esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the SSUKF Integrated Navigation Algorithm Based on Adaptive Factors
## 3 INS/GNSS Integrated Navigation Algorithm Design  
In a navigation system, the statistical characteristics of the process noise are time-varying. To improve the system's robustness and accuracy, a noise estimator is introduced into the filtering algorithm. In this study, we combine the Simplified Spherical Unscented Kalman Filter (SSUKF) with a noise estimator, proposing the Adaptive Simplified Spherical Unscented Kalman Filter (ASSUKF) method, which can effectively adaptively adjust the statistical characteristics of the process noise, thereby improving the filtering performance.  
Compared to the standard Unscented Kalman Filter (UKF), the SSUKF simplifies the process of generating sampling points and the nonlinear transformations during state estimation, reducing computational complexity and matrix operations. This enables the filtering algorithm to maintain high accuracy at a lower computational cost. This makes SSUKF more efficient and accurate for high-dimensional nonlinear systems.
# Welding Image Data Augmentation Method Based on LRGAN Model
## 2 LRGAN Model
### The Generator Part of the LRGAN Model
#### 2.2.2 Generator Activation Functions  
In the backpropagation process of neural networks, division is required to compute the derivative of the activation function in order to obtain the error gradients. This can result in high computational complexity and is prone to the vanishing gradient problem. The sigmoid function, when used in deep neural networks during backpropagation, exhibits a low rate of change in the saturated region, causing its derivative to approach zero. As a result, this can lead to the loss of information during propagation, resulting in information saturation, which hinders normal training of deep networks. Therefore, in the output layer of the generator, this paper abandons the sigmoid activation function with a Lipschitz constant of \(1/4\) and instead opts for the tanh activation function with a Lipschitz constant of 1 to better control the output range of the generator and avoid information saturation. The expression of the tanh function is shown in Equation (20), and the function curve is depicted in Figure 7.  
\[\tanh(x)=\frac{\sinh(x)}{\cosh(x)}=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{20}\]  
As shown in Figure 7, the Tanh function is centrally symmetric with a mean of 0, and it can map a 0-1 Gaussian distribution to values near 0, maintaining the zero mean property. Additionally, because it has both positive and negative values, it allows for faster discrimination of inputs from the previous layer. This leads to faster convergence and  
Figure 6: LRGAN generator structure diagram.  
helps mitigate the issue of high computational complexity caused by backpropagation. It reduces the computational overhead in multi-layer networks, making it more suitable for the generator's requirement of multi-layer network backpropagation.
# Survey and Perspectives of Vehicular Wi-Fi versus Sidelink Cellular-V2X in the 5G Era
## 4 Results
* **IEEE 802.11p with LTE-V2X PHY vs. LTE-V2X:** If the LTE-V2X PHY is assumed for IEEE 802.11p, the gap to LTE-V2X with Mode 4 becomes small. In this case, the PRR is comparable with that of LTE-V2X with \(p_{\rm k}=0.8\) and the UD is always lower. If Mode 3 is considered, LTE-V2X still remains preferable in terms of PRR, with a UD that is similar in Highway and Cologne and higher in Bologna. Figure 3: Packet reception ratio and update delay in Highway. Figure 4: Packet reception ratio and update delay in Bologna. Figure 5: _Cont._  
Given the best MCS per each technology in terms of PRR, inferred from Figures 3-5, a comparison in terms of PRR, varying the transmitter-receiver distance, and in terms of UD, varying the target, is provided in Figures 6 and 7, respectively. If we compare the performance of LTE-V2X Mode 3 with the other cases, a higher PRR is always observable and the UD is only slightly worse than that of IEEE 802.11p in Bologna. Even if the used algorithm is only based on the positions of the nodes (not taking channel variability and NLOS into account), still the wider knowledge of the network allows for achieving better performance. Focusing on a the standard IEEE 802.11p compared to LTE-V2X Mode 4, although a lower range is overall observable, it can be noted that its performance until a certain distance is comparable in  
Figure 5: Packet reception ratio and update delay in Cologne. Figure 6: Packet reception ratio vs. distance. Figure 7: Update delay vs. target. terms of PRR (especially if \(p_{\rm k}=0\)) and better in UD (especially if \(p_{\rm k}=0.8\)). The UD is worse only in the Highway scenario, where it is calculated within 200 m and thus affected by a low PRR. If IEEE 802.11p with LTE PHY layer is considered, then similar performance in terms of PRR and better in terms of UD can be noted. This suggests that, when out-of-coverage scenarios are addressed, most of the improvement shown in the literature with LTE-V2X compared to IEEE 802.11p is due to the PHY layer.
# Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy
## 1 Introduction
Although the above enhancement methods have their own advantages for different types of images, most of them ignore an important point for medical image enhancement, especially for CBCT images: the medical images may contain noise due to the interference of the surrounding environment [22]. Based on this motivation, we propose an adaptive visual saliency feature enhancement method of CBCT for image-guided radiotherapy, which takes the denoising process into account to overcome the influence of potential noise on the enhancement results. First, we propose a structural  
Figure 1: Examples of unregistered CBCT (the first row) and CT (the second row) images. similarity-based low-rank approximation model (SSLRA) to construct the source CBCT images to denoise the noise that may exist. Low-rank approximation (LRA) is an extension of singular value decomposition (SVD) in machine learning. Unlike in our previous work [23], we propose an unsupervised adaptive denoising model based on structural similarity considering the characteristics of CBCT images. Then, we propose a visual saliency feature-based enhancement (VSFE) method to enhance the important regions in CBCT images. The main contributions of this work are as follows: (1) A novel unsupervised adaptive denoising model called a structural similarity-based low-rank approximation (SSLRA) model is proposed for CBCT images. (2) A visual saliency feature enhancement (VSFE) method is proposed, in which more salient and important regions of CBCT can be enhanced. (3) By analyzing the support for subsequent registration tasks of CBCT and CT, we prove that the proposed method can effectively improve the registration performance, indicating its application prospects in IGRT. The rest of this paper is organized as follows. Section 2 describes the proposed method in detail. Experimental results and analyses are presented in Section 3. Section 4 discusses the application of our work in IGRT. Section 5 gives the conclusion of our work.
## 3 Results and Discussion
The impact leads to temporary change in the mean pressure (orange), which may result in a slippage of the connection. The slippage is indicated by the reduction in the reaction moment (blue). Figure 10: Upper subfigure is showing the contact pressure in the shaft–hub connection without impulse. The lower subfigure is showing the immediate influence of the impact on the contact pressure which is decreasing between \(0^{\circ}\) and \(90^{\circ}\) and increasing between \(90^{\circ}\) and \(300^{\circ}\). The local change in contact pressure may result in a slippage of the connection. The result of the simulation with a cylinder instead of a sphere-shaped mass (\(m=1.6\) kg, \(v=3000\) mm/s) is shown in Figure 4:0 Instead of being point-shaped, the contact during the impact is line-shaped. This results in a small decrease in the reaction moment of approximately 1 Nm. This underlines the influence of the contact between the impact mass and the shaft. A change in the shape of the contact leads to a change in the energy transfer and a different tangential vibration between the point mass and the shaft due to the impulse. This results in a different deformation due to the reduction in the friction force of the shaft and the slip zone between the shaft and the hub which may arise from several influencing factors. Furthermore, a different vibration excitation of the shaft can occur. Different studies on the influence of the impact velocity, the preload, the mass of the ball, and the distance between the impact and the shaft-hub connection were carried out. The results of all those factors are shown in Figure 4:1 The biggest decrease in the transmittable torque is obtained at the highest impact energies (big mass and high velocity), the closest distance to the hub, and with the highest preload. All of these changes can be attributed to the effects of vibration excitation of the shaft [17] and the temporary reduction in the contact pressures caused by the deformation and the reduction in the frictional force [19; 26; 27; 28], with the associated increase in slippage [22]. The highest energies lead to the biggest decrease in the contact pressure in the connection and the decrease in the transmittable torque. The highest preload puts so much tension on the connection that even  
Figure 4:2 FE results from time dependent progress of kinetic energy and transmittable torque in the shaft–hub connection after overall three impacts. The impact is causing a decrease in contact pressure which also results in a decrease in the transmittable torque. Figure 4:3 Influence of the distance h between impact point and shaft–hub connection. The smallest distance h = 10 mm results in the biggest slippage accompanied with the biggest decrease in transmittable torque while the biggest distance h = 35 mm results in the smallest slippage/decrease in transmittable torque. a slight reduction in the contact pressure can cause slippage. The influence of the distance may be related to damping effects in the material. Further studies may follow on this.
## 2 Materials and Methods
For the two-dimensional cross-sections, the samples were embedded in an epoxy-resin (Epofix, Struers), cut, and polished with different polishing cloths in combination with a diamond suspension of 9, 3, and 1 \(\upmu\)m (Tegramin, Struers). The acceleration voltage of the SEM was adjusted to 1.5 (tomography) and 3 kV for investigation of the particles' surface and the CBD and to 15 kV for AM and for particle cracking visualization in the cross-sectional images of the embedded electrodes. Three-dimensional surface images and quantification of electrode surface densification were performed by a confocal laser scanning microscope (VK-X200, Keyence, Osaka, Japan) (CLSM). For the FIB/SEM image segmentation and digital modeling, the FIB/SEM image data were segmented and processed using the GeoDic2020 software by Math2 Market GmbH. A filter to remove the so-called curtaining-effect was applied. A non-local means filter was applied to reduce noise. The misalignment of the image stack was corrected using a specially designed algorithm. Since it was not possible to segment the CBD from the AM and the pore space, the segmentation was focused on segmenting the AM, while assigning the CBD as pore. Afterwards, the CBD was digitally modeled into the microstructure. The known information about the CBD regarding the density and weight percent for this specific sample was combined with general values from the literature for porosity, PSD, cluster distribution, and tortuosity [27]. Firstly, the CBD was created as a non-porous phase and was distributed via a geometric algorithm in the pore space, connecting the active material with a contact angle of 40\({}^{\circ}\) between AM and CBD. Different approaches modeling the CBD have recently been reported [36]. Afterwards, the porosity of the CBD was modeled in an additional step using previously published information [27] and a fibrous base shape of the pores with a Gaussian diameter distribution. The porosity of the CBD phase was 50.2% with a minimal pore diameter of 37.2 nm, which is equivalent to the modeled voxel length. MIP and granulometry (mathematical description of the pore size by filling the pores with spheres) were simulated using PoroDict in the GeoDict software [38; 39] with a batch size of the voxel length. The composition of the electrodes in wt% and vol% with the used densities are given in Table 1. The solid-volume-percentage and density of the combined CB and binder to CBD is also shown.
# How Biological and Environmental Factors Affect the Quality of Lavender Essential Oils
## 2 Lavender Essential Oil Production  
### Lavender Essential Oil Composition  
The International Organization for Standardization (ISO) strictly regulates the properties of essential lavender oils, and their economic value is determined. The L. angustifolia essential oil composition is described in the ISO 3515:2002/Cor 1:2004, and its description concerns its different origins [17]. The essential oil composition of the variety 'Grosso' (LI) is defined by ISO 8902:2009 as follows: linalool (24-37%), linalyl acetate (25-38%), camphor (6-8.5%) and 1.8-cineole (4-8%) [18]. Moreover, Pharmacopoeia regulates the pharmaceutical quality of true lavender EO [19]). Table 1 presents the main compounds in lavender's composition, according to two ISOs [17; 18], Etherio from Greek native L. angustifolia [13] and European Pharmacopoeia [20].  
Many factors alter lavender EO quality, including genotype, plant organs, plant growth, flower development, harvesting time, environmental conditions, and drying and extraction methods [19; 21].  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multicolumn{2}{c}{ISO **3515:2002**} & \multicolumn{2}{c}{ISO **8902:2009**} & \multirow{2}{*}{\begin{tabular}{c} **European** \\ **Pharmacopoeia** \\ **L. angustifolia** \\ **etherio** \\ \end{tabular} } & \multicolumn{2}{c}{**European**} \\  & **[17]** & **[**18**]** & & **\begin{tabular}{c} _etherio_[13]** \\ \end{tabular} & & \begin{tabular}{c} _Ch. angustifolia_[20]** \\ \end{tabular} \\ \hline \hline \begin{tabular}{l} Linalool \\ 1,8-Cineole \\ Camphor \\ Limonene \\ b-Ocimene \\ Terpinen-4-ol \\ Lavandulol \\ Lavandulol \\ a-Terpineol \\ Borneol \\ \end{tabular} & \begin{tabular}{c} 25-38 \\ 25-45 \\ 28-38 \\ 4-7 \\ \end{tabular} & \
\begin{tabular}{c} 24-35 \\ 28-38 \\ 0.4-2 \\ 0.5-1 \\ 0.5-1.5 \\ 0.5-1.5 \\ 0.5-1.5 \\ 0.1-0.6 \\ 1.7-2.4 \\ \end{tabular} &
\begin{tabular}{c} 28-35 \\ 25-46 \\ 25-46 \\ \textless{}2.5 \\ \textless{}1.2 \\
## 4 Voltage Generation Benefit Analysis  
We use the fourth-order Runge-Kutta method and the small perturbation method to find the theoretical voltage of this system. The theoretical model of the slapping force on the PZT at the root of the elastic steel sheet and the overall voltage generation equation is constructed as follows.  
We assume that the impact force of the rigid plate lever slapping the PZT is regarded as:  
\[\overline{F}=\rho A\overline{W}_{F}{}^{**}\overline{\delta}(\overline{t}- \overline{T}),\ \overline{t}>0 \tag{40}\]  
where \(\overline{T}\) is the slapping period and \(\overline
# Detecting and Responding to Concept Drift in Business Processes
## 2 Related Work
### Markov Models  
A discrete-time Markov chain (DTMC) is usually used to characterize a stochastic process, in which the next state depends only on the current state with transition time/sojourn time/waiting time as one unit time [18]. A continuous-time Markov chain or a semi-Markov process (SMP) is commonly employed if the transition time is exponentially or randomly distributed [33]. SMP models are widely used for, e.g., modelling highway bridge maintenance [34], characterising patient pathways via hospital care [35] and optimising mode control [36]. The model also suits business process mining, but it has not been studied much. In general, a business process can be naturally represented in terms of an SMP framework by considering process activities as the state space, describing the probability of moving to the next activity from the current by transition probabilities and simulating the transition time by non-exponential distributions. It offers a single model for characterizing the process control-flow and the time spent by customers. A hierarchical MM is a stochastic process including at least two layers that usually follow the Markov property. [PERSON] et al. [37] proposed a hierarchical MM to discover the relationship between low-level process activities and the high-level description of business processes. [PERSON] et al. [38] integrated the Coxian phase-type model with the continuous-time Hidden Markov model to describe the length of stay of patients in hospital.  
A Markov decision process (MDP) is a discrete-time stochastic process aiming to model decision-making in situations [18; 39]. That is, an MDP is a DTMC with a decision/action selection policy in every transition. MDP can be solved by using dynamic programming-based approaches [39], which require updating the transition probability matrix and the transition reward matrix for every decision, leading to the cost of a substantial amount of computational resources [18]. Reinforcement learning (RL), especially approximation-based methods that estimate the value of state-action pairs (e.g., Deep Q network) [40], can be considered as a near-optimal solution for large and complex MDP problems [41]. On the other hand, such methods lack the ability to model transition probabilities of moving from the current to the next state via a selected action. MDP has been successfully applied in a wide range of action selection areas such as robotics and control [42], games [43], trading [44] and resource allocation [11; 41], but it has received less attention in PM.
## 3 System Mode and Terminologies  
### System Mode  
We assume such a MWSN scenario: A wide open area is covered with a large number of homogeneous sensor nodes; there are \(N\) source nodes and \(M\) mobile sinks. Sinks can move anywhere within the sensor field, at any time with a fixed speed. Each source node sends its sensed data through multi-hops to the nearest sink via one and only one routing path. Several nodes can route packets to the mobile sink. In our network mode, individual node only owns the local information, such as its unique ID, residual energy level, delay and the distances to its neighbors, which can be estimated based on the received signal strength [24; 25]. Our model does not need any extra positioning system such as GSP.  
We use the same energy model and fault model proposed in [10]. The energy consumption equation of sensing and transferring m bit data is as follows:  
\[E_{sens}(m)\;=\;\alpha_{1}\times m \tag{1}\]  
\[E_{tx}(m,\,d)\;=\;(\beta_{1}\;+\;\beta_{2}\times\text{{dis}}(i,\,j)^{n})\times m \tag{2}\]  
\[E_{rx}(m)\;=\;\gamma_{1}\times m \tag{3}\]where \(dis\)\((i,j)\) is the distance between \(node\)\(i\) and \(node\)\(j\), \(E_{sens}\) is the energy consumption of sensing m bits of data, \(E_{tx}(m,d)\) and \(E_{rx}(m)\) are the energy consumption of sending and receiving \(m\) bits of data, \(n\) is the channel attenuation index, \(\alpha_{1}\), \(\beta_{1}\), \(\beta_{2}\) and \(\gamma_{1}\) are energy consumption parameters of sensing circuit, sending circuit, sending amplifier and receiving circuit, respectively. Let \(P_{fault}\) be the probability of sensor node's failure in the network. We assume that the probability of the mobile sink failure is approximately 0. If any relay node is failed, it is handled by our routing algorithm.
# Enabling Horizontal Collaboration in Logistics Through Secure Multi-Party Computation
## 3 Optimization Problem
### Optimization Goal  
The optimization goal of the plain CFDP problem is to minimize the average empty space on a truck. For this, let \(\mathcal{A}\) denote all truck rides in a valid solution. For each ride \(A\in\mathcal{A}\), \(C_{T_{A}}\) denotes the capacity of the truck used for this ride, and \(\mathcal{O}_{A}\) are the orders of this ride, where \(v_{O}\) is the volume of an order \(O\in\mathcal{O}_{A}\).  
The optimization goal is then the fraction \(\frac{C_{T_{A}}-\sum_{O\in\mathcal{O}_{A}}v_{O}}{C_{T_{A}}}\) of empty space for a specific truck ride, averaged over all truck rides \(A\in\mathcal{A}\), resulting in a normalized value in the interval \([0,1]\). Specifically, the optimization goal is given by the following:  
\[\text{minimize}\;\frac{1}{|\mathcal{A}|}\cdot\sum_{A\in\mathcal{A}}\frac{C_{ T_{A}}-\sum_{O\in\mathcal{O}_{A}}v_{O}}{C_{T_{A}}}=1-\frac{1}{|\mathcal{A}|} \cdot\sum_{A\in\mathcal{A}}\sum_{O\in\mathcal{O}_{A}}\frac{v_{O}}{C_{T_{A}}}\,. \tag{1}\]  
An objective value of \(0\) indicates that all trucks are completely filled, whereas an objective value of \(1\) indicates that all trucks are completely empty.  
Note that Equation (1) minimizes the fraction of empty deck space per truck ride. Minimizing the fraction of empty deck space per truck-kilometer is easily achieved by adding appropriate scaling factors to the summands.  
Clearly, as an extension to the above CFDP, the prioritization of orders can be reflected in the objective function, with adjustable weights to adjust the relative importance of those two aspects (i.e., timing and load factor). However, while being under ongoing development, for the sake of brevity, we will not elaborate on the prioritization aspects in more detail in this paper, as it does not change the overall approach and validity of our results, and was also not considered in [3], which we use as a baseline for our evaluation.