a sudden change boundary simulation device, as illustrated in Figure 4. This device rapidly moves a plug to connect the flow path of the test specimen with the atmosphere, facilitating a swift change in the flow path boundary. The device can complete boundary changes within 6 ms, which is comparable to the time scale of structural changes occurring during shaft failures [3], thereby meeting the experimental requirements.
## 5 Simulation Verification
### Simulation of SPMSM Speed Regulation  
To illustrate the important role of the proposed parameter identification algorithm when designing the high-performance controller, the simulation experiment of motor speed regulation is carried out based on the double closed-loop control system described in Figure 8. The given speed is periodically adjusted between 300 rpm and 1000 rpm, and the motor torque load is set to increase by 5 N.m at 0.4 s and restores the original value at 0.9 s. Three adjustable laws based on ADRC, switched PI, and PI are considered for the MRAS, and the speed regulation results are shown in Figure 15.  
From Figure 15, when the motor torque load changes suddenly at 0.4 s and 0.9 s, the speed curve under the ADRC adaptive law has a relatively low-speed drop, and the speed curves of the switched PI and PI adaptive laws have more obvious fluctuations than that of the ADRC adaptive law. Obviously, the proposed adaptive law based on ADRC has better anti-disturbance performance than the switched PI and classical PI adaptive laws.
###### Abstract  
Differential privacy, a cornerstone of privacy-preserving techniques, plays an indispensable role in ensuring the secure handling and sharing of sensitive data analysis across domains such as in census, healthcare, and social networks. Histograms, serving as a visually compelling tool for presenting analytical outcomes, are widely employed in these sectors. Currently, numerous algorithms for publishing histograms under differential privacy have been developed, striving to balance privacy protection with the provision of useful data. Nonetheless, the pivotal challenge concerning the effective enhancement of precision for small bins (those intervals that are narrowly defined or contain a relatively small number of data points) within histograms has yet to receive adequate attention and in-depth investigation from experts. In standard DP histogram publishing, adding noise without regard for bin size can result in small data bins being disproportionately influenced by noise, potentially severely impairing the overall accuracy of the histogram. In response to this challenge, this paper introduces the SReB_GCA sanitization algorithm designed to enhance the accuracy of small bins in DP histograms. The SReB_GCA approach involves sorting the bins from smallest to largest and applying a greedy grouping strategy, with a predefined lower bound on the mean relative error required for a bin to be included in a group. Our theoretical analysis reveals that sorting bins in ascending order prior to grouping effectively prioritizes the accuracy of smaller bins. SReB_GCA ensures strict \(\epsilon\)-DP compliance and strikes a careful balance between reconstruction error and noise error, thereby not only initially improving the accuracy of small bins but also approximately optimizing the mean relative error of the entire histogram. To validate the efficiency of our proposed SReB_GCA method, we conducted extensive experiments using four diverse datasets, including two real-life datasets and two synthetic ones. The experimental results, quantified by the Kullback-Leibler Divergence (KLD), show that the SReB_GCA algorithm achieves substantial performance enhancement compared to the baseline method (DP_BASE) and several other established approaches for differential privacy histogram publication.  
differential privacy; histogram publishing; relative error +
Footnote †: journal: _Algorithms_
# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 4 Field Study
### Data Acquisition and Delivery  
Initializing the IMU integration constants is essential before data collection can begin. Tactical-grade IMUs generally have gyro biases less than the earth rate (\(15^{\circ}/\mathrm{h}\)), allowing them to determine the vehicle's heading reliably in static mode [46]. An alternative is a kinematic alignment, which involves distinctive driving maneuvers to align the IMU data and the GNSS Course-Over-Ground. In our study, both systems performed a kinematic alignment.  
In the measurement campaign, we strive to create comparable conditions for both systems. Therefore, the two vehicles drove in convoy at 60 to 100 km/h, and the MLS systems collected data from the same area less than five seconds apart.  
As mentioned above, we did not participate in processing trajectories and the georeferenced point clouds. However, we defined boundary conditions for a consistent processing strategy and supplied our partners with GNSS raw data from four surrounding base stations of the Austrian Positioning Service (APOS). The data delivery included  
**Point clouds** with RGB, intensity, and time information, georeferenced in UTM33N;  
**High-frequency trajectory data** including position and attitude (200 Hz); and  
**Trajectory quality data** such as **standard deviation** and **separation** in the east, north, up, roll, pitch, and yaw directions (1 Hz).  
The separation is a quality parameter that measures the trajectory deviation between the forward and reverse solutions. These two refer to the same trajectory but are processed forward and backward in time. Differences may occur due to variations in the satellite geometry and availability. Suppose an MLS survey includes a long tunnel and the effects of IMU drifts are maximum at the end of the tunnel. Therefore, forward and reverse processing yield different results. The trick is to combine both solutions by inverse variance weighting to obtain the best possible trajectory. The separation itself provides a good first indicator of the quality of the final trajectory. As Figure 9 shows, the deviations decrease the further the vehicle travels from the tunnel portal. Accordingly, we must assume a lower absolute positioning accuracy for our object of interest (RW1).
## 3 Background
The parameter \(s\) is either automatically learned during the training process or manually set. Due to the neural SDF representation, NeuS demonstrates strong capabilities in reconstructing the surfaces of novel objects. However, its application in real-time robotic systems is constrained by the time-consuming nature of the learning process and the significant computational costs involved. To address this limitation, we adopt Voxurf [35], a model that leverages voxel-based representations for accelerating the surface reconstruction process. Voxurf enhances the training speed by combining an explicit voxel grid with a neural SDF representation. Specifically, Voxurf directly learns the SDF values of the geometry through an SDF voxel grid \(V^{(\text{SDF})}\in\mathbb{R}^{1\times N_{x}\times N_{y}\times N_{z}}\) and predicts the color using a feature voxel grid \(V^{\text{(feat)}}\in\mathbb{R}^{C\times N_{x}\times N_{y}\times N_{z}}\) with a lightweight MLP. For a specific \((\mathbf{x},\mathbf{d})\), the SDF value \(d^{\prime}\) and the color \(\mathbf{c}\) can be obtained using trilinear interpolation as follows:  
\[d^{\prime}=\text{interp}(\mathbf{x},\mathcal{G}(V^{\text{(SDF)}},k_{g},\sigma_ {g})), \tag{6}\]  
\[\mathbf{c}=\text{MLP}(\text{interp}(\mathbf{x},V^{\text{(feat)}}),\mathbf{x}, \mathbf{d},n). \tag{7}\]  
For \(d^{\prime}\), \(\mathcal{G}\) represents a 3D convolution on the voxel grid \(V^{\text{(SDF)}}\) using a Gaussian kernel with size \(k_{g}\) and standard deviation \(\sigma_{g}\) to smooth the voxel SDF values rather than using the raw data. For \(\mathbf{c}\), the normal vector \(n\) is derived from the \(V^{\text{(SDF)}}\). Lastly, \(d^{\prime}\) is substituted into Equation (5) to calculate \(\alpha_{i}\) and then volume rendering is performed as in Equation (4) to obtain the final pixel color.
# Challenges and Possibilities of ICT-Mediated Assessment in Virtual Teaching and Learning Processes
## 3 Results
### Systematic Review of ICT-Mediated Assessment
#### 3.1.3 What Are the Assessment Possibilities Offered by the Current ICT Platforms?
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strategy** & **Samples Reference** & \multicolumn{2}{c}{**Description**} & **Challenges** \\ \hline Self-assessment & [35; 97] & \(\bullet\) & Students are involved in their monitoring, evaluating their performance, and building learning plans. & \(\bullet\) & Requires further research to establish validity and impact on learners \\  & & \(\bullet\) & Increase learning motivation and contributes to a better understanding. & & \\ \hline Peer-assessment & [22; 30; 34; 40; 49; 60; 98; 99] & \(\bullet\) & Students rate their peers & \(\bullet\) & Some question their validity and reliability \\  & & \(\bullet\) & Develop reflection skills and encourages responsibility & \(\bullet\) & Requires proper instruction to train the students to grade their peers \\  & & \(\bullet\) & Useful for online, blended, and massive courses & & \\ \hline Mobile assessment & [15; 57; 52; 71; 89; 94; 95; 96; 97; 99] & \(\bullet\) & Use a mobile device (cellphones and tablets) for assessment & \(\bullet\) & There may be distractions during the evaluation process \\  & & \(\bullet\) & Allows periodic evaluations & & \\  & & \(\bullet\) & It can be developed anywhere & & \\ \hline Gamification & [56; 70; 100] & \(\bullet\) & Use game for problem-solving and skill development & & \\  & & \(\bullet\) & Increases student interest and engagement & \(\bullet\) & Requires specific developments according to the area of knowledge \\ \hline Augmented Reality & [43; 50] & \(\bullet\) & Emulation of real-world environment for interaction & \(\bullet\) & Requires specific developments according to the area of knowledge \\  & & \(\bullet\) & Increases student interest and engagement & & \\ \hline Learning analytics & [36; 55; 87; 88; 101; 102; 103; 104; 105] & \(\bullet\) & Captures data during the training process to identify students’ strengths, opportunities, and limitations & \(\bullet\) & Incorporating data analysis into educational practices \\ assessments & & \(\bullet\) & Allows to adapt the training process according to the skills of each student & & \\ \hline Automated assessment & [56; 57; 59; 64; 71; 106; 107; 108; 109; 110; 111] & \(\bullet\) & Allows the generation, scoring, and automatic feedback of evaluations & \(\bullet\) & Requires specific developments according to the area of knowledge \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Key strategies to incorporate ICT-mediated assessment into learning processes.
# Study on Water Inrush Characteristics of Hard Rock Tunnel Crossing Heterogeneous Faults
## 6 Discussion  
The study reveals two distinct water inrush patterns that occur when crossing water-rich faults in igneous tunnels. The first pattern is characterized by a rapid decrease in water inflow and a short duration when the supply is less than the tunnel inrush flow. This inrush pattern occurs within the fault damage zone, manifesting as a high-speed water inrush. The second pattern occurs when there is a continuous external supply, resulting in a steady inflow and a longer duration. This inrush pattern is found within the fault core, manifesting as a low-speed water inflow. These findings align with the results obtained from tuff injection experiments conducted in hard rock tunnels in Sichuan, China by [PERSON], et al. (2018). The results of this study have important implications for the management and mitigation of sudden water hazards in igneous faults.  
It is important to highlight that the law derived in this study is applicable specifically to highly heterogeneous faults with a stable external recharge. It should be noted that these types of faults pose significant challenges during construction. Furthermore, this paper primarily focuses on the concept of constructing a non-homogeneous model based on the internal structural properties of the fault. Different engineering situations require specific analysis.