esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulations and Experimental Results
### Case 2: Collision Avoidance Experiment in Which a Ship Misunderstands  
Case 1 demonstrates an ideal situation in which mutual understanding and cooperation can be fully achieved. However, the BTC method is only a communication method that promotes mutual trust and does not increase the actual understanding of the ship's pilot. Therefore, if the communication fails, the process of communicating the ship's intention to avoid collision may be experienced several times during the CA process. In case 2, ship 2 is set up as the \"black sheep\" that does not understand the other ships.  
In the case 2 scenario (Figure 11), in subfigure t = 500, ship 2 has already started its right turn. In fact, ship 2 is a give-way ship with respect to ship 3 and ship 4, where ship 2 meets ship 3 at a small angle and ship 2 meets ship 4 at a large angle. Therefore, the right-turn decision made by ship 2 in subfigure t = 500 s is consistent with the COLEGs if there is no communication of CA intent. However, this causes confusion in the decisions of the other three ships, because if the situation in case 1 is followed, the behavior of ship 2 adds to the confusion instead. However, since an emergency CA decision scheme based on the ship domain is set up in this algorithm (Section 3.2), a randomized CA decision can be implemented by the path-planning algorithm, including APF between ships, when an abnormal decision is detected. Therefore, it can be seen that ship 1, ship 3, and ship 4 perform slightly more complex CA paths in subfigure t = 1000 s and subfigure t = 1500 s than in case 1. Since only one ship did not understand the CA communication results, the impact on the scenario was always limited, and ships 1, 3, and 4 finally completed the ship CA according to the initial plan and left the encounter area safely. The time of broadcasting is also illustrated in Figure 9 (subfigures t = 100 s, t = 500 s, and t = 1500 s). It can be seen that, due to the uncoordinated CA maneuver of S2, [PERSON] had to take additional action at t = 280 s, and [PERSON] also broadcasted the new CAB after she made the decision.  
It can be seen that, although there was a problem in understanding the ship's intention of CA in this case, successful CA was still achieved in the end. In future maritime entanglements, any similar loss in safety and economy of other encounter ships caused by one ship not acting according to the communication can be verified using the stored blockchain.
## 3 Feasible Trajectory Planning Algorithm
### LSTM-Improved Heuristics  
The features allowing classification or regression using classical machine learning approaches have the major limitation of being manually designed by developers using a combination of domain expertise and iterative experimentation. [PERSON] et al. [61] proposed a deep neural network capable of learning optimization heuristics, achieving performance improvements of up to 14% over traditional machine learning approaches. In fact, deep learning can extract information directly from raw data without the need for human experts to label the dataset. It can also use transfer learning to adapt knowledge from one optimization problem to another. A long short-term memory (LSTM) neural network is a deep learning-based approach developed by [PERSON] and [PERSON] [62].  
It is a type of recurrent neural network designed to increase the storage capacity of the network and preserve historical information over subsequent time steps. It is particularly well suited to nonlinear prediction problems and is able to capture temporal dependencies by correlating time series data. A four-layer neural network was designed with a sequence input layer to process the sequential data of three features, followed by a bidirectional LSTM (BiLSTM) recurrent layer that processed the input sequence in both forward and backward directions to capture dependencies in the data over time as follows. The bidirectional LSTM (BiLSTM) layer processes the input sequence in both the forward and backward directions to capture temporal dependencies in the data. The output at each timestep \(t\) can be expressed as  
\[h_{t}=\tanh(W_{h}\cdot\chi_{t}+b_{h}) \tag{30}\]  
Therein, we have the following:  
* \(\chi_{t}\) is the input instance at time step \(t\), where \(\chi_{t}=\{\chi_{t1},\chi_{t2},\chi_{t3}\}\) comprises the length of the candidate segment, the change in direction, and the \(x\) value in the relevant \(\Pi\) vector.
* \(W_{h}\) is the weight matrix associated with the input features.
* \(b_{h}\) is the bias term.
* \(h_{t}\) is the output (hidden state) at time \(t\) produced by applying the hyperbolic tangent (\(tanh\)) activation function.  
Next, a fully connected layer is used to classify the data into the target classes, while a softmax is used layer to compute class probabilities using a softmax function as follows:  
\[P(y=c\mid\chi)=\frac{e^{z_{c}}}{\sum_{j}e^{z_{j}}} \tag{31}\]  
where \(z_{c}\) is the score in terms of the weighted sum of the input features for class \(c\), and \(j\) iterates over the two classes (_feasible_ and _unfeasible_). The LSTM architecture used in this paper is shown in Figure 3.
## 5 Applications of Thermal Images to Future Space Missions  
It has been proven that thermal infrared imaging does not only give a strong contribution to understanding the physical state of planetary surfaces but also shows potential applications to future space missions such as optical navigation especially in the nighttime ore dawn/dusk regions, as well as a hazard detection. Here we show some examples of the applications.
# Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN
## 5 Experimental Results
### Performance Comparison
#### 5.4.5 Time Utilization Rate  
In this section, we compare the differences in time utilization rates between the D-JERDG and MODDPG algorithms. The experimental results are presented in Figure 11. In Figure 11, we observe that the time utilization of D-JERDG initially increases and then decreases as the number of CH nodes varies. This trend aligns with the analysis discussed earlier. In general, D-JERDG exhibits a similar pattern to MODDPG, and after 300 nodes, their time utilization becomes comparable. When the number of nodes is 150 to 350, the average growth rate of D-JEGDG in terms of time utilization rates is about 23%. The reason behind this behavior lies in the D-JERDG method, which utilizes the SA algorithm to adjust the sequence of CH node visits, thereby reducing the UAV flight distance and time. Before reaching 300 nodes, with a smaller network size, the total flight time includes longer hover times. At 200 nodes, the time utilization reaches its peak, which aligns with the trend observed in the average UAV energy consumption curve. However, beyond 300 nodes, as the number of CH nodes increases, the flight time also increases. With the total flight time remaining constant, the time utilization decreases accordingly. In Figure 11, we examine the impact of UAV flight speed on time utilization under both algorithms. Notably, D-JERDG consistently achieves higher time utilization compared with MODDPG, maintaining the highest level throughout the different flight speeds. As the speed of UAV increases, the average growth rate of D-JEGDG in terms of time utilization rates is about 36%.
# Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model
## 3 Methodology
### CNNs  
CNNs usually consists of several convolutional, pooling, and fully-connected layers, and it has shown its effective performance in analyzing images due to their remarkable performance on benchmark datasets such as ImageNet. CNNs have the ability to extract features and classify classes with a large number of parameters learned from the training dataset. VGGNet [18] can achieve good classification accuracy and it is relatively simpler compared to ResNet [25] and Inception [26]. Pretrained VGGNet model is easy to be fine-tuned for different classification tasks, and it can be used as the base model instead of training a model from scratch. The process of fine-tuning a pretrained CNN can be shown in Figure 3. In this study, the VGGNet-16 model was chosen as the basic architecture for detecting collapsed buildings using pre- and post-event VHR remote sensing imagery. The VGGNet-16 model contains 16 weight layers including 13 convolutional layers and 3 fully-connected layers, and it utilizes small-size convolutional filters (\(3\times 3\)) with a fixed stride of 1 for convolutional layers and \(2\times 2\) pixels with a stride of 2 for max-pooling from the beginning to the end. The dropout probability is set at 0.5 to reduce overfitting. Each convolutional block contains two or three convolutional layers and a pooling layer. There are five convolutional blocks, in which the first and second convolutional blocks have two convolutional layers, and the last three blocks have three convolutional layers.
## 4 Experiments
### Application to Real Data
In this data, on the horizontal plane, the vertical and horizontal spacing of temperature measuring points were 4.3 m and 4.6 m, respectively, with a total of six rows and 11 columns, a height distance of 6.7 m, and a total of five layers. The sensor arrangement is shown in Figure 10a. The data, mainly from October 2016, showed that it cooled during the November period, until June 2017, the data were mainly temperature data, and the collection time was once a week. To estimate grain warehouse temperature,  
Figure 8: Temperature images from the PST data set. (**a**) Ground truth; (**b**) Estimates of the resampled points using our algorithm; and (**c**) Predicted against resampled points for all real points. we used data from the first 10 weeks for training and predicted data from the 14 th week. From week 5, a small amount of sensor information was incorrectly collected and the value is null. In weeks 7 and 8, information collection of the entire temperature measurement cable was lost. We applied the Kriging filter with the data covariance function in Equation (47), drift functions as \(f(x)=[\ 1,x,y,z]^{T}\), and cubic exponential smoothing model parameters \(\zeta=0.8\), \(\tau=0.5\), \(\gamma=0.3\), and \(L=0\). In this case, we chose \(\sigma=0.5\) and \(a=10\). The results are shown in Figure 10.  
The granary temperature monitoring system is a three-dimensional storage system. As such, this analysis was carried out in 3D, as showing in Figure 10b-d, with an expanded view of the outer surface. Unsurprisingly, the change was not very dramatic, given the huge size of the silo and the fact that bulk grains are poor conductors of heat. However, for the temperature forecast from week 11 onwards, we obtain better results, as shown in Figure 11. The reason for this lies in the particularity of grain storage. Grain storage is  
Figure 10: Analysis of grain temperature through our algorithm: (**a**) Distribution of sensors in the grain storage silo; (**b,c**), the first 12 weeks were sampled data for gradual fitting and (**d**) prediction at the 14 th week. Figure 9: Time-series temperature changes at predicted and original points. Among them, the first 120 months were used for training and the next 24 months were predicted: (**a**) The resampled points; and (**b**) the points estimated though our algorithm, using resampled points. mainly affected by external conditions and shows periodic changes, making it a suitable scenario for the dynamic linear regression model.
## 3 System Configuration  
A typical LAS consists of a laser, a photodetector, and an optical configuration for light interaction with gas. For WMS-based LAS, there are, additionally, a laser modulator and a signal demodulator, the latter usually by a lock-in amplifier (LIA).  
The laser is the LAS's key component; it usually needs to be continuously tunable mode-hop-free, reliable, single frequency with narrow linewidth (typically \(<\)1 MHz), and to have low noise intensity. Historically, lead-salt diode lasers have been developed in a MIR gas sensor. However, these lasers require cooling to liquid nitrogen temperatures and present problems for mode hops and multi-mode operation. Recently, great progress in laser technology has brought many types of excellent lasers, e.g., QCLs and ICLs.  
High-sensitive and low-noise detectors are essential for trace gas detection. The most popular commercial infrared detector is a mercury-cadmium-telluride (MCT, or HgCdTe) photoconductive semiconductor-based detector. The MCT detector enjoys a very wide spectral response (2 to 25 \(\upmu\)m) and higher speed of detection. Its main limitation is that it needs cooling to reduce noise due to the thermally excited current carriers. Alternatively, newly developed quantum heterostructure detectors could play a vital role in future infrared detection [31].  
The optical configuration provides interaction between light and gas' the interaction length directly relates with the sensitivity of gas detection. Thus, a long interaction length is desired to achieve high sensitivity. Multiple-pass cells (MPCs) and open long path are commonly used in LAS to measure low-concentration components or to observe weak spectra in gas. The requirements of compactness, small sample volume, and fast response time stimulated the development of a new type of gas cell. Recently, a hollow waveguide (HWG)-based gas cell has been found to boast the advantages of small sample volume and fast response time [8; 32], whereas substrate-integrated hollow waveguides (iHWG) are compact integrated sensors [33]. On the other hand, the need for non-fixed open-path gas detection, e.g., leak detection, aroused the development of standoff remote sensing without a retroreflector [34; 35; 36].
## 5 Experiment and Result Analysis
### Parameter Optimization Results  
The MISAO parameters were set as follows: the number of populations was 30, the maximum number of iterations was 30, the optimization range of parameter \(\gamma\) was [0.1, 1000], and the optimization range of parameter \(\sigma\) was [0.01, 100]. The \(Nstd\) search range was [0.15, 0.6], the \(NE\) search range was [50, 600], and the minimum average envelope entropy was used as the fitness function. The optimized values of \(Nstd\) and \(NE\) were [0.461, 452], [0.180, 236], and [0.241, 197] for sunny, cloudy, and rainy weather, respectively.
# An Automatic Partition Time-Varying Markov Model for Reliability Evaluation
## 2 Materials and Methods
### The Automatic Partition Time-Varying Markov Model for Reliability Evaluation
#### 2.2.1 The Automatic Partition Method for State Number Determination of the Markov Model
Cluster initial probability:  
\[\alpha_{p}=\frac{1}{L}\underset{l=1}{\overset{L}{\sum}}\mu_{p,l} \tag{13}\]  
Cluster center:  
\[\mathrm{v}_{p}^{\mathrm{x}}=\frac{\underset{l=1}{\overset{L}{\sum}}\left(\mu _{p,l}\right)^{m}\left(\mathrm{x}_{l}-W_{p}\left\langle y_{p,l}\right\rangle \right)}{\underset{l=1}{\overset{L}{\sum}}\left(\mu_{p,l}\right)^{m}} \tag{14}\]  
where \(\left\langle y_{p,l}\right\rangle=M_{p}^{-1}W_{p}^{T}(x_{l}-\mathrm{v}_{p}^{ \mathrm{x}})\), \(M_{p}=\sigma_{p,x}^{2}I+W_{p}^{T}W_{p}\);  
Weight update:  
\[\widetilde{W}_{p}=F_{p}W_{p}(\sigma_{p,x}^{2}I+M_{p}^{-1}W_{p}^{T}F_{p}W_{p})^ {-1} \tag{15}\]Variance update:  
\[\sigma_{p,x}^{2}=\frac{1}{Q}tr(F_{p}-F_{p}W_{p}M_{p}^{-1}\widetilde{W}_{p}^{T}) \tag{16}\]  
Distance norm:  
\[A_{p}=\sigma_{p,x}^{2}I+\widetilde{W}_{p}\widetilde{W}_{p}^{T} \tag{17}\]  
Calculation of cluster prototype center and standard deviation parameter of time parameter for sample sequence:  
\[v_{p}^{t}=\frac{\sum\limits_{l=1}^{L}{{{({\mu_{p,l}})}^{m}}_{l}}}{\sum\limits_ {l=1}^{L}{{{({\mu_{p,l}})}^{m}}_{l}}},\sigma_{p,t}^{2}=\frac{\sum\limits_{l=1}^ {L}{{{({\mu_{p,l}})}^{m}}{{({t_{l}}-{v_{p}^{t}})}^{2}}}}{\sum\limits_{l=1}^{L}{ {{({\mu_{p,l}})}^{m}}}} \tag{18}\]  
(ii) Cluster merging  
The similarity factor, \(S_{PCA}^{a,b}\), based on PCA proposed by [PERSON] is applied as one of the merging criteria for two adjacent segments, \(Sub_{5}^{a}\), \(Sub_{5}^{b}\), to determine whether they need to be merged or not. \[S_{PCA}^{a,b}=\frac{1}{Q}\sum\limits_{a=1}^{Q}\sum\limits_{b=1}^{Q}\cos^{2}{ \theta_{a,b}}=\frac{1}{Q}tr(U_{a,q}^{T}U_{b,Q}U_{b,Q}^{T}U_{a,Q}) \tag{19}\]  
where \(U_{a,Q}\), \(U_{b,Q}\) are the first \(Q\) eigenvalues of the eigenvectors for segments \(Sub_{S}^{a}\) and \(Sub_{5}^{b}\), respectively. The distance between the cluster centers of \(Sub_{5}^{a}\) and \(Sub_{5}^{b}\) is another merging criterion. \[D(v_{o}^{x},v_{p}^{x})=\|v_{o}^{x}-v_{p}^{x}\| \tag{20}\]  
Since the clustering process is carried out in the whole range of \(S\), the clustering similarity of each state space is measured by the fuzzy decision algorithm proposed by \(U\). [PERSON] et al. are featured in [40], in which the overall similarity matrix is \(H=\left\{h_{a,b}|1\leq a\leq P,1\leq b\leq P\right\},\)  
\[h_{a,b}=\sqrt{\frac{\left(\frac{1}{P(P-1)}\sum\limits_{a=1}^{P}\sum\limits_{ b=1}^{P}S_{PCA}^{a,b}\right)^{2}+\left(\frac{1}{P(P-1)}\sum\limits_{a=1}^{P} \sum\limits_{b=1}^{P}D(v_{a}^{d},v_{b}^{d})\right)^{2}}{2}}\]  
If the clustering similarity factor, \(h_{a,a+1}\), of two adjacent segments, \(Sub_{5}^{a},Sub_{5}^{a+1}\), is exceeded the set threshold, \(Sub_{5}^{a},Sub_{5}^{a+1}\) will be merged. It ends when the algorithm termination condition, \(\epsilon\), is reached.
# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 2 Classification of Research Perspectives
### Modeling Methodology  
From the perspective of modeling methods, surface taxi time prediction research can be broadly categorized into two approaches: analytical modeling [2; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77].  
Analytical modeling relies on historical data analysis, in which different feature variables are selected to establish surface taxi time prediction models. Mainstream methods encompass regression algorithms, neural networks, support vector machines, integrated learning, fuzzy rule systems, and other machine learning techniques.  
Simulation modeling involves modeling and analyzing the surface departure process. There are two primary aspects: (1) Fast-time simulation methods (e.g., SIMMOD, AIRTOP, LINOS, and other mainstream commercial software) accurately simulate the physical structure and topological modeling of the surface, enabling high-precision simulation of the surface running process. Statistical analysis is then performed on the simulation data to  
Figure 6: Aircraft arrival and departure process.  
determine surface taxi times. (2) Queuing theory-based prediction models. The departure process of aircraft is divided into two stages: taxiing from the stand to the runway-end waiting point (considered as unimpeded time or fixed taxi time), and waiting in the departure queue for takeoff. For the first phase, the average taxi time for different aircraft positions can be derived from historical data or established surface posture measures. For the second phase, appropriate queuing models are used to calculate waiting time at the runway end to establish a prediction model for takeoff waiting time [5].  
In addition, there is some literature [7; 8; 9; 10] that combines taxi time prediction with taxi route optimization to build taxi route optimization models with the objective of finding the shortest taxi time, or solving the taxi time by the shortest route.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 5 Result Analysis and Performance Evaluation
### Under Jellyfish Periodic Drop Attacks
#### 5.1.1 Packet Delivery Ratio (PDR)  
The packet delivery ratio can be calculated as the proportion of the total number of packets successfully received at the destination node as compared to the number of packets sent by the source node. This metric is measured as follows:  
\[\text{PDR}=\frac{\Sigma\text{Packets}_{\text{received}}*100}{\Sigma\text{ Packets}_{\text{sent}}} \tag{8}\]  
where \(\text{Packets}_{\text{received}}\) is the successfully received packets by the destination node and \(\text{Packets}_{\text{sent}}\) is all of the packets sent by the source node.  
In accordance with the number of attacker nodes, Figure 10 compares the variations in the PDR of the proposed SecE-V2X with the aforementioned security algorithms. For all of the protocols, it can be observed that the PDR decreases whenever the number of malicious nodes in the network grows. In a network without any malicious nodes, we note that the suggested protocol produces better results than those of the other schemes and has a higher PDR of almost 95.5%. The outcomes for three malicious nodes indicate that SecE-V2X performs at 93.21%, significantly better in terms of the PDR than [37], 2017 (74.21%), and even [20], 2018 (90.22%), demonstrating that our recently designed protocol works effectively even in the presence of a few malicious nodes. When there are six malicious nodes, the proposed protocol's PDR drops somewhat to 89.67%, which is still much higher than [37], 2017 (about 64.67%), and [20], 2018 (about 76.72%). The PDR of our suggested protocol reduces significantly to 76.83% when nine malicious nodes are taken into account, since the number of attacker nodes is exaggerated, which degrades the network. The results show that the Honesty metrics of SecE-V2X allow it to use the reading of the networks to make a decision about the best route to take in terms of the PDR.