esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Fundamentals of GLI-Split
### Security Concerns and Countermeasures
#### 3.8.2 ID Hijacking through Locator Gleaning  
Locator gleaning means that nodes store ID-to-GL mappings in their local caches when they see incoming packets with new ID/GL mappings. This possibly saves queries to the mapping service, but it causes a security problem so that locator gleaning should be avoided. Figure 6 illustrates how an attacker can hijack the ID of another node when GLI-hosts use locator gleaning. The attacker behind GLI-gateway X pretends to be node 1. It sends a packet with ID 1 in the source address to node 3. Node 3 receives the global GLI-address X.1 and updates its local cache with the mapping entry 1\(\rightarrow\)X (\"locator gleaning\"). When node 3 contacts node 1 later, it uses the wrong locator from the local cache and the packets destined to node 1 will be delivered to the spoofing node behind X instead of the correct node behind E.  
A countermeasure against that type of attack is implemented in the upgraded stack of GLI-nodes and GLI-gateways. When a packet is received with an unknown ID/GL combination in the source address, this mapping should be validated by a query to the mapping service before storing it in the local cache. Classic IPv6 nodes including those inside a GLI-domain are not affected by wrong mapping information since they are unaware of locators, identifiers, and mappings.  
Figure 6: When a GLI-host gleans locators from incoming source addresses, a malicious node can send a packet with a spoofed source ID and ste  
Figure 6: When a GLI-host gleans locators from incoming source addresses, a malicious node can send a packet with a spoofed source ID and steal traffic intended for that ID.
# Deep Convolutional Neural Network-Based Approaches for Face Recognition
## 4 Methodology and Experiments
### Experiments and Results  
This section presents the experimental results that were obtained in face recognition using the three deep convolutional neural networks--AlexNet and ResNet-50 with SVM classifier, and transfer learning from AlexNet based on various standard datasets. The main three experiments in our study were conducted to compare the difference in performance between pre-trained CNN architectures. First, we evaluated the performance when extracting the learned image features from a pre-trained CNN AlexNet, followed by SVM as a classifier. Second, we extracted the learned image features from ResNet-50, followed by an SVM classifier. Third, we evaluated the performance when transfer learning from the AlexNet network was used for the classification task. The analysis and evaluation were carried out on the basis of the performance recognition accuracy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Identities** & **Images** & **Images Per** & **Images** & **Images** \\  & & & **Identities** & **Size** & **Type** \\ \hline ORL & 40 & 400 & 10 & \(92\times 112\) & JPEG \\ GTAV face & 44 & 704 & 16 & \(240\times 320\) & BMP \\ Georgia Tech face & 50 & 700 & 14 & \(131\times 206\) & JPEG \\ FEI face & 50 & 700 & 14 & \(640\times 480\) & JPEG \\ Labeled faces in the wild (LFW) & 50 & 700 & 14 & \(250\times 250\) & JPEG \\ Frontalized labeled faces in the wild & 50 & 700 & 14 & \(272\times 323\) & JPEG \\ (F\_LFW) & 50 & 700 & 14 & \(320\times 240\) & JPEG \\ YouTube face (YTF) & 210 & 2880 & 10-16 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Face datasets and their specifications used in our experiments.  
Figure 3: Sample images from all datasets: (**a**) sample images from ORL dataset; (**b**) sample images from GTAV face dataset; (**c**) sample images from Georgia Tech face dataset; (**d**) sample images from FEI dataset; (**e**) sample images from LFW dataset; (**f**) sample images from F_LFW dataset; (**g**) sample images from YTF dataset.
## 2 Spatially Fixed Virtual Sensing Algorithms
### The virtual sensing problem  
The virtual sensing problem and notation used throughout this paper are introduced in this section. It is assumed here that there are \(M_{p}\) physical microphones, \(M_{v}\) spatially fixed virtual microphones and \(L\) secondary sources. The vector of the total pressures at the \(M_{p}\) physical microphones, \(\mathbf{e}_{p}(n)\), is defined as  
\[\mathbf{e}_{p}(n)=\left[\begin{array}{cccc}e_{p1}(n)&e_{p2}(n)&...&e_{pM_{p} }(n)\end{array}\right]^{\mathrm{T}}. \tag{1}\]  
The total pressures at the \(M_{p}\) physical microphones, \(\mathbf{e}_{p}(n)\), is the sum of the sound fields produced by the primary and secondary sound sources at the physical microphone locations, and may be written as  
\[\mathbf{e}_{p}(n)=\mathbf{d}_{p}(n)+\mathbf{y}_{p}(n)=\mathbf{d}_{p}(n)+ \mathbf{G}_{pu}\mathbf{u}(n), \tag{2}\]  
where \(\mathbf{d}_{p}(n)\) is a vector of the primary pressures at the \(M_{p}\) physical microphones, \(\mathbf{y}_{p}(n)\) is a vector of the secondary pressures at the \(M_{p}\) physical microphones, \(\mathbf{G}_{pu}\) is a matrix of size \(M_{p}\times L\) whose elements are the transfer functions between the secondary sources and the physical microphones, \(\mathbf{u}(n)\) is a vector of the secondary source strengths and \(n\) is the time step.  
Similarly, the vector of the total pressures at the \(M_{v}\) spatially fixed virtual locations, \(\mathbf{e}_{v}(n)\), is defined as  
\[\mathbf{e}_{v}(n)=\left[\begin{array}{cccc}e_{v1}(n)&e_{v2}(n)&...&e_{pM_{v}}(n )\end{array}\right]^{\mathrm{T}}. \tag{3}\]  
The total pressures at the \(M_{v}\) virtual microphones, \(\mathbf{e}_{v}(n)\), is the sum of the sound fields produced by the primary and secondary sources at the \(M_{v}\) virtual locations and may be written as  
\[\mathbf{e}_{v}(n)=\mathbf{d}_{v}(n)+\mathbf{y}_{v}(n)=\mathbf{d}_{v}(n)+ \mathbf{G}_{vu}\mathbf{u}(n), \tag{4}\]  
where \(\mathbf{d}_{v}(n)\) is the vector of the primary pressures at the \(M_{v}\) virtual locations, \(\mathbf{y}_{v}(n)\) is the vector of secondary pressures at the \(M_{v}\) virtual locations and \(\mathbf{G}_{vu}\) is a matrix of size \(M_{v}\times L\) whose elements are the transfer functions between the secondary sources and the virtual locations.  
Using the physical error signals, \(\mathbf{e}_{p}(n)\), a virtual sensing algorithm is used to estimate the pressures, \(\mathbf{e}_{v}(n)\), at the spatially fixed virtual locations. Instead of minimising the physical error signals, the estimated pressures are minimised with the active noise control system to generate zones of quiet at the virtual locations.
# Assessment of Pohang Earthquake-Induced Liquefaction at Youngil-Man Port Using the UBCSAND2 Model
## 2 Methodology
### UBCSAND2 Constitutive Model
#### 2.1.3. Hardening and Flow Rule on Maximum Shear Stress Plane  
When the yield locus moves from A to B in Figure 1, it induces a plastic shear strain increment \(\mathrm{d}y^{p}1\) related to the shear stress ratio \(\eta_{1}\). It is assumed that the relationship of stress ratio \(\eta_{1}\) and plastic strain \(\gamma^{p}1\) is a hyperbolic curve, which is controlled by plastic shear modulus \(G^{p}1\) as shown in Figure 3. This correlation can be expressed as follows:  
\[\mathrm{d}\gamma^{p}_{1}\;=\;\frac{1}{G^{p}_{1}/\sigma^{\prime}m}\mathrm{d} \eta_{1} \tag{5}\]  
where \(\mathrm{d}\gamma^{p}1\) is the plastic shear strain increment, \(G^{p}1\) is the plastic shear modulus, \(\mathrm{d}\eta_{1}\) is the stress ratio increment.  
\(G^{p}1\) is the plastic shear modulus and given by  
\[G^{p}1\;=\;G^{p}_{max}\bigg{(}1-\frac{\eta_{1}}{\eta_{f}}R_{f}\bigg{)}^{2} \tag{6}\]  
where \(\eta_{1}\) is the stress ratio at failure (= \(\sin\phi_{f}\)), \(R_{f}\) is the failure ratio for the hyperbolic relationship (= \(\eta/\eta_{\mathrm{i}uth}\)). \(R_{f}\) varies from 0.6 to 1.0 and decreases with increasing relative density. \(G^{p}max\) is the plastic modulus at \(\eta\) = 0 and can be expressed as follows:  
\[G^{p}_{max}\;=\;k^{p}_{G}\cdotp\mathrm{d}\bigg{(}\frac{\sigma^{\prime}m}{P_{a }}\bigg{)}^{np} \tag{7}\]  
where \(k^{p}_{G}\) is the plastic shear modulus number. \(np\) is the stress exponent for \(G^{p}max\) (approximately 0.4).  
Herein, the position of the yield locus \(\mathrm{d}\phi_{ml}\) can be calculated as follows:  
\[\mathrm{d}\phi_{ml}\;=\;\bigg{(}\frac{G^{p}_{1}}{\sigma^{\prime}m}\bigg{)} \mathrm{d}\gamma^{p}_{1} \tag{8}\]  
The flow rule acting on maximum shear stress plane, which is obtained from the well-known Rowe stress-dilatancy theory [21], is given as follows:  
Figure 3. Hardening rule of the UBCSAND2 model.  
\[\frac{d\varepsilon_{v1}^{p}}{\left|\mathrm{d}\gamma_{1}^{p}\right|}\ =\ \sin\psi_{1}\ =\ ( \eta_{1}-\sin\phi_{cv}) \tag{9}\]  
where \(\mathrm{d}\varepsilon_{v1}^{p}\) is the plastic volumetric strain increment, \(\phi_{cv}\) is the friction angle at constant volume, \(\eta_{1}\) is the current stress ratio, and \(\psi_{1}\) is the dilatancy angle.
## 1 Introduction
### Contributions  
In this paper, a smart UAV positioning mechanism is proposed by taking such regulation constraints into account to provide sustainable wireless coverage and services to the ground users under more realistic conditions. In particular, we propose a \(Q\)-learning-based approach for UAV-assisted communication systems. The optimal position of UAVs are determined under the constraints of altitude regulations, NFZs, and transmit power. The main contributions of the paper are as follows:  
* A smart UAV positioning mechanism for a sustainable UAV communication system is proposed, under certain constraints.
* A multiobjective optimization model is formulated, that is, minimizing the energy consumption of UAV, while maximizing the number of users covered.
* A weighting mechanism is developed to prioritize the two objectives given in the previous item over each other for different scenarios.
* \(Q\)-learning-based algorithm is used to find the optimal position of UAV. The convergence of the developed algorithm is first tested, followed by comparing its performance with the baseline \(k\)-means method in terms of number of users covered and energy consumption.
## 4 Experiments and Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Protein** & **Molecular Weight** & **Spectrometer Frequency** & **Sampling Type** &
\begin{tabular}{c} **Date Point** \\ **(\(t_{2}\)\(\times t_{1}\))** \\ \end{tabular} & **References** \\ \hline HSQC & GB1 & \(-\)8.0 kDa & 600 MHz & Full & \(1466\times 170\) & Figure 3 \\ \hline TROSY & Ubiquitin & \(-\)8.6 kDa & 800 MHz & Full & \(683\times 128\) & Figure 4 \\ \hline Solid NMR & \(-\)68 Da & 900 MHz & Full & \(249\times 150\) & Figure 5 \\ \hline \hline \end{tabular} Note: The NUS and reconstruction are performed on the indirect dimension, which is noted as \(t_{1}\).  
\end{table} 
Table 2: Important parameters of experimental 2D spectra. Figure 4: Reconstructed 2D \({}^{1}\)H-\({}^{15}\)N best-TROSY spectra of ubiquitin with 20% NUS data. (**a**) The fully sampled spectrum; (**b–e**) reconstructed spectra using the LRHM, LRHMF, SLS, and SLSMF, respectively. Note: The contours of spectra are at the same level. Figure 5: Reconstructed solid NMR spectra with 15% NUS data. (**a**) The fully sampled spectrum; (**b–e**) reconstructed spectra using the LRHM, LRHMF, SLS, and SLSMF, respectively. Note: The contours of spectra are at the same level. Avance 600 MHz spectrometer using a room temp HCN TXI probe, equipped with a z-axis gradient system. The fully sampled spectrum consists of \(1676\times 170\) complex points; the direct dimension (\({}^{1}\)H) has 1676 data points, while the indirect dimension (\({}^{15}\)N) has 170 data points. The 2D \({}^{1}\)H-\({}^{15}\)N best-TROSY spectrum of ubiquitin was acquired at 298.2 K temperature on an 800 MHz Bruker spectrometer and was described in a previous paper [37]. The fully sampled spectrum consists of \(683\times 128\) complex points; the direct dimension (\({}^{1}\)H) has 683 data points, while the indirect dimension (\({}^{15}\)N) has 128 data points. The solid-state NMR spectrum was acquired from an imidazole sample at room temperature on a 21.1 T Bruker AVANCE-III spectrometer. The sample was spinning at 60 kHz inside a 1.3 mm probe. The 2D \({}^{1}\)H-\({}^{1}\)H double-quantum spectrum was recorded using a symmetry-based R1225 pulse sequence with a recycle delay of 5 s. Data were recorded by co-adding 16 transients with 300 t1 increments. The number of fully sampled data points in the indirect dimension is 150. In addition, to quantify the reconstruction error, a relative \(l_{2}\)-norm error (RLNE) defined as:  
\[\mathrm{RLNE}(\hat{\mathbf{x}})=\frac{\|\hat{\mathbf{x}}-\mathbf{x}\|_{2}}{ \|\mathbf{x}\|_{2}} \tag{16}\]  
where \(\hat{\mathbf{x}}\) is the reconstructed signal and \(\mathbf{x}\) is the fully sampled signal, is adopted.
# A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm
## 5 Conclusions  
Aiming at the problem of insufficient numbers and missing categories of trajectory label datasets, a two-stage semi-supervised high maneuvering target trajectory data classification algorithm was proposed. The algorithm referred to the principles of pre-training models and metric learning methods in the semi-supervised field, combined the autoencoder with the Siamese neural network to design a classification model, and designed a corresponding two-stage model training method.  
Experiments showed that the algorithm improved the classification ability by using labeled data and had the ability to cluster unlabeled categories at the same time. It effectively dealt with the special cases of insufficient label data and missing categories. For the problem of missing categories, when only 25% types were labeled, the purity still reached 82%, which is about eight percentage points higher than the directly training the network; when there were two problems at the same time, the purity was maintained at the average above the clustering results. Compared with the directly trained network, the performance improved by about three percentage points on average.
## 2 Framework of Proposed Method  
As illustrated in Figure 1, the proposed method contains six major stages which give full consideration of uncertain expert evaluation for essential factors, subsystem division, as well as the dynamic and uncertain propagation of product failure.  
* Stage I: Preparation of the product information and data Relevant data and information are collected. First, the minimum value of product reliability allowable defined as \(R^{\star}\) is determined according to market requirements. The reliability of a product obtained by optimization calculation is represented by \(R\). Function module classification is conducted based on the different product sub-functions, and a set of target products are regarded as the samples to obtain the data of module failures.
* Stage II: Subsystem division based on module failure decoupling Establish the adjacency matrix and accessibility matrix based on the module classification and data of module failures, and the subsystem division is carried out by the module failure decoupling. The detailed operator can be seen in Section 3.1.
* Stage III: Calculation of the subsystem weight considering the failure diagnosis based on FRPNSubsystem failure diagnosis with FRPN is performed based on the data and information collected in Stage I. Principles and mathematical description of the FRPN model are introduced to build the accessibility matrix for FRPN so that the failure probability of each subsystem is available. Then, the subsystem weight considering the failure diagnosis based on FRPN can be obtained. The detailed operator can be seen in Section 3.2.
* Stage IV: Calculation of the subsystem weight considering development cost based on interval expert evaluation  
Interval expert evaluation for subsystem considering development cost for six essential factors is conducted. The detailed operator can be seen in Section 3.3.
* Stage V: Establishment of the reliability-based and cost-oriented optimization model  
Mathematical method aiming the product reliability and cost is established with two types of subsystem weights obtained in Stage III and Stage IV, which aim to achieve the maximum product reliability and minimum cost. The detailed operators can be seen in Section 3.4.
* Stage VI: achievement of the product optimized design solution with DMOPSO  
Adopt DMOPSO to obtain the relative Pareto fronts and employ the fuzzy-based mechanism for the optimal VAR dispatch problem to extract the best compromise solution [32]. The detailed operators can be seen in Section 3.5.  
Figure 1: General framework of the proposed reliability optimization.
# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
## 3 Experiments and Results
### Data Description  
In the diverse landscape of deep learning and computer vision, the fidelity and precision of model performance are predominantly dictated by the quality of datasets and meticulously engineered preprocessing pipelines [41]. To rigorously assess the efficacy of our proposed model--especially its adaptability across multiple scales and contexts--we elected to utilize two industry-standard road extraction datasets: the Massachusetts Roads Dataset [42] and the DeepGlobe Road Dataset [43]. Beyond conventional preprocessing routines, we integrated a suite of data augmentation techniques--including image rotation, flipping, and color adjustments--to further enrich the training corpus and bolster model generalizability. Notably, the experimental design was tailored to the individual datasets, resizing images in the Massachusetts Roads Dataset to 512 \(\times\) 512 pixels while retaining the native 1024 \(\times\) 1024-pixel dimensions in the DeepGlobe Road Dataset. This stratified approach was not solely predicated upon dataset specific characteristics, such as resolution and information density or computational efficiency and resource allocation; it also furnished a comprehensive evaluative framework for model performance at different scales. Training and testing on larger image dimensions afforded the enhanced capture of intricate road networks and topographical features, thereby augmenting the model's generalizability across multiple scales and scenarios. Through this holistic preprocessing and data augmentation strategy, our intent was to provide an exhaustive and insightful assessment of model performance under a plethora of experimental conditions, thereby lending robust support and precise limitations analysis for its real-world applications.
## 3 Simulation
### Bistatic Mode Simulation  
For the bistatic configuration in Figure 1b, the target, the source and the receiver were all at the same depth of 25 m. The distance between the fixed source and the fixed receiver was 60 m. The spherical target with a diameter of 0.5 m moved along the line which was 60 m away from the line where the source and the receiver were located.  
Figure 4 shows the interference pattern observed by the above configuration for frequencies: 400\(-\)800 Hz, 4\(-\)8 kHz, and 40\(-\)80 kHz. As the target moved from 78 m to 60 m, the shape of the striations followed the shape of the target's trajectory. The striations bent more in this configuration, compared to that in the monostatic configuration. The striations were discontinuous, and the sound field intensity of the high-frequency part was more pronounced than that of the low-frequency part. The interference striations were denser at high-frequency bands. In addition, the striations were wide in the high-frequency part and narrow in the low-frequency part at one frequency band.
# Effect of Electrospun Nanofiber Additive on Selected Mechanical Properties of Hardened Cement Paste
## 3 Results and Discussion
### Thermogravimetric Analysis Results  
Figures 7 and 8 present the thermogravimetric analysis (TGA) and the derivative thermogravimetric (DTG) results of the five samples in this study. As shown by the DTG curves in Figure 7, the boundaries of various phases or groups of phases in the hardened cement paste samples were determined and these results also agreed with the findings of many works in the literature [35; 36; 37]. For instance, the corresponding decomposition temperature range of each phase or group can be ascribed as follows: the temperature range until 200 \({}^{\circ}\)C was attributed to the dehydration of CSH, C\({}_{2}\)SH\({}_{8}\), ettrinigite, and AFm; from 200 to 400 \({}^{\circ}\)C was attributed to the dehydration of C\({}_{2}\)AH\({}_{8}\) and C\({}_{2}\)AH\({}_{6}\); from 400 to 470 \({}^{\circ}\)C was the dehydration of CH; finally, from 550 to 900 \({}^{\circ}\)C was the decarbonation of calcite. In addition, according to [33; 38] and [39; 40], the decomposition temperature of nylon-66-based electrospun nanofibers is from around 400 to 500 \({}^{\circ}\)C, and this parameter of TEOS/PVP-based electrospun nanofibers is from around 350 to 470 \({}^{\circ}\)C. Therefore, the peaks from around 350 to 500 \({}^{\circ}\)C in Figure 7 can be attributed to the decomposition of both CH and the electrospun nanofibers in the cement pastes. It is worth mentioning that the weight loss before 145 \({}^{\circ}\)C was due to the evaporation of the arbitrary water that was included in each paste [41]; therefore, the comparison of TGA results was made with 0% weight loss at 145 \({}^{\circ}\)C, as shown in Figure 8. In this manner, the content of hydration products observed from the cement paste containing CNTs-TEOS/PVP NFs was highest, followed by that of the paste containing TEOS/PVP NFs, then the pastes containing CNTs-N66 NFs and N66 NFs, compared with the hydration product contents of the plain paste. From the inset plot in Figure 8, at 200 \({}^{\circ}\)C, there were different weight losses of the TGA results from the pastes containing TEOS/PVP-based nanofibers compared with that from others, and the lowest belonged to the TGA result of the plain paste. Therefore, the contents of the CSH phase in the pastes containing TEOS/PVP-based nanofibers were higher than those in the others pastes. In addition, according to [32], the CSH phase makes up around 60% of the solid volume of the hydrated products and plays a major role in forming the strength and durability of cementitious materials. Consequently, the TGA results explain the order of the increase of compressive strength in this study.  
Figure 7: Derivative thermogravimetric (DTG) curves of the hydrated cement pastes.