esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Intelligent Prediction of Link States  
To improve the reliability of routing decisions, this paper proposes a link intelligent prediction scheme (CNN-EAA) based on the combination of a Convolutional Neural Network and attention mechanism for judging the link connectivity status to assist subsequent routing decisions. The algorithm is useful for opportunistic networks where nodes have high mobility to extract valid information about the nodes and focus on learning the connectivity status of the links. Applying this link prediction algorithm before routing decisions can effectively improve the communication efficiency and communication period of opportunistic networks.
###### Abstract  
Fifth-generation (5G) mobile systems are a necessary step toward successfully achieving further increases in data rates. As the use of higher-order quadrature amplitude modulation (QAM) is expected to increase data rates within a limited bandwidth, we propose a method for orthogonal frequency division multiplexing (OFDM)-based 1024- and 4096-QAM transmission with soft-decision Viterbi decoding for use in 5G mobile systems. Through evaluation of the transmission performance of the proposed method over multipath fading channels using link-level simulations, we determine the bit error rate (BER) performance of OFDM-based 1024- and 4096-QAM as a function of coding rate under two multipath fading channel models: extended pedestrian A (EPA) and extended vehicular A (EVA). We also demonstrate the influence of phase error on OFDM-based 1024- and 4096-QAM and clarify the relationship between phase error and the signal-to-noise ratio (SNR) penalty required to achieve a BER of \(1\times 10^{-2}\). This work provides an effective solution for introducing higher-order modulation schemes in 5G and beyond.  
mobile communication; higher-order modulation; 1024- and 4096-QAM; OFDM; multipath fading channels; SNR penalty; link-level simulations +
Footnote †: journal: Sensors. Actuator Netw., **2019**, \(8\), 19; doi:10.3390/jsan8020019
## 5 Performance Evaluation  
We evaluate the performance of the proposed aggregation scheme to assess the overheads. The performance metrics used in our empirical evaluation are defined as follows:  
1. Computation overhead: node's runtime of the proposed scheme in terms of spatial and temporal aggregation.
2. Communication overhead: the size of a message transmitted between the nodes and GW (number of bits).
3. Security parameter \(k\): we analyze the impact of the different value of \(k\) on the two overheads.  
We compare these results against several existing works [23; 24] using performance metrics based on Friendly ARM [25] and the library in [17]. By comparison with them, we intend to illustrate our computing and communication advantages in terms of the combination of PRF and modular addition methods adopted, respectively, in the scheme [23] and [24]. Each experiment consists of 50 independent trials and the averaged results of these trials are reported. The computation time required for these tasks is listed in Table 2.  
We fix the number of users at 1 million; the number of \(C\) is 10; the number of GW ranges from 1 to 20. Let n denotes a possible number of users in a group, and it ranges from 1 to 5000. We present the impact of a different number of users in the GW and a different value of \(k\) (ranging from 1 to 100) on the performance. We also assume, for simplicity, that all SMs can be functioning normally.
## 4 Mechanical Test Results and Discussion
### Tensile Behavior  
#### 4.2.1 Tensile Stress and Strain Curve  
In Figure 10, the tensile stress versus strain curves for specimens cured in a 40 \({}^{\circ}\)C water tank for 7 d and 28 d are presented. It was shown that some of the 7 d cured specimens have higher post-cracking stiffness than that of 28 d cured specimens. For the 7 d cured specimens, the tensile stress drastically increased up to 1.6% of the strain. Then, the post-cracking stiffness decreased until the strain hardening behavior stopped. On the other hand, in cases of 28 d cured specimens, the post-cracking stiffness was maintained almost linearly up to the peak point. Moreover, it was noted that the tensile stress-strain curves consistently fluctuated, exhibiting rapid stress drops in the strain-hardening section. In general, the 7 d cured specimens generally had a lower number of drops in tensile stress in the strain-hardening section as compared to those of 28 d cured specimens.  
The types of mineral additives also largely influenced the post-cracking behavior, as shown in Figure 10. Specim A7, constituting only OPC as a binder, exhibited a drastic increase and few drops in tensile stress in the strain-hardening section. The silica flour and GGBS increased the initial post-cracking stiffness of specimens ranging from B7 to E7 without increasing the number of drops in the tensile stress. Specimens F7 and G7 uniquely demonstrated linear post-cracking stiffness without a drastic increase at an early stage as compared to other 7 d cured specimens. CKD also reduced the initial post-cracking stiffness in specimens H7 and I7 as compared to that of specimen A7. However, the post-cracking stiffness was still higher than those of specimens F7 and G7, using SF. Nevertheless, CKD enabled the 7 d cured specimens to exhibit excellent strain hardening performance similar to that of the 28 d cured specimens. For all specimens, the post-cracking stiffness decreased as the curing time increased from 7 d to 28 d, and the 28 d cured specimens demonstrated a higher number of drops in tensile stress and maintained strain hardening behaviors over 6%.  
Figure 9: Compressive strength test result.
## 5 Simulation and Evaluation Results
### Privacy Protection Effectiveness Evaluation  
In this section, we verify the effectiveness of the privacy protection scheme proposed in this paper. Specifically, we first design a privacy-preserving evaluation metric and then conduct comprehensive experiments to evaluate the privacy-preserving performance.
# Efficient Dissemination of Safety Messages in Vehicle Ad Hoc Network Environments
## 3 The Proposed Safety Message Dissemination Scheme
### Cluster Management
#### 3.2.1 Cluster Subscription  
The purpose of clustering is to differentiate the roles of each vehicle, reduce duplicate messages, and improve the reception rate of safety messages. However, in urban vehicle environments with high mobility and varying travel directions, clusters frequently need to be reconfigured. If the urban vehicle environment is not taken into consideration, high cluster management costs are incurred. The purpose of the proposed cluster subscription algorithm is to form clusters with vehicles that have similar locations, speeds, and directions, allowing the cluster to last longer.  
Algorithm 1 shows the cluster subscription algorithm. Vehicles periodically broadcast basic safety messages (BSMs) to share their basic state information with nearby vehicles. The CH uses BSMs to send CHAs to unsubscribed vehicles (IN). Vehicles that receive the CHA send a cluster invitation confirmation message to the CH. The CH compares the cluster's direction, location, and speed with the IN, and if the score is above a certain threshold, it sends a subscription completion message, and then the other vehicle is subscribed to the cluster. 't' indicates the wait time for a cluster invitation message. f = 1 indicates the cluster search state. Each vehicle ends the cluster search state (f = 0) once the cluster subscription is completed within the time 't'. When 't' reaches 0, each vehicle declares itself as a CH and broadcasts a CHA to its surroundings.  
Figure 2: Cluster management scheme process.
# Building Survivable Software Systems by Automatically Adapting to Sensor Changes
## 2 Settings and Notations
### Notations  
Suppose we are given \(K\) individual sensors, among which \(K^{\prime}\) sensors are reference sensors. We assume that  
* All sensors generate sensor values at fixed time intervals, and sensor values are temporally aligned.
* Sensor values start at time 1. At time \(S+1\), \(K-K^{\prime}\) sensors are replaced by \(P\geq 0\) new sensors (\(P=0\) corresponds to sensor failure). We have sensor values until time \(S+T\).
* There is only a single change point, i.e., time \(S+1\), and it is already given.  
Let \(\mathbf{x}_{1},\mathbf{x}_{2},\cdots,\mathbf{x}_{S}\) be sensor values before the change point, where \(\mathbf{x}_{s}\in\mathcal{R}^{K}\) represents sensor values at time \(s\in\{1,2,\cdots,S\}\), and \(\mathbf{x}_{s,k}\) represents the corresponding sensor value from sensor \(k\in\{1,2,\cdots,K\}\). Additionally, let the replaced sensors be sensors \(K^{\prime}+1,K^{\prime}+2,\cdots,K\). Let \(\mathbf{z}_{1},\mathbf{z}_{2},\cdots,\mathbf{z}_{T}\) denote sensor values after the change point, where \(\mathbf{z}_{t}\in\mathcal{R}^{K^{\prime}+P}\) represents sensor values at time \(S+t\), for \(t\in\{1,2,\cdots,T\}\). Note that we use \(s\) to index \(\mathbf{x}\) and \(t\) to index \(\mathbf{z}\). Based on this setting, \(\{\mathbf{x}_{s,k}\}\) and \(\{\mathbf{z}_{t,k}\}\) for \(k\in\{1,2,\cdots,K^{\prime}\}\) represent sensor values of reference sensors, and \(\{\mathbf{z}_{t,k}\}\), for \(k\in\{K^{\prime}+1,K^{\prime}+2,\cdots,K^{\prime}+P\}\) represent sensor values of the \(P\) new sensors. Figure 2 illustrates the above notations.  
In the following, we often refer to sensor values before the change point as the source domain, and sensor values after the change point as the target domain. Similar notions are used in the domain adaptation and transfer learning communities [15].
# Technology Behavior Model--Beyond Your Sight with Extended Reality in Surgery
## 2 Literature Review  
### Study Context  
Smart glasses are hands-free interactive computing devices that include real-time remote command and monitoring. Wearability and hands-free are the main advantages of SG. XRSG can be used in different areas of the healthcare industry [20]. There are a few types of XRSG available in the market. The commonly used ones are Google Glass, Eyewear, and PicoLinker, which are mainly used for live surgical video communication [21; 22; 23]. PicoLinker has been used commercially in the Japanese industry for two years [23]. Surgeons started using SG for surgical observation, such as vision correction, skin surgery, and plastic surgery. However, Google Glass is not widely used because it is expensive [21]. Therefore, most medical professionals do not use XRSG in general surgery.  
XRSG are devices that can support medical experts. During surgery, the surgeon needs to view various visualization devices to overlook the patient's signs [22]. This information is displayed on a display. In research and exploration, it was found that medical experts can detect information about the patient during surgery with SGs without the need to consult the case book or the pathology records at the computer terminal. Realistic images are obtained by superimposing 3D anatomical composite images inside the lens [24]. These navigational images can be displayed to the surgeon through the XRSG display. The displayed images include vital signs, computer navigation, and conventional images (X-ray images, computed tomography, color ultrasound, MRI, and other instrumental images.) The PicoLinker allows the surgeon to focus on the surgical task without leaving the scene [23]. In some literature, the authors report that the use of the extended reality smart eye can improve accuracy, reduce radiation, and shorten surgery time. Minimally invasive surgery is not new to people, but with the help of visualization and fluoroscopic navigation, the extended reality smart eye can monitor surgical data projected into the surgeon's field of view [25].  
Based on the studies in the relevant literature, the future technological route of XRSG in clinical surgery has been prospected. As shown in Table 1, future tracking technologies and image processing will be more intelligent, and displays and sensors will be closely related to the perception of human organs [3; 26; 27]. Realization of highly intelligent technologies offer more freedom of operation.
# Vehicle Control Strategy Evaluation Based on the Driving Stability Region
## 2 The Vehicle Dynamics Model with the Control Strategy
### The Vehicle Model with 4 WS  
In this section, the classical feedback control four-wheel steering system control method [27] is selected. The model expression of the rear wheel angle of the vehicle system is:  
\[\delta_{r}=-\omega(L/v_{x}-K_{D}) \tag{9}\]  
where \(K_{D}\) is a system parameter, which is determined by the vehicle mass, wheelbase, and cornering stiffness of the rear wheel:  
\[K_{D}=\sqrt{\frac{2 ml_{f}}{k_{r}}} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Longitudinal Slip Coefficient** & **Lateral Slip Coefficient** & **Lateral Slip Coefficient** \\ \hline \(r_{x,1}\) & \(r_{x,2}\) & \(r_{y,1}\) & \(r_{y,2}\) \\
35 & 40 & 40 & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The tire combined slip correction coefficient.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Lateral/Longitudinal Force** & **Front/Rear Wheels** & **B** & **C** & **D** & **E** \\ \hline \multirow{2}{*}{lateral force} & front wheel & 11.275 & 1.56 & 2574.7 & \(-\)1.999 \\  & rear wheel & 18.631 & 1.56 & 1749.7 & \(-\)1.7908 \\ \hline \multirow{2}{*}{longitudinal force} & front wheel & 11.275 & 1.56 & 2574.8 & 0.4109 \\  & rear wheel & 18.631 & 1.56 & 1749.6 & 0.4108 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tire force parameters under the condition of low adhesion.  
Similarly, for the single-track vehicle model, under the control of 4 WS, the rear wheel steering angle is applied to the 5 DOF vehicle system model. The new 5 DOF vehicle system model, after introducing 4 WS, is as follows:  
\[\left\{\begin{array}{l}\dot{v_{y}}=-v_{x}\omega+\frac{\bar{F}_{lf}\text{sin} \,\delta_{f}+\bar{F}_{x}\text{cos}\,\delta_{f}+\bar{F}_{ir}\text{cos}\,\delta_{ f}+\bar{F}_{ir}\text{sin}\,\delta_{r}-\text{sgn}\left(v_{y}\right)\text{-} \text{-}\text{c}_{\text{air},y}\,A_{\text{L},y}\,\frac{\theta}{2}v_{y}^{2}}{m} \\ \dot{v}_{x}=v_{y}\omega+\frac{\bar{F}_{lf}\text{cos}\,\delta_{f}-\bar{F}_{rf} \text{sin}\,\delta_{f}+\bar{F}_{ir}\text{cos}\,\delta_{r}-\bar{F}_{ir}\text{sin} \,\delta_{f}-\text{sgn}\left(v_{x}\right)\text{-}\text{c}_{\text{air},x}A_{ \text{L},y}\,\frac{\theta}{2}v_{x}^{2}}{m}\\ \dot{\omega}=\frac{\left(\bar{F}_{lf}\text{sin}\,\delta_{f}+\bar{F}_{if}\text{ cos}\,\delta_{f}\right)\bar{I}_{lf}-\left(\bar{F}_{ir}\text{cos}\,\delta_{f}+ \bar{F}_{ir}\text{sin}\,\delta_{r}\right)\bar{I}_{r}}{I_{z}}\\ \dot{\omega}_{f}=\frac{T_{df}-\text{sgn}\left(\omega_{f}\right)\bar{T}_{bf}- \bar{R}_{r}\text{-}\bar{F}_{lr}}{I_{\omega}}\\ \dot{\omega}_{r}=\frac{T_{dr}-\text{sgn}\left(\omega_{r}\right)\bar{T}_{bf}- \bar{R}_{r}\text{-}\bar{R}_{r}\text{-}\bar{F}_{lr}}{I_{\omega}}\end{array}\right. \tag{11}\]  
The tire model still uses the Magic Formula tire model with the same parameters as described in Section 2.1.
## 4 Performance Evaluation
### Average Data Communication Overhead  
In order to evaluate the average data communication overhead, we vary the velocity of the sink from 5 m/s to 30 m/s in increments of 5 m/s. Each source node generates a data packet 10 times. In other words, Figure 11 shows that after finishing location service, data packets are sent by multi-hop transmission. We can see that average data communication overhead is the highest with ALS. TTDD consumes less overhead than ALS. This is because TTDD can find a new path by updating to a new grid node. However, this is not the shortest path. Therefore SDLS performance is the best.  
Figure 11: Average data communication overhead.  
Figure 10: Network lifetime.
# Fractal Dimension and Retinal Pathology: A Meta-Analysis
## 1 Introduction  
### Retinal Vasculature and Fractal Dimension  
The retina is of crucial importance to eye care professionals as retinal diseases are the leading cause of blindness worldwide. The retina is a thin, light-sensitive neural layer and is supplied by a sophisticated microvascular network that delivers nutrients and carries away waste. As part of the human circulatory system, the network's development tends to seek configurations which minimize operational energy expenditure, reflected by [PERSON]'s Law of Minimal Work which relates the radii of parent and daughter vessels, giving rise to the network's branching pattern [1]. Often diseases will have a vascular component that can manifest as abnormalities in this network and thus the network can be observed to acquire insight into the presence(or absence) of disease [2, 3, 4, 5]. With advancements in non-invasive ocular imaging techniques such as optical coherence tomography angiography (OCTA) permitting the segmentation of the vasculature into well-defined layers [6], the retinal vasculature has become more accessible than ever for researchers. Hence, increasing attention has been paid to analyzing its quantitative characteristics as a potential diagnostic tool.  
The retinal blood vessels form a complex branching pattern that has been shown to be fractal [5]. Therefore, a natural parameter for describing the retinal vasculature is the fractal dimension, first described by [7] and then introduced into ophthalmology by [4]. Thefractal dimension is a real number that describes how an object's detail changes at different magnifications. It can be thought of as an extension of the familiar Euclidean dimensions to allow for intermediate states. The fractal dimension of the retinal vascular tree lies between 1 and 2 [5], indicating that its branching pattern fills space more thoroughly than a line, but less than a plane. Thus, the retinal fractal dimension provides a measure of the tree's global branching complexity, which can be altered by the rarefaction or proliferation of blood vessels in a disease scenario. In healthy human subjects, the retinal FD is around 1.7, which is similar to that of a 2D diffusion-limited aggregation process [4; 5]. It has been postulated that this is because the retinal vasculature grows through diffusion of angiogenic factors in the retinal plane [8].
###### Abstract  
The transmission accuracy and meshing performance of the gearbox is determined by the internal helical gears pair. Thermal deformation of internal helical gears pair is derived from sliding friction between the contacting teeth surface, resulting in shock, vibration, and misalignments. The purpose of this paper is to compare the influence of a modified gear and an unmodified gear on the temperature field and transmission characteristics of a planetary gear system under the same working conditions. This study presents an innovative temperature field model for gear pairs utilizing Surf152 elements, integrating Hertzian contact theory, tribological principles, and finite element analysis. For the first time, we quantitatively demonstrate the enhancement of thermo-mechanical performance through topological modification in helical gears. Under light-load conditions (200 rpm), the modified gear configuration exhibits a 6.38% reduction in tooth surface temperature and a 46.5% decrease in thermal deformation compared to conventional designs. Experimental validation confirms these improvements, showing an average 62.35% reduction in transmission error. These findings establish a novel methodology for high-precision gear design while providing critical theoretical foundations for planetary gear systems, ultimately leading to significant improvements in both transmission accuracy and operational lifespan.  
internal mesh helical planetary wheel system; sliding friction; topological modification; temperature field; thermal deformation; transmission error
## Materials and Methods
### Orientation Control
#### 2.6.2 Fuzzy Logic Controller  
An FLC is applied for the position control of the DC motor of each axis. The controller takes the error signal (\(e\)) of the angular position to produce a control signal (\(u\)). This control technique emulates the experience of a human operator and has the advantage that it can deal with nonlinear systems and it is not necessary to know the mathematical model of the system. The FLC is graphically presented in Figure 12. A fuzzification block converts the numerical values of the controller inputs into linguistic variables (fuzzy sets). Later, an inference engine determines the value of the output fuzzy sets by using an \"if-then\" rule base. Finally, a defuzzification block combines the values of the scaled output fuzzy sets to get a crisp output value [22].  
Figure 11: Heliostat angles.  
The block diagram of the proposed FLC is shown in Figure 13. It is a two-input (error \(e\) and the change of error \(de\)) and one-output (control signal \(u\)) controller. Furthermore, the FLC uses a nine rules rule base and the center of sums (CoS) defuzzification method, which is faster than many defuzzification methods due to its computational simplicity [23]. The rule base is shown in Table 4, whereas the CoS defuzzification method is given by Equation (6).  
\[u=\frac{\sum\limits_{i=1}^{n}\left[\mu(\overline{x}_{i})A_{i}\right]}{\sum \limits_{i=1}^{n}\left[A_{i}\right]} \tag{6}\]  
where \(u\) represents the output value, \(n\) represents the number of the output fuzzy sets, \(\overline{x}_{i}\) represents the centroid of the fuzzy set, and \(A\) represents the area of the scaled output fuzzy set \(i\).  
Figure 12: Fuzzy logic controller.  
Figure 13: Block diagram of the FLC.  
Additionally, there is a conversion block in the inputs and output signal due to the difference between the signal values and the fuzzy sets values. The processing signals are given by:  
\[e^{*}=\frac{e}{\pi} \tag{7}\]  
\[de^{*}=\frac{(de)(T_{s})}{\pi} \tag{8}\]  
\[u^{*}=uV_{\max} \tag{9}\]  
where \(e^{*}\), \(de^{*}\), and \(u^{*}\) represent the scaled values of the inputs and the output, \(T_{s}\) represents the sample time of the controller, and \(V_{\max}\) represents the maximum DC motor supply voltage signal.  
The fuzzy sets and the output surface of the FLC are shown in Figures 14 and 15, respectively. The fuzzy sets in each input signal and the output signal are denoted by N for the negative values, Z for the middle values and P for the positive values. The values of the membership functions were selected in order to the control signal of the FLC can modify the position of the DC motor due to the smallest change in the error signal.