esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
They continue to overlook the possibility of an FIDVR phenomena that still exists on power systems subjected to disturbance. As a result, a complete method capable of assessing both the voltage instability mode and the FIDVR phenomenon concurrently within the short observation time window (OTW) is required. Conventionally, machine learning algorithms and deep learning algorithms for the assessment of voltage stability require the manual labeling of a large number of data samples, which is computationally expensive and time consuming [17; 18; 19; 22; 23]. When the network topology changes, most of the machine learning and deep learning algorithms fail in prediction, requiring frequent model updates with large data sets, which is time consuming and infeasible for online application [24; 25]. Recently, many research studies have been conducted based on transfer learning (TL) where the same model trained to solve one particular task can be utilized to solve another but related task with few labeled samples [26; 27; 28; 29]. However, none of the studies in the literature found so far have utilized the deep learning-based transfer learning concept for the assessment of STVS for the N-1 contingency cases using a single learning model. As a result, developing an STVS assessment approach that can acclimate to the new environment using a small set of labeled data sample is critical. The objective of this research is to resolve the research gaps mentioned above. It is an extension of our previous study [30] which further proposes the combined temporal convolutional neural network and long short-term memory neural network for the assessment of STVS considering the impact of RESs. The following are the primary contributions of this work:  
1. Proposal of an approach for real-time STVS assessment, including both voltage instability and FIDVR phenomenon based on a deep learning algorithm combining a temporal convolutional neural network and a long short-term memory neural network (TempCNN-LSTM). The proposed TempCNN-LSTM model can extract spatiotemporal STVS features from the data samples available through PMUs. 2. The performance of the proposed approach in assessing STVS is compared with other deep learning models and conventional machine learning models under different conditions such as PMUs measurement error, missing PMUs data, and different observation time windows. 3. The proposed approach also uses a transfer learning technique for the assessment of STVS that can adapt to the new environment (N-1 contingency case) with a few labeled data samples. Section 2 describes the theoretical background about the procedure of STVS assessment. The detailed explanation about the proposed TempCNN-LSTM model is given in Section 3. Subsequently, results and discussion for different case studies are presented in Section 4. Finally, the conclusions of this study are summarized in Section 5.
## 4 Node Importance Evaluation Model
### Indices for Evaluating the Importance of Urban Rail Transport Nodes
#### 4.1.1 Subsubsection  
The network efficiency loss and maximum connected sub-network loss after the failure of urban rail transit stations are selected as dynamic evaluation indices to reflect the impact of station failure on network topology performance.  
The network efficiency of urban rail transit, as a convenient and efficient mode of public transport, quantifies the efficiency of the network on a global scale. After the failure of an urban rail station, the shortest paths between certain starting and finishing points in the network changed, resulting in efficiency losses for the network. The loss of network efficiency is shown in Formulas (1) and (2).  
\[E=\frac{1}{N(N-1)}\sum_{i\
eq j}\frac{1}{d_{ij}} \tag{1}\]  
\[\Delta E_{a}=E_{0}-E_{a} \tag{2}\]  
\(E\) is the network efficiency, \(N\) is the total number of stations in the network, \(d_{ij}\) is the shortest path length between stations \(i\) and \(j\). Then, \(E_{0}\) is the initial network efficiency, \(E_{a}\) is the efficiency of the network after the failure of station \(a\), and \(\Delta E_{a}\) is the loss of network efficiency before and after the failure of station \(a\).  
(2) Maximum connected sub-network loss  
After the failure of urban rail transit network stations, the network becomes divided into different connected sub-networks. The sub-network with the highest number of connections is referred to as the maximum connectivity sub-network. The calculation of the maximum connected sub-network loss can measure the impact of the station failure on network connectivity. The loss of the maximum connected sub-network is represented by Formula (3).  
\[\Delta S_{a}=1-\frac{N_{a}}{N}=\frac{N-N_{a}}{N} \tag{3}\]  
\(N_{a}\) is the number of stations in the maximum connectivity sub-map of urban rail transit after the failure of station \(a\), and \(\Delta S_{a}\) is the maximum connected sub-network loss before and after the failure of station \(a\).
## 2 FANET Architecture and Routing Protocol
### Routing Protocols  
The routing protocol in FANET refers to the mechanism and rules for data transmission between UAV nodes by establishing routing paths [24; 25]. As shown in Figure 4, we have divided the routing protocols into eight categories based on the routing methods.
# Dynamic Response of Reinforced Recycled Aggregate Concrete Pavement under Impact Loading
## Experimental Methods
### Fine Aggregate  
The fine aggregate used in the mixes was locally-sourced river sand with a maximum particle size of 4.75 mm and a fineness modulus of 2.60, which met the requirements for a Class II gradation medium sand.  
1. Coarse Aggregate  
The coarse aggregate involved two types of natural coarse aggregate (NCA) and recycled coarse aggregate (RCA). The NCA was crushed calcareous limestone from the stone quarries which had continuous gradation with a particle size of 5-20 mm. The RAC was supplied by the local plant from the demolition of twenty- to thirty-year-old concrete pavements, the original strength grade of which was unknown. Prior to the experiment, the aggregates larger than 20 mm were screened, and impurities such as bricks and wood were removed.  
Tests were conducted on the NCA and RCA according to the Code of GB/T 25177-2010 and GB/T 14685-2011; the physical properties of the coarse aggregates are shown in Table 2,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Loss on** \\ **Ignition** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Initial Setting Time** \\ **(min)** \\ \end{tabular} & \begin{tabular}{c} **Final Setting Time** \\ **(min)** \\ \end{tabular} & \begin{tabular}{c} **Specific** \\ **Surface Area** \\ **(m\({}^{2}\)/kg)** \\ \end{tabular} & \
\begin{tabular}{c} **Compressive Strength (Mpa)** \\ **28 Days** \\ \end{tabular} &
\begin{tabular}{c} **Flexural Strength** \\ **(Mpa)** \\ \end{tabular} \\ \cline{2-7}
2.35 & 170 & 290 & 337 & 27.3 & 45.6 & 5.6 & 8.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of cement.  
and Figure 1 shows the sieve analysis of the coarse aggregates. Both NCA and RCA met the specification that ensured the appropriate properties of the fresh and hardened concrete.  
(4) Steel reinforcement  
The diameter of the longitudinal reinforcing bars in the RCP specimens was 8 mm, and the relevant properties were tested by using a tensile test machine. The bar response showed an obvious yield plateau, and the measured yield strength (\(f_{\text{yk}}\)), ultimate strength (\(f_{\text{uk}}\)) and elongation after fracture were 426 Mpa, 600 Mpa, and 18.2%, respectively.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 1 Introduction
In addition, an insight is provided about the technological trends in topics like smart home/smart building, integration of RES and grid management, since they are found to be directly or indirectly related to smart metering applications. A small feedback is given about the telecommunication technologies used in the MV grid through projects that combine research on LV smart metering applications with research on the MV grid. However, a thorough analysis of the aspects of the MV smart grid network would be out of the scope of this work. For reasons of completeness we also give the main characteristics of the telecommunication technologies that are mostly found to be used in practice through our survey in order to help the reader understand their usage and the main concepts behind them. The rest of the paper is organised as follows: Section 2 lists the wired and wireless telecommunication technologies respectively, which are available for the LV smart grid, as they are found in the literature. Particular emphasis is given to the technologies that are found to be used in reality at a great extent as smart metering solutions. In Section 3 the technologies selected for smart metering applications mainly in Europe are presented. In Section 4 some of the main smart  
Figure 1: AMI—Communication from the smart meters to the utility system with several technological solutions. grid projects are presented with special focus on the role of smart meters and the technologies used. Section 5 concludes the paper.
# Thangka Image--Text Matching Based on Adaptive Pooling Layer and Improved Transformer
## 3 Results  
### Experimental Data and Preprocessing  
To conduct comparative and ablation experiments, this paper chose two datasets: a self-constructed Thangka image-text dataset and the widely employed Flickr30k dataset in the field of image-text matching [19].  
The Thangka image-text dataset comprises 3000 samples of Thangka-themed images. In constructing image-text pairs for Thangka, we referenced the text structure of the commonly used Wikipedia dataset [20], combining it with the characteristics of Thangka themes, such as academic nomenclature, production techniques, thematic symbolism, cultural backgrounds, and more. For each Thangka theme category, we selected corresponding textual descriptions, resulting in an initial set of 2231 Thangka image-text pairs. Subsequently, data augmentation techniques were applied to expand the image and text datasets. For image data, in addition to conventional methods like brightness, contrast, rotation, and flipping, we employed a cut-and-replace approach to substitute entity parts within the Thangka images, thereby increasing the dataset size. To address the issue of limited textual data, we utilized Easy Data Augmentation (EDA) [21] for data augmentation. Following data augmentation, a total of 5500 Thangka image-text pairs were obtained. Consistent with the practice of the commonly used Wikipedia dataset for cross-modal matching, the constructed Thangka-themed image-text pairs were divided into a dataset in a 6:2:2 ratio. Specifically, 3300 pairs were randomly chosen for the training set, 1100 pairs for the validation set, and 1100 pairs for the testing set. An example of the Thangka image-text dataset is illustrated in Figure 5.
## 4 The SMA-LRBs
### The Hysteresis Model of SMA Wires  
Researchers have wrapped SMA wires around the base isolators in three different arrangements, namely, straight [18], cross [16; 26], and double-cross [24; 25]. These isolators are schematically shown in Figure 6. It should be noted that, in these isolators, wires pass through steel hooks, which are mounted on the top and the bottom supporting plates. It is assumed that there is a smooth contact between the steel hook and the SMA wire. In order to reduce the complexity of the finite element simulation, [PERSON] and [PERSON] [16] proposed to consider the applied forces on the isolator due to the SMA wires, instead of modelling the steel hooks and their contact with the wire. They calculated the force generated in SMA wires as a function of time, and then applied the effect of SMA wire on the isolator. For calculating this axial force of the wire, first, by specifying the geometrical properties of the isolator and configuration of wire, the strain in the wire at each time step can be found. Second, with the help of the stress-strain relationship of SMA, the axial stress in the wire is determined. Finally, using the amount of the axial stress and the direction of wire at each time step, the force vectors exerted from the SMA wire to the LRB isolator as an external force are computed.  
As mentioned above, the strain in SMA wire at each time step depends on the geometry of the isolator and wires arrangement. In this study, the SMA wires are wrapped around the LRBs of the benchmark bridge, so the geometry of the LRB is constant. Therefore, calculations must be performed for the three different configurations of wires separately.  
Figure 6: (**a**) Straight, (**b**) cross and (**c**) double-cross configuration of shape memory alloy (SMA)-LRB.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Yield Displacement(m)** & \
\begin{tabular}{c} **Post-Yield Stiffness** \\ **(kN/mm)** \\ \end{tabular} &
\begin{tabular}{c} **Initial Stiffness** \\ **(kN/mm)** \\ \end{tabular} \\ \hline
**Abument Isolators** & 0.015 & 0.6 & 4.8 \\ \hline
**Mid-pier Isolators** & 0.015 & 1.2 & 9.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Lead rubber bearing (LRB) characteristics of the benchmark bridge.
# Pressure Drop and Particle Settlement of Gas-Solid Two-Phase Flow in a Pipe
## 4 Results and Discussion
### Relationship between the Pressure Drop and Mixture Inlet Velocity
#### 4.5.2 Effect of Particle Mass Flow  
Figure 11 shows the relationship between the pressure drop per unit length \(p/L\) and mixture inlet velocity \(V\) for different particle mass flows. For a given inlet velocity, the values of \(p/L\) increase as the particle mass flow increases because the particle mass flow is directly proportional to the particle volume concentration.
# Locating the Epidemic Source in Complex Networks with Sparse Observers
## 2 Model and Method
### Locating the Epidemic Source in Tree Network
#### 2.2.2 Parameter Estimation of \(S\)  
Let \(\mathcal{T}_{O}=[\tau_{s1},\tau_{s2},...,\tau_{sk}]\), so the posterior probability of node \(s\) as epidemic source shows:  
\[P(s,t_{0}|\mathcal{T}_{O}=T_{O})=\frac{P(\mathcal{T}_{O}=T_{O}|s,t_{0})P(s,t_{ 0})}{P(\mathcal{T}_{O}=T_{O})} \tag{5}\]  
Suppose that there is no prior knowledge of the epidemic source and the observers, i.e., the probability of any node as the epidemic source in the network keeps equal and the probability that any node becomes an observer is equal too. Then the following derivation can be obtained.  
\[P(s,t_{0}|\mathcal{T}_{O}=T_{O})\propto P(\mathcal{T}_{O}=T_{O}|s,t_{0})\approx \prod_{o_{k}\in O}P(\tau_{sk}=t_{o_{k}}|s,t_{0}) \tag{6}\]  
Let \(L(s,t_{0}|\mathcal{T}_{O}=T_{O})=\sum_{o_{k}\in O}\log P(\tau_{sk}=t_{o_{k}}|s,t_{0})\), so the epidemic source localization problem can be transformed into following optimization problem.  
\[(s,f_{0})=\underset{s\in V,t_{0}\in\mathbb{Z}}{argmax}L(s,t_{0}|\mathcal{T}_{ O}=T_{O}) \tag{7}\]  
To solve this optimization problem, we first calculate initial time \(f_{0}=argmax_{t_{0}\in\mathbb{Z}}\ L(s,t_{0}|\mathcal{T}_{O}=T_{O})\) of each node \(s\) and then drag in Equation (7) to get the objective function value \(L(s,f_{0}|\mathcal{T}_{O}=T_{O})\). And the node with the maximum objective function value is the epidemic source in network.  
When the network is not a tree but a general network, the first idea is to build a maximum spanning tree and then locate the epidemic source same as before. Considering that the epidemic spread between nodes prefers to occur in a shorter path, we can build a BFS tree randomly and then find the node with maximum objective function as the epidemic source. In fact, from Equation (4), it is even unnecessary to actually construct these BFS trees. If only the distance \(d_{sk}\) between node \(s\) and each observer \(o_{k}\) is known, parameter estimation and likelihood probability can be calculated. As the core of the whole algorithm is to optimize the Pascal distribution, it is called the Pascal-based Epidemic Source Localization algorithm (PESL).
# Matching of Water Breakthroughs in a Low-Resistivity Oil Reservoir Using Permeability Anisotropy
## 4 Results and Discussion
### Permeability Anisotropy (Kv/Kh)  
Permeability anisotropy (Kv/Kh), which is often represented as an integer ratio ([PERSON] et al. 2006) [19], is an essential parameter that controls fluid production rates, OPR, and WPR. If the fractured samples with Kv/Kh above 1000/1 are excluded, the data of all wells converge between the upper line of Kv/Kh = 1/1 and the lower line of Kv/Kh = 2/3 (Figure 3). Most of the data follow the line of Kv/Kh = 1/1. Well G data are more widely scattered, while some of the deviating points belonged to Well C. Most of the data were below 10 mD, which may mean that Kv/Kh has little significance in terms of recovery because such low permeabilities affect flow rate and the fluids are diverted almost exclusively through the more permeable cells ([PERSON] 1970) [20].  
Despite the absence of fissures in Well G, certain intervals within the very tight formation exhibited Kh up to 200 mD. However, irrespective of the high values of the permeabilities, ranging up to 10,000 mD, the data of Well B closely followed a 1/1 line. The data deviating above the 1/1 line indicate secondary fabrics with increased Kv, while the data below indicate a depositional pore structure. In [21], it can also be seen that a group of data followed a 1/1 line, with other data below it indicating a higher Kh.  
Simulations often employ a default Kv/Kh ratio of 0.1, which indicates a laminar (layered) rock structure, as suggested by [PERSON] and [PERSON] [22]. For example, a Kv/Kh ratio of 0.19 was used for the Arbuckle Formation, which is a laminar rock structure without fractures [11]. In contradiction with the commonly held notion that laminated sandstones have a low Kv/Kh, it was found that the values for the laminated sandstones of the Virgelle Member varied between 0.5 and 0.8, while it was 0.7 for the homogeneous sandstone [12], which is close to the 2/3 that we found for the Gharif Formation [14].  
A Kv/Kh value of 1/1 indicates that the LHS sandstone is very homogeneous, through which the water flow can be equally diverted in all directions, with the upward water inflow into the upper layers controlled only by gravity. The relatively higher vertical water mobility can be a substantial factor for the early breakthrough observed in LHS.  
Figure 2: Data of Well G measured in horizontal (h) and vertical (v) directions: (**a**) porosity (Phi); (**b**) ratio Kv/Phi_v vs. ratio Kh/Phi_h.
# Towards a Near-Real-Time Protocol Tunneling Detector Based on Machine Learning Techniques +
## 7 Conclusions  
In this paper, we proposed a software prototype for detecting protocol tunneling attacks in a monitored network. Relying on a combination of machine learning and deep learning techniques, the proposed solution identifies anomalous connections that deviate from the ones usually established in the network. Since machine learning models are only built based on legitimate traffic, the proposed solution is therefore able to deal with zero-day attacks, because malicious traffic is not required for the learning phase. The prototype has been evaluated both on malicious and benign datasets: results show a very high accuracy in detecting malicious samples and a low false positive rate on legitimate traffic. As future work, we plan to optimize the algorithm through a deeper analysis on how the choice of byte-stream length affects the computational time, in order to find a value which guarantees the best trade-off between efficiency and accuracy. Indeed, in this work, we mainly focused on accuracy. Secondly, we envision that the engineered prototype will be integrated into a streaming architecture, where new data will be analyzed by the proposed prototype as soon as they are collected to provide the fastest possible response. In parallel, the models of the protocol tunneling detector are periodically retrained to keep them up-to-date with possible deviations from the usual behaviour of the monitored network. It is important to mention that the envisioned streaming architecture can always count on a trained model to process incoming traffic during possible retrainings; old models will be available until the new ones are ready. Finally, in Section 6, we discussed the benefits of IP whitelisting filters. Once in production, the prototype can be easily extended with other SOC-defined whitelists (e.g., whitelists regarding domains and/orautonomous systems), allowing security analysts to enrich the proposed detector with their domain-specific knowledge, further reducing possible false positives and improving the overall performance. Conceptualization, F.S., D.U. and F.B. ; methodology, F.S. and D.U. ; software, F.S. and B.C. ; validation, F.S., B.C., D.U. and F.B. ; formal analysis, F.S. and B.C. ; investigation, F.S. and B.C. ; resources, D.U. and F.B. ; data curation, F.S. and B.C. ; writing--original draft preparation, F.S., B.C, D.U. and F.B. ; writing--review and editing, F.S., B.C. and D.U. ; supervision, D.U. and F.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding.
## 5 The Experiment and Analysis
#### 5.1.2 Comparison Analysis  
To prove the effectiveness of PGMCKD, the MCKD and MCKD based on Shannon entropy are selected to compare with PGMCKD. For MCKD, the value of \(T\) is 85, and \(L\) can be any value from 100 to 500. In this experiment, the value of \(L\) is 460, which is the same as PGMCKD. The Shannon entropy optimization parameter method sets the value range of \(T\) as [87; 83], the step size as 1, the search range of \(L\) as [100, 500], the step size is 5, and the final result of \([L,T]\) is [87; 325]. The power spectrum by different methods is given in Figures 4 and 5.  
From Figure 4, after the reconstructed signal is processed by MCKD, the extracted fault features are still weak, so they cannot meet the requirements of fault diagnosis. In Figure 6, it can be seen 1/5 harmonic of the rotating frequency (5.859 Hz), and the fault frequency \(f_{0}\) (140.6 Hz) and its 1/3 harmonic (46.88 Hz). However, compared with Figure 4, the power spectrum of the signal processed by PGMCKD has less frequency component of noise, and the effect is more obvious. Therefore, the experimental comparison results can prove the effectiveness of PGMCKD for fault diagnosis.
## 2 Background
### Distortion  
In ideal optics, the magnification of each field of view is identical. However, in reality, after light passes through an optical system and is focused on the imaging surface, the magnification differs at different image heights, and the objects in the images are then deformed, as depicted in Figure 5. This is called distortion. In positive distortion, also called pinclusion distortion, the actual imaging surface is larger than the ideal imaging surface. In negative distortion, also called barrel distortion, the actual imaging surface is smaller than the ideal imaging surface.