esa-sceva/satcom-chunk-collection huggingface dataset:
# Transient Phenomena of Dynamic Contact Angle in Micro Capillary Flows +
## 2 Theoretical Approach  
Figure 1 shows the schematic of the capillary-driven micro flow through a circular tube. For a Newtonian viscous liquid, if flow is laminar and incompressible, the governing equation for the unsteady flow velocity \(u(r,t)\) can be written as follows:  
\[\frac{\partial u}{\partial t}=-\frac{1}{\rho}\frac{\partial p}{\partial x}+ \frac{\mu}{\rho}\frac{1}{r}\frac{\partial}{\partial r}\bigg{(}r\frac{\partial u }{\partial r}\bigg{)} \tag{1}\]  
where \(p\) is the pressure inside the tube, \(\mu\) is the fluid viscosity, and \(\rho\) is the density. From the Laplace-Young equation, the driving capillary force at the meniscus is \(2\sigma\cos\theta/R\), where \(\sigma\) is the surface tension coefficient, \(\theta(t)\) is the dynamic contact angle, and \(R\) is the tube radius. The pressure gradient inside the tube is assumed to be linear and the fully developed laminar velocity profile can be used.  
\[-\frac{dp}{dx}=\frac{1}{L(t)}\frac{2\sigma\cos\theta}{R} \tag{2}\]  
\[u(r,t)=2L^{\prime}(t)\bigg{(}1-\frac{r^{2}}{R^{2}}\bigg{)} \tag{3}\]  
where \(L(t)\) is the wetting distance at time \(t\). In most previous approaches, the transient term \(\partial u/\partial t=L^{\prime}\) is neglected. By substituting Equations (2) and (3) for Equation (1), an ordinary differential equation for the wetting distance is given as follows:  
\[\frac{8\mu L}{\rho R^{2}}L^{\prime}-\frac{2\sigma\cos\theta(t)}{\rho R}=0 \tag{4}\]  
Figure 1: Capillary-driven micro flow through a circular tube.  
If the contact angle is assumed to be constant as a static contact angle \(\theta_{s}\), the solution for the wetting distance is the well-known Washburn equation [4].  
\[\text{Washburn model}:\ L=\sqrt{\frac{2R\sigma\cos\theta_{s}}{4\mu}}t \tag{5}\]  
Meanwhile, [PERSON] and [PERSON] [15] analyzed the interface dynamics of capillary flow in a tube considering inertial acceleration at the inlet of the tube and energy dissipation just after the meniscus. The dynamic balance of force in a horizontal tube is described as  
\[(L+l)L^{\prime\prime}+\frac{m}{2}\big{(}L^{\prime}\big{)}^{2}+\frac{8\mu L}{ \rho R^{2}}L^{\prime}-\frac{2\sigma\cos\theta}{\rho R}=0 \tag{6}\]  
where \(l\) and \(m\) are correction factors for the inertial force and energy dissipation, respectively. The first and second terms in Equation (6) represent the inertial forces by tube entrance and liquid velocity itself, respectively. The third term is viscous force, and the fourth term is capillary force. [PERSON] [18] assumed the values of \(l=7R/6\) from the inertial force in a hemispherical region of the tube inlet and \(m=3.41\) from an empirical relation. There is no analytic solution for Equation (6), but it can be solved numerically using the Runge-Kutta method by considering the dynamic variation of contact angle.
# Shock Vibration Control of SDOF Systems with Tubular Linear Eddy Current Dampers
## 3 Analytical Model for Transient Responses of an SDOF System with the ECD  
### Shock Excitation  
A shock pulse can be used to describe shock excitation, usually in terms of the time history of excitation of acceleration, velocity, displacement, or force. Although shock situations are quite complicated in practice, idealized and simple forms of the time history of shock excitation are commonly used to approximate practical situations. Figure 3 shows the common idealized forms of shock excitation. Various kinds of shock excitation are determined by different shapes, peak magnitudes, and durations.  
In this study, the common acceleration time history is considered to represent shock excitation, and three profiles of acceleration are considered, including the half-cycle sine, initial peak triangular, and rectangular. These three types of idealized shock excitation are mathematically expressed as:  
Half-cycle sine shock excitation:  
\[a(t)=a_{0}sin(\pi t/t_{d})\ (0<t<t_{d}) \tag{2}\]  
Initial peak triangular shock excitation:  
\[a(t)=a_{0}(1-t/t_{d})\ (0<t<t_{d}) \tag{3}\]  
Rectangular shock excitation:  
\[a(t)=a_{0}\ (0<t<t_{d}) \tag{4}\]
## 2 Methodology
### Inverse Radon Transformation and Reconstruction  
To reconstruct the spatial-domain CT section image with rich information and no artifacts, the filtered back-projection approach optimizes the sinogram, which is then subjected to the inverse Radon transform. Projecting the projection value \(\hat{P}(\theta,s)\) at each angle \(\theta\) in the opposite direction along the corresponding ray direction into the two-dimensional image space is the fundamental concept of the inverse Radon transform. And we use multi-angle superposition to obtain the image \(\hat{f}(x,y)\). The following is its formula:  
\[\hat{f}(x,y)=\int_{0}^{\pi}\hat{P}(\theta,x\cos\theta-y\sin\theta)d\theta \tag{16}\]  
where \(\theta\) is the coordinate of the matching picture and \(\theta\) is the projection angle. The projected values in all directions along their corresponding directions are accumulated in reverse during the integration process.
## 3 Results and Discussion
### The FD Factor of Odor-Active Compounds in Six CSSs  
To screen more important odor-active compounds from 55 odorants identified in six CSSs, their FD factors were measured via GC-O, combined with AEDA. The results obtained are listed in Table 2.  
Based on Table 2, it can be seen that 4-ethylguaiacol (burnt, smoky) had the highest FD factor of all the CSS isolates obtained by both SE-SAFE (FD factor = 4096) and SPME (FD factor = 800). Both 3-methylbutanoic acid (sweaty, cheese-like) and DMHF (caramel-like) possessed the highest FD factor (4096) among all the extracts obtained by SE-SAFE; both methional (cooked potato) and guaiacol (burnt, smoky) possessed the highest FD factor (800) among all the isolates obtained by SPME. Aside from the compounds mentioned above, some odor-active substances, such as 2-ethyl-3,5-dimethylpyraizine (roasty, earthy), benzeneacetaldehyde (honey-like), phenethyl alcohol (floral), HEMF (caramel-like), 2,6-dimethoxyphenol (burnt, smoky), etc., also had higher FD factors in either SE-SAFE extracts or SPME isolates. These compounds might cause the six CSS samples to possess some common odor characteristics. There were also some odorants which had the highest FD factor only in one sample. For example, dimethyl trisulfide had a higher FD factor (sulfur/cabbage, FD factor = 1024) only in J3, EHMP (fruity, FD factor = 2048) only in C1, methionol (cooked potato, FD factor = 1024) only in J3, maltol (caramel-like, FD factor = 1024) only in C1, vanillin (vanilla, FD factor = 1024) only in C2 and phenylacetic acid (honey, FD factor = 1024) only in C1. These odorants resulted in the odor differences among the six CSSs. Most of the RIs of these compounds with higher FD factors on the DB-Waxcolumn were more than 1400. They were likely to have contributed the most to the overall aroma profile of CSS.
## 2 Related Work
### Cyber-Layer Security Modeling  
Cyber-layer security modeling focuses on representing adversarial behavior, system vulnerabilities, and defense strategies through formal analytical methods. Common tools include attack graphs [34], stochastic Petri nets [35], Markov models [36; 37], game-theoretic frameworks [38], and Bayesian networks [39], etc. These methods support probabilistic analysis of cyber risks, identification of critical nodes, and evaluation of system security under varying threat scenarios. Their primary strength lies in capturing uncertainty, intent, and interaction within cyber processes.  
Despite their analytical rigor, these models often operate in isolation from the physical layer. They typically do not account for how cyber incidents propagate to affect physical performance or safety. As a result, their applicability to CPSs is limited. Simplifying assumptions--such as static network topologies or security controls for specific types of attacks [40; 41]--can further reduce realism and applicability. Without explicit integration of cyber and physical domains, such models cannot offer a complete representation of system resilience.  
Overall, each of the four categories reviewed provides valuable insight into specific aspects of cyber resilience. However, they also exhibit inherent limitations, particularly in addressing the interdependence of cyber and physical processes. To address this gap, the following sections present an integrated modeling framework that jointly considers security dynamics in the cyber layer and performance degradation in the physical domain.
# Survey and Perspectives of Vehicular Wi-Fi versus Sidelink Cellular-V2X in the 5G Era
## 2 Overview of the Technologies and Comparative Discussion
### Sidelink LTE-V2X
#### 2.2.1 Next Generation: 5G and New Radio  
In the cellular ecosystem, standardization processes are continuously proceeding and the first steps towards 5G and new radio (NR) have been done inside Release 15, frozen in March 2019.  
After a debate on how to support each V2X use case in the 3 GPP standard, the conclusion is that, while 5G NR will provide enhancements in future releases to support advanced services, Release 14 C-V2X (i.e., LTE-V2X) will be the only core of basic safety communications. Differently from Wi-Fi, the principle does not have backward compatibility, but rather adds an optional second interface with improved performance on different channels [20].  
5G-V2X modifications designed to enhance sidelink PC5 interface will include:  
* Carrier aggregation with support of up to eight bands;
* Use of frequencies above 6 GHz;
* Flexible numerology, with the possibility of subcarrier spacing of 30 kHz and 60 kHz at 5.9 GHz;
* Higher order of MCSs, also including 64-QAM;
* Possibility to transmit over single slots and even portions of slots;
* Addition of a sidelink feedback channel to allow higher reliability and lower latency;
* LDPC and polar codes designed to offer higher robustness without increasing encoding and decoding complexity;
* Use of MIMO receiving antennas to enable spatial diversity especially useful to mitigate multipath in urban scenarios;* A modification of the scheduling Modes in order to enhance the sensing-based procedures and to deal with aperiodic traffic, also including the possibility of group reservations.
## 4 Hybrid Action Space SAC-Based MLLM Task Offloading and Resource Allocation Scheme
### [PERSON] Decision Process
#### 4.1.3 Reward Function  
To align the DRL objective with the aim of minimizing the total energy consumption, which contrasts with the conventional goal of maximizing cumulative rewards, we define the reward function as the inverse of the weighted sum of system latency and energy consumption for MLLM task requests. The reward function can be defined as  
\[r(s(t),a(t))=\frac{K\beta_{r}}{\sum_{k=1}^{K}(\eta_{l}T_{k}(t)+\eta_{e}E_{k}(t))}, \tag{34}\]  
where \(\beta_{r}\) is the adjustable coefficient.
## 4 Analyzing Experimental Results  
### Assessment Indicators  
We installed a threshold in the model to determine whether a prediction box is correct or not, which can be called the intersection over union (IoU). If the IoU between the predicted box and the actual box was bigger than the value we set, then the predicted box was true and valid. In Figure 7, the red box in the upper left corner represented the true box and the blue box in the bottom right corner represented the model prediction box. Through Equation (10), we showed how the IoU was calculated.  
\[\text{IoU}=\frac{S_{GT}\cap S_{PD}}{S_{GT}\cup S_{PD}} \tag{10}\]  
Figure 6: Loss variation with the number of training iterations.  
Figure 7: The calculation process of the IoU.  
We also introduced standard evaluation criteria in deep learning to assess the models, mainly using \(P\) (precision), \(R\) (recall), and mean average precision (mAP). The precision rate indicates the ratio of all results where the model predicts a positive sample and the model predicts it correctly, which is expressed as the ratio of all examples predicted to be 'crack' that are truly labeled as 'crack' in this study. The accuracy rate is defined in Equation (11) as follows:  
\[P=\frac{TP}{(TP+FP)} \tag{11}\]  
In the equation, two kinds of samples need to be defined: \(TP\) and \(FP\). \(TP\) denotes the count of examples that were 'crack' and predicted by the model to be 'crack'. \(FP\) denotes the quantity of examples that were 'background' but predicted by the model to be 'crack'.  
The recall represented the magnitude of the positive examples predicted by the model, accounting for the total number of positive examples. In Equation (12), we defined how to calculate the recall.  
\[R=\frac{TP}{(TP+FN)} \tag{12}\]  
In the equation, \(FN\) denotes the count of examples that were 'crack' but were predicted by the model to be the background.
# A Survey of Secure Time Synchronization
## 2 Time Synchronization
### Time Synchronization Requirements
#### 2.4.5 Cost and Size  
Sensor nodes must be cost effective and miniature in hardware size. These are reasons which make attaching them to large and expensive hardware devices (such as a GPS receiver and storage devices) an unacceptable solution [35]. For example, it makes no sense to attach a GBD 10 sensor node to a GBD 100 GPS receiver, especially when you are using a network of thousands of sensor nodes which will increase the size of the device and result in a marginally high price. The LTS and TS/MS protocols require larger bandwidth and larger storage space, the RBS protocol requires complicated hardware, and these expensive hardwares increase the cost. The FTSP is a hardware-based protocol which means that, even for smaller networks, it will require expensive hardware similar to that required for larger networks [21].  
These aspects of a time synchronization protocol need to be balanced according to the requirements in order to achieve efficient time synchronization.
## 4 Routing Protocols Using Ant-Like Mobile Agents
### AntNet  
AntNet uses two types of ant-like agents for routing in the communication networks. They are the _forward ant_ and the _backward ant_[25; 26; 27]. AntNet uses the _forward ants_ to explore the network topology proactively. Each network node launches ant-like agents, _i.e._, the _forward ants_, asynchronously toward randomly selected destination nodes. This happens concurrently with the data traffic at regular intervals. Each agent searches for a minimum cost path joining the source node and the destination node. While moving, the ant-like agents collect information about the time length and the congestion status of the followed paths. When the agents arrive at the destination node, they create the other ant-like agent, _i.e._, the _backward ants_, and make them go back to their source nodes through the same paths as they come but in the opposite direction to reflect the traffic statistics that the _forward ants_ collected. During the backward travel, the ant-like agents update the routing tables in the nodes along the paths with information they have collected during the travel. Thus, they adaptively build routing tables [25; 26; 27]. [PERSON] _et al._ successfully showed the ability of artificial ants to optimize the network configuration. By using two different types of ant-like agents, _i.e._, the _forward ant_ and the _backward ant_, each node in the network has shortest path to all the destinations. However, they cause extra overhead and reduce the available traffic capacity for actual data communication. AntNet is an algorithm for traffic adaptive routing for multiple paths in wired IP networks. The effectiveness of this algorithm is demonstrated in the work of [PERSON] _et al._[28; 29]. They have applied the algorithm for relatively static telephone networks, and the effectiveness in a network that dynamically changes its topology, such as MANET, may be limited.  
Since the main purpose of AntNet is to investigate how data packets travel along a certain route, the _forward ants_ and data packets share the same queue in the intermediate nodes. Thus the _forward ants_ can measure the estimated travelling time that the actual data packets would experience. The problem, however, is that the _forward ants_ may take a significantly lengthy travelling time to reach their destinations. In order to mitigate this problem, [PERSON] _et al._ developed an improved AntNet, AntNet-FA. In AntNet-FA, _forward ants_ also use high-priority queues in order to try to reduce the travelling time to their destinations [30]. As _backward ants_ simply update the routing tables at the intermediate nodes based on the _forward ants_' experience, they need not measure anything, and they use high-priority queues in both AntNet and AntNet-FA. The _forward ants_ in AntNet-FA estimate the virtual trip time based on the properties of the link such as bandwidth and propagation delay, instead of measuring actual trip time. AntNet-FA has improved AntNet with respect to efficiency, but it does not help it adapt to dynamically changing networks such as MANET.
## 3 Experiments
### Experimental Setup
#### 3.1.3 Comparison Methods  
To achieve a comprehensive and comparative analysis of our model, we compare it with a series of models:  
* **CopyRe** is a seq2 seq model with a copy mechanism, and uses the copy mechanism to generate triplets in a sentence in order. The model can jointly extract entities and relations from sentences of any type.
* **GraphRel** is an end-to-end relation extraction model. The model constructs a complete word graph for each sentence, using graph convolutional networks (GCNs) to jointly extract entities and relations.
* **HRL** applies a hierarchical paradigm. The entire extraction process is decomposed into a two-level reinforcement learning strategy hierarchy, which is used for relation detection and entity extraction, respectively. It first performs relation detection as a high-level reinforcement learning process, and then identifies the entity as a low-level learning process.
* **ETL-Span** decomposes the joint extraction task into two interrelated subtasks. Firstly, distinguishing all head entities, and then identifying the corresponding tail entities and relations.
* **WDec** designed a new representation scheme and uses the seq2 seq model to generate the entire relation triplet.
* **RSAN** uses the relation-aware attention mechanism to construct a specific sentence representation for each relation, and then performs sequence labeling to extract its corresponding head and tail entities.
* **BERT-JEORE** uses a BERT-based parameter sharing layer to capture joint features of entities and overlapping relations, assigning entity labels to each token in a sentence, and then extracting entity-relation triplets.  
The datasets used in our experiment are Chinese medical datasets. In the input part of the model, the combination of character and word information in the Chinese datasets is more complicated than that of the English datasets. The above models are all experiments done on the English datasets, so when we apply them to the Chinese medical data set, the input of these models all use Chinese character embedding.
# Acquisition and Processing of UAV Fault Data Based on Time Line Modeling Method
## 5 Experimentation  
In order to demonstrate the validity of the data set, ten classical machine learning algorithms, as well as a convolutional neural network model (CNN), were used to evaluate the data set in this paper. The evaluation results are expressed as the precision, recall, and F-Measure of each machine learning algorithm using this data set. Four sensor information, namely GPS information, Inertial Measurement Unit (IMU), desired and achieved vehicle attitude rate (RATE), and processed acceleration vibration information (VIBE), were selected as data sets. Before feeding the data into the model training, the feature processing method of Algorithm 2 was applied to each information data to filter the features about time as well as the features that are not generic.  
In the data set, sensors or telemetry information are recorded at different scales, which leads to some features whose values are not in the same order of magnitude, which can lead to an imbalance in linear operations and can affect the results of machine learning. The \(Max-Min\) feature scaling method was used to scale each feature between 0 and 1, using the method shown in Equation (6).  
\[Feature_{scaled}=\frac{Feature_{i}-Min(Feature_{i})}{Max(Feature_{i})-Min(Feature_ {i})} \tag{6}\]  
Precision can be understood as the ratio between the number of detected features of a certain class and the number of all detected features; Recall can be understood as the ratio between the number of detected features of a certain class and the number of all features of that class in the data set. Since Precision and Recall values sometimes appear  
Figure 18: The proportion of each label after processing.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Normal** & **GPS Fail** & **Accelerometer Fail** & **Engine Fail** & **RC Fail** & **Total** \\ \hline GPS & 3609 & 1096 & 1305 & 606 & 1277 & 7893 \\ IMU & 18,032 & 5480 & 6528 & 3033 & 6393 & 39,466 \\ RATE & 7215 & 2188 & 2610 & 1215 & 2558 & 15,786 \\ VIBE & 7218 & 2189 & 2612 & 1210 & 2557 & 15,786 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Data distribution table of the data set after the balancing process.  
contradictory, they need to be considered together, and F-Measure is the weighted summed average of Precision and Recall. Precision, Recall, and F-Measure are calculated as shown in Equations (7) and (8).  
\[Precision=\frac{TP}{(TP+FP)} \tag{7}\]  
\[Recall=\frac{TP}{(TP+FN)} \tag{8}\]  
In Equations (7) and (8), TP refers to the number of positive samples correctly identified, FP refers to the number of negative samples that are misreported, and FN refers to the number of negative samples predicted by the classifier that is actually positive.  
\[F_{1}Measure=\frac{2*Precision*Recall}{Precision+Recall} \tag{9}\]  
In Equation (9), Precision is the calculation result of Equation (7) and Recall is the calculation result of Equation (8).
# Performance Evaluation of Bedding Sand and Load Transfer Characteristic of Concrete Block Pavements
## 3 Results  
### Performance of Bedding Sand  
The results of the mechanical performance evaluation according to sand type and particle size are summarized in Table 4. It was found that as the vertical stress increased, the shear strength of all sands increased accordingly, and that of the river sand was the highest. At a vertical stress of 150 kPa, the shear strength of river sand was measured to be approximately 5% and 13% higher than the shear strengths of quartz sand and manufactured sand, respectively. Evaluating shear strength and internal friction angle according to the 0.6 mm passing ratio, it is seen that the difference in shear strength was not large for the river sand, but the shear strength at the 40% passing ratio was the highest. The evaluation of shear strength and internal friction angle, according to the 0.075 mm content of river sand, showed that the larger the 0.075 mm content is, the greater the shear strength.