esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Study on the Load-Bearing Performance of RC Beams Subjected to ASR Expansion
## 4 Load-Carrying Capacity of RC Beams with ASR Deterioration
### Challenges and Future Prospects  
The model proposed in this study provided some insight into the cracking behavior associated with ASR expansion and the relationship between ASR expansion and the flexural fracture behavior of RC beams. It is expected that the results obtained will contribute to the quantitative evaluation of the remaining load-bearing capacity of actual structural members and structures that have been degraded by ASR. However, in order to obtain a more accurate assessment, several issues must be resolved in the future. For example, problems with the constitutive law and the modeling of the steel bars meant that the effect of expansion cracking was not properly reflected in the mechanical properties, increasing the maximum load in cases where expansion deterioration occurred.  
One of the possible methods to reconsider the constitutive law is to change the currently applied linear Drucker-Prager yielding condition and apply a nonlinear constitutive law that can reproduce plastic behavior in more detail. However, the RC beam model proposed in this analysis has many elements, and it is difficult to directly apply a nonlinear constitutive law from the viewpoint of convergence. Therefore, generating a mesh partition with an improved element aspect ratio and reducing the number of elements is necessary to create a model that can more straightforwardly depict fracture.  
The future prospect of this study is to propose a model to reproduce the effects of combined deterioration (steel bar corrosion) associated with ASR deterioration, that is, to apply a model of steel corrosion to the model proposed in this study in order to understand the effects of combined deterioration on structural performance quantitatively. Specifically, the simplest method is to apply the expansion with moisture as a parameter based on the solid-type rebar model proposed in this study. In addition, we believe that a more precise ASR degradation can be reproduced by applying an expansion model with alkali as a parameter instead of the proposed single-dependent formula for aggregate expansion that is currently applied.
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
## 4 TSN Standards for Automotive Communications
### Security  
The TSN family of standards does not include a standard dealing with confidentiality, but there is a close cooperation between the IEEE 802.1 TSN Task Group and the IEEE 802.1 Security Task Group to investigate how to adopt the existing IEEE security mechanisms in TSN networks. Currently, the P802.1 AEdk project, an amendment to the IEEE Std 802.1 AE17--2018 standard, is running. The standard specifies a MAC Privacy protection encapsulating protocol and its use in conjunction with the MAC Security protocol (MACsec)to hide the source and destination MAC addresses of user data frames and to reduce any correlation between observable frame sizes and transmission timing. The MACSec protocol provides link-to-link encryption and protection to the data exchanged by ECUs within the car and adds Security TAG, Integrity Check Value, Packet number fields, and encryption. In MACSec, all communications, i.e., unicast, multicast, and broadcast messages of all protocols, are protected. MACSec makes it possible to maintain communications confidentiality and to take action against security breaches [42]. Moreover, used in combination with IEEE 802.1 Qci, i.e., per-frame filtering and policing, MACsec limits Denial-of-Service (DoS) attacks. Recent presentations at the 2022 IEEE Standards Association Ethernet & IP @ Automotive Technology Day [43; 44] evince a strong interest in leveraging MACsec and improving its startup performance to use it as the next state-of-the-art automotive network security solution. However, MACsec for automotive usage needs hardware support in controllers and switches, thus increasing the cost of the components.
## 3 Results and Discussion
### Relationship between Wind Resistance and Experimental Capsule Radius
\[(\tau_{ij})_{eff}=\mu_{eff}(\frac{\partial u_{j}}{\partial x_{i}}+\frac{ \partial u_{i}}{\partial x_{j}})-\frac{2}{3}\mu_{eff}\frac{\partial u_{k}}{ \partial x_{k}}\delta_{ij}, \tag{17}\]  
where \(\delta_{ij}=0(i\
eq j)\) and \(\delta_{ij}=1\)\((i=j)\). Under steady-state conditions, the wind resistance power should be entirely converted into viscous heat; therefore, studying the viscous heating term \((\tau_{ij})_{eff}\). is equivalent to investigating wind resistance power. For ease of studying the relationship between wind resistance power, effective viscosity, and velocity distribution, Equation (17) is transformed into Equation (18) using the ideal gas equation, as follows:  
\[(\tau_{ij})_{eff}=[v_{eff}(\frac{\partial u_{j}}{\partial x_{i}}+\frac{ \partial u_{i}}{\partial x_{j}})-\frac{2}{3}v_{eff}\frac{\partial u_{k}}{ \partial x_{k}}\delta_{ij}]\ \frac{p}{RT}, \tag{18}\]  
where \(T\), \(P\), and \(R\) are the temperature, pressure, and gas constant, respectively. For fluids with constant density, the term \(\frac{\partial u_{k}}{\partial x_{k}}\) is zero. Equation (18) clearly indicates that under conditions where changes in the temperature and pressure spatial distributions are negligible, the viscous heating term is solely related to the effective dynamic viscosity and spatial distribution of velocities. In our simulation, we observed that the spatial distribution of velocity near the armature did not exhibit significant variations. Thus, we focused on another influencing factor: the effective dynamic viscosity. To visualize the changes in the effective dynamic viscosity throughout space, we utilized the average sectional effective dynamic viscosity, as indicated by the yellow circles in Figure 7. We noticed that the effective dynamic viscosity increased as the radius increased from 4350 to 4650 mm. Figure 8a,b shows the sectional contour plots of the effective viscosity within the MRF rotating domain at radii of 4350 and 4650 mm, respectively. These figures reveal that when the radius increases, the effective dynamic viscosity within the wake region behind the armature increases significantly. Based on the above analysis, we discovered that when the radius increases from 4350 to 4650 mm, the increase in wind resistance is not due to a decrease in the air entrainment speed or an increase in the relative velocity between the armature and entrained air but to an increase in the effective viscosity within the wake region.
# A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features
## 3 Method
### Feature Extraction
#### 3.3.2 Immediate Motion Orientation  
Another geometric feature used in our method is immediate motion orientation. Suppose \(\vec{Vi},t=(x,y,z)\) is a 3D vector of joint \(i\) in frame \(t\). First, we map the 3-dimensional vector to the three planes \(xy\), \(xz\), and \(yz\) to obtain three 2-dimensional vectors. Then, the immediate motion orientation can be obtained by calculating the cosine angles of the three 2-dimensional vectors on the corresponding coordinate axis. The movement orientation \(O=(\cos\theta x,\cos\theta y,\cos\theta z)\) can be calculated as follows:  
\[\begin{array}{l}\cos\theta x=\frac{x}{\sqrt{\Delta x^{2}+\Delta y^{2}}}\\ \cos\theta y=\frac{y}{\sqrt{\Delta x^{2}+\Delta y^{2}}}\\ \cos\theta z=\frac{z}{\sqrt{\Delta y^{2}+\Delta z^{2}}}\end{array} \tag{3}\]  
where \(\theta\) is the angle between the motion vectors and the coordinate axis, and \(\Delta x,\Delta y,\Delta z\) are the displacements of joint \(i\) on the \(x,y,z\) axis, respectively. In particular, the values of the immediate motion of the 1 st frame are initialized to zero.
## 3 Performance Analysis
### Average Symbol Error Rate  
There are two main methods to get the ASER: A moment generating function or CDF. In this paper, the end-to-end CDF of the receiving SNR has already been obtained using Equation (20). Therefore, we prefer to use the CDF method to calculate the ASER. The typical value of necessary ASER is \(10^{-6}\), and the smaller value of ASER means the better diversity performance. Based on Ref. [30], and the modulating M-PSK and M-PAM using the Gray code mapping constellation, the ASER can be represented or approximated as \(P_{e}=\mathrm{E}\left[aQ_{1}\left(\sqrt{b\gamma}\right)\right]\), where \(Q_{1}\left(\cdot\right)\) denotes the Gaussian function defined as \(Q_{1}\left(x\right)=\left(1/\sqrt{2\pi}\right)\int_{x}^{\infty}\exp\left(-t^{ 2}/2\right)dt\). The letters \(a\) and \(b\) are constants related to the modulation method. For example, when \(a=1,b=1\), the corresponding modulation method is BPSK. \(a=2,b=\sin^{2}(\pi/M)\) corresponds to M-PSK modulation, while \(M\geq 4\), \(a=2\left(M-1\right)/M,b=3/(M^{2}-1)\) corresponds to M-PAM modulation. To obtain an expression for \(P_{e}\), we can integrate the PDF of \(\gamma\). After simple variable substitution, \(P_{e}\) can be reformulated as:  
\[P_{e}=\frac{a}{2}\sqrt{\frac{b}{\pi}}\int_{0}^{\infty}\frac{\exp\left(-bx \right)}{\sqrt{x}}F_{\gamma}\left(x\right)dx \tag{27}\]  
Substituting Equation (20) into Equation (27), \(P_{e}\) can be written as:  
\[P_{e}=\frac{a}{2}\sqrt{\frac{b}{\pi}}\int_{0}^{\infty}\frac{\exp\left(-bx \right)}{\sqrt{x}}\frac{\exp(-tr(\uplambda))|\Psi(x\upsigma^{2}/\delta^{2} \gamma_{s})}{|\mathbf{V}|\left(\Gamma(N_{Hr}-N_{rH}+1)\right)^{N_{rH}}}dx \tag{28}\]
###### Abstract  
The introduction of medical Internet of Things (IoT) for biomedical applications has brought about the era of proactive healthcare. Such advanced medical supervision lies on the foundation of a network of energy-constrained wearable or implantable sensors (or things). These miniaturized battery-powered biosensor nodes are placed in, on, or around the human body to measure vital signals to be reported to the sink. This network configuration deployed on a human body is known as the Wireless Body Area Network (WBAN). Strategies are required to restrict energy expenditure of the nodes without degrading performance of WBAN to make medical IoT a green (energy-efficient) and effective paradigm. Direct communication from a node to sink in WBAN may often lead to rapid energy depletion of nodes as well as growing thermal effects on the human body. Hence, multi-hop communication from sources to sink in WBAN is often preferred instead of direct communication with high transmission power. Existing research focuses on designing multi-hop protocols addressing the issues in WBAN routing. However, the ideal conditions for multi-hop routing in preference to single-hop direct delivery is rarely investigated. Accordingly, in this paper an optimal transmission policy for WBAN is developed using Markov Decision Process (MDP) subject to various input conditions such as battery level, event occurrence, packet transmission rate and link quality. Thereafter, a multi-hop routing protocol is designed where routing decisions are made following a pre-computed strategy. The algorithm is simulated, and performance is compared with existing multi-hop protocol for WBAN to demonstrate the viability of the proposed scheme.  
medical IoT; WBAN; energy efficient; Markov Decision Process; routing; specific absorption rate; transmission strategy +
Footnote †: journal:  
_Article_
## 3 Results and Discussion
### Characteristics at Middle Latitudes
During the June solstice, the Ne over the Pacific longitudes at middle latitudes in both hemispheres is comparable and symmetrical at 800 km, which is similar to the scintillation results (Figure 12g,h). During December solstice, the electrons are significantly higher at middle latitudes in the southern hemisphere and are concentrated at Weddell Sea Anomaly (WSA) longitudes (from South America to the central Pacific region), which is more consistent with the distribution of high scintillation occurrence during the December solstice in the southern hemisphere (Figure 12g,h). The WSA behavior may be due to ionospheric long-term photoionization during the local summer period and vertical plasma drift caused by neutral winds under geomagnetic field geometry effects [33]. Meanwhile, the higher scintillation occurrence in Swarm A may be explained by the ionospheric irregularities mainly occurring at lower altitudes. The higher absolute perturbations in the electron density of irregularities is more likely to cause scintillation. More observations are needed to verify these inferences.
# BDS/IMU Integrated Auto-Navigation System of Orchard Spraying Robot
## 2 Materials and Methods
### Control Model of the System
#### 2.3.2 Kinematic Model of the Spraying Robot  
The chassis of the spraying robot adopts a track structure and adopts the principle of sliding steering to realize steering by controlling the relative speed of the tracks on both sides. Assuming that the spraying robot does not slip when driving, and the mass division of the robot is uniform, and the center of mass is located in the geometric longitudinal symmetry line of the robot, the coordinate system of the spraying robot is shown in Figure 7. According to the right-hand rule, the center of mass CM of spraying robot is the coordinate origin, the forward motion direction is the positive direction of the X-axis, the left is the positive direction of the Y-axis, the Z-axis is perpendicular to the paper facing outward, and OC is the center of rotation center of the sprayer, \(v_{c}\) is the linear velocity of the center of mass, \(r_{c}\) is the turning radius of the center of mass, \(v_{l},v_{r}\) are the linear velocities of the left and right driving wheels, \(d_{lr}\) is the distance between the two track centers, which is 0.62 m. The angular velocity \(\omega\) of steering around the OC can be expressed as follows:  
\[\omega=\frac{v_{c}}{r_{c}}=\frac{v_{r}}{r_{c}+d_{lr}/2}=\frac{v_{l}}{r_{c}-d_{ lr}/2} \tag{12}\]  
\[\omega=(v_{r}-v_{l})/d_{lr} \tag{13}\]  
The linear velocity of the spraying robot centroid can be expressed as follows:  
\[v_{c}=(v_{l}+v_{r})/2 \tag{14}\]  
The turning radius of the spraying robot can be expressed as follows:  
\[r_{c}=\frac{(v_{l}+v_{r})d_{lr}}{2(v_{r}-v_{l})} \tag{15}\]  
The forward kinematics model of the spraying robot can be expressed as follows:  
\[\begin{bmatrix}v_{c}\\ \omega\end{bmatrix}=\begin{bmatrix}1/2&1/2\\ 1/d_{lr}&-1/d_{lr}\end{bmatrix}\begin{bmatrix}v_{r}\\ v_{l}\end{bmatrix} \tag{16}\]  
The inverse kinematics model of the spraying robot can be expressed as follows:  
\[\begin{bmatrix}v_{r}\\ v_{l}\end{bmatrix}=\begin{bmatrix}1&d_{lr}/2\\ 1&-d_{lr}/2\end{bmatrix}\begin{bmatrix}v_{c}\\ \omega\end{bmatrix} \tag{17}\]  
Figure 6: The structure of the path tracking system.
## 4 Overview of the Proposed Error Recovery Mechanism  
The work in [14] is the non-NC based error recovery mechanism that covers the widest range of QoS requirements compared to others. However, it is not adaptive to the network's channel conditions. Moreover, [14] applies a clustering mechanism, and it is very difficult to make a clustering algorithm adaptive to the network's dynamic channel condition. On the other hand, NC-based error recovery mechanisms [13, 13, 23, 24, 25, 26, 27] have great potential to be efficiently adaptive to the network channel conditions. Considering the security issue, this work will consider XoR-based NC for WBSNs. However, XoR-based existing works [26, 29] could suffer in security and privacy, too, as they need to decode all or most of the packets at relay nodes. Therefore, [PERSON]'s ([PERSON]'s) XoR coding-based approach [13] is selected as the base work in this work. This work will extend this NC-based mechanism in order to make it adaptive to the network's channel conditions and user QoS requirements. Adaptiveness to network channel condition and users' requirements will help the mechanism to improve the performance in terms of reliability, energy efficiency and real-time delivery. As the proposed work is based on [PERSON]'s work, in the following, first, we briefly present [PERSON]'s mechanism (for details, please see [13]) and then present the proposed one.
## 5 Discussion
### Number of Vehicles, Vehicle Delay, and Level of Service  
The total number of vehicles passing through the simulation model was slightly higher in the DLA scenario (0.02%, 0.05%, and 0.03%), which indicates that DLA implementation increased the intersection flow by reallocating lanes to match directional demand during peak hours. Overall, more vehicles were processed per hour, followed by a significant reduction in the average vehicle delay. The results of the DLA implementation were most notable at the middle-signalized intersection (Vukovar Street-Nova Cesta Street), where vehicle delay was reduced by 58.05%. At the edge intersections of the simulation model, the situation was slightly different because DLA did not have a direct impact on the traffic situation. On the Vukovar Street-Trakoscanska Street-Ozaljska Street intersection, the vehicle delay increased by 16.97%, and on the Vukovar Street-Florijana Andraseca Street intersection, the vehicle delay was reduced by 5.73%.  
As for the level of service values, it must be noted that the values in the tables represent the internal PTV VISSIM performance score, which is not based solely on the average delay but also on the volume-to-capacity ratio, signal plan complexity, and other parameters. Nevertheless, the values follow the number of vehicles and vehicle delay trend, so at the middle intersection, the LoS is increased from D to C, suggesting that DLA has a positive effect on operational performance. At the first intersection (eastbound direction), the LoS was slightly decreased from B to C following the same explanation as the vehicle delay, and at the last intersection, the LoS was increased from the value C to B. It can be noted that with the implementation of DLA, the capacity in the eastbound direction is increased, following smoother traffic flow and better lane utilization.
## 5 Results  
To validate the proposed method, the modified IEEE nine-bus system is utilized, which is sketched in Figure 1. Two generators locate at Bus 1 and Bus 2; one wind farm and one ESU connect to Bus 3. The system data are provided in Table 1. Considering a time horizon of 24 h, i.e., \(T=24\) h, the load curve is drawn in Figure 2. Ten averagely weighted representative scenarios of wind power are generated by the weather data in Qinghai Province, China, 2019, as in Figure 3. Given \(\beta=95\%\), the value of \(\varepsilon\) is set to 0.3, according to (15). Complete data are provided in Reference [34].  
Furthermore, we define the parameter set as follows:  
\[\Theta=\Big{\{}\,\theta=[P^{s}(\text{MW}),E^{s}(\text{MWh})]^{T}\Big{|}0\leq P^{s }\leq 100,0\leq E^{s}\leq 150\Big{\}}\]
# Multi-Label Classification Based on Associations
## 1 Introduction  
In data mining, classification is a common activity. The goal is to properly anticipate the class label of unseen instances using the rules or functions learned from a labeled set, or training set [1; 2]. Many researchers [3; 4; 5; 6; 7; 8] have been attracted to classification in recent decades, and have used a wide variety of learning approaches and strategies, including decision trees, neural networks, fuzzy logic, Bayesian and statistical approaches, rule-set induction, and more to create highly accurate classifiers [9]. In categorization, there are three major categories [10]. Each data point in the first two categories must match only one of the predefined classes. The third category [11], on the other hand, enables numerous class labels to be assigned to specific dataset instances. The first, referred to as a \"binary classification\" has just two class labels, but the second, referred to as a \"multi-class classification\" contains more than two [12; 13]. The more general multi-label classification (MLC) system [11; 14] is the third classification scheme. This study focuses on a particular categorization strategy that employs a single-label classification (SLC) to handle the multi-label problem.  
Associative classification (AC) is one of the primary approaches that has been actively used in addressing the classification problem [15]. AC is a rule-set induction approach that uses the Association Rule Mining (ARM) task to solve the cassification issue [1]. In general, the AC approach has several distinguishable features over other learning approaches, such as the highly accurate rules produced by AC algorithms, the simplicity of representing the learned rules through the \"IF-THEN\" format, and its applicability to a wide range of real-life classification problems, i.e., medical diagnosis, e-mail phishing, fraud detection, and software defects [16]. Most AC-based methods have only been used for binary andmulti-class classification problems [17]. In contrast, only a few efforts have been presented to apply AC in a broader form of classification termed MLC [16].  
This research presents an update to the classification based on associations (msCBA) [18] algorithm. The improved prediction phase is a result of the new version's usage of the local positive dependencies among labels to reduce the large size of the problem search space. Multi-label classification based on associations (ML-CBA) is the new name for the improved system.ML-CBA is one of the first methods to employ AC to address the MLC problem by exploiting local labels' dependencies.  
The paper is organized as follows: the next section briefly overviews the main concepts related to the AC approach and surveys some of the algorithms that have attempted to utilize AC in MLC. Section 3 describes the proposed ML-CBA algorithm and the results of comparing it to several other MLC algorithms that use different learning strategies. Finally, Section 4 concludes and introduces significant future work.