esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results  
### Unconditional Image Generation and Visual Evaluation  
Using the trained model, we first unconditionally generated head MR images [i.e., \((\epsilon,\delta)=(0,0)\)]. Three medical doctors (radiologists) evaluated the quality of those images (six volumes) and real images (six volumes) to show the capability of the model to generate realistic but fictional high-resolution volumetric medical images. They evaluated the appearance of anatomical structures and the contrast of the cortical white matter in five stages on the basis of the criteria shown in Table 1. Tables 2 and 3 show the results of the evaluation of real and fake cases, respectively. Moreover, in Figure 2, we show three representative slices (i.e., axial, coronal, and sagittal slices) of a generated head MR image (we selected case 1 in Tables A1-A3.) from pseudo random noise, i.e., from \(t=200\) in (17). The medical images obtained in the present study were cropped and enlarged to enhance their visibility.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Slages** & **Criteria** \\ \hline
1 & The structure is invisible. \\
2 & The structure is slightly identifiable. \\
3 & The structure is visible but not sufficient. \\
4 & The structure is visible as in real cases \\  & with the resolution of \(256\times 256\times 256\). \\
5 & The structure is well visible as in real cases \\  & with the resolution of \(256\times 256\times 256\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation Criteria.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Doctor** & **A** & **B** & **C** & **Ave. (256\({}^{3}\))** \\ \hline \hline
**Brain regions** & & & & \\ Anterior commissure & 5.0 & 5.0 & 5.0 & 5.0 \\ Posterior commissure & 5.0 & 5.0 & 5.0 & 5.0 \\ Cerebral aqueduct & 5.0 & 5.0 & 5.0 & 5.0 \\ Tegmentum of midbrain & 5.0 & 5.0 & 5.0 & 5.0 \\ Cerebellar hemisphere & 5.0 & 5.0 & 4.7 & 4.9 \\ sulcus & & & & \\ Cerebral peduncle & 5.0 & 5.0 & 5.0 & 5.0 \\ Corpus callosum & 5.0 & 5.0 & 5.0 & 5.0 \\ Third ventricle & 5.0 & 5.0 & 5.0 & 5.0 \\ Fourth ventricle & 5.0 & 5.0 & 5.0 & 5.0 \\ Lateral ventricle & 5.0 & 5.0 & 5.0 & 5.0 \\
**Cortical white matter** & & & & \\ \hline Hippocamp & 5.0 & 5.0 & 4.8 & 4.9 \\ Frontal lobe & 5.0 & 5.0 & 4.8 & 4.9 \\ Occipital lobe & 5.0 & 5.0 & 4.8 & 4.9 \\ Temporal lobe & 5.0 & 5.0 & 5.0 & 5.0 \\ Parietal lobe & 5.0 & 5.0 & 5.0 & 5.0 \\ Basal ganglia & 4.8 & 4.8 & 4.8 & 4.8 \\
**Other regions** & & & & \\ First cervical vertebra & 5.0 & 5.0 & 5.0 & 5.0 \\ Second cervical vertebra & 5.0 & 5.0 & 5.0 & 5.0 \\ Optic nerve & 5.0 & 5.0 & 4.8 & 4.9 \\ Extraocular muscles & 5.0 & 5.0 & 5.0 & 5.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Visual evaluation results. A, B, and C indicate averaged evaluation results for six real cases. Ave. indicates the averaged results (A, B, and C) from the three medical doctors.
# Configuration of Non-Pumping Reactive Wells Considering Minimum Well Spacing
###### Abstract  
A non-pumping reactive well (NPRW) is a subsurface structure that prevents contaminant spread using many non-pumping wells containing reactive media. For the construction of an effective NPRW, a sufficiently small spacing between wells is an important design factor to prevent contaminant leakage. However, close well construction is not recommended because of concerns about the decreased stability of adjacent wells under field conditions. In this research, we proposed a sawtooth array of NPRW as a practical configuration to minimize well spacing while meeting stability requirements in the field. To evaluate the performance of the novel NPRW configurations, a numerical modeling was conducted considering different well diameters and well spacings and their performance was compared taking into account the number of wells and the mass of the reactive material. The comparison results showed that the sawtooth configuration was more practical than a line of wells. The performance curve of NPRWs with the saw-toothed configuration was constructed from the relationship between the contaminant removal and configuration components (diameter and spacing of the well). This can be used to predict the contaminant removal performance of NPRWs with a sawtooth array.  
non-pumping reactive well; well diameter; minimum well spacing; practical configuration; sawtooth array; performance curve
## 5 Classification of Smart Grid Cyberattack
### Data Secrecy Attacks
#### 5.2.7 Man in the Middle (MITM)  
The goal of MITM attacks is to intercept and change communications between field devices and the control center. This allows an attacker to pass false data to the victim's device or system. During the protocol session, the attacker seems to be the intended destination for both the source and the target. The three CIA parameters are all impacted by MITM attacks, which can be conducted in various layers, particularly the transport and MAC layers. Comprehensive packet analysis software should be a part of cyber security systems, and strong authentication procedures can thwart MITM breaches [100].  
In smart grid systems the primary goal of authentication is to stop harmful, unauthorized components, like fraudulent smart meters, from imitating real components and launching MITM attacks that can grant specific access to the smart grid. Getting access to the smart grid components' data during an exchange can cause various vulnerabilities, such as corrupting or dropping data packets, decreasing or stopping the performance of the smart grid network, or starting additional attacks against the system, such as DoS and flooding attacks. MITM can be of various types such as an ARP (address resolution protocol) spoofing-based man-in-the-middle attack, which can be used to breach a smart grid SCADA control system [9].
## 5 Ground-Based Calibrations
### Energy Calibration  
#### 5.1.1 Calibration Methods  
The energy calibration evaluates each energy channel's measurement error. Additionally, the evaluation result determines the actual energy index and detection accuracy of the instrument. The energy range calibration determines the actual boundary point of the detector energy channel range. The basic principle is that, when a particle with the energy of the boundary point enters the detector, the probability of it falling into the upper and lower energy ranges is equal. It can be expressed by the following formula:  
\[\mathrm{P(i,E_{i})=P(i+1,E_{i})} \tag{2}\]  
where i is a certain energy range, E\({}_{i}\) is the actual energy boundary, and P is the probability of particles falling into the energy range.  
During calibration, a high-energy electron or proton accelerator was used to output a continuously adjustable single-energy particle beam. The whole high-energy particle detector measured the particle beam so that the response of the instrument to particles of different energies can be obtained. According to the actual response situation, the actual energy range that can be recorded by each energy channel can be obtained. Adjusting the particle beam energy and combining with measured data fitting can find the beam energy when the counts of two adjacent channels are equal, that is, the actual measurement boundary of the energy boundary.  
As shown in Figure 13, when seeking the actual boundary point of two energy ranges (E5 and E6), the beam energy selects multiple energy points. The two lines represent the probability of protons with different energies falling into the two energy ranges (E5 and E6). When the two lines intersect, it means that the probability of particles falling into the two channels is equal. The incident energy corresponding to the intersection of the two lines is the actual boundary point of the energy ranges (E5 and E6) [39; 40].  
Figure 12: Schematic diagram of the HEPD’s electronics principle.
## 3 Methodology
### Results of Predictive Modeling
The improved model used batch normalization, while the proposed model added a Time2 Vec layer. lower mean absolute errors, indicating that the model performed better at predicting nitrogen concentrations during these months. * In Al Riyadh, the highest mean absolute errors were recorded in March and September, indicating that the model had greater difficulty accurately predicting nitrogen concentrations during these months. The model performed better in forecasting in February and May. * In Al Juhail, the months of July and September were associated with the highest mean absolute errors, indicating that the model had difficulty making predictions during these months. The months of January and June were associated with the lowest mean absolute errors, indicating that the model performed best during these months. Moreover, we noticed a large variation in the mean error values in Al Riyadh compared to the other cities, especially during December, March, and May. This may indicate that the model makes larger errors with higher concentrations, which is expected when we use the mean squared error to calculate the loss function of the model during training. This is because the mean squared error loss function is sensitive to outliers, causing outliers to dominate the learning process. Consequently, we suggest that the selection of the loss function should depend on the existence of outliers in the data collected, given that these outliers are not errors but indicators of larger concentrations of the pollutant. Figure 7: Training and validation of the improved LSTM model with Time2 Vec for the three cities. (**a**) Al Juhail; (**b**) Al Riyadh; and (**c**) Najran.
# Layout Optimisation of Frame Structures with Multiple Constraints and Geometric Complexity Control
## 3 Mechanical Constraints
### Stress  
The stresses of the 2D beam element are sampled at two end nodes (\(n_{1}\), \(n_{2}\)). Taking a hollow circular cross-section as an example, the sampling points for stress are shown as red dots in Figure 2.  
The 2D stress component for element-\(e\) at the sampling point-\(j\) is  
\[\sigma_{e,j}=[\sigma_{e,j}^{xx}\ \ \tau_{e,j}^{xy}]^{\mathrm{T}}=\mathbf{B}_{e,j}k_{e} \mathbf{T}_{e}\mathbf{D}_{e}\mathbf{U} \tag{14}\]  
where \(\mathbf{B}_{e,j}\) is a matrix used to calculate stress from the force vector and is constructed based on cross-sectional properties (area, moment of inertial, etc. ), \(\mathbf{k}_{e}\) represents the element stiffness matrix in the local coordinate system, \(\mathbf{T}_{e}\) is the transformation matrix used to transform the element displacement vector from the global coordinate system to local. \(\mathbf{D}_{e}\) is a matrix used to extract the element displacement from the global displacement vector. \(j\) is the index of the stress sampling point. Based on Equations (5)-(14), the sensitivity of the stress component with respect to the design variable is  
\[\frac{\partial\sigma_{e,j}}{\partial d_{e}}=\frac{\partial B_{e,j}}{\partial d _{e}}\mathbf{k}_{e}\mathbf{T}_{e}\mathbf{D}_{e}\mathbf{U}+\mathbf{B}_{e,j}\frac{\partial\mathbf{k}_{e }}{\partial d_{e}}\mathbf{T}_{e}\mathbf{D}_{e}\mathbf{U}-\mathbf{B}_{e,j}\mathbf{k}_{e}\mathbf{T}_{e} \mathbf{D}_{e}\mathbf{K}^{-1}\frac{\partial\mathbf{K}}{\partial d_{e}}\mathbf{U} \tag{15}\]  
The von Mises stress at the sampling point can be calculated from the stress component:  
\[\sigma_{e,j}^{\mathrm{vm}}=\sqrt{\left[\sigma_{e,j}\right]^{\mathrm{T}}\left[ \begin{smallmatrix}1&0\\ 0&3\end{smallmatrix} \right]\left[\sigma_{e,j}\right]} \tag{16}\]  
The stress constraints formulated based on von Mises stress can be expressed as  
\[G_{e,j}^{\mathrm{vm}}\ =\ \frac{\sigma_{e,j}^{\mathrm{vm}}}{\overline{\sigma}^{ \mathrm{yield}}}\leq 1,\ \ \ \ \ \ e\in[1,N],\ j\in[1,8] \tag{17}\]  
where \(\overline{\sigma}^{\mathrm{yield}}\) is the predefined yield stress of the material. \(N\) is the number of elements in the structure. \(j\in[1,8]\) indicates that there are eight stress sampling points for these elements, four at each end, as shown in Figure 2.  
Similar to the node-wise displacement constraints, the element-wise stress constraints are aggregated into a global measure to improve the computational efficiency of the sensitivity analysis. By using the same \(p\)-norm function and the same value of \(p=4\), the global measure of stress constraints can therefore be expressed as  
\[P_{e^{\mathrm{vm}}}=\left[\sum_{i=1}^{N}\sum_{j=1}^{8}\left(\sigma_{i,j}^{ \mathrm{vm}}\right)^{p}\right]^{1/p}\leq 1 \tag{18}\]  
Figure 2: Stress sampling points for a 2D beam element. The global measure of stress constraints is normalised in the same way as displacement (Equation (9)) but is omitted here for brevity. Based on Equations (5) and (14)-(18), the sensitivity of the global stress constraint with respect to the design variable can be derived as  
\[
## 2 Materials and Methods
### Statistics  
The coefficient of individual agreement (CIA) was used to assess rater equivalence [25], which was adapted from the coefficient for assessing individual bioequivalence criteria (IBC) by Food and Drug Administration (FDA) guideline 2001 [26]. CIA was obtained when the inter-rater difference was so small that measurements from different raters were considered to be equivalent to the rater's own repeated measurements. The threshold of CIA was adapted from the bound of IBC recommended by FDA, 2.495, which corresponds to 0.445 of CIA [25]. Equivalence was considered acceptable if CIA greater than 0.445. The 95% confidence interval was estimated with bootstrapping method [25]. Intraclass correlation coefficient (ICC) was used to assess the intra-rater reliability. An ICC less than 0.40 was considered to be \"poorly\" agreement, from 0.40 to 0.59 was considered to be \"acceptable\", from 0.60 to 0.74 was considered to be \"good\", and 0.75 to 1.00 was considered to be \"excellent\" [27]. The intra-rater variability was calculated as within-group mean square (WMS) [28]. F-test was used to determine the significance of the intra-rater variability of mbRSA-EGS compared with the corresponding of marker-based RSA (normality was tested by Kolmogorov-Smirnov test). Chi-square test for the variance was used to determine whether intra-rater variability was significantly less than the upper limit of RSA measurement accuracy. Consistent with previous studies [29; 30], the upper limit of RSA accuracy (0.5 mm for translation, 1.15 deg for rotation) was used as the threshold. In addition, implants with translation more than 0.15 mm within two years was considered to have higher risk of loosening according to a previous literature [31]. Therefore, 0.15 mm was used as an additional threshold for the intra-rater variability considering the ability of mbRSA-EGS to predict the loosening of the femoral stem. The significance level of 0.05 was considered in all the tests mentioned above. All statistical analyses were performed by R (R Foundation, Vienna, Austria) [32].
## 5 Energy Efficiency and Sustainability
### Energy Efficiency in the Context of RIS and HAP Integration
#### 5.1.1 Energy Efficiency Through Adaptive Beamforming  
One of the primary advantages of incorporating RISs into HAPs is the ability to adapt to the propagation environment dynamically. RISs can effectively modify the phase and amplitude of the incoming EM waves to create constructive interference in desired directions [125]. This beamforming capability means that signals can be focused more accurately on specific receivers, significantly reducing the power required to maintain a reliable communication link, thus enhancing the overall energy efficiency of the HAPs [72; 116; 125].
## 2 Characterization of Surface Structure and Properties of Titanium Alloy Turning Operations  
### Turning Tool Structure and Instantaneous Cutting Position  
In order to reveal the instantaneous dynamic characteristics of the cutting edge during the turning process, the structure of the turning tool and its cutting edge are characterized by geometric modeling, and the corresponding coordinate system is established, as shown in Figure 1.  
The tool coordinate system, designated as \(o_{a}\)-\(x_{a}y_{a}z_{a}\) in Figure 1, was established through the following geometric definitions: The origin \(o_{a}\) coincides with the intersection of the cutting edge's apex plane and the radial maximum overhang projection. The \(z_{a}\)-axis extends along the line connecting \(o_{a}\) to the cutting edge apex, while the \(x_{a}\)-axis aligns with the radial overhang direction. The \(y_{a}\)-axis completes the right-handed Cartesian frame. Key geometric parameters include: q (cutting edge left boundary), m (upper boundary), \(p\) (arbitrary edge point), \(l\) (edge segment length), \(\phi_{a}\) (transition arc angle), \(\phi_{a}\) (angular offset from tool nose center), and \(l_{a}\) (edge profile equation). Dimensional parameters comprise (shank length), \(x_{D}\) (tool height), \(x_{B}\) (shank width), and \(r_{a}\) (nose radius). The tool geometry is characterized by seven fundamental angles: principal cutting edge angle (\(\kappa_{r}\)), auxiliary cutting edge angle (\(\kappa_{r}\)'), tool included angle (\(\varepsilon_{r}\)), inclination angle (\(\lambda_{s}\)), radial components (rake \(\gamma_{n}\), clearance \(\alpha_{n}\), wedge \(\beta_{n}\)), and axial components (rake \(\gamma_{0}\), clearance \(\alpha_{0}\), wedge \(\beta_{0}\)). As can be seen from Figure 1, the main cutting edge is solved by solving the equation in the lathe coordinate system, as follows:  
\[L_{a1}(x_{a},y_{a},z_{a})=\left\{ \begin{array}{l}x_{a}=r_{a}+l\cdot\sin\kappa _{r}\\ y_{a}=\sin\kappa_{r}\cdot(r_{a}\cdot(1-\cos\phi_{a})+l\cdot\sin\lambda_{s})\\ z_{a}=-l\cos\kappa_{r}\end{array} \right. \tag{1}\]  
The tip arc of the transition section of the cutting edge is solved by solving the equation in the tool tooth coordinate system, as follows:  
\[L_{a2}(x_{a},y_{a},z_{a})=\left\{ \begin{array}{l}x_{a}=r_{a}\cdot(1-\sin\phi _{a}\prime)\\ y_{a}=l\sin\lambda_{s}\\ z_{a}=r_{a}\cdot(1-\cos\phi_{a}\prime)\end{array} \right. \tag{2}\]  
The sub-cutting edge is solved by solving the equations in the tool tooth coordinate system, as follows:  
\[L_{a3}(x_{a},y_{a},z_{a})=\left\{ \begin{array}{l}x_{a}=r_{a}\cdot(1-\cos \kappa_{r}\prime)+l\cdot\sin\kappa_{r}\prime\\ y_{a}=\cos\kappa_{r}\prime\cdot(r_{a}\cdot(1-\sin\phi_{a})+l\cdot\cos\lambda_{ s})\\ z_{a}=r_{a}\cdot(1+\sin\kappa_{r}\prime)+l\cdot\cos\kappa_{r}\prime\end{array} \right. \tag{3}\]  
Figure 1: Tool structure parameters and its coordinate system. The cutting edge is solved by solving the equations in the tool tooth coordinate system, as follows:  
\[L_{a}(x_{a},y_{a},z_{a})=\left\{ \begin{array}{l}L_{a1}(x_{a},y_{a},z_{a})\\ L_{a2}(x_{a},y_{a},z_{a})\\ L_{a3}(x_{a},y_{a},z_{a})\end{array} \right.
## 3 Numerical Results
### Deformation Characteristics under Different Compression Rates  
In Sections 3.3 and 3.4, for the \(\mathrm{A_{e}}\), \(\mathrm{B_{e}}\), and \(\mathrm{D_{e}}\) nodes in the roller-shaped DVWH structure, based on numerical simulation results, the equivalent stress and radial displacement variation curves of these nodes under different compression rates and rotational speeds are plotted. Preliminary analysis was conducted from the curves' peak size, variation amplitude, and fluctuation degree.
# Design of a Heterogeneous-Based Network Intrusion Detection System and Compiler
## 4 Deployment of Experimental Environment
### Evaluation Criteria  
We use average packet size and anomaly traffic ratio to evaluate the throughput and matching performance of the rule compiler and its corresponding FPGA Pre-Filtering module, and use system throughput and accuracy to assess the performance of the heterogeneous anomaly intrusion detection system.  
**Average packet size:** When the input traffic bandwidth remains constant, smaller average packet sizes increase packet counts and the number of five-tuple matching operations required by the system. When exceeding the system's processing threshold, the overall throughput drops significantly with severe packet loss. Additionally, identical five-tuples across multiple rules increase matching pressure on the module. By adjusting PKtgen's packet sizes, we test the system's throughput under 100 Gbps.  
**Anomaly traffic ratio:** This is a critical metric for testing IDS performance. By increasing the number of anomalous packets and gradually raising the anomaly traffic ratio, we evaluate the throughput of the fast-pattern matching module to indirectly validate the rule compiler's performance. The original CIC IDS2017 dataset contains approximately 15% anomaly traffic. We adjust the anomaly packet ratio in PKtgen-generated traffic to test the three rule sets under different anomaly ratios.  
**Throughput and accuracy:** Since the performance of signature-based IDS is also affected by unknown attacks and traffic visibility, we configure attacks explicitly defined in the rule sets during throughput experiments to ensure all anomalies trigger rules accurately, thereby improving evaluation reliability. The attack types solely manifest as rule-matched anomalous packets without involving inter-packet behavioral characteristics. The throughput formula is as follows:  
\[\text{Throughput (Gbps)}=\frac{Number\ of\ packets\times 700B\times 8}{Testing\ duration(s)} \tag{1}\]  
For accuracy rate calculation, we compute the ratio between correctly detected anomalous packets and the total test packets to derive the accuracy metric
## 6 Conclusions  
With the increasing number of robot tasks involving interaction with unknown environments, ensuring stability, speed, and steady force tracking during the interaction process has become increasingly important. This paper proposes a fractional-order PID adaptive impedance control strategy to enhance the stability and force tracking performance of robot-environment interactions and provides the theoretical stability and validity derivations. The algorithms CIC, AIC, VIC, and FOAIC were compared and validated through two aspects of simulation--robustness testing and force tracking testing. The results indicate that when unexpected changes in the parameters occur, such as desired force, FOAIC maintains excellent force tracking performance while nearly eliminating overshoot and exhibiting fast adjustment times, showcasing robustness. Regarding force tracking control in different environments, CIC is only suitable for plane environments. In sinusoidal and complex surface environments, AIC demonstrates an overshoot of 38.3% and 43.6%, respectively, while VIC shows an overshoot of 33.4% and 39.2%. FOAIC, on the other hand, has almost no overshoot. According to the MSE analysis, the force tracking accuracy of FOAIC surpasses that of AIC and VIC by 29% and 45%, respectively, in the sinusoidal environment, and by 40.3% and 58.6%, respectively, in the complex surface environment, showcasing outstanding force tracking performance.  
Moreover, the proposed strategy demonstrates strong scalability, as it can adapt to various interactive tasks by estimating environmental information to update the robot's reference trajectory online. Therefore, this control strategy introduced in this paper can be applied to a range of force control tasks involving interactions with the environment, such as deburring, polishing, assembly, machining tasks in deformable environments, and production tasks based on robot force control.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Control Mode** & **AIC** & **VIC** & **FOAIC** \\ \hline MAE & 0.2527 & 0.3041 & 0.1963 \\ RMSE & 0.2848 & 0.3421 & 0.2201 \\ MSE & 0.0811 & 0.1170 & 0.0484 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Force tracking errors: MAE, RMSE, and MSE.  
Due to constraints, this paper only conducts simulation validation of the proposed strategy's effectiveness. Future work can involve experimental validation on a physical manipulator to further verify and enhance our research.  
Conceptualization, Q.H.; methodology, L.G.; software, Q.H.; validation, L.G. and Q.H.; data curation, L.G.; writing--original draft preparation, L.G.; writing--review and editing, L.G. and Q.H.; supervision, L.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.