esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Contact Model
### Assembly of Stiffness and Mass Matrices  
The derived local matrices \([M]\) and \([K]\) from the reduced models of the beam and two supports are used to calculate the global matrices \([M_{GLOB}]\) and \([K_{GLOB}]\) of the assembly. As described in Figure 15, the degrees of freedom of the reduced models were ordered for both beam and slots.  
By dividing the degrees of freedom into two categories, namely free (F) and the constrained (C), it was possible to split the mass and stiffness matrices as:  
\[\left(-\omega^{2}[M_{GLOB}]+[K_{GLOB}]\right)\{\mathbf{q}\}=\{\mathbf{0}\}+\{\mathbf{R_{0}} \}\rightarrow\left\{\begin{array}{c}\left(-\omega^{2}[M_{FF}]+[K_{FF}] \right)\{\mathbf{q_{F}}\}=\{\mathbf{0}\}\\ \left(-\omega^{2}[M_{CF}]+[K_{CF}]\right)\{\mathbf{q_{F}}\}=\{\mathbf{R_{0}}\}\end{array}\right. \tag{12}\]  
The modal analysis was then carried out using the matrices \([M_{FF}]\) and \([K_{FF}]\), obtained from the global matrices \([M_{GLOB}]\) and \([K_{GLOB}]\) by eliminating the rows and columns corresponding to the degrees of freedom of the bases of the two slots. The structure of the resulting matrices is exhibited in Figure 16.  
To insert the contact elements to the interfaces, it is necessary to define a connectivity matrix to associate the degrees of freedom of the coinciding nodes of the blade simulacrum and supports. Each row of the connectivity matrix corresponds to a pair of coinciding nodes between which a contact element operates. Coincident nodes were identified by looking for nodes with the same identification number used in commercial finite element software, according to the procedure explained above. Linear contact elements were created, i.e.,  
Figure 16: Global matrices considering constraints: (**a**) mass matrix and (**b**) stiffness matrix.  
Figure 15: Assembly of super-element: (**a**) mass matrices and (**b**) stiffness matrices.  
contact elements that do not allow sliding nor the separation at the contact region. The stiffness matrix for the contact elements in the local coordinate system is:  
\[\left[K_{(\epsilon)}^{L}\right]=\left[\begin{array}{cccccc}k_{n}&0&0&-k_{n}&0&0 \\ 0&k_{t}&0&0&-k_{t}&0\\ 0&0&k_{t}&0&0&-k_{t}\\ -k_{n}&0&0&k_{n}&0&0\\ 0&-k_{t}&0&0&k_{t}&0\\ 0&0&-k_{t}&0&0&k_{t}\end{array}\right] \tag{13}\]  
The rotation matrix \([\Lambda]\) was used to switch from the local to the global coordinate system. Indeed, since the total potential energy is invariant with respect to the reference system, the global stiffness matrix becomes:  
\[\left[K_{(\epsilon)}^{G}\right]=\left[\Lambda_{(\epsilon)}\right]^{T}\left[K_{ (\epsilon)}^{L}\right]\left[\Lambda_{(\epsilon)}\right] \tag{14}\]  
where  
\[\left[\Lambda\right]=\left[\begin{array}{cccccc}c&0&s&0&0&0\\ 0&1&0&0&0&0\\ -s&0&c&0&0&0\\ 0&0&0&c&0&s\\ 0&0&0&0&1&0\\ 0&0&0&-s&0&c\end{array}\right] \tag{15}\]  
with \(c\) and \(s\) denoting the trigonometric functions cos and sin. Eventually, the corresponding stiffness matrix of contact elements for each pair of coincident nodes was inserted into the global matrix of the assembly \([(K_{GLOB})_{FF}]\) (see Figure 17).
## 5 Experiments and Results
### Results  
Based on the simulation and pool experiments, a comprehensive comparison is given to the control performances of PID, LADRC, and LADRC-TD in AUH depth control and heading control in this paper. Among them, PID has poor performance, a sizeable steady-state error in the depth and heading controls, and a worse anti-interference performance when compared to LADRC and LADRC-TD. LADRC demonstrates a performance between the LADRC-TD and PID. The steady-state accuracy and anti-interference of LADRC and LADRC-TD are almost the same. As LADRC-TD is added with tracking differentiation, the overshoot decreases, and the response time is slightly increased by approximately 2 s. However, more attention was paid to the accuracy, overshoot, and anti-interference at low speeds of the algorithms. As discovered, the response time of the LADRC-TD algorithm fully meets the task requirements. Table 2 summarizes the results of each algorithm in the simulations and the fixed-point experiment. In addition, the AUH performed PID and LADRC-TD in the dynamic experiment. As demonstrated, LADRC-TD has excellent dynamic depth-keeping performance and superior heading tracking performance compared to PID.
# Thermal Threat Monitoring Using Thermal Image Analysis and Convolutional Neural Networks
## 3 Materials and Methods
### Proposed Methodology
Then, three convolutional layers with kernels of different sizes were used (which aimed to extract image features at different levels--simple features, complex features). Each one of them was followed by Max-pooling layers (which enabled data size reduction). Then, the efficiency was tested for different numbers of filters and finally 24 filters were proposed in the Conv1, Conv2 layers and 48 filters in the Conv3 layer. Taking into account the number of classes of examined images, the fully connected layers were also reduced, the first one (Fc1) to 16, and the second one (Fc2) to 2 neurons, which made it possible to significantly reduce the model size. After each layer, Conv1,2,3 and Fc1, a ReLU activation function layer was added (in order to accelerate model convergence and reduce the vanishing gradient, similarly to [29]). After the Fc1 layer, a layer with the ReLU activation function was added to achieve model nonlinearity. The input image size was reduced in subsequent stages by Max-pooling layers. Further modifications of the network involving changes in the convolutional layers resulted in a decrease in operational efficiency by several percent, so the final version shown in Figure 5 was proposed. Figure 4: Block diagram of the thermal threat detection algorithm. Figure 6 presents selected filter masks generated in subsequent layers of convolutional networks (24, 24, 48 filters). Figure 7 presents the network training process. It can be observed that the model achieves high accuracy relatively quickly and after 10 iterations, the increase in effectiveness is much slower. The stochastic gradient descent method with momentum values = 0.9 and the learning rate drop factor which reduced by 0.1 every 8 epochs was used. The L2 regularization parameter was equal to 0.004. The hyperparameters of the learning process were also established, i.e., minibatch size = 256 and number of epochs = 20. Figure 5: Network structure. Figure 6: Sample masks of generated filters for the Conv1, Conv2, Conv3 layers. Figure 7: Network training process.
## 4 Results and Discussion
### Experimental Settings  
#### 4.1.1 Study Area and Data  
We evaluated FedDeep on 13 prefecture-level cities in Jiangsu Province.  
Jiangsu province, positioned on the Yangtze River in East China, is one of the most developed regions in terms of economy. The province boasts a substantial population and highly developed industry. It covers a region of about 107,200 square kilometers and is divided into 13 prefecture-level cities. In 2019, the total population crossed 80.5 million [34; 35]. In the past few years, the urban \(PM_{2.5}\) problem in Jiangsu Province has become very serious, attributed to rapid economic development. Thus, to assist government departments in solving air pollution issues, we selected research regions in 13 prefecture-level cities of Jiangsu province, containing Nanjing, Suzhou, Wuxi, Changzhou, Zhenjiang, Nantong, Taizhou, Yangzhou, Yancheng, Huaian, Suqian, Xuzhou, and Lian yunggang. A total of 133 air quality monitoring sites were referenced, of which the number of sites per urban is depicted in Table 1. Each site composed an hourly averaged concentration of \(PM_{2.5}\), ranging from 31 May 2018 to 31 December 2021. The external factors utilized in this paper were derived from district-level meteorological sites. We first measured the distance among \(PM_{2.5}\) monitoring sites and each meteorological station. Weather data from the nearest meteorological station were then leveraged as external factors for the \(PM_{2.5}\) monitoring site. The external factors of each site contained weather conditions, temperature, and wind speed, for which the data collection period was aligned with \(PM_{2.5}\). In the global ESTNet, we treated the 133 sites as predictive objectives. In each local ESTNet, the predictive objective was the number of sites contained in the corresponding prefecture-level city.
# Optimisation of Cable Dome Structure Design for Progressive Collapse Resistance
## 2 Dynamic Response and Model Analysis of Progressive Collapse
### Cable Elements, Strut Elements, and Equivalent Force Model  
Ansys LS-DYNA was used to simulate cable and strut elements by selecting LINK167 and LINK160, and the offset amount was defined to apply prestress. The equations used are as follows:  
\[\mathrm{F}=\mathrm{K}\times\mathrm{max}\{\Delta\mathrm{L},0.0\} \tag{1}\]  
\[\mathrm{K}=\mathrm{EA}/(\mathrm{L}_{0}-\mathrm{offset}) \tag{2}\]  
where \(\Delta\mathrm{L}\) is the amount of change in strut length, \(\mathrm{L}_{0}\) is the initial strut length, \(\mathrm{E}\) is the elastic modulus, \(\mathrm{A}\) is the cross-sectional area, and offset is the amount of offset. For the LINK160 strut element, a bilinear dynamic material model was used, with the failure strain set at 0.01. In other words, any strut elements with a strain level higher than 0.01 were removed from the structure [34].  
Analyses were performed using the full dynamic equivalent load-based instantaneous unloading method, which involves removing one component of the structure and replacing it with an equivalent force before unloading the equivalent force. The replacement time, duration, and unloading time of the equivalent force were set as twice, 20 times, and 1/10, respectively, of the remaining natural period of vibration of the structure (which was set to 2 s in this study) [34; 40]. Figure 2 depicts the entire process of the equivalent force action.
# Performance Evaluation of Routing Protocols for Underwater Wireless Sensor Networks
## 3 Performance Evaluation and Simulation Results
### Results  
This section describes the results of all analyzed routing protocols. This work has been designed with a height of 1000 m \(\times\) 1000 m \(\times\) 1000 m with 200 sensor nodes moving randomly in a 3D environment.
# Model-Based Design of Aircraft Landing Gear System
## 4 Simulation Results
### Longitudinal Dynamics  
The first set of curves obtained from the dynamic model are the longitudinal forces and kinematic behavior of the aircraft, which are, respectively, reported in Figures 8 and 9.  
From Figure 8a, we can confirm the behavior of the aerodynamic and rolling resistance trend of the three aircraft: the former shows an exponentially decreasing behavior, due to its proportionality to the square value of the aircraft speed, while the latter presents a constant value, thereby showing the simplification of a constant rolling coefficient. The curve shown in Figure 8b is the braking force: we can see that the actuation started at 5 s from touching the ground due to the imposed actuation delay. It then took a few more seconds to reach a constant actuation. In all three cases, the linearly increasing braking force was appreciable, which was derived from the ramp signal used to provide the actuation force on the brakes, which then saturated at the maximum imposed value. A similar trend  
Figure 8: Longitudinal forces acting on the aircraft during landing. Similar behavior can be seen in [7; 26].  
Figure 9: Longitudinal motion of the aircraft during landing. Similar behavior can be seen in [26].  
can also be found in literature [7; 26]. Additionally, regarding the ATR42-600 and the MDD F-15D curves, the intervention of the antiskid logic was evident in the first few seconds of the braking phase: an alternating curve was noticeable due to the disengagement of the brakes returning the braking force to zero, which was due to the insufficient vertical force on the tires to avoid the locking of the wheels. This effect was absent in the 737-800, which, due to its higher mass, can provide high enough longitudinal grip to the tires. It can be also confirmed that for the first few seconds of the landing phase, the aerodynamic resistance was comparable to the maximum braking force, thus justifying a postponed braking.  
Finally, by looking at Figure 9, the kinematic behavior of the aircraft can be confirmed. In particular, the landing distances of the three aircraft are reported, alongside the longitudinal speed and acceleration characteristics. In the former, an almost linear trend can be noticed, which replicates the typical landing behavior [26]. In the latter, a sudden spike in deceleration can be seen at around 5 s, which corresponds to the start of the braking action.
## 2 Related Research  
### Position Fingerprinting Method  
Indoors radio signals reflect and are affected by many things, such as obstacles, temperature, and human movement. Unique RSSI values are generated at different locations.  
RSSI values are measured and collected at as many points as possible indoors, and when estimating location, the RSSI values are compared to the values of the location points. Since this is similar to the relationship between a person and a fingerprint, it is also known as the position fingerprinting method [9; 10]. The position fingerprinting method is divided into two parts: the \"online phase\" and the \"offline phase\". Figure 1 illustrates the procedure of the position fingerprinting method.  
In the offline phase, first, the experimental environment is determined, classified, and displayed on a map. In the experimental environment, multiple APs are set up in advance, and the RSSI values of each reference point within the entire map are measured sequentially.  
In the online phase, when performing location estimation, the RSSI value collected from the terminal AP at the location point is used to compare with the fingerprint points stored in the location fingerprint database, the fingerprint point with the most similar RSSI value characteristics is selected, and the location coordinates of the location point can be estimated by the location estimation algorithm based on the RSSI distribution rules.
# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 2 Related Work  
There is much research on different aspects of malware detection. Practical and effective experimental results in the classification of malware are obtained by using different types of methods. In this section, a brief overview of some studies has been provided particularly those focusing on deep learning-based methods for malware detection.  
[PERSON] et al. [7] explored CNNs for malware detection by converting binaries to grayscale images, achieving 99.77% accuracy on Linux samples but only 86.83% on Windows, highlighting challenges with polymorphic malware. [PERSON] et al. [8] proposed a deep neural network for Windows malware classification using the EMBER dataset. [PERSON] et al. [9] combined texture features (SIFT, KAZE, HOG) with a BoVW model on the Malimg dataset, attaining 98.34% accuracy through ensemble learning.  
For advanced threats, [PERSON] et al. [10] analyzed fileless malware via memory forensics, while [PERSON] et al. [11] evaluated n-gram and deep learning against metamorphic samples from the Microsoft Malware Challenge. [PERSON] et al. [12] introduced sequential pattern mining (SPM) for API call analysis, focusing on WannaCry and Zeus variants. [PERSON] et al. [13] achieved 96% accuracy via dynamic API monitoring, and [PERSON] et al. [14] developed the Fantasm framework to study polymorphic malware behavior on bare-metal systems.  
Alternative approaches include supervised ML ([PERSON] et al. [15]), hybrid signature/anomaly detection ([PERSON] and [PERSON] [16]), and behavioral modeling ([PERSON] et al. [17]). [PERSON] et al. [18] proposed a graph-based method for Android malware, outperforming image-based techniques. [PERSON] [19] leveraged ontology knowledge graphs, while [PERSON] et al. [20] employed multi-scale learning with spatial pyramid pooling. [PERSON] et al. [21] introduced BinImg2 Vec, combining self-supervised and supervised learning for binary image classification.  
According to the analysis principle, it can be discussed in three different classes, as shown in Figure 1. In the static analysis method, the image of the malware before it is executed is analyzed. In the dynamic analysis method, the behavior of the malware and the traces it leaves behind during its execution are analyzed. Memory images and other runtime data also need to be examined. For this reason, Memory-based methods are used.
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 5 Earthquakes
### Systems Requirements and Constraints for Earthquake EW Systems  
Because of the moderate seismicity across Europe, the main focus and requirement for European Earthquake EW has been on speed rather than source characterization, though research on this aspect is increasing [6]. Meantime, low latency (in terms of both data processing, chosen prediction algorithms, and data transmission) should always be a focus as to provide warnings before the main earthquake event occurs. In particular, data latency is often the most important factor contributing to the delay of an Earthquake EW system [60], and as such the packetization format, data serialization and compression methods, the structure of the seismic network and datalogger, etc., should be optimized.  
An Earthquake EW system generally has limitations in terms of accuracy of prediction and detection, which may lead to both false alarms and missed warnings. An alerting threshold lower than the damaging threshold (or multiple thresholds) will produce more false alarms, but it will minimize the missed alerts and possible damage to the user [74].  
Other aspects that came to light from the reviewed papers were: (i) the essential role played by synchronization and the subsequent need for synchronization protocols in the deployed WSNs; (ii) the need for fault tolerant sensor networks; (iii) the correlation between node density and performance, both for quality of prediction and resilience, since WSNs show less data corruption after some nodes fail [63]. Many efforts in the reviewed literature were focused on the design of low-cost sensor networks that could allow a denser deployment of seismic networks in this regard. Density also plays a factor in the overall latency of the system [60]. Redundancy for both devices and communication channels should be considered while retaining low costs [67], and improved battery life should also be an objective. Since latency is a very essential requirement, Fog/Edge computing can be exploited to reduce data transmissions and computational times at the main server, for example embedding Machine Learning models or prediction algorithms in sensing devices or gateways to make a first prediction on a lower layer.
# An Optimization Model for a Desert Railway Route Scheme Based on Interval Number and TOPSIS
## 2 Research Methods
### Comprehensive Weight of the Indicators
#### 2.3.2 Analytic Hierarchy Process  
The analytic hierarchy process (AHP) is a subjective weight calculation approach that combines qualitative and quantitative analysis [33]. This method employs the experience of decision-makers to judge the relative importance of each indicator and then the weight of the index is obtained. It has the advantages of simple calculation and strong systematization. The steps for calculating the weight via the chromatography approach are given as follows:  
(1) The factor set \(U\) is constructed with \(n\) indicators:  
\[U=\left\{b_{1},b_{2},\cdots,b_{i},b_{n}\right\} \tag{7}\]  
(2) The relative important judgment matrix \(P\) is constructed according to the relative importance of each indicator:  
\[P=\left(b_{\bar{\eta}}\right)_{n\times n} \tag{8}\]  
in which \(b_{\bar{\eta}}\) represents the relative importance value of the index \(b_{i}\) to the index \(b_{\bar{\eta}}\) (\(i=1,2,\ldots,m\); \(j=1,2,\ldots,n\)), \(b_{\bar{\eta}}=1\), \(b_{\bar{\eta}}=1/b_{\bar{\eta}}\), and \(b_{\bar{\eta}}=b_{\bar{\eta}}/b_{\bar{\eta}}\). The ratio scale can be expressed by 1, 3, 5, 7, and 9 as \"equally important\", \"slightly important\", \"obviously important\", \"strongly important\", and \"extremely important\", respectively. The numbers 2, 4, 6, and 8 are used to indicate the scale between the two scales mentioned above.  
(3) Each column \(b_{\bar{\eta}}\) of the judgment matrix \(P\) is normalized to obtain \(c_{\bar{\eta}}\):  
\[c_{ij}=\frac{b_{\bar{\eta}}}{\sum\limits_{k=1}^{N}b_{ki}},i,j=1,2,\cdots n \tag{9}\]  
(4) Each column \(c_{\bar{\eta}}\) of the normalization matrix is added in rows per the following relation:  
\[w_{ij}=\sum\limits_{j=1}^{N}c_{ij},i,j=1,2,\cdots,n \tag{10}\]  
(5) The column vector \(\overline{w}=\left(\overline{w}_{1},\overline{w}_{2},\cdots,\overline{w}_{n} \right)^{\text{T}}\) is normalized to arrive at the feature vector \(w=\left(w_{1},w_{2},\cdots,w_{n}\right)\) as follows:  
\[w_{i}=\frac{\overline{w}_{i}}{\sum\limits_{i=1}^{n}\overline{w}_{i}},i=1,2, \cdots n \tag{11}\]The random consistency ratio (\(CR\)) is employed to judge the rationality of the weights:  
\[CR=\frac{CI}{RI}=\frac{(\lambda_{\max}-n)}{(n-1)\cdot RI} \tag{12}\]  
where \(CR\) denotes the ratio of random consistency, \(\lambda_{\max}\) is the maximum eigenvalue of the judgment matrix \(P\), and \(RI\) represents the average random consistency index of the judgment matrix, which can be determined by the order of the judgment matrix [34]. In the case of \(CR\) < 0.10, the judgment matrix can be assumed to have satisfactory consistency.