esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Drivetrain Hybridization on Transmission Lifetime
## 3 Results
### Conventional Drivetrain  
The load spectrum at the transmission input resulting from the applied driving cycle is shown in Figure 8. Here, the transmission input torque is shown versus the transmission input speed. The color indicates the frequency of occurrence of the respective operating point. The torque at the transmission input is to be considered here as a representation of the bearing load, since the gear forces to be supported by the bearings are proportional to the torque. The range of positive torques arises during drive operation. Negative torques occur during coast operation of the ICE. Negative torques beyond the thrust characteristic curve of the ICE occur during downshifts in coasting. In this case, an overtorque is applied by the clutches to synchronize the ICE to the transmission input speed.  
The failure probabilities of the individual rolling bearings of the transmission and their superposition over the travel distance are shown in double-logarithmic form in Figure 9. In [37], the transmission B\({}_{10}\)-lifetime is given as 150,000-250,000 km, depending on the vehicle type. In [12; 38], the lifetime of 300,000 km for the vehicle and transmission is given respectively without specifying the probability of failure. Therefore, the superposed lifetime of the bearings with about 195,000 km in the  
Figure 8: Load spectrum of the transmission input torque for the conventional drivetrain in the WLTC.  
WLTC is considered plausible by the authors. The failure distribution is dominated by bearings 16 and 18. They are used as locating bearings for the two intermediate shafts, and are particularly frequently present in the power flow. In addition, as locating bearings, they are subjected to the combined load of axial and radial bearing forces resulting from the gears. The high scattering of bearing lifetimes of two orders of magnitude according to the authors' assessment can be attributed to the fact that different load assumptions than those caused in the WLTC and the vehicle under consideration were used for the design of the transmission.  
According to the calculation approach presented, the needle roller bearings used are fatigue-resistant, since they only experience significant operating loads in the synchronized case, i.e., without relative motion of the bearing raceways. Loads that occur during synchronization and due to bearing friction do not contribute to damage in this case. In fact, wear in the form of indentations in the raceway under high stationary loads is mentioned in the literature as a frequent cause of failure [38; 39]. If a lifetime model becomes available, the influence of the needle bearings can be included in the approach.
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 3 Results
### Dryland Areas in America
#### 3.2.3 Region Analysis  
We separated the USA into four regions (West, Midwest, Northeast and South). The dryland areas are mainly located in West, Midwest and South (Figure 11). We regionally attributed the vegetation growth to different factors such as CO\({}_{2}\), CC, LU, CV and other factors our methods cannot attribute (Figure 12). For all American dryland areas, we found that CO\({}_{2}\) is the dominant factor accounting for 47.9%. LU accounts for 36.8%. Among the dryland areas, the West shows the most significant positive trend. CO\({}_{2}\) plays the most important role accounting for 61.7%, LU is 30.2%. It is notable that CV contributed negative effects on vegetation growth for 40 decades. For the Midwest, LU contributed the most on greening here, accounting for 44.6%. For the South, only a few areas are included in dryland and the observed change can be ignored.  
Figure 11: Regional separation for analysis of America. American dryland areas mainly locate in West, Midwest and South.  
Figure 10: Map of three dominant factors’ distribution in American dryland areas. The areas controlled by CO\({}_{2}\) are shown in red. The areas controlled by LU are shown in green. The areas controlled by CC are shown in blue.
# Convolutional Two-Stream Network Using Multi-Facial Feature Fusion for Driver Fatigue Detection
###### Abstract  
Road traffic accidents caused by fatigue driving are common causes of human casualties. In this paper, we present a driver fatigue detection algorithm using two-stream network models with multi-facial features. The algorithm consists of four parts: (1) Positioning mouth and eye with multi-task cascaded convolutional neural networks (MTCNNs). (2) Extracting the static features from a partial facial image. (3) Extracting the dynamic features from a partial facial optical flow. (4) Combining both static and dynamic features using a two-stream neural network to make the classification. The main contribution of this paper is the combination of a two-stream network and multi-facial features for driver fatigue detection. Two-stream networks can combine static and dynamic image information, while partial facial images as network inputs can focus on fatigue-related information, which brings better performance. Moreover, we applied gamma correction to enhance image contrast, which can help our method achieve better results, noted by an increased accuracy of 2% in night environments. Finally, an accuracy of 97.06% was achieved on the National Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) dataset.  
Keywords:fatigue detection; multi-task cascaded convolutional networks; optical flow; gamma correction; feature fusion +
Footnote †: journal: Future Internet
# Context-Aware Caching Distribution and UAV Deployment: A Game-Theoretic Approach
###### Abstract  
This paper investigates the problem of the optimal arrangement for both unmanned aerial vehicles' (UAVs) caching contents and service locations in UAV-assisted networks based on the context awareness, which considers the influence between users and environment. In the existing work, users within the coverage of UAVs are considered to be served perfectly, which ignores the communication probability caused by line-of-sight (_LOS_) and non-line-of-sight (_NLOS_) links. However, the links are related to the deployment of UAVs. Moreover, the transmission overhead should be taken into account. To balance the tradeoff between these two factors, we design the ratio of users' probability and transmission overhead as the performance measure mechanism to evaluate the performance of UAV-assisted networks. Then, we formulate the objective for maximizing the performance of UAV-assisted networks as a UAV-assisted caching game. It is proved that the game is an exact potential game with the performance of UAV-assisted networks serving as the potential function. Next, we propose a log-linear caching algorithm (LCA) to achieve the Nash equilibrium (NE). Finally, related simulation results reflect the great performance of the proposed algorithm.  
context-aware; UAV-assisted networks; communication probability; cache content; potential game +
Footnote †: journal: Applied Sciences
# Satellite IoT Edge Intelligent Computing: A Research on Architecture
###### Abstract  
As the number of satellites continues to increase, satellites become an important part of the IoT and 5G/6G communications. How to deal with the data of the satellite Internet of Things is a problem worth considering and paying attention to. Due to the current on-board processing capability and the limitation of the inter-satellite communication rate, the data acquisition from the satellite has a higher delay and the data utilization rate is lower. In order to use the data generated by the satellite IoT more effectively, we propose a satellite IoT edge intelligent computing architecture. In the article, we analyze the current methods of satellite data processing, combined with the development trend of future satellites, and use the characteristics of edge computing and machine learning to describe the satellite IoT edge intelligent computing architecture. Finally, we verify that the architecture can speed up the processing of satellite data. By demonstrating the performance of different neural network models in the satellite edge intelligent computing architecture, we can find that the lightweight of neural networks can promote the development of satellite IoT edge intelligent computing architecture.  
Keywords:IoT; satellite internet of things; edge computing; edge intelligent computing; deep learning +
Footnote †: journal:
## 2 Chaos Oscillators
### Colpitts Chaos Oscillator  
The chaos oscillator under investigation is a nonlinear dynamical system that produces chaotic signals distinguished by their sensitivity to initial conditions and noise-like waveforms. These oscillators are critical in secure communication systems because their chaotic nature is used to ensure PLS. Figure 1 shows the oscillator's circuit diagram, which includes components such as resistors, capacitors, inductors, and a nonlinear element. The system shows chaotic dynamics under certain parameter values [44].  
Figure 1: Colpitts chaos oscillator [45] with the following parameters: \(R_{L}=35\)\(\Omega\), \(L_{1}=10\)\(\mu\)H, \(C_{1}=5.4\) nF, \(C_{2}=5.4\) nF, \(R_{1}=400\)\(\Omega\), \(V_{1}=5\) V, \(V_{2}=5\) V, with a 2N222 transistor. These parameters resulted in a fundamental frequency of 968.59 kHz.  
The oscillator's chaotic behavior can be mathematically characterized by a set of differential equations based on its circuit design. Let \(v_{\text{CE}}\), \(v_{\text{BE}}\), and \(i_{L1}\) represent the voltages across the capacitors \(C_{1}\) and \(C_{2}\) and the current through the inductor \(L_{1}\). The state equations are stated as follows:  
\[\begin{cases}C_{1}\dfrac{\text{d}v_{CE}}{\text{d}t}=i_{L}-i_{C}\\ C_{2}\dfrac{\text{d}v_{BE}}{\text{d}t}=\dfrac{V_{1}+v_{BE}}{R_{1}}-i_{L}-i_{B} \\ L_{1}\dfrac{\text{d}i_{L1}}{\text{d}t}=V_{2}-v_{CE}+v_{BE}-i_{L}\cdot R_{L} \end{cases}, \tag{1}\]  
where \(v_{CE}\) corresponds to the collector-emitter voltage, \(v_{BE}\) the base-emitter voltage, \(V_{1}\) and \(V_{2}\) are the source voltages, \(i_{L}\) is the inductor current, \(i_{C}\) is the collector current, and \(i_{B}\) is the base current.  
The oscillator's chaotic behavior depends on its nonlinear element. A piecewise linear function approximates the nonlinear voltage-controlled resistance the transistor provides in the Colpitts oscillator. This function is described by:  
\[i_{B}=\begin{cases}0,&\text{if }v_{BE}\leq V_{TH}\\ \dfrac{v_{BE}-V_{TH}}{R_{ON}},&\text{if }v_{BE}>V_{TH}\end{cases}, \tag{2}\]  
\[i_{C}=\beta_{F}\cdot i_{B}, \tag{3}\]  
where \(V_{TH}\) denotes the threshold voltage, \(R_{ON}\) represents the small-signal on-resistance of the base-emitter, and \(\beta_{F}\) indicates the transistor's forward current gain.
# Modeling Trust in IoT Systems for Drinking-Water Management
## 3 Methods and Proposed Approach
### Proposed Approach
#### 3.2.2 Formalization of Trust Calculation
First, the trust calculation equation \(C_{ij}=\sum_{k=1}^{m}W_{ijk}\times F_{ijk}\) was crafted with careful consideration given to its ability to integrate multiple factors influencing trust between critical elements within a water-treatment plant. This approach allows for a holistic and comprehensive analysis, taking into account various dimensions of the relationship between these elements. As for the factors \(F_{ijk}\), their selection based on ISO 8000-61 ensures an objective foundation. This standard establishes rigorous criteria for choosing specific characteristicsthat influence trust. Thus, by adhering to this standard, the trust calculation model ensures adequate consideration of essential aspects of the relationship between critical elements. Similarly, the weights \(W_{ijk}\) associated with these factors are calculated using the Analytic Hierarchy Process (AHP) method. AHP was preferred for its ability to decompose a complex problem into a hierarchy of criteria and sub-criteria, therefore offering a systematic and structured approach to quantitatively evaluate and compare these elements. By employing AHP, we were able to assign weights to different factors rationally, taking into account their relative importance within the specific context of water-treatment management. Furthermore, the normalization of weights ensures fair and consistent weighting, thus enhancing the objectivity and reliability of the model. It is crucial to note that this model was specifically tailored to the unique conditions and requirements of water-treatment plants, such as those in Sherbrooke. By incorporating the special characteristics of each critical element and relationship, this model provides an accurate and personalized representation of trust within this critical context. The choices of functions, methods, and particularly the integration of AHP, made for trust calculation, were carefully considered to ensure a robust, objective, and tailored approach to the specific needs of water-treatment management.
## 2 Bernoulli-Euler Beam with Variable Cross-Section  
### Differential Equation of Motion  
In the case of small deformation, for the Euler beam with equal cross-section, the differential equation of transverse motion is as follows:  
\[EI\frac{\partial^{4}y(x,t)}{\partial x^{4}}+\rho A\frac{\partial^{2}y(x,t)}{ \partial t^{2}}=0 \tag{1}\]  
where \(EI\) is the bending stiffness of the beam, \(\rho A\) is the linear density of the beam, and \(y(x,t)\) is the transverse deflection function of the beam.  
The general solution of this equation is  
\[T(t)=C_{1}cos\omega t+C_{2}sin\omega t \tag{2}\]  
\[Y(x)=Asin\beta x+Bcos\beta x+Csinh\beta x+Dcosh\beta x \tag{3}\]  
where \(\omega\) is the natural frequency of the beam, \(Y(x)\) is the mode shape function of transverse free vibration of the beam, and \(\beta^{4}=\omega^{2}(\rho A)/(EI)\).  
When the beam with variable cross-section is divided into different segments according to the cross-sectional shape, each segment can be considered as a free vibrating beam with equal cross-section, so it follows the motion relation in Equation (1). Assuming that the variable-cross-section beam with a total length of L is divided into \(n\) segments, as shown in Figure 1, the mode shape function of the \(i\)-th segment beam is  
\[Y_{i}(x)=A_{i}sin\beta_{i}X_{i}+B_{i}cos\beta_{i}X_{i}+C_{i}sinh\beta_{i}X_{i}+ D_{i}cosh\beta_{i}X_{i} \tag{4}\]  
where \(X_{i}=x-x_{i-1}\), \(x_{i}=\sum\limits_{k=0}^{i}l_{k}\), \(x_{i-1}\leq x\leq x_{i}\), \(\beta_{i}^{4}=\omega^{2}(\rho A)_{i}/(EI)_{i}\), and \(\omega\) is the natural frequency of the beam with variable cross-section. If the structural and the division method of the beam with variable cross-section are determined, the unknowns in the mode shape function of the beam are the natural frequency \(\omega\) and the mode shape coefficient.  
The displacement, angle, moment, and shear force of two adjacent segment beams are continuous at the joint [22], so the boundary relation between the \(i\)-th segment beam and \(i\)+1-th segment beam can be obtained as  
\[\left\{\begin{array}{c}Y_{i+1}(x_{i})=Y_{i}(x_{i})\\ Y_{i+1}^{\prime}(x_{i})=Y_{i}^{\prime}(x_{i})\\ (EI)_{i+1}Y_{i+1}^{\prime\prime}(x_{i})=(EI)_{i}Y_{i}^{\prime\prime}(x_{i})\\ \left((EI)_{i+1}Y_{i+1}^{\prime\prime}(x_{i})\right)^{\prime}=\left((EI)_{i}Y_ {i}^{\prime\prime}(x_{i})\right)^{\prime}\end{array}\right. \tag{5}\]  
Simplifying Equation (5), the transfer matrix of the adjacent segment beams can be established as follows:  
\[K_{i+1}=Z_{i}K_{i} \tag{6}\]  
where \(K_{i}=\begin{bmatrix}A_{i}&B_{i}&C_{i}&D_{i}\end{bmatrix}^{T}\) and \(Z_{i}\) is as follows:  
\[Z_{i}=\begin{bmatrix}c_{1,i}cos\beta_{i}i_{i}&-c_{1,i}sin\beta_{i}l_{i}&c_{  
Thus, the boundary relationship between the first segment beam and the \(N\)-th segment beam can be obtained:  
\[K_{N}=ZK_{1} \tag{7}\]  
where \(Z=Z_{N-1}Z_{N-2}\ldots Z_{1}\).
## 2 Structural Assessment. Limit States Calculation Methods, Design Situation, and Loads
### Loads
#### 2.4.2 Wind Action  
Agreeing with the considerations of [PERSON] and [PERSON] [19], EC1 and CTE-DB-SE provide remarkably similar formulation approaches to determine wind action over structural elements. EC1 states that the wind pressure acting on an external surface \(\left(w_{E}\right)\) can be obtained from the Equation (62), where \(q_{b}\) is the basic velocity pressure, \(c_{e}(z_{e})\) is the exposure factor at the reference height and \(c_{pe}\) is the pressure coefficient, whose value is based on the shape and geometry of the structure. Likewise, CTE-DB-SE calculates the wind pressure on an external surface \(\left(q_{e}\right)\) by Equation (64) using very similar factors as EC1, namely: the basic velocity pressure \(\left(q_{b}\right)\), the exposure factor \(\left(c_{e}\right)\) and the pressure coefficient \(\left(c_{p}\right)\). \[w_{E}=q_{b}\cdot c_{e}(z_{e})\cdot c_{pe} \tag{62}\]  
\[q_{e}=q_{b}\cdot c_{e}\cdot c_{p} \tag{63}\]  
According to the first factor of Equations (62) and (63), the basic velocity pressure \(\left(q_{b}\right)\), is obtained by Equation (64), where \(v_{b}\) is the basic wind velocity and \(c_{prob}\) is the probability factor, given by Equations (65) and (66), respectively; and \(\rho\) is the air density (recommended value 1.25 kg/m\({}^{3}\)). Additionally, \(c_{dir}\) is the directional factor, \(c_{season}\) is the season factor (set equal to 1 in both cases following recommendations) and \(v_{b,0}\) is the fundamental value of the basic wind velocity, whose value is specified in each National Annex. Additionally, \(K\) is the shape parameter depending on the coefficient of variation of the extreme-value distribution, \(n\) is a constant (their recommended values are 0.2 and 0.5, respectively) and \(p\) is the annual probability of wind velocity exceeding the fundamental value of basic wind velocity (expressed as the inverse of return period). \[q_{b}=\frac{1}{2}\cdot\rho\cdot\left(v_{b}\cdot c_{prob}\right)^{2} \tag{64}\]  
\[v_{b}=c_{dir}\cdot c_{season}\cdot v_{b,0} \tag{65}\]\[c_{prob}=\left(\frac{1-K\cdot\ln(-\ln(1-p))}{1-K\cdot\ln(-\ln(0.98))}\right)^{n} \tag{66}\]  
The exposure factor at the reference height \((c_{e}(z_{e}))\) is obtained according to Equation (67). It should be noted that the numerator is known as the peak velocity pressure at the reference height \(\left(q_{p}(z_{e})\right)\) whereas the denominator is the basic velocity pressure introduced above. The terms \(I_{\text{\tiny{P}}}(z_{e})\) and \(v_{m}(z_{e})\) are the turbulence intensity and the mean wind velocity at the reference height \((z_{e})\) respectively, determined by Equation (68a,b) and (69) as appropriate. Additionally, \(k_{1}\) is the turbulence factor and \(c_{0}(z_{e})\) is the orography factor (their recommended values are (1); and \(z_{o}\) is the roughness length. Finally, a new parameter is introduced in this last expression, called the roughness factor \((c_{r}(z_{e}))\).
# Boundary-Aware Transformer for Optic Cup and Disc Segmentation in Fundus Images
## 4 Experiment Implementation
### Evaluation Metrics  
The segmentation performance of the OD and OC on fundus images was quantitatively evaluated using the Intersection over Union (IoU) and Dice Coefficient, as shown in Figure 6.  
The IoU, also referred to as the Jaccard Index, evaluates the similarity between the predicted segmentation mask and the ground truth by calculating the ratio of their intersection area to their union area.  
\[\text{IoU}=\frac{\text{Area of Overlap}}{\text{Area of Union}}=\frac{|\hat{Y} \cap Y|}{|\hat{Y}\cup Y|} \tag{10}\]  
where \(\hat{Y}\) is the predicted segmentation mask, and \(Y\) is the ground truth.  
The Dice Coefficient score is conceptually similar to the IoU but places greater emphasis on the degree of overlap between two regions. It is defined as  
\[\text{Dice}=\frac{2\times\text{Area of Overlap}}{\text{Total Area}}=\frac{2|\hat{Y} \cap Y|}{|\hat{Y}|+|Y|} \tag{11}\]  
where \(\hat{Y}\) and \(Y\) represent the predicted and ground truth segmentation masks, respectively.  
The Dice Coefficient score ranges from 0 to 1, with values closer to 1 indicating a high level of agreement between the prediction and the ground truth. Compared to the IoU, the Dice Coefficient score is more sensitive to small objects, making it particularly effective for evaluating the segmentation of smaller anatomical structures such as the OC.  
Figure 6: Comparison of IoU (Intersection over Union) and Dice Coefficient scores across different models.
# A Key Management Scheme for Mobile Wireless Sensor Networks
## 4 Evaluation and Comparison
### Communication Efficiency  
PPWK and PGK do not require any key establishment, since each node knows the key matched to all its links.  
Table 2 shows the communication efficiency for the state-of-the-art schemes and PCAT. The new parameter \(l_{enc}\) represents the minimum length of an encrypted message with public encryption. In 2C, 1-5C, RSDTMK, LEAP+, FLEAP, EG, and SKKE, the key establishment involves the transmission of two one-hop messages with various sizes. In PCAT, four messages are required: two messages  
Figure 4: Size upper bound for the network, according to the size of the memory used to store the secret material.  
Figure 5: Ratio between the number of nodes directly connected and the total quantity of nodes in the network at which PCAT and FPWK require the same memory area.  
contain the ID of the sender and its public key, the other two messages contain the ID of the sender and an encrypted random number.  
Considering \(l_{k}=16\) bytes, \(l_{ID}=1\) bytes, \(l_{sID}=2\) bytes, \(r=10\), \(l_{kID}=1\) byte (2 in RSDTMK), \(l_{sign}=64\) bytes, \(l_{enc}=64\) bytes, and \(l_{pu}=l_{pr}=32\) bytes, the key establishment in LEAP+ and FLEAP requires 18 bytes, in SKKE 67 bytes, in RSDTMK 40 bytes, in EG 29 bytes, in 2C 38 bytes, in 1-5C 3 bytes, in SSL 260 bytes, and in PCAT 196 bytes. Comparing the communication efficiency, PCAT has greater requirements than the schemes based on symmetric encryption, but not as many as SSL.
## 3 Proposed Scheme
### Correctness Analysis
#### 3.3.2 Temporal Aggregation  
\[\begin{array}{ll}AT(i,T)&=\sum\limits_{j\in ind_{i}[s]}LT_{(j,d)}^{i}\bmod M (1\leq s\leq k)\\ &=\sum\limits_{j\in ind_{i}[s]}\sum\limits_{d=1}^{T}E_{(j,d)}^{i}\bmod M\\ &=\sum\limits_{d=1}^{T}\sum\limits_{j\in ind_{i}[s]}(x_{(j,d)}^{i}+r_{(j,d)}^{ j})\bmod M\\ &=\sum\limits_{d=1}^{T}x_{d}^{j}\bmod M\end{array} \tag{12}\]  
Equation (12) shows that the temporal aggregation for one user node equals to the sum of local temporal aggregation from its pairwise nodes, i.e., the sum of its individual data in a series of timestamps \(T\). It proves further the correctness of our temporal aggregation.