esa-sceva/satcom-chunk-collection huggingface dataset:
# Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet
## 3 Cross-Guide-UNet (CG-UNet)
### Joint Loss Function  
In Section 3.1, the structure of the edge detection model is redesigned. Further, the loss function is crucial for ensuring detection accuracy. The edge detection loss function primarily comprises a weighted cross-entropy loss function, given as follows:  
\[f_{ce}(G,P)=-W_{i}\sum_{i}^{N}\left(G_{i}\text{log}p_{i}+(1-G_{i})\text{log}(1 -p_{i})\right), \tag{6}\]  
where \(G_{i}\) represents the real label value, \(p_{i}\) represents the predicted value, \(N\) represents the number of pixels, and \(W_{i}\) represents the weight. The weighted cross-entropy loss function can be considered as a pixel-level loss calculation, comparing the predicted value of each pixel with the true value to compute the error and derive the loss. However, a single loss function fails to differentiate pixels near real edges effectively and often misclassifies non-edge pixels as edge pixels. The boundary difference over union (DoU) loss function [44] can be used to calculate the loss based on the boundary difference between predicted and true values, enabling focused attention on pixel information proximal to the edge. Distinct from  
Figure 4: Diagram of spatial attention.  
weighted cross-entropy, the boundary DoU loss function can be viewed as an object-level loss calculation. The DoU loss is computed using Equation (7).  
\[L_{DoU}=\frac{G\cup P-G\cap P}{G\cup P-\alpha*G\cap P}, \tag{7}\]  
where \(G\) represents the true value, \(P\) represents the predicted value, and \(\alpha\) is set to 0.8, controlling the width of the boundary calculation; the smaller the value, the more distance between the predicted boundary and the true boundary, and the lower the punishment intensity for prediction failure. On the contrary, it will increase the punishment intensity for prediction failure. We combined the weighted cross-entropy loss function and the boundary DoU loss function to create a joint loss function. While the weighted cross-entropy loss function emphasizes pixel differences, the boundary DoU loss function addresses overall differences. The joint loss is calculated using Equation (8).  
\[L=L_{DoU}+\beta L_{CE}, \tag{8}\]  
The parameter \(\beta\) is set to 0.001 to control the proportion of the loss function. The joint loss function optimizes the edge detection model from two distinct perspectives and effectively distinguishes pixels with high uncertainty near the real edge.  
The joint loss function combines cross-entropy loss and boundary loss to perform differential constraints on different types of pixels during model training. Cross-entropy loss focuses on the classification accuracy of each pixel, while boundary loss focuses on optimizing the difference between predicted and true boundaries. The combination of the two effectively improves the model's ability to distinguish ice edge pixels and reduces the occurrence of misjudgments.
## 2 Mathematical Model of HWHE
### Equations of HWHE
#### 2.3.2 Mathematical Model of Heat Transfer from the Fluid to the Pipe Wall  
Heat Transfer Process in the Pipe Wall  
For Method A, one part of heat is transferred from the fluid to the fin with a length of \(L\) which equals \(0.5(\pi-\theta)d\), and the other part is transferred from the fluid to tube wall with a length of \(H_{1}\), which is decided by the radian of the contact angle \(\theta\). For Method B, heat is transferred from the fluid to the fin with a length of \(L\) which equals \(0.5\pi d\).  
To spread the tube wall with length of \(L\) as expressed before, it could also be regarded as a fin with one side contacted with fluid, and there exists similarity in the form of the heat conduction differential equation and the boundary condition for the plate and the pipe wall. So, the heat conduction differential equation and the boundary condition for the pipe wall are no longer given repeatedly.  
The temperature on the wall can be expressed as Equation (14).  
\[t=t_{f}+(t_{j}-t_{f})\frac{\cosh[m_{t}(L-y)]}{\cosh(m_{t}L)} \tag{14}\]  
where, \(t\) is the temperature of the tube wall, \({}^{\circ}\)C and \(t_{f}\) is the fluid temperature in the pipe, \({}^{\circ}\)C.  
For Equation (14), \(m_{t}\) and \(\lambda_{tl}\), which are used to calculate \(m_{t}\) for Method B, share a similar equation form with Equations (5)-(7). In the process of calculating \(m_{t}\) for Methods A and B, all the parameters in Equations (5)-(7) such as \(\alpha_{a}\), \(\lambda_{b}\), \(\delta_{b}\), \(\lambda_{bl}\), and \(\rho_{b}\) should be replaced with \(\alpha_{f}\), \(\lambda_{t}\), \(\delta_{t}\), \(\lambda_{tl}\), and \(\rho_{t}\), respectively.  
Where, \(\lambda_{tl}\) is the thermal conductivity of the tube combining with the foil, W/(m \({}^{\circ}\)C) and \(\rho_{t}\) is the density of the tube, kg/m\({}^{3}\). \(\alpha_{f}\) is fluid convection heat transfer coefficient inside the pipe, W/(m \({}^{2}\)\({}^{\circ}\)C); \(\lambda_{t}\) is the thermal conductivity of the pipe, W/(m \({}^{\circ}\)C); and \(\delta_{t}\) is the thickness of the pipe, m.  
Heat Transfer for the Fluid to the Pipe Wall  
For Method A, as mentioned above, the heat transferred from the fluid to the pipe is composed of two parts: the heat transferred by the length \(H_{1}\) and by length \(L\), respectively; the heat can be expressed as Equation (15).  
\[Q_{f}=\eta_{t}\alpha_{f}(t_{f}-t_{j})\times 0.5(\pi-\theta)d+\alpha_{f}(t_{f}-t_ {j})\times H_{1} \tag{15}\]For Method B, the heat transferred from the fluid to the tube could be expressed as Equation (16).  
\[Q_{f}=\eta_{t}\alpha_{f}(t_{f}-t_{j})\times\frac{\pi d}{2} \tag{16}\]  
where, \(Q_{f}\) is the heat transferred from the fluid to the whole tube wall, W/m and \(\eta_{t}\) is the copper pipe fin efficiency which can be expressed as Equation (17).  
\[\eta_{t}=\frac{\tanh(m_{t}\times L)}{m_{t}\times L} \tag{17}\]
## 3 Results and Discussion
### Impact of Stirring Rate on Products Formation  
The stirring rate was reported to influence the production of 1,3-PD in _K. pneumoniae_ and _Shimvella plattae_[19, 33, 35, 38]. To optimize this parameter for _C. freundii_ AD119, four different stirring values were tested, from 40-160 rpm. The kinetics of the fed-batch fermentations performed using these conditions are presented in Figure 8.  
There were no significant differences in the final concentration of 1,3-PD, lactic acid, succinic acid, and ethanol obtained at stirring rate values ranging from 40 to 160 rpm (Table 3).  
Figure 7: Comparative kinetics of 1,3-propanediol (1,3-PD) production with _C. freundii_ AD119 in fed-batch cultures at a maintained pH = 7.0 or pH = 7.5 and in a process with an initial pH value of 7.5 then shifted to 7.0 after 24 h.  
Although no premature cessation of the fermentations was observed, the concentration of 3-HPA was analyzed. It did not exceed 4.5 mmol/g of bacterial cell dry mass in any of the batches. However, the higher the stirring rate was, the lower the observed concentration of 3-HPA (Figure 9). The reported optimal values of the stirring rate range between 110 and 350 rpm [19, 33, 38, 40]. These depend on the strain and the type of bioreactor used. Excessively vigorous stirring has been found to decrease 1,3-PD production, probably due to over-aeration of the medium [40], or to have a negative effect on bacterial growth [33]. Excessively low stirring rate, on the other hand, led to the accumulation of 3-HPA [16, 38]. The value of 80 rpm was selected as the optimal stirring rate, as it prevents foaming and additional, unjustified power demand.  
Figure 8: Time course of dry cell weight (DCW) and metabolite production (1,3-propanediol (1,3-PD), acetic acid, lactic acid, succinic acid, ethanol) with \(C\). _freundii_ AD119 in a fed-batch culture with a fixed temperature of 30 \({}^{\circ}\)C and pH value of 7.0 at a stirring rate of (**a**) 40 rpm, (**b**) 80 rpm, (**c**) 120 rpm, and (**d**) 160 rpm.
## 4 Results and Analysis
### Driving Behavior  
Based on the different lanes where participants were located, there were three main driving behaviors in the transition area, namely, straight-through vehicles not affected by merging in the left lane (X), straight vehicles affected by merging vehicles in the adjacent lane to the left of the closed lane (Y), and merging vehicles merging into the adjacent lane to the left of the closed lane (Z).  
Our experiment allowed participants to freely choose lanes, with the fewest participants choosing to merge (Z) and most going straight (X), with an overall ratio of 42% (X):33% (Y):25% (Z); the greater the number of lanes, the fewer participants chose to merge (Z), with a ratio of 10% (4 lanes):26% (3 lanes):39% (2 lanes). When the transition area length was 200 m, the distributions of vehicle speed and driving workload for different driving behaviors were as shown in Figure 9.  
Figure 8: Fitting curve of the transition area length with vehicle speed and driving workload.  
The straight-through vehicles (X) had completed lane changing in the advance warning area (A), and they were able to travel normally within the transition area. Their average vehicle speed (67.17 km/h) was significantly higher than that of other vehicles and the speed limit, with a low speed limit compliance rate and low speed dispersion. As participants were not affected by other factors, the overall driving workload remained stable, and majority of drivers stayed within the standard driving workload range, with only a few higher-risk sections (2.14%).  
The straight-through vehicles in the adjacent lane (Y) on the left side of the closed lane were affected by merging vehicles from the right lane when crossing through the transition area. Speed needs to be reduced according to the situation of merging vehicles to ensure smooth merging; therefore, the average vehicle speed (58.97 km/h) was lower, and the speed dispersion was higher due to different deceleration conditions. The driving workload was higher, and the proportion of higher-risk sections (9.62%) and high-risk sections (0.86%) increased.  
The merging vehicles (Z) were required to slow down in the transition area to complete merging through lane changing, so the vehicle speed was low (55.24 km/h) and the overspeed rate and the speed dispersion were low. When merging, participants had to constantly observe the traffic conditions in the adjacent lanes through their rearview mirrors and look for suitable gaps to change lanes and merge; therefore, the impact of adjacent lanes was significant, and the driving workload was significantly higher than other driving behaviors. The proportion of higher-risk sections (17.82%) and high-risk sections (4.32%) was high, which increased the possibility of traffic conflicts and posed a greater risk to traffic safety in the transition area.
# A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network
## 4 Simulation Results and Discussion
### Environment and Datasets  
The simulation was carried out assuming a square field that is an obstacle free target area. All simulation experiments were performed in MATLAB (R 2013a) and IBM SPSS (Version-23) was used for statistical analysis. Table 1 shows parametric characteristics that were used for simulation and experimentation. Eight datasets were generated by using four mobility models (RPGM, RWP, GM, and MH). The datasets include one hundred nodes. For individual mobility model, two ranges of speeds were considered: 0.5-1.5 m/s and 5-7.5 m/s. The simulation time duration was 1000 s. Please note that the source code of the simulator is not public yet, but we will share it at request for research purposes under a non-disclosure agreement.
# A Novel Hybrid Edge Detection and LBP Code-Based Robust Image Steganography Method
## 3 Proposed Work
### Data Extraction Cover Image Restoration  
Step 1: Let the stego image is \(S1\) and an instance of it by \(S\). First, \(n-\)bits of LSBs are cleared from every pixel of \(S\) by Equation (9).  
\[S(i,j)=S(i,j)-f(S(i,j),2^{n}); \tag{9}\]  
where function \(f\) returns the remainder value when one divides \(S(i,j)\) by \(2^{n}\).  
Step 2: We have applied \(m-\)number of edge detection operators, e.g., canny, sobel, fuzzy, Robert, Prewitt, log, etc., on the cleared image I to detect edge pixels, separately. We have generated the edge image by Equation (10).  
\[eS(i)=\psi(S,\Omega); \tag{10}\]  
where \(\psi\) is one of the \(m\) edge detection operators, i.e., \(\Omega\in\{canny, sobel,log,fuzzy, Robert, Prewitt,etc.\}\) and \(1<=I<=m\) and \(\psi\) returns the edge image \(el\) from \(I\) for a specific edge detector \(\Omega\). We also make all possible combinations of edge images if needed. We then hybridize edge images using the logical OR operator by Equation (11). Each edge image is a binary image. For each pixel, the edge image holds a 0 or 1. A 1-in-edge image means the corresponding pixel of \(I\) is in the detected edge.  
\[ReS=eS(1)|eS(2)|.........|eS(m); \tag{11}\]  
The hybridized edge image helps in measuring the edge pixels in an image. To meet a bit higher embedding capacity, the number of edge pixels has been increased by applying a morphological dilation operation by Equation (12). Emphasis was given on horizontal and vertical edges. Hence, the dilation operator, i.e., mask, is prepared by a \(3\times 3\) matrix.  
\[dReS=F(ReS,mask); \tag{12}\]  
where \(F\) returns the dilated edge images and we then select only edge pixels. Say, the detected edge pixels have formed another image \(P\). That image \(P\) is partitioned into different \(3\times 3\)-sized blocks.  
Step 3: Let L be the 8-bit LBP codes that are obtained from the block using Equations (13) and (14).  
\[g(SK_{c},SK_{p}^{i})=\begin{cases}1&\text{if }SK_{p}^{i}<SK_{c}\\ 0&\text{else}\end{cases} \tag{13}\]  
\[SL=g(SK_{c},SK_{p}^{i}); \tag{14}\]  
Step 4: Now trace matrix TC is XORed with LSB of Stego block SB by Equation (15).  
\[IS=TC\oplus LSB(SB); \tag{15}\]  
Step 5: Again, the trace matrix IS is XORed with trace matrix TC by Equation (16).  
\[HS=IS\oplus TC; \tag{16}\]  
Step 6: Next, the XORed values are shuffled by Equation (17).  
\[H=Shuffle(HS); \tag{17}\]  
Step 7: To extract the secret message, again shuffled bits H XORed with LBP code SL by Equation (18).  
\[M=H\oplus SL; \tag{18}\]  
Step 8: The original edge pixels of the cover image were recovered and a cover image is generated by following the way.  
**if \(SL(i)=1\) then**  
\(C(i)\gets S(i)+TC(i)\)  
**else**  
\(C(i)\gets S(i)-TC(i)\)  
**endif**  
Step 9: Data extraction and cover image restoration are completed.
## 4 Experiment and Results  
### Dataset  
To validate the proposed methodology, this study utilizes the NEU-DET dataset, provided by the research lab at Northeast University, USA. This dataset comprises images depicting various types of manufacturing defects on steel surfaces during the production process [31]. It is categorized into six defect types: inclusion, scratches, pitted surface, patches, crazing, and rolled-in-scale, each represented by 300 grayscale images of 200 \(\times\) 200 pixels, totaling 1800 images. The complex characteristics of these defects, varying in size and appearance, make them challenging to identify and locate accurately through manual inspection. Figure 7 presents sample images of the six defect types included in the dataset. The NEU-DET dataset is accessible at [[https://www.kaggle.com/datasets/kaustubhdikshit/neu-surface-defect-database](https://www.kaggle.com/datasets/kaustubhdikshit/neu-surface-defect-database)]([https://www.kaggle.com/datasets/kaustubhdikshit/neu-surface-defect-database](https://www.kaggle.com/datasets/kaustubhdikshit/neu-surface-defect-database)) (accessed on 5 May 2024).  
Table 1 presents the dataset composition used for defect detection in stage 1. This dataset also includes images without defects, generated by replacing defective areas in defect images with normal background pixels using interpolation methods. To simulate various factors that may occur in real production processes, augmentation techniques such as adjustments in resolution, lighting conditions, and random vertical and horizontal flips were applied. The resulting dataset comprises 1440 images for training and 360 images for testing, with the validation set created by separating 20% of the training set.  
Table 2 shows the dataset composition used for object detection in Stage 2. The dataset, consisting of 300 images for each defect type, was divided into training and test sets at an 8:2 ratio. The validation set was formed by taking 20% of the training set.  
The experiments were conducted on a Windows 11 operating system using the PyTorch ver. 3.10.12 deep learning framework. As demonstrated in Table 3, the hardware setup included an Intel Core i7 CPU, 32 GB RAM, and an NVIDIA 14 GPU.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Class** & **Train Set** & **Validation Set** & **Test Set** & **Total** \\ \hline With Defects & 1152 & 288 & 360 & 1800 \\ Without Defects & 1152 & 288 & 360 & 1800 \\ \hline Total & 2304 & 576 & 720 & 3600 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Defect detection dataset.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Defect Category** & **Train Set** & **Validation Set** & **Test Set** & **Total** \\ \hline Crazing & 192 & 48 & 60 & 300 \\ Inclusion & 192 & 48 & 60 & 300 \\ Patches & 192 & 48 & 60 & 300 \\ Pitted & 192 & 48 & 60 & 300 \\ Roll-in scale & 192 & 48 & 60 & 300 \\ Scratches & 192 & 48 & 60 & 300 \\ Without Defect & 1152 & 288 & 360 & 1800 \\ \hline
**Total** & **2304** & **576** & **720** & **3600** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Object detection dataset.  
Figure 7: NEU-DET dataset.
# Optimization Methods of Compressively Sensed Image Reconstruction Based on Single-Pixel Imaging
## 3 Methods
### Binary Sensing Matrix Optimization
Thus, the iterative gradient projection (GP) based strategy is used to seek the minimum value of the loss function \(F(\hat{\Phi})\). The gradient \(\
abla F(\hat{\Phi})\) of \(F(\hat{\Phi})\) is expressed as:  
\[\
abla F(\hat{\Phi})\equiv\frac{\partial F(\hat{\Phi})}{\partial\hat{\Phi}}= \left(\hat{\Phi}\Psi-\Theta\right)\Psi+4\tau\hat{\Phi}\Psi\!\left(\left(\hat{ \Phi}\Psi\right)^{T}\!\hat{\Phi}\Psi-I\right)\!\Psi^{T}. \tag{16}\]  
The sensing matrix \(\hat{\Phi}\) is updated by continuous iterations, and \(\Phi^{(k)}\) is the intermediate estimated solution of \(\Phi\) during the iterations. That is, from the \(k\)-th iteration to the \(k\)+1-th iteration, \(\Phi^{(k)}\) is updated to \(\Phi^{(k+1)}\). The following equation is defined with a positive scalar parameter \(\alpha^{(k)}\), \(\alpha^{(k)}>0\), as:  
\[w^{(k)}=\left(\Phi^{(k)}-\alpha^{(k)}\
abla F\!\left(\Phi^{(k)}\right)\right)_{ +}. \tag{17}\]  
At each iteration of \(\Phi^{(k)}\), the algorithm searches along the negative gradient direction \(-\
abla F(\Phi^{(k)})\), projecting onto the non-negative orthant and taking a backtracking line search until sufficient decrease is sought out in \(F(\Phi^{(k)})\). Then, the second scalar parameter \(\lambda^{(k)}\) is defined, \(\lambda^{(k)}\in[0,1]\). So \(\Phi^{(k+1)}\) is updated from \(\Phi^{(k)}\) as follows:  
\[\Phi^{(k+1)}=\Phi^{(k)}+\lambda^{(k)}\!\left(w^{(k)}-\Phi^{(k)}\right)\!. \tag{18}\]Using \(\lambda^{(k)}\) and \(\alpha^{(k)}\) reduces the possibility that \(F(\hat{\Phi})\) may increase at some time during iterations and enhances the efficiency to search the global optimal solution of \(\Phi\) in our algorithm.
# Characteristics of Moduli Decay for the Asphalt Mixture under Different Loading Conditions
## 3 Test Result and Analysis
### The Initial Values of Moduli at Different Fatigue Stress Levels  
The moduli of 50 th cycle is widely used as the initial moduli of fatigue test [29]. However, the fatigue lives of the same material vary at different stress levels, so the initial moduli values that were obtained by this method has a large deviation. In this paper, the initial moduli \(E_{0}\) was defined as the average one of the 10 moduli, which were near the cycle ratio \(N/N_{f}=0.01\). Because the specimen of the indirect tensile test is in the state of two-dimensional stress state, the pattern of double moduli decay of the indirect tensile test was considered. The average initial moduli values of four parallel specimens under different stress state and different stress levels are summarized in Table 8:  
From Table 8, it can be observed that the initial moduli value varies in different stress levels. It also varies in the different fatigue tests, which have the different stress states. However, it shows a common characteristic that the initial values of the fatigue moduli increase with the increase of the stress levels. The initial compression moduli are larger than that of the tensile moduli.
## 7 Testing of the Algorithm to Discriminate Switching Events from Faults
### Capacitive Switching  
The performance of the algorithm is evaluated for the event of switching the capacitor bank with a capacity of 20 MVAR. The switching on the capacitor bank event is realized at the fourth cycle, and the switching off the capacitor bank event is realized at the eighth cycle. The respective FI for these events is presented in Figure 11. It can be seen that the value of the FI is less relative to the threshold magnitude of \(5\times 10^{9}\). Hence, the technique introduced in this paper is efficient in the differentiation of the switching (capacitor bank) events from the faults.
# A Secure Protocol against Selfish and Pollution Attacker Misbehavior in Clustered WSNs
## 3 Model Establishment
### Assumptions about the Network and Sensor Nodes  
Our scheme makes the following assumptions.  
* The network has a hierarchical topology. Sensor nodes are grouped in clusters. Each cluster is headed by a cluster head (\(CH\)) selected at the beginning of the protocol by the base station (\(BS\)).
* The clustering is static and determined by a centralized K-means algorithm, which provides optimized choices of cluster heads and their member nodes that favor minimized distances between them.
* Member nodes of each cluster are responsible for detecting external events. They are called source nodes. \(CH\)s are the nodes in charge of forwarding received data to the \(BS\). They are the ones susceptible to being compromised.
* Each sensor node is equipped with a unique matrix of randomly chosen integers of large size (\(l\times L\)) considered as a secret key, where \(l>t\) and \(L>p\). These matrices are pre-loaded before the network deployment and are shared with the \(BS\).
* The \(BS\) cannot be compromised and has essentially unlimited processing power compared to sensor nodes. Moreover, the \(BS\) is aware of the positions of all sensor nodes and their distances to each other in the network.  
Figure 2: An example of linear network coding.  
The total number of \(CH\)s in the network is a minimum of \(p\), chosen once by the user before deploying the network according to its size and scale, which are related to the network domain of application.
* Each source node has several paths to the \(BS\). A node can send data to the \(BS\) through \(p\) different \(CH\)s. For simplicity, we assume that these \(CH\)s are of approximately similar distances to the source node.
* Each source node proceeds to send sensed data to the \(BS\) through the routing protocol after \(t\) sensed data measurements. In other words, each routing round ensures the transmission, for each source node, of a data message composed of \(t\) sensed original data packets.