esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation of Vertical Density Profile of Medium Density Fiberboard in Hot Press
###### Abstract
Medium density fiberboard's strength, hardness, and other important properties are determined by its vertical density profile, which is one of the most important factors that is determined in laboratory tests of MDF performance. This factor is the combination of the results of many process parameters [14]. During hot pressing, the internal condition of the sheets, temperature, and moisture content continuously change with time [15]. A one-dimensional computer model depends on fundamental analysis, which shows the relationship between process parameters and density profile arrangement. Such parameters are important board characteristics that correlate with the properties of the MDF board [16]. MDF boards with appropriate VDPs are created with careful determination and accurate pressing plan. Existing MDF in the wood market faces issues in regard to moisture, dampness, primary execution, and toughness [17]. Producing quality MDF products that satisfy customer requirements will definitely result in a notable increase in profit for the industry as well as enhancement of the credibility from the perspective of customers [18]. In the last few decades, many researchers have worked on MDF board properties, i.e., bending, thickness swelling, internal bonding, and occurring chemical changes, but only a few studies have examined the vertical density profile of MDF board mathematically. The aims of this study are to increase the bending and internal bonding strength of MDF board, to experimentally study its vertical density profile under varying hot press parameters, and to determine how its peak and core densities are affected by various hot press parameters.
# An Extended Car-Following Model Considering Generalized Preceding Vehicles in V2X Environment
## 5 Numerical Simulation
### Simulation of Braking Process  
To simulate the car-following behavior of vehicles in the starting process at the intersection when the traffic light turns from red to green in a realistic traffic system, the simulation scenario about vehicle starting process is set as following: 10 identical vehicles in every single of three lanes are moving in the same direction with the same initial velocity 5 m/s and headway of 10 m between any two consecutive vehicles. The vehicles in the middle of all three lanes are selected as object vehicles and marked as 1 to 10 according to the distance to the intersection from near to far. Considering that GPV is introduced in our model, the first object vehicle of the fleet is following its GPV in the scenario. Moreover, regarding the comparability of simulation results, the scenario for simulation with the FVD model is set as the same. At the beginning of the simulation, the traffic light turns from green to red, and all vehicles brake in sequence. The termination condition of this simulation is set as all object vehicles have stopped. The velocity and the acceleration during the braking process of object vehicles are studied, as shown in Figures 8 and 9.  
Figure 8 depicts the simulated velocity of object vehicles with the GPV model and the FVD model, respectively, during the braking process. As shown in Figure 8a, it costs 16 s that the first vehicle of the fleet decelerates to 0 m/s, and all object vehicles stop at 75 s from the initial time in the simulation with the GPV model. By comparison, it takes 35 s that the first vehicle of the fleet decelerates to 0 m/s, and all object vehicles stop at 87 s from the beginning time in the simulation with the FVD model as shown in Figure 8b.  
The simulated deceleration of object vehicles with the two models are as shown in Figure 9. In contrast with the FVD model, the deceleration of the GPV model is more rapid, and the response delay time is much shorter. As shown in Figure 9a, the first vehicle of the fleet reaches the maximum deceleration of 0.83 m/s\({}^{2}\) at 2.8 s, and the last vehicle of the fleet reaches the maximum deceleration of 0.5 m/s\({}^{2}\) at 61.1 s. By comparison, the first vehicle of the fleet reaches the maximum deceleration of 0.63 m/s\({}^{2}\) at 23.8 s, and the last vehicle of the fleet reaches the maximum deceleration of 0.63 m/s\({}^{2}\) at 76.1 s in this simulation with the FVD model as shown in Figure 9b.  
From Figures 8 and 9, one can see that there is a certain brake delay in the simulation with FVD and this result consistent with the results of data fitting in Section 3. Furthermore, it is worth noting that there are two deceleration fluctuations of each object vehicle during the braking process with the GPV model, while there is only one deceleration fluctuation with the FVD model. This phenomenon will be discussed in the following section.
## 3 Results and Discussions
### Effect of Size of Square Inclusions  
The effect of using squares of different side lengths within a single unit cell was investigated. In the four-square inclusions, the inclusion sizes were controlled by the square side lengths \(a_{i1}\) and \(a_{i2}\), and the filling fraction was calculated as FF = 2(\(a_{i1}\)\({}^{2}\) + \(a_{i2}\)\({}^{2}\))/\(a_{0}\). In the case of nine squares, three square side lengths were defined as \(a_{i1}\), \(a_{i2}\), and  
Figure 4: (**a**) Phononic crystals with square inclusions rotated by an angle of \(\theta\) = 45\({}^{\circ}\). (**b**) Bandwidth versus rotation angle of composites with filling fractions of 0.1–0.5, for one- (**b**), four- (**c**), and nine-square (**d**) inclusions within the host.  
\(a_{i3}\). The filling fraction was FF = \(3({a_{i1}}^{2}+{a_{i2}}^{2}+{a_{i3}}^{2})/{a_{0}}\). In each case, the side lengths were defined, such as \(a_{i1}<a_{i2}<a_{i3}\), to achieve the gradient effect, as shown in Figure 5a,b.  
In the case of four inclusions, phononic crystals with five different filling factions were studied, the square dimensions of which are listed in Table 2. Similarly, in the case of nine-square inclusions, phononic crystals with four different filling factions are presented in Table 3. The lower bound frequency (LBF), upper bound frequency (UBF), and bandwidth versus filling fraction are shown in Figure A3a,d. The simulation result shows that for four- and nine-square inclusions the gradient dimensions of inclusions lead to a higher LBF and a lower UBF, which indicates a narrower bandgap as compared to the equally sized inclusions, as shown in Figure 5c,d. This can be explained by the effective size of the phononic crystal with square inclusions of graded sizes. For example, when the FF = 0.3, the effective size of graded square inclusions was 0.2658 m, which is less than the effective size of normal inclusions, 0.2738. The larger bandwidth is due to the larger effective inclusion size, as indicated by the study of bandwidth versus filling fraction in Section 3.1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \# & FF & \(a_{i1}\) & \(a_{i2}\) \\ \hline
1 & 0.1 & 0.1 & 0.2 \\
2 & 0.2 & 0.15 & 0.2783 \\
3 & 0.3 & 0.2 & 0.3316 \\
4 & 0.4 & 0.25 & 0.3708 \\
5 & 0.5 & 0.3 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The dimensions of the phononic crystals with 4 gradient square inclusions.  
Figure 5: (**a**) Phononic crystals with four-square inclusions, where their dimensions are arranged in an increasing gradient. (**b**) Phononic crystals with nine-square inclusions, where their dimensions are set in a growing gradient of size. (**c**) The bandwidth of the phononic crystals with graded square inclusions plotted against their filling fraction for four inclusions. (**d**) The bandwidth of the phononic crystals with graded square inclusions plotted against their filling fraction for nine inclusions.
# Machine Failure Prediction Using Survival Analysis
## 4 Materials and Methods
### Types of Machine Failure
#### 4.3.3 Power Failure (PWF)  
Power failure (PWF) happens when the machining process is not supplied with enough power or when there is an excessive amount of power being delivered. Insufficient power can lead to a reduction in the material removal rate, while excessive power can cause the tool to break or lead to workpiece damage. PWF is an important failure mode to monitor because it can directly impact the productivity and quality of the machining process.
# Recent Advances in Efficient Dynamic Graph Processing
## 4 Algorithms
### Cohesive Subgraph Search  
Cohesive subgraph search is an important and fundamental research problem in graph analysis with many practical applications, including community detection and friend recommendation in social networks, production recommendation and commercial advertising in shopping networks, complex protein detection in PPI networks, etc. [28,144]. Based on different criteria for cohesiveness, multiple cohesive subgraph models are proposed.
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 3 Advanced Techniques in Finite Element Modeling for Fatigue Crack Propagation
### Extended Finite Element Method (XFEM)
#### 3.1.1 ABAQUS  
ABAQUS is one of the most popular commercial software packages utilized for XFEM-based crack growth simulation. ABAQUS provides a comprehensive set of capabilities for modeling and analyzing complex crack propagation problems. With its XFEM module, ABAQUS enables accurate capture of crack paths without the need for extensive mesh refinement, thereby reducing computational costs. A user-friendly interface is offered, making it accessible to engineers and researchers [23; 104; 105]. ABAQUS offers a limited free version for academic use, typically via licenses provided to educational institutions, but comprehensive features require a commercial license.
# Channel Estimation for UAV Communication Systems Using Deep Neural Networks
## 2 System Model and Problem Formulation  
Communications and transportation technologies are major assets for our lifestyle. In the move toward the integration of these advancements in one field, a transceiver can be attached to a drone to assist future wireless networks. Significantly, in natural disasters, especially when communications infrastructure (e.g., base stations) is damaged, the communication network must be maintained for search and rescue efforts. Currently, existing methods have limitations in terms of flexibility and resource availability. Coping with such issues requires that unmanned aerial vehicles (UAVs) be employed to work wirelessly because of their multifunctional qualities and flexibility. A drone flies at low altitude and is equipped with transceivers to work with a wireless network as a drone cell in the coverage area. Many parameters determine the channel modeling between the drone transceiver and the BSs, such as the altitude, antenna directivity, location, transmission power, and the characteristics of the environment. To investigate the effects of these parameters on channel modeling between the drone transceiver and the BSs, we proposed a framework of GBRBMs for dimension reduction and pre-training utilization incorporated with an autoencoder-based deep neural network.
## 4 Implementation and Analysis
### Customized Emotional Analysis
#### 4.7.2 Infinitive Text  
We limited the verbs to their infinitive form to make building the dictionary easier. Moreover, because our classification relies on word counting, we keep the conjugated verbs. To count the verbs in all forms, we developed a function that identified conjugated verbs and put them into their infinitive form. Firstly, we tokenized our texts and then post-tagged the tokenized texts. Then, we found the infinitive form of the tokens identified as verbs. Next, we used the WordNetLemmatizer function of the nltk.stem library on each of the tokens identified as a verb by the post-tagging. This function lemmatizes the word by finding its lemma, the form of the word in the dictionary. The problem with this solution is that it ignored the past participtive of verbs conjugated with an auxiliary. This is because the verbs are tagged as adjectives. To overcome this issue, we removed any verb identified as an auxiliary, such as 'be', 'can', 'could', 'dare', 'do', 'have','may','might','must', 'need', 'ought','shall','should', 'will', and 'would'. This was further simplified by removing negation contractions in the previous step. After removing the auxiliaries, we post-tagged the text a second time to make the past participiples identified as verbs and repeated the steps. Finally, we converted the text to their plural nouns from their singular form to fit our dictionary completely.
# Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm
## 3 Materials and Methods
### Hardware Development
#### 3.2.4 Single Board Computer (SBC)  
An SBC is a complete computer system integrated onto a single board, with a CPU, memory, and I/O interfaces; this component is required for remotely operating the E312. The Raspberry Pi 4 with 8 GB RAM is utilised. Regarding SwaP-C, this 47 g SBC has an 85 \(\times\) 56 \(\times\) 17 mm form factor, and a low power consumption that is compatible with the drone power supply via a voltage converter. The 2.4 and 5 GHz WiFi and Bluetooth 5.0 module provides wireless connectivity for operation control via Secure Shell Protocol (SSH). The gigabit ethernet port is used to connect the Pi and E312.
# Fast and Accurate Visual Tracking with Group Convolution and Pixel-Level Correlation
## 4 Experiments
### Results on UAV123  
UAV123 [34] is a collection of 123 high-definition videos captured using UAVs during aerial photography, containing a variety of targets such as pedestrians, ships, planes, and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**L3** & **L4** & **L5** & **Correlation** & **MCA Block** & **GSA Block** & **AUC** \\ \hline  & & \(\surd\) & DW & & & 0.616 \\  & \(\surd\) & \(\surd\) & DW & & & 0.620 \\ \(\surd\) & \(\surd\) & \(\surd\) & DW & & & 0.628 \\ \(\surd\) & \(\surd\) & \(\surd\) & Pix & & & 0.636 \\ \(\surd\) & \(\surd\) & \(\surd\) & Pix & \(\surd\) & & 0.647 cars; a variety of scenes including fields, roads, and water, with many activity styles; and occlusions, scale changes, lighting changes, and camera movements in order to increase the tracking challenge. The evaluation metrics include success, precision, and norm precision. Precision is the center position error, using the average center position error of all frames in a sequence to evaluate the performance of the trackers. Success is the proportion of area overlapped between the detection and the real area; generally, the area under the curve is used as its value.  
We compared our work with other state-of-the-art trackers, including SiamRPN++ [3], Ocean [36], SiamBAN [37], and SiamGAT [38]. As shown in Figure 9, compared with SiamCAR, our tracker shows a 4.0% improvement in success and a 4.8% improvement in precision.  
We also compared the trackers in terms of visual attributes, including illumination changes, occlusion, scale changes, and background clutter, as shown in Figure 10. Our tracker ranks first, which shows that our tracker has the ability to cope with illumination changes, occlusion, and scale changes.  
Figure 9: (**a**) Overall success and precision plots of our tracker on UAV123 compared with other trackers. (**b**) Success plot for visual attributes. (**c**) Precision plot for visual attributes.
# A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
## 1 Introduction
To the best of our knowledge, this is the first attempt to build trust models based on packet dropping properties other than the total number. Our trust model takes consecutive packet drops into consideration and can be seamlessly integrated into existing models. To show that evaluating the trust value of a node based on its consecutive packet drops can effectively improve the detection speed and accuracy, we propose a new trust model based on the concept of consecutive drops and then build a hybrid trust model that can adaptively choose between this new trust model and an existing trust model. We conduct extensive simulations using a commercially available network simulator, OPNET Modeler [19], to demonstrate that our model outperforms the beta trust model for all types of inside packet drop attacks, not only in terms of detection speed and accuracy as it is designed for, but also in terms of other important network performance metrics, such as packet delivery rate, routing reliability, and energy efficiency. The rest of the paper is organized as follows. We first describe the background in Section 2. In Section 3, we give the motivation for considering consecutive packet drops in evaluating trust in order to quickly and accurately detect inside packet drop attackers. In Section 4, we elaborate our hybrid trust model based on consecutive drops. In Section 5, we evaluate the performance of our model. Finally, we conclude in Section 6.
## 2 Design and Development of Experimental Apparatus
### Data Acquisition Cabinet and Sensor System  
The data acquisition cabinet, which is equipped with meteorological and temperature demodulators, serial ports, gateway interfaces, and power supplies, is responsible for collecting and processing the experimental data. It connects to the environmental chamber via data transmission cables, enabling unified collection of the surface temperature, humidity, wind speed, and other data from the structure within the chamber. The collected data are then transmitted to a computer through the gateway and stored in text format using serial port software. Figure 2 illustrates the data acquisition chassis.
## 4 Analytical Implementation of VB-TPHD
### The Implementation of Gaussian Mixture VB-TPHD Filter
#### 4.1.4 Other Contents about the Implementation  
The biggest difference between TPHD and PHD lies in the different object within the filter processing, although they have similar prediction and update steps. The former is the processing of state sequence, namely trajectory. The longer the filtering time, the more states need to be calculated and stored as well as the greater the computational burden [23]. Here, the L-scan trajectory processing method is proposed. When the update window length \(L\) is small, its computational efficiency is almost the same as PHD. When the filtering duration \(I<L\), only the trajectory state within the duration range is processed, while once the filtering duration \(I\geq L\), only the trajectory state within the current moment, and the previous \(L\) time is processed.  
It is necessary to prune and absorb the Gaussian components in the current extended state space after the update step in order to ensure the efficiency of operation and achievebetter joint extraction of state and noise covariance. Set the upper limit of the number of Gaussian components \(I_{max}\) and the weight threshold \(\varepsilon_{p}\). Cut out the weight number \(I\) whose weight is greater than the threshold:  
\[I=\!\!\left\{j\;\in\;\{1,\;\ldots,\;I_{k}\}\colon\;w_{k}^{j}>\;\varepsilon_{p}\right\} \tag{53}\]  
Above, the number of Gaussian components is directly processed from the threshold value. In order to ensure the validity of the remaining Gaussian components, the current Gaussian components with high similarity are retained based on the Mahalanobis distance principle. This process is absorption. Specifically, by calculating the Mahalanobis distance between each Gaussian component and the maximum Gaussian component of the current moment weight, if the distance is less than the set threshold \(\varepsilon_{a}\), the serial number \(S\) of Gaussian component is selected as  
\[S=\!\!\left\{j\;\in\;I\!:\!\left(\hat{m}_{k}^{j}\hat{m}_{k}^{i}\right)^{T}\! \left(\hat{p}_{k}^{j}\right)^{-1}\!\left(\hat{m}_{k}^{j}\!-\!\hat{m}_{k}^{i} \right)\;\leq\;\varepsilon_{a}\right\}\!, \tag{54}\]  
where \(i\;=\;\text{argmax}(w_{k}^{j})\), then the weight of one of the Gaussian components corresponding to \(L\) is appropriately increased and the other Gaussian components are deleted so as to achieve the purpose of controlling the number of Gaussian components.  
The last step of VB-TPHD is the extraction of trajectory in extended state space. Similar to PHD, the number of trajectory can be estimated as  
\[N_{k}=round\left(\sum_{j=1}^{I_{k}}w_{k}^{j}\right) \tag{55}\]  
Sort all Gaussian components from high to low according to their weights, screen out \(N_{k}\) Gaussian components with high weight and finally extract the joint covariance of trajectory as follows:  
\[\left\{\left(t_{1},t_{k}^{j},\hat{m}_{k}^{j},\left(a_{k}^{l,j},\beta_{k}^{l,j} \right)_{l=1}^{d}\right)\right\}\!,\;j\;=\;1:N_{k} \tag{56}\]
## 3 Proposed Model
### Matching Module  
Two approaches are possible for the matching process: combining MM and FEM as a CNN block or using a separate classifier like SVM, K-nearest neighbor (KNN), or random forest (RF). The main differences between MM and REM are the number of existing samples for each class. In the REM, all input images belong to one class (palmprint), and therefore all database images can be used for learning of that class. On the other hand, there are several classes--one per individual--in the matching phase. To explain further, consider \(n\) as the number of images in the pertinent database, \(p\) as the number of individuals, and \(x\) as the number of existing samples per class. There are \(n\) samples for each class in REM (\(x_{REM}=n\)), while it is \(n/p\) for MM (\(x_{MM}=n/p\)). As an example, in the typical contact-based Hong Kong Polytechnic University Palmprint (HKPU) database ([[http://www4.comp.polyu.edu.hk/~biometrics/](http://www4.comp.polyu.edu.hk/~biometrics/)]([http://www4.comp.polyu.edu.hk/~biometrics/](http://www4.comp.polyu.edu.hk/~biometrics/))), \(x_{REM}=7752\) and \(x_{MM}\approx 20\). Due to a lack of training samples per individual, combining MM and FEM as a CNN block may lead to overfitting, even by exploiting the transfer learning.  
Based on the aforementioned considerations, we chose a separate classifier for the MM. In this manner, three different classifiers were evaluated as the MM: support vector machines (SVMs), K-nearest neighbor (KNN), and random forests (RFs). SVMs are supervised learning models with associated learning algorithms that analyze data used for classification. Given a set of training examples, each marked as belonging to one class, the SVM training algorithm builds a model that assigns new examples to one class, making it a linear classifier. SVMs are the representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. At this point, new examples are then mapped into that same space and predicted to belong to a class based on which side of the gap they fall. In more detail, SVM constructs a set of hyperplanes in a high-dimensional space to classify the input features. The main advantages of SVMs are [61]: (i) effective in high-dimensional spaces, (ii) effective in cases where the number of samples is lower than the number of dimensions, (iii) efficient memory usage (because it uses a subset of training points in the decision function called support vectors), and (iv) flexibility (different Kernel functions can be used for the decision function).  
KNN is a non-parametric technique that classifies new cases based on the majority vote of its k-nearest neighbors, where the votes are considered according to the distances between testing and training samples. RF is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control overfitting.