esa-sceva/satcom-chunk-collection huggingface dataset:
# Resampling Imbalanced Network Intrusion Datasets to Identify Rare Attacks
## 7 Metrics Used for the Assessment of Results
### Classification Metrics Used  
For evaluating the classification of the random forest runs, the following matrices were used: accuracy, precision, recall, F-Score, and macro precision.  
AccuracyAccuracy is the number of correctly classified instances (i.e., True Positives and True Negatives) divided by the total number of instances [28].  
Accuracy = [True Positives (TP) + True Negatives (TN)]/[TP + False Positives + TN + False Negatives]  
PrecisionPrecision is the proportion of predicted positive cases that are correctly labeled as positive [29]. Precision by label considers only one class and measures the number of times a specific label was predicted correctly, normalized by the number of times that label appears in the output.  
Precision = Positive Predictive Value = [True Positives]/[True Positives + False Positives]  
RecallRecall is the ability of the classifier to find all the positive samples, or the True Positive Rate (TPR). Recall is also known as sensitivity, and is the proportion of Real Positive cases that are correctly predicted as Positive (TP) [29].  
All Real Positives = [True Positives + False Negatives]  
All Real Negatives = [True Negatives + False Positives]  
Recall = True Positive Rate = [True Positives]/[All Real Positives]  
F-ScoreThe F-score is the harmonic mean of a prediction's precision and recall metrics. It is another overall measure of the test's accuracy [29].  
\[F-Score=2*[\text{Precision}*\text{Recall}]/[\text{Precision}+\text{Recall}] \tag{8}\]  
Recall, sensitivity, and TPR connate the same measure.  
Macro Precision: Macro precision finds the unweighted mean of the precision values. This does not take label imbalance into account [30].
# Semantic Distributed Data for Vehicular Networks Using the Inter-Planetary File System
## 7 Experimental Results
### Discussion of Results
When comparing IPFS experiment results with the results obtained with the simulations, it is important to take into account that these simulations did not include any kind of encryption nor authentication procedure. The access-centric standard simulation performed the request using a plain and insecure TCP connection. CDVC, however, used the Content-Centric Networking (CCN) protocol stack proposed by [PERSON] et al. at [44]. Therefore, CDVC did not include the authentication of the participant nodes, the vehicle did not require any kind of initial connection to the network, and the request did not have the overhead of a TCP or QUIC connection. The fact that IPFS uses TLS for encryption and authentication, and the connection-oriented nature of this protocol, make IPFS less efficient when only one small content request is done by the vehicle. The Table 2 shows a comparison between IPFS, CDVC, and the standard address-centric protocol. IPFS is shown in two rows, one with the cost of doing a single and isolated content request, and the second when multiple requests are done. The results show that the real implementation of IPFS is capable of matching, during the operation of the system, the best performance shown in the state-of-the-art, even when compared with a simulation, which includes many simplifications that clearly make it compete in better conditions. Figure 10: Total time required to fetch content from another node, including the initial connection. Figure 9: Total number of bytes required to fetch content from another node including the initial connection.
# An Iterative Closest Point Method for Lidar Odometry with Fused Semantic Features
## 4 Methods  
### Point Clouds Filtering  
During mobility, Lidar continually gathers information of its surroundings in the form of point cloud representations. We filter the point clouds beforehand to efficiently extract feature points and decrease the burden of semantic segmentation. First, the invalid points collected by the Lidar are removed. Second, point clouds that are too close to the Lidar coordinate system are also filtered, as these point clouds usually do not belong to the surrounding environment. These points generally belong to the machine on which the Lidar is loaded, such as the machine surface. Therefore, we remove the point clouds within 0.15 m from the origin of the Lidar coordinate system.
# Hybrid-Compliant System for Soft Capture of Uncooperative Space Debris
## 4 Impedance Controller
### Hybrid Compliance  
The useful terms of _active_ and _hybrid_ compliance are described in this section to understand the proposed impedance controller better. For this purpose, we used a reduced version of the three-mass system previously described in Figure 11. The reduced version is  
Figure 12: Block diagram of the impedance control strategy for the soft capture of space debris.  
a two-mass equivalent ADR system with only the ACU to control its interaction with the debris, as presented in Figure 13a.  
The system equations of motion for the equivalent ADR system in Figure 13a, during the contact with the debris, can be written as:  
\[m_{s}\ddot{x}_{s}=-F_{a} \tag{11}\]  
\[m_{c}\ddot{x}_{c}=-F_{i}+F_{a}. \tag{12}\]  
Substituting \(\ddot{x}_{s}\) of Equation (11), and \(\dot{x}_{c}\) of Equation (12), into the impedance filter that describes the desired impact behavior of Equation (5) yields  
\[-\frac{m_{f}}{m_{c}}F_{i}+\frac{m_{f}}{\mu_{ef}}F_{a}+b_{f}(\dot{x}_{c}-\dot{x }_{s})+k_{f}(x_{c}-x_{s}-k_{m})=-F_{i}, \tag{13}\]  
where \(\mu_{ef}\) is given by  
\[\mu_{ef}=\frac{m_{c}m_{s}}{m_{c}+m_{s}}. \tag{14}\]  
Solving for the actuator force \(F_{a}\) and selecting \(m_{f}\) equal to \(m_{c}\) so that \(F_{a}\) does not depend on the impact force \(F_{i}\), yields  
\[F_{a}=k_{p}(x_{s}-x_{c}+l_{m})+k_{d}(\dot{x}_{s}-\dot{x}_{c}), \tag{15}\]  
where \(k_{d}\) and \(k_{p}\) are the impedance controller's gains given by  
\[k_{d}=\frac{\mu_{ef}}{m_{f}}b_{f} \tag{16}\]  
and  
\[k_{p}=\frac{\mu_{ef}}{m_{f}}k_{f}. \tag{17}\]  
Observing Equation (15) for the actuator's force command \(F_{a}\), one can conclude that the impedance-controlled actuator behaves in active (virtual) compliance with spring coefficient \(k_{a}\) of length \(l_{a}\) and damping coefficient \(b_{a}\), as shown in Figure 13b; in this example, equal to the controller's gains \(k_{p}\) and \(k_{d}\), respectively.  
Furthermore, the proposed ADR system, as modelled in Section 4.1 and shown in Figure 11, incorporates, additionally to the active compliance, passive physical compliance with spring coefficient \(k_{s}\) of length \(l_{s}\) and damping coefficient \(b_{s}\), see Figure 14a.  
Figure 13: CubeSat-ADR system with two masses connected by (**a**) impedance-controlled actuators, (**b**) an active (virtual) compliance equivalent to (**a**).  
The active and the passive compliance in series can be combined to form an equivalent _hybrid_ compliance of length \(l_{m}=l_{s}+l_{a}\) with spring coefficient \(k_{m}\) and damping coefficient \(b_{m}\) as shown in Figure 14b.  
The stiffness and the damping coefficients \(k_{m}\) and \(b_{m}\) of the hybrid system are of paramount importance as they affect the ADR system's impedance, the contact time of the ADR system with the debris and, thus, the success of the debris capture. Therefore, the reduced hybrid-compliant system shown in Figure 14b is used in Section 5.4 to showcase the necessity of hybrid compliance in an ADR system.
# A Building Segmentation Network Based on Improved Spatial Pyramid in Remote Sensing Images
## 3 Results and Discussion
### Evaluation Metrics  
To verify the effectiveness of the Detail Enhancement module, we used the Peak Signal-to-Noise Ratio (PSNR) as an evaluation metric. In the experiments, the original images from the dataset were used as \(I_{HR}\) images. The corresponding \(I_{LR}\) were obtained by downscaling the images with the factor \(U\) through bicubic interpolation. A low spatial resolution image \(I_{LR}\) with \(W\) columns, \(H\) rows, and \(C\) bands can be expressed by a tensor of size \(W\times H\times C\). Its corresponding \(I_{HR}\) had a size of \(UW\times UH\times UC\). We compared our module with some super-resolution state-of-the-art methods. MSE represents the mean square error between the reconstructed image \(F\) and the original high-resolution image \(I\). Assume the size of the reconstructed image and original high-resolution image is \(m\times n\), MSE can be calculated as:  
\[MSE=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}[I(i,j)-F(i,j)]^{2} \tag{8}\]  
Let \(k\) represent the number of bits per pixel. The PSNR can be calculated as:  
\[PSNR=10*log_{10}(\frac{(2^{k}-1)^{2}}{MSE}) \tag{9}\]  
To verify the effectiveness of the Semantic Segmentation module, we used the Pixel Accuracy (PA) and Mean Intersection over Union (mIoU) as evaluation metrics. PA is used to indicate the proportion of correctly classified pixels to the total pixels. Assuming thatthere are a total of \(k+1\) types of targets in the image, \(p_{ij}\) represents the number of pixels that belong to class \(i\) but are predicted to be class \(j\), while \(p_{ii}\) represents the number of pixels that are actually classified correctly.  
\[PA=\frac{\sum_{i=0}^{k}p_{ii}}{\sum_{i=0}^{k}\sum_{j=0}^{k}p_{ij}} \tag{10}\]  
The mIoU is used to measure the accuracy of an object detector on the datasets. The mIoU can therefore be calculated as:  
\[mIoU=\frac{1}{k+1}\sum_{i=0}^{k}\frac{p_{ii}}{\sum_{j=0}^{k}p_{ij}+\sum_{j=0}^ {k}p_{ji}-p_{ii}} \tag{11}\]
# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 5 Tool Wear Monitoring Method Based on Deep Convolutional Neural Network
### Deep Convolutional Neural Network
It converted the direct learning of the target value into learning the residual between the input and the output. To a certain extent, it solved the problem of information loss and reduced learning difficulty. [PERSON] et al. [115] optimized the parameters and training algorithm of the original AlexNet convolutional neural network. The findings suggested that the improved AlexNet convolutional neural network algorithm could better judge the degree of tool wear. [PERSON] et al. [116] employed a convolutional autoencoder to pre-train the network model, combined with the BP and Adam algorithms to fine tune the model parameters, thereby establishing an effective model for tool wear type recognition. [PERSON] et al. [117] selected two features of cutting force and cutting vibration as initial data and used the wavelet packet analysis method to reduce data noise for feature extraction. Then, the entropy result of the monitoring data was calculated as the input of the predictive model. Through training and testing the deep CNNs, the tool remaining life prediction model was finally formulated. [PERSON] et al. [118] used CNN to develop a tool condition monitoring model, implementing hyperparameter adjustments to improve prediction accuracy. Figure 6: The LeNet-5 network structure (drawn with the permission of Reference [110]. Copyright 2011 IEEE). With the continuous development of computer hardware and deep learning algorithms, deep CNNs achieved great success in computer vision tasks such as image classification, target monitoring, and image segmentation. Many deep CNN methods were applied to tool wear monitoring. In order to improve the generalization performance of the network, the number of layers of the network can be increased according to the situation, so that the model can mine the tiny information of the machining feature and avoid gradient dispersion. The model operation should facilitate adaptive feature extraction. However, the deeper the network layers, the better; they must be judged according to the actual application. If the data are insufficient, it will lead to problems such as insufficient training and over fitting.
## 2 Materials and Methods
### Numerical Simulation
#### 2.3.2 Establishing the Model  
Based on the geometry of the longwall face, the ratio of the first weighting interval of the main roof to the panel width is 0.2, while the ratio of the periodic weighting interval of the main roof to the panel width is within 0.1-0.18. Therefore, the longwall face can be simplified as a plane problem [53; 54; 55]. Hence, a \(400\times 173\) m\({}^{2}\) PFC2D numerical model of the midspan of the longwall face along its width direction was built, as shown in Figure 11. From the top, the model was divided into five layers: the loess layer, siltstone, mudstone, coal, and griststone. In the computation, 22 particles at different positions on the surface (loess layer) were monitored in real time to record the dynamic change in particles on the surface. The open-off cut distance to the left boundary was 50 m, the upper boundary is free, and the left and right boundaries constrained horizontal lateral displacement and allowed vertical movement. The mining height was 8 m, each advance was 8 m, and gravitational acceleration was 9.8 m/s\({}^{2}\).  
Each rock stratum in the model has the same particle radius; the minimum radius is 20 cm and the maximum minimum radius ratio is 1.6, with a total of 206,899 particles. A common method used to generate model particles is the expansion method. Although it is simple, the coefficient of expansion is difficult to control when the particle is generated, resulting in a high overlap among particles that often causes a particle overflow boundary. Therefore, the model in this study was established using the brick technique (surrounding block technique), with individual blocks in equilibrium within space. Although the time needed for the model to obtain equilibrium increased with its size, when a model is composed of blocks already in equilibrium, it does not take long for it to reach equilibrium, which facilitates the establishment of a large-scale model.  
Figure 11: Numerical model of particle flow in the studied working face.  
Figure 10: Calculation cycle process of Particle flow code (PFC).  
With the advance of the working face, the overlying strata undergoes dynamic processes, such as deformation and collapse. To be more consistent with engineering practice, during the advance process, blocks that collapsed and accumulated in the coal seam floor were not eliminated (the collapsed blocks will influence the development of fractures inside the rock formation). To determine whether each advance phase of the face was in equilibrium, the model was considered to be in equilibrium under the following conditions [55]: (1) The average unbalanced force ratio was less than or equal to \(10^{-4}\), where the average unbalanced force ratio was defined as the ratio of the average value of the sum of the contact force of all particles and the body force to the sum of all externally applied loads and body force. (2) The rate of crack growth was less than or equal to five per 1,000 steps. (3) The displacement and stress regions of all measuring points were in equilibrium.
## 2 Methodology  
The flowchart of modal property identification and FE model updating for flexible PV support structures is shown in Figure 1. First, field modal testing of the flexible PV support structure is carried out. Subsequently, the VMD-SH method proposed by [PERSON] et.al. [41] is employed to identify the modal frequencies and damping ratios. In the VMD-SH method, multi-sensor signals are first decomposed into a series of single-mode components using the variational mode decomposition (VMD) [42] method. The natural excitation technique (NExT) [43] is then used to convert these single-mode components into free decay signals. The smoothed discrete energy separation algorithm (SDESA) [44] is applied to the free decay signals to identify time-varying modal frequencies, while the half-cycle energy operator (HCEO) [45] is used to identify time-varying modal damping ratios. The modal shapes are identified from multi-sensor signals using a VMD-based approach. For FE model updating, the response surface model for modal frequencies is first constructed. Then, the particle swarm optimization (PSO) [46] algorithm is employed on the response surface to search for the combination of modeling parameters that minimizes the error between the FE modal frequencies and the field-measured modal frequencies, thereby obtaining the updated FE model.  
Figure 1: Flowchart for modal property identification and FE model updating of flexible PV support structures.
## 5 Conclusions  
This study explored the imperfections of scalar approximation for diffractive optics calibrators in calibration of computer-generated holograms. The findings are summarized as follows:  
1. When the grating period is less than 5\(\uplambda\), the scalar approximation fails to accurately describe the characteristics of CGH by diffractive optics calibrators.
2. Reconstruction geometric errors are concentrated in cases in which the period is small and the depth is large. For example, a 10% error for the duty cycle and a 180 nm error for the depth are introduced owing to the imperfections of scalar approximation in the 2 \(\upmu\)m grating case.
3. Wavefront aberration is more sensitive to fabrication errors in small-period regions of a CGH, and the effect of the duty cycle error on the wavefront cannot be ignored.  
In future, the developing method for CGHs with small periods to solve the imperfections of scalar approximation will be presented in another article.  
Performing conceptualization, investigation, experimental methodology and drafting the original manuscript, Y.B.; supervision, funding acquisition, writing--review & editing, Z.Z.; guiding the research conducted, treatment of the experimental data, R.W. and T.C.; resources, funding acquisition, X.W.; resources and project administration, X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Nature Science Foundation of China (Nos. 51775531, 11803037). This work was partially supported by the Advanced Science Key Research Project, Chinese Academy of Science (No. QYZDJ-SSW-JSC038), Key Foreign Cooperation Projects of International Cooperation Bureau, Chinese Academy of Science (181722 KYSB20180015).  
Not applicable.  
The authors declare that they have no conflicts of interest.
## 3 Case Studies
### Sham Tseng San Tsuent Debris Flow
In Figure 9, we provide the numerical results obtained from the GeoFlow-SPH code regarding the basal excess pore-water pressures relative to liquefaction. At the start, the relative pore-water pressure \(\left(p_{w}^{rel}\right)\) was taken as 1, indicating that the flowing material was completely liquefied. Then, the pore-water pressure dissipated during the propagation stage until the debris-flow reached the deposition area. Figure 7: General view of the landslide location. (Provided by [PERSON] et al. (2005) [42]). In the authors' opinion, the back-analysis of both case studies indicates that the numerical results of ravaflow are sensitive to variations in the basal friction angle \(\phi_{B}\) and the initial solid fraction \(n_{0}\); and on the other hand, the numerical results of GeoFlow-SPH are more sensitive to the consolidation coefficient \(c_{v}\) obtained by using parameters of terminal velocity \(V_{T}\) and stiffness of the mixture \(k_{v}\); consequently, the characteristic times of propagation and consolidation are the key aspects of the GeoFlow-SPH model. There exist many different structural countermeasures to reduce the impacts of landslides. Bottom drainage screens [2] could be used as an effective control to work against channelized flows with narrow widths, such as the Sham Tseng San Tsuen debris flow case. Artificial barriers and walls [3] could be used as protection against rock and debris avalanches, such as the Acheron rock avalanche, with widespread deposition. Figure 8: A comparison between two sets of code. The results on the left side were obtained with GeoFlow-SPH, and those on the right side with ravaflow. They show the flow propagation path, with the numerical results of flow-depth and maximum velocity at different times: (**a**) 0 s, (**b**) 20 s, (**c**) 40 s, and (**d**) 60 s.
## 3 Results and Discuss
### Discussion  
The application of microbially induced carbonate precipitation (MICP) technology demonstrates significant potential for improving the expansive characteristics and mechanical properties of expansive soils. This finding aligns with previous studies which reported substantial enhancements in soil mechanical parameters through microbial treatment. For  
Figure 6: CBR value results.  
Figure 7: Load expansion rate test results.  
instance, [PERSON] et al. [34] observed a 33.5% increase in soil cohesion (from 29.52 kPa to 39.41 kPa) and a 46.94% improvement in the internal friction angle (from 20.13\({}^{\circ}\) to 29.58\({}^{\circ}\)). Similarly, Chittoori and Neupane [20] documented a 252% enhancement in unconfined compressive strength (UCS), with values increasing from 0.25 MPa to 0.88 MPa.  
Notably, some discrepancies exist between the improvement levels reported in these studies and our current findings. These variations may be attributed to differences in the mineralogical composition of expansive soils across geographical regions.  
Furthermore, it is important to recognize that the efficacy of microbial soil modification can be influenced by multiple factors, including the bacterial solution concentration and curing conditions (temperature, duration, and pH) [35]. While the present study primarily examines the effect of cementation solution concentration while maintaining constant bacterial solution concentrations and curing parameters, future investigations should systematically evaluate the impacts of these additional variables to optimize the MICP treatment process.
# Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
## 5 Proposed CCAM Solution for Autonomous Driving
### Computation  
It goes without saying that the sensory data collected through on-road deployed sensors need to be processed and analyzed in the roadside unit. Pushing it to the central clouds of webscalers is contradictory to the fundamental concept of the proposed CCAM. This asks for the compute infrastructure, accelerators in the roadside unit. For this, we suggest to have a greater set of GPUs than CPUs and other accelerators. This is due to the nature of analytics that are expected at the roadside unit of the proposed CCAM infrastructure. The on-road deployed sensors collect the video stream, photos, radar data, lidar data, etc., which require strong GPU equipped computer infrastructure.
# Improving Accuracy of Impervious Surface Extraction Based on a Threshold Hierarchical Method (THM)
###### Abstract  
Impervious surface area (ISA) is an important representation of urban area. It is very popular to extract ISA by using linear spectral mixture analysis (LSMA). However, there are still some defects in this method: underestimated in areas with a large amount of ISA. Hence, we designed a threshold hierarchical method (THM) to test this underestimation and understand which scale is the best to mixture. The capacity of the THM and the optimal threshold in the impervious surface extraction are the focus in this work. In THM model, the medium-resolution image (Landsat 8 OLI) and the high-resolution image (Gaofen-2, GF-2) were used, the LSMA and the object-oriented method (OOM) were applied for the area with a larger amount of impervious surfaces, which was extracted from the Landsat 8 OLI image after finishing the LSMA procedure by a threshold of the ISA abundance data, the GF-2 image was employed to extract the ISA by OOM. The results show that the THM had the capacity to achieve higher ISA extraction accuracy and ameliorate the ISA underestimate problem.  
Keywords:impervious surface; linear spectral mixture analysis; threshold; Gaofen-2 +
Footnote †: journal: Applied Sciences