esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Reinforcement Learning Combined with ANN
### Q Learning Combined with ANN  
The traditional Q learning method uses the Q table to store the corresponding value of each state-action pair. However, the state space gets larger when the structure of the energy system is complex, and it may occupy a considerable space to store the Q table. In order to alleviate computational burdens and improve the efficiency of the algorithm, an artificial neural network is combined with a Q learning algorithm.  
In this section ANN is combined with Q learning, a multi-layer neural network is adopted to approximate the Q value of each action. The diagram of a multi-layer neural network is proposed in Figure 4.  
As shown in Figure 4, the input of the neural network is the state of the Q learning state space. Thus, the value of the state-action pair can be expressed as:  
\[Q=\sum_{j=1}^{m}\omega_{j}^{(2)}h_{j}^{(2)}, \tag{30}\]  
Figure 4: Diagram of a multi-layer neural network.  
where \(m\) is the node number of the proposed ANN, \(h_{j}^{(2)}\) is the output of the \(jth\) node, \(\omega_{j}^{(2)}\) is the connection weight between the output layer and hidden layer. What is more, \(h_{j}^{(2)}\) can be defined by the sigmoid function as:  
\[h_{j}^{(2)}=\frac{1}{1-e^{-h_{j}^{(1)}}}, \tag{31}\]  
\[h_{j}^{(1)}=\sum_{i=1}^{n}s_{i}\omega_{ij}^{(1)}, \tag{32}\]  
where \(h_{j}^{(1)}\) is the input of the hidden layer, \(\omega_{ij}^{(1)}\) is the connection weight between the input layer and the hidden layer.  
Therefore, the connection weights of the ANN can be updated by gradient descendent according to the updating strategy of the single-step Q learning:  
\[\omega_{ij,t+1}^{(1)}=\omega_{ij,t}^{(1)}+\alpha[r_{t+1}+\gamma\underset{a \in A}{\text{max}}Q_{t}(s_{t+1},a)-Q_{t}]\frac{\partial Q_{t}}{\partial\omega_ {ij,t}^{(1)}}, \tag{33}\]  
\[\omega_{j,t+1}^{(2)}=\omega_{j,t}^{(3)}+\alpha[r_{t+1}+\gamma\underset{a\in A }{\text{max}}Q_{t}(s_{t+1},a)-Q_{t}]\frac{\partial Q_{t}}{\partial\omega_{j,t }^{(2)}}, \tag{34}\]  
where \(\frac{\partial Q_{t}}{\partial\omega_{ij,t}^{(1)}}\) and \(\frac{\partial Q_{t}}{\partial\omega_{j,t}^{(2)}}\) are as follows:  
\[\frac{\partial Q_{t}}{\partial\omega_{ij,t}^{(1)}}=\omega_{j,t}^{(2)}s_{t}h_{ j,t}^{(2)}\left(1-h_{j,t}^{(2)}\right), \tag{35}\]  
\[\frac{\partial Q_{t}}{\partial\omega_{j,t}^{(2)}}=h_{j,t}^{(2)}. \tag{36}\]
# Toward Vulnerability Detection for Ethereum Smart Contracts Using Graph-Matching Network
## 4 Methodology
### Graph Similarity Learning
#### 4.3.2 Graph Matching Network  
Rather than separately mapping each contract graph to a vector in GNN, GMN takes a pair of contract graphs at a time and learns the embeddings through cross-graph attention-based matching. Initially, the encoding step of GMN is the same as that of GNN, as shown in Equation (1). The main difference between the two models lies on the propagation process, which can be described as the following:  
\[\mathbf{m}_{j\to i} =f_{\text{message}}\left(\mathbf{h}_{i}^{(t)},\mathbf{h}_{j}^{(t)}, \mathbf{e}_{ij}\right),\quad\forall(i,j)\in E_{1}\cup E_{2} \tag{4}\] \[\mathbf{h}_{i}^{(t+1)} =f_{\text{update}}\Bigg{(}\mathbf{h}_{i}^{(t)},\sum_{j}\mathbf{m }_{j\to i},\sum_{j^{\prime}}\mathbf{\mu}_{j^{\prime}\to i}\Bigg{)},\]  
where we still use an MLP and a GRU for \(f_{\text{message}}\) and \(f_{\text{update}}\), respectively, and \(f_{\text{match}}\) is introduced to exchange cross-graph information through  
\[a_{j\to i} =\frac{\exp\Big{(}s_{h}\Big{(}\mathbf{h}_{i}^{(t)},\mathbf{h}_{j}^{(t) }\Big{)}\Big{)}}{\sum_{j^{\prime}}\exp\Big{(}s_{h}\Big{(}\mathbf{h}_{i}^{(t)}, \mathbf{h}_{j}^{(t)}\Big{)}\Big{)}} \tag{5}\] \[\mathbf{\mu}_{j\to i} =a_{j\to i}\Big{(}\mathbf{h}_{i}^{(t)}-\mathbf{h}_{j}^{(t)}\Big{)},\]  
with \(s_{h}\) as a vector similarity metric and \(a_{j\to i}\) as the attention weight. In this paper, we use cosine similarity for \(s_{h}\). It can be observed that the updater \(\mathbf{h}_{i}^{(t+1)}\) of GMN not only considers the last hidden state and the aggregated message, but also takes \(\sum_{j^{\prime}}\mathbf{\mu}_{j^{\prime}\to i}\) as input, which measures the differences between \(\mathbf{h}_{i}^{(t)}\) and all its neighborhoods. For the readout function of GMN, we still follow Equation (3), and the similarity of a graph pair can be computed by  
\[\mathbf{h}_{G_{1}} =f_{\text{G}}\bigg{(}\left\{\mathbf{h}_{i}^{(T)}\right\}_{i\in V _{1}}\bigg{)} \tag{6}\] \[\mathbf{h}_{G_{2}} =f_{\text{G}}\bigg{(}\left\{\mathbf{h}_{i}^{(T)}\right\}_{i\in V _{2}}\bigg{)}\] \[s =f_{\text{s}}\big{(}\mathbf{h}_{G_{1}},\mathbf{h}_{G_{2}}\big{)},\]where \(s\) is a similarity score and \(f_{s}\) is a similarity metric.  
According to the above setting, most parts of our GMN model are similar to those of the GGNN model, while differences mainly occur at the propagation stage. We will evaluate the effectiveness of GMN on improving the performance of vulnerability detection in smart contracts later.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 3 Performance Metrics of V2X Technologies
### Transmission Latency (L)
#### 3.2.2 L for 802.11 bd Protocol (L\({}^{11 bd}\))  
The calculation L\({}^{11 bd}\) is carried out in a similar way to that of the 802.11p protocol, as shown in Equation (4):  
\[n_{sym}^{11 bd}=\frac{P_{b}.8}{n_{dc}^{11 bd}.CR^{11 bd}.n_{bps}} \tag{4}\]where \(n_{sym}^{11 bd}\) is the number of OFDM symbols needed to transmit \(P_{b}\) bytes, \(N_{dc}^{11 bd}\) is the number of data subcarriers, which is 52 [33], and \(n_{bps}\) is the number of bits carried by the modulation scheme (see [32], Table 8.6.1-1). It is also necessary to calculate the number of symbols used to midamble (\(n_{ma}\)), which can be calculated by the following:  
\[n_{ma}=\left\lceil\frac{n_{sym}^{11 bd}-1}{t_{ma}}\right\rceil \tag{5}\]  
where \(t_{ma}\) is the midamble periodicity (eight OFDM symbols for MCS0-4 and MCS10 and four OFDM symbols for MCS5-9). Finally, L11 bd can be calculated by the following:  
\[L^{11 bd}=t_{pre}^{11 bd}+t_{AIFS}+t_{sym}.t_{sym}^{11 bd}+t_{sym}.n_{ma}+t_{BO} \tag{6}\]  
where \(t_{pre}^{11 bd}\) is the preamble duration (i.e., 80 \(\upmu\)s), \(t_{AIFS}\) is the arbitrary inter frame space (i.e., 32 \(\upmu\)s) for priority data, \(t_{sym}\) is the OFDM symbol duration (i.e., 8 \(\upmu\)s). Moreover, \(t_{BO}\) is the random time interval preceding the start of a transmission, with the same purpose as the time period used for 802.11p. Similarly to 802.11p, in this study, we consider the worst-case scenario corresponding to 32 slots \(\times\) 20 \(\upmu\)s = 640 \(\upmu\)s.
## 3 Robustness and Reliability: New Challenges in CMR Segmentation
### Challenges to Robust Segmentation
#### 3.3.2 Data Acquisition  
Data acquisition may deteriorate the quality of an image and its visual appearance, but differently from domain shift, it does not alter the image's statistical properties. Several factors affect the quality of a CMR image during its acquisition. Some of them are under the control of the clinician (e.g., the number of acquired slices), some depend on the subject being scanned (e.g., bulk or respiratory motion), and some are out of control (e.g., arrhythmias, blood flow or magnetic field inhomogeneities) [70]. When the quality is compromised, CMR images may contain artifacts like ghosting, blurring and smearing. During manual labelling, these images can be discarded for training. At inference, low-quality input images may not be possible to discard. Potentially, they could be the only information available for a patient. However, these low-quality inputs images may lead to poor segmentation results, if the segmentation model is not capable of handling invalid inputs.
## 2 Methods and Materials
### Type of Defects
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Material**} \\ \cline{2-3}  & **Metallic** & **Composite** \\  & Fatigue cracks & \(\blackdiamond\) Disbonds \\  & Pores and voids & \(\blackdiamond\) Delamination \\  & Corrosion & \(\blackdiamond\) Foreign Inclusion \\  & Material inclusions & \(\blackdiamond\) Fibre/Matrix Cracking \\  & Welded sheets without metal diffusion & \(\blackdiamond\) Honeycomb cell wall damage \\  & Overload & \(\blackdiamond\) Porosity \\  & Wear & \(\blackdiamond\) Thermal stress cracking \\  & Creep & \(\blackdiamond\) Fatigue \\  & Impact damage and BVID \\  & Water ingress \\  & Absence of adhesive \\  & Skin-to-core debonding \\  & Core crushing \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Common Types of Damage in Metallic and Composite Aerospace Materials [105]. Figure 11: (**a**) Illustration of typical material defects for composite structures; (**b**) jet engine turbine blades; (**c**) honeycomb panels; (**d**) metallic aircraft and spacecraft components [39]. [PERSON] et al. [46] prepared an AD31T5 aluminium alloy specimen with dimensions of 360 mm in length, 120 mm in width, and 5 mm in thickness. The sample included artificial circular defects arranged in various cluster densities, sizes, and configurations. The Figure 12 below shows the specimen, detailing the number of holes and their actual depth values. AD31T5 aluminium alloy is commonly used in the aviation industry for aircraft frames, support structures, and interior fittings due to its strength and corrosion resistance. In another study, [PERSON] et al. [42] employed an autonomous UAV-based ultrasonic system to inspect an aluminium specimen. The researchers created a 1000 mm \(\times\) 1000 mm \(\times\) 15 mm aluminium sample with twenty-five flat-bottom holes of various diameters and depths to simulate sub-surface defects. In the study by [PERSON] et al. [20], two types of honeycomb sandwich aircraft panels--a flat panel and a curved panel--were fabricated to assess surface damage using 3D scanning technology. These panels were constructed with specific materials: the flat panel utilised Al 7075-T6 for the top face sheet, Al 5052 for the core, and epoxy/fibreglass for the bottom face sheet. On the other hand, the curved panel used Al 2024-T3 for both the top and bottom face sheets, with Al 5052 as the core. Each panel featured a heat-resistant epoxy adhesive for structural integrity. Figure 13a,b display images of the flat and curved panels, respectively, illustrating the setup for dent inspection and the panel geometry. This experiment aimed to simulate real-world damage scenarios and evaluate the precision and reliability of 3D scanning as a non-destructive inspection method. Figure 12: Sample made of AD31T5 alloy [46]. Figure 13: (**a**) Honeycomb sandwich aircraft panel inspection specimens; (**b**) Flat panel Curved panel [20].
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 2 Background and Preliminaries
### Beyond IoT Technologies
#### 2.6.4 Network Function Virtualization  
NFV stands for Network Function Virtualization and offers an adaptable and economical way to implement essential network security features, such as firewalls or intrusion detection systems, on-demand across smart grid infrastructures without requiring additional physical installations [62]. NFV enables security services, when used in conjunction with IoT, to track distributed or mobile IoT nodes, providing scalable services such as remote diagnostics or secure energy trading, as well as adaptive protection [63].
## 4 Experimental Results
### Generalization Analysis  
The first series of experiments has shown that the generator module proposed in this work allowed the 3D-GAN to attain comparable and even better results than the supervised methods, despite only using 25% of the labeled anomalous instances. However, it is not a general-purpose generator. This may constitute a limitation as the generator cannot easily fit a various range of problems. In order to analyze this aspect, in this second series, we conducted experiments using the external dataset UHCTD.  
The results shown in Table 4 indicate an order relation between the CNN-based methods investigated for the UHCTD dataset in terms of performance. C3D reached the highest AUC, accuracy and precision. Then, 3D-ResNet was slightly better than the customized 3D-CNN, except in recall. Finally, MoViNet was 8% worse in AUC when compared to C3D. On the other hand, it provided the highest recall among the CNN-based methods. All supervised methods outperformed the 3D-GAN model, while, again, the autoencoder provided the worst results. As expected, the reason for the significantly degraded performance of the 3D-GAN in UHCTD is probably the problem-dependency of the custom generator. Especially noteworthy is the recall rate obtained using the 3D-GAN model, which was the lowest recall performance among all models investigated. This result indicates how unsuccessful the 3D-GAN was in trying to identify all anomalies. In terms of FPS, the customized 3D-CNN was also the best approach.  
However, if the generator is customized, e.g., by providing some modifications in the defects used to generate the anomalous instances, for a specific-purpose application, the final performance will improve. Taking into account the fact that the 3D-GAN method used in this paper is semi-supervised, considering all the available data as normal and the existence of a few anomalous instances to customize and train the generator, the 3D-GAN approach is highly recommended.
## 2 Materials and Methods
### Dependent Variables
#### 2.6.1 Sensory Analysis  
The NeuroCom EquiTest software computes sub-equilibrium scores which include the somatosensory (SOM), visual (VIS), vestibular (VEST), and the management (PMAN) ratios between the average equilibrium scores for specified pairings of sensory test conditions to describe the finding in the different perspectives (Tables 3 and 4). They determined the importance of each sensory system influencing the balance allowing the identification of the use of somatisensory (SOM), visual (VIS), and vestibular (VEST) information, as well as the ability to manage altered proprioceptive inputs (PMAN). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Predictors** & \multicolumn{2}{c}{**Unstandardized Coefficients**} & \multicolumn{2}{c}{**Standardized Coefficients**} & \multirow{2}{*}{**T**} & \multirow{2}{*}{**Sig.**} \\ \cline{3-3} \cline{5-6}  & & \(\mathbf{\delta}\) & & & & \\ \hline \multirow{4}{*}{Condition 1} & Constant & 57.000 & 15.109 & & 3.773 & 0.001 \\ \cline{2-6}  & Knee OA & \(-\)0.890 & 0.608 & \(-\)0.217 & \(-\)1.463 & 0.153 \\ \cline{2-6}  & Age & 0.025 & 0.028 & 0.123 & 0.868 & 0.392 \\ \cline{2-6}  & Condition 1 & \(-\)0.620 & 0.159 & \(-\)0.578 & \(-\)3.894 & 0.000 \\ \hline \multirow{4}{*}{Condition 2} & Constant & 56.791 & 7.747 & & 7.990 & 0.000 \\ \cline{2-6}  & Knee OA & \(-\)0.158 & 0.726 & \(-\)0.024 & \(-\)0.217 & 0.829 \\ \cline{1-1} \cline{2-6}  & Age & \(-\)0.017 & 0.035 & \(-\)0.051 & \(-\)0.472 & 0.640 \\ \cline{1-1} \cline{2-6}  & Condition 2 & \(-\)0.620 & 0.088 & \(-\)0.771 & \(-\)7.063 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the effects of textured insoles. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ratios** & **Formula** & **Significance** \\ \hline SOM & Condition 2/Condition 1 & Question: Is there an increase in sway when visual information is removed? \\  & Low scores: Inadequate reliance on somatosensory references \\ \hline VIS & Condition 4/Condition 1 & Question: Does sway increase in the absence of somatosensory information? \\  & Low scores: Ineffective use of visual references \\ \hline VEST & Condition 5/Condition 1 & Question: Does sway increase in the absence of visual information and when somatosensory information is incorrect? \\  & Low scores: Inefficient use of vestibular information or the absence of vestibular information. \\ \hline PMAN & Condition 4+5 +6/ & Question: Does inaccurate somatosensory information cause more sway than accurate somatosensory information? \\  & Condition 1+2 +3 & Low scores: Inadequate compensation for disruptions in specific sensory inputs. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Descriptions of sensory analysis [42].
# Operation Optimization of the Sea Container Fleet Based on the Double-Level Planning Model
## 2 The Establishment of the Model
### The Establishment of Comprehensive Optimization Model
#### 2.2.2 Model Construction  
Based on the above analysis, we established an optimized model of ship allocation on the basis of speed and route. The objective function is as follows:  
\[\min W=\sum\limits_{r=1}^{R}\sum\limits_{v=1}^{V}\sum\limits_{v=1}^{V}\Big{(}P _{1}F_{1,\
u}+7K_{v}s_{v}^{3}\Big{)}x_{vr}n_{vr}+\sum\limits_{r=1}^{R}\sum \limits_{v=1}^{V}x_{vr}t_{r}C_{v}/7+ts \tag{8}\]  
The constraints of the objective function are as follows:  
\[s.t.\left\{\begin{array}{l}\sum\limits_{v=1}^{V}x_{vr}C_{ap,v}\geq Q_{r}^{k}, k=1,2,\dots,m_{r},\forall r\in\{1,2,\dots,R\}\\ t_{r}=T_{port}+L_{r}/24\sum\limits_{v=1}^{V}x_{vr}s_{v},\forall v\in\{1,2,\dots,V\},r\in\{1,2,\dots,R\}\\ n_{vr}=x_{vr}\Big{[}\frac{\
u}{V}\Big{]},\forall v\in\{1,2,\dots,V\},r\in\{1,2, \dots,R\}\\ S_{\min,v}\leq s_{v}\leq S_{\max,v},\forall v\in\{1,2,\dots,V\},r\in\{1,2,\dots,R\}\\ \sum\limits_{v=1}^{V}x_{vr}=1,\forall r\in\{1,2,\dots,R\}\\ x_{vr}\in\{0,1\},\forall v\in\{1,2,\dots,V\},r\in\{1,2,\dots,R\}\end{array}\right. \tag{9}\]  
The specific constraints include the following:  
Constraint 1 (cargo-carrying capacity): Ensure that the ship's carrying capacity on the route is adequate to meet the freight demand of each segment. This prevents cargo backlog or transportation delays by ensuring that the ship can meet the demand for cargo transportation.  
Constraint 2 (speed and voyage time): Consider the relationship between the ship's speed and the time required to complete a voyage. This includes both the port berthing time and sea navigation time to ensure timely arrival at the destination and successful completion of the cargo transportation mission.  
Constraint 3 (ship allocation number on route): ensure that the number of ships deployed on the route matches the desired frequency of departure per week, maintaining the continuity and reliability of transportation services.  
Constraint 4 (speed limits): set minimum and maximum speeds for ships to ensure their safe operation and technical compliance.  
Constraint 5 (uniform ship types): there is a requirement for consistency in the type of ships used on each route for improved operational efficiency, facilitated management, and consistency in ships.  
Constraint 6 (flexible ship type configurations): allows for adjustments to the ship's configuration based on specific route needs and regulations.  
By comprehensively considering these constraints, it is possible to develop a rational and feasible solution process for ship speed and route allocation problems. By balancing these constraints appropriately, an ideal route allocation plan can be developed that minimizes operating costs while meeting practical business and technical constraints.
## 6 Performance Evaluation  
In order to evaluate the performance of the secure cluster-based routing protocol, several simulation experiments (over 100) with various random topologies were run. We implemented our proposed protocol using NS-2 version 2.34. NS-2 [29] is an open source, discrete event simulator which is widely used for research purposes. It has an excellent implementation of the 802.11 standards at physical, Data link and higher layers. We simulate the proposed routing protocol assuming a multi-hop network of size 500 m \(\times\) 500 m deployed with different number of sensor nodes ranging from 50 to 200 in randomized grid. The sink is located in the center of the network. The traffic is CBR of 600 packets/sec and the packet size is 316 bytes. Table 2 shows our simulation environment and other parameters used in our simulation.
# Leveraging IGOOSE-XGBoost for the Early Detection of Subclinical Mastitis in Dairy Cows
## 5 Prediction Model for Subclinical Mastitis in Dairy Cows Based on IGOOSE-XGboost
### Building the IGOOSE-XGBoost Regression Model for Subclinical mastitis in Dairy Cows
#### 5.3.2 Evaluation Indicators for Regression Models  
Four indicators--the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R2)--were taken into consideration in order to test and assess the prediction accuracy and performance of the regression method machine learning model examined in this study [33].  
The regression problem's loss was mostly measured by the mean square error, or MSE. The model's prediction performance is higher if the MSE value is lower, since it indicates a lower error between the actual outcomes predicted by the approach and the original data. On the other hand, a higher MSE value indicates a greater discrepancy between the data and the expected outcomes, a higher error, and a less effective model. It is expressed as follows:  
\[MSE=\frac{1}{n}\underset{i=1}{\overset{n}{\sum}}\left(\overset{\wedge}{y_{i}} -y\right)^{2} \tag{14}\]  
The root means square error (RMSE) falls between 0 and +\(\infty\). RMSE = 0, or an ideal model, occurs when the true value and the predicted value are the same. The RMSE value increases with the size of the error. The formula is expressed as follows:  
\[RMSE=\sqrt{\frac{1}{m}\underset{i=1}{\overset{m}{\sum}}\left(y_{i}-\overset{ \wedge}{y_{i}}\right)^{2}} \tag{15}\]  
The advantage of the mean absolute error (MAE) is that it is not sensitive to extreme values, it has a stable gradient regardless of the input value, and it has a robust solution. The range is [0, +\(\infty\)). A smaller score indicates a better model, because there is less discrepancy between the true and predicted values. On the contrary, a greater score indicates that the model is poor. The formula is expressed as follows:  
\[MAE=\frac{1}{n}\underset{i=1}{\overset{n}{\sum}}\left|\overset{\wedge}{y_{i}} -y_{i}\right| \tag{16}\]  
The primary purposes of the adjusted R-squared (R2) are to assess the degree of fit between the expected and actual values and to ascertain whether the statistical model can adequately fit the data. The range of the value is [0, 1]. The model performs better in terms of prediction the closer it approaches to 1. The specific expression is as follows:  
\[R^{2}=1-\frac{\underset{i}{\overset{n}{\sum}}\left(\overset{\wedge}{y_{i}}-y \right)^{2}}{\underset{i}{\overset{n}{\sum}}\left(\overline{y_{i}}-y_{i} \right)^{2}} \tag{17}\]