esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method to Determine BeiDou GEO/IGSO Orbital Maneuver Time Periods
## 2 Theory of Orbital Maneuver Period Detection
### Method for Predicting the Orbit  
The spatial coordinates of the station, pseudo-range observations, and satellite position are needed to calculate the start time and end time factors. Once the orbit maneuver occurs, the orbital parameters in broadcast ephemeris are inaccurate, with increasing errors for several hours (generally about 7-8 h for BDS) and the precise orbit products of the maneuvering satellite are lost from IGS and iGMAS, causing difficulty in determining the time period of orbital maneuvers.  
In order to determine the start time of orbital maneuvers, the satellite position is calculated by the backward predicted orbit, which can be predicted from the precise orbit products on the day after orbit maneuvering. The orbital parameters of satellite after orbit maneuvering are consistent with the day after orbit maneuvering, which can calculate the correct position of satellite. Furthermore, the orbital parameters of satellite before orbit maneuvering are consistent with the day before orbit maneuvering, which can calculate the correct position of satellite. The forward and backward predicted orbit can be determined by the precise orbit products using the following formula:  
\[\left(r_{t},\dot{r}_{t}\right)=g\left(t,r_{0},\dot{r}_{0},q\right) \tag{10}\]  
where \(r_{0}\) is the initial position vector; \(\dot{r}_{0}\) is the initial velocity vector; \(q\) is the status vector of the perturbation parameter for the satellite; and \(t\) is the time vector. The perturbation parameters of the satellite are calculated by orbit fitting by the measured orbit. The orbit is predicted by orbit integrations with parameters, including \(r_{0}\), \(\dot{r}_{0}\), and the perturbation parameters. Specific mathematical models of orbit fitting and integration can be found in [20].  
To detect the end time of orbital maneuvers, the satellite position is calculated by the forward predicted orbit. The orbit before maneuvering is predicted by precise products on the day before orbital maneuvers, and can also be replaced by broadcast ephemeris or ultra-rapid orbit products at 00:00:00.  
In this study, we used precise orbit products from the Chang'an University BeiDou Analysis and Service Center (CHD) to predict the backward and forward orbits for orbit maneuvering of BeiDou satellites.
# Application of a Local Dynamic Model of Large Eddy Simulation to a Marine Propeller Wake
## 1 Introduction
Reference [11] adopts a new type of underwater micro PIV system, which utilizes underwater particle image velocimetry and acoustic Doppler velocimetry to in-situ measure sediment resuspension caused by propeller erosion. Literature [12] carried out PIV analysis on the wake of rotating propeller in cavitation wind tunnel, and studied the wake characteristics of four propellers at high Reynolds number using dual frame PIV technology. In this paper, from a theoretical and applied point of view, a local dynamic \(k\)-equation subgrid-scale (LES-LDKSGS) model is applied to study the flow over a marine propeller. This local dynamic SGS model of LES have already been used in simple flows such as classical Taylor-Green vortex flow, rearward facing step and so on to investigate its robustness. In this paper it will be applied to a more complex flows, i.e., simulation of a marine propeller wake, to further evaluate its ability range. Results from this SGS model of LES show promising trend to predict the complex dynamics of rotor flows effectively and accurately. The purpose of this study is to:  
1. Perform a LES simulation with a local dynamic \(k\)-equation eddy-viscosity model (named LES-LDKSGS in this paper) and a RANS simulation with a standard \(k-\varepsilon\) model (named RANS-kEpsilon, just for comparison with LES-LDKSGS) for flow over a submarine propeller at design and off-design advance ratios;
2. Evaluate the ability of LES-LDKSGS model to capture the complex evolution of propeller wake, and compare the results of LES-LDKSGS model with the results of PIV measurements, dynamic algebraic model (LES-DASGS, also called LES-DSM) and RANS-kEpsilon model;
3. Study the flow field evolution mechanics of the propeller and the origin of loads on blades from the perspective of engineering applications;
4. Discuss vortex identification of the wake in detail from an academic point of view. The organized structure of this article is as follows: firstly, numerical experiments are described in Section 2, including governing equations of the flow, LES models, propeller geometry, computation domain setting, initial conditions (ICs), boundary conditions (BCs), meshing strategy and numerical methods; then Section 3 provides validation of numerical simulations, including comparisons with physical experiments and other researcher's LES-DSM simulation results; numerical experimental results are presented and discussedin Section 4; finally, based on the previous description and discussion, a summary is given in Section 5.
# SEA and GATOR 10 Years Later
## 4 Structural Features of the SEA and GATOR Complexes
### Posttranslational Modifications of SEA/GATOR  
The majority of the information about post-translational modifications of the SEA/GATOR components came from whole proteome studies, essentially in yeast [46; 47; 48; 49; 50; 51]. All the SEA and GATOR members are heavily phosphorylated and ubiquitinated (except of Sec13), with many modifications occurring at the disordered regions of proteins. However, there are still very few studies that explore the functional role of these modifications. Several papers, which describe the effect of ubiquitination, are manly focused on the role of this modification on protein stability. Thus, Npr2 in yeast interacts with Grr1, the F-box component of the SCFGrr1 E3 ubiquitin ligase [52]. Moderately unstable Npr2 is stabilized in \(grr1\Delta\) mutants. In response to amino acids, CUL3-KLH22 E3 ubiquitin ligase induces K48 polyubiquitination on multiple DEPDC5 sites leading to its degradation [53]. Accordingly, DEPDC5 levels are increased during amino acid starvation. In the rich media, NPRL3 is more resistant to proteasome degradation than NPRL2 [54]. The data about the stability of SEA/GATOR proteins during amino acid starvation are contradictory and vary considerably in different species. For example, the level of practically all the SEA members in yeast decreases both during amino acid starvation and rapamycin treatment [13]. In _Drosophila_ S2 cell lines, amino acid deprivation increases Nprl3 stability [55], although the reports in human cell lines indicate that the amount of NPRL2 and NPRL3 is not changed at least after 30 min of amino acid starvation [53]. It is reasonable to expect in the following years that we will gain more information about the role of posttranslational modifications not only on the stability of SEA/GATOR members, but also on their function.
# SMS: A Secure Healthcare Model for Smart Cities
###### Abstract  
Technological innovations have enabled the realization of a utopian world where all objects of everyday life, as well as humans, are interconnected to form an \"Internet of Things (IoT).\" These connected technologies and IoT solutions have led to the emergence of smart cities where all components are converted into a connected smart ecosystem. IoT has envisioned several areas of smart cities including the modern healthcare environment like real-time monitoring, patient information management, ambient-assisted living, ambient-intelligence, anomaly detection, and accelerated sensing. IoT has also brought a breakthrough in the medical domain by integrating stake holders, medical components, and hospitals to bring about holistic healthcare management. The healthcare domain is already witnessing promising IoT-based solutions ranging from embedded mobile applications to wearable devices and implantable gadgets. However, with all these exemplary benefits, there is a need to ensure the safety and privacy of the patient's personal and medical data communicated to and from the connected devices and systems. For a smart city, it is pertinent to have an accessible, effective, and secure healthcare system for its inhabitants. This paper discusses the various elements of technology-enabled healthcare and presents a privacy-preserved and secure \"Smart Medical System (SMS)\" framework for the smart city ecosystem. For providing real-time analysis and responses, this paper proposes to use the concept of secured Mobile Edge Computing (MEC) for performing critical time-bound computations on the edge itself. In order to protect the medical and personal data of the patients and to make the data tamper-proof, the concept of blockchain has been used. Finally, this paper highlights the ways to capture and store the medical big data generated from IoT devices and sensors.  
healthcare; IoT; SMS; SDN; Twofish; WSN; mobile edge computing; blockchain; OAuth
# A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System
## 3 Materials and Methods
### CNN Model
#### 3.2.2 Modified CNN  
The optimum way to balance the depth, width, and resolution of a CNN is suggested in a recent article by [PERSON] et al. [18]. With the use of their own custom-developed compound coefficient, the scientists were able to successfully scale up Mobile and ResNets. The EfficientNet CNN architecture was also introduced in the same publication. To enhance the finest ResNet-18 model, this model will be used. Their default model includes squeeze and excite blocks sporadically along with inverted residual blocks similar to Mo-bileNetV2 [6]. When using the compound coefficient's set of formulas, this structure can be enlarged effectively.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**Year**} & \multicolumn{1}{c}{**Size**} & \multicolumn{1}{c}{**Number of**} & \multicolumn{1}{c}{**Top-1**} & \multicolumn{1}{c}{**Top-5**} & \multirow{2}{*}{**Depth**} \\ \cline{1-1} \cline{3-6} Xception [5] & 2013 & 88 MB & 22,910,480 & 79.0\% & 94.5\% & 126 \\ \hline VGG-16 [27] & 2014 & 528 MB & 138,357,544 & 71.3\% & 90.1\% & 23 \\ \hline VGG-19 [27] & 2014 & 549 MB & 143,667,240 & 71.3\% & 90.0\% & 26 \\ \hline ResNet-50 [28] & 2015 & 99 MB & 25,636,712 & 74.9\% & 92.1\% & 168 \\ \hline Inception V3 [29] & 2015 & 92 MB & 23,851,784 & 77.9\% & 93.7\% & 159 \\ \hline InceptionResNetV2 [30] & 2016 & 215 MB & 55,873,736 & 80.3\% & 95.3\% & 572 \\ \hline DenseNet121 [31] & 2016 & 33 MB & 8,062,504 & 75.0\% & 92.3\% & 121 \\ \hline MobileNet [32] & 2017 & 16 MB & 4,253,864 & 70.4\% & 89.5\% & 88 \\ \hline MobileNetV2 [33] & 2018 & 14 MB & 3,538,984 & 71.3\% & 90.1\% & 88 \\ \hline EfficientNetB0 [18] & 2016 & 7.8 MB & 5.3 million & 76.3\% & 93.2\% & 224 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CNN different pre-trained models.
# Image Anomaly Detection Using Normal Data Only by Latent Space Resampling
## 4 Experiment
### Dataset  
We evaluate the performance of the proposed method on the comprehensive natural image anomaly detection dataset: MVTec AD dataset [15]. The MVTec AD dataset provides over 5000 high-resolution  
Figure 3: Visual examples of the anomaly detection process. (**a**) images with anomalies, where red circles represent the ground truth of anomalous area. (**b**) \(\ell^{2}\)-distance of (**c**,**d**); (**c**) images decoded from resampled index tables; (**d**) reconstructed images of VQ-VAE.  
images divided into five texture and 10 object categories. Texture types cover both regular (carpet, grid) and random (leather, tile, wood) cases, while the remaining 10 objects contain rigid, fixed appearance (bottle, metal nut), deformable (cable), and natural variation (hazelnut) cases. The dataset configures a scenario that only provides normal images during training. For each class, the training set is only composed of anomaly-free images, and the test set consists of anomaly-free images as well as images containing 73 different types of fine-grained anomalies, such as defects on the objects' surface like scratches or dents, structural defects like distortion of object parts, or defects due to the missing parts of certain objects. Pixel-precise ground truth labels are provided for each anomaly image region. The dataset overview is shown in Figure 4. The MVTec AD dataset can be downloaded in [[https://www.mvtec.com/company/research/datasets/mvtec-ad/](https://www.mvtec.com/company/research/datasets/mvtec-ad/)]([https://www.mvtec.com/company/research/datasets/mvtec-ad/](https://www.mvtec.com/company/research/datasets/mvtec-ad/)).
## Appendix A Existence and Local Stability Results Proof  
The proof of the existence result (Proposition 1) is as follows. First, by the classical differential equations theory, we can confirm that system (1) has a unique local solution \((x(t),s(t),y(t),v(t),z(t))\) in \([0,t_{m})\).  
First, the solution \(x(t)\) is strictly positive for all \(t\in[0,t_{m})\). Indeed, assume the contrary, and let \(t_{1}>0\) be the first time such that \(x(t_{1})=0\) and \(\dot{x}(t_{1})\leq 0\). From the first equation of system (1), we have \(x(t_{1})=\lambda>0\), which presents a contradiction. Therefore, \(x(t)>0\) for all \(t\in[0,t_{m})\). We also have the following:  
\[\dot{s}|_{s=0}=\frac{k_{1}xv}{x+v}\geq 0,\]  
\[\dot{y}|_{y=0}=k_{2}s\geq 0,\]  
\[\dot{v}|_{v=0}=ay\geq 0,\]  
and  
\[\dot{z}|_{z=0}=0\geq 0.\]  
This shows that \(s(t)\geq 0\), \(y(t)\geq 0\), \(v(t)\geq 0\) and \(z(t)\geq 0\) for all \(t\in[0,t_{m})\). On the other hand, for the boundedness of the solutions, assume first that  
\[X=x+s+y;\]  
then, we will have  
\[\dot{X} =\lambda-d_{1}x-d_{2}s-d_{3}y-pyz,\] \[\leq\lambda-\delta X,\]  
where \(\delta=min(d_{1},d_{2},d_{3})\). Therefore,  
\[X(t) \leq X(0)e^{-\delta t}+\frac{\lambda}{\delta}(1-e^{-\delta t})\] \[\leq X(0)e^{-\delta t}+\frac{\lambda}{\delta}.\]  
it is easy to check that we have always \(a_{1}>0\), \(a_{2}>0\) and \(a_{1}a_{2}-a_{3}>0\). In addition, it is easy to see that \(a_{3}>0\) when \(R_{0}<1\). From the Routh-Hurwitz criterion, it follows that \(E_{f}\) is locally asymptotically stable.
# BBSF: Blockchain-Based Secure Weather Forecasting Information through Routing Protocol in Vanet
## 3 Methodology
### Weather Forecasting Servers  
Weather forecasting servers were part of the services offered through the proposed methodology. These servers were set up through highly efficient machines, and provided effective and reliable content management and processing delivery. They captured the data coming from the sensor modules and weather forecasting units. The server deployed a blockchain Hyperledger Sawtooth transaction mechanism to secure the routing and weather forecasting information. The purpose of the security in this section was to enhance the working features of the proposed technique. The working of the weather forecasting server is as follows:  
* Initially, it received the weather forecasting information from the sensory modules and stored it in memory. The record for every entry is stored in a separate location using log files kept for future data searching.
* The server periodically checks the data coming from sensory modules for weather forecasting information. The network setting and road conditions are also checked based on the provided setting and reliable content delivery framework.
* After gathering the data from sensory modules, the blockchain-based Hyperledger Sawtooth framework is applied to secure the provided data. The blockchain framework provides two types of communications: one is in the form of secure and trusted routing, and the second is in the form of secure weather information.
* In this section of the thesis, we take the routing and pre-secured framework and enhance the working of the proposed approach through secure routing and weather forecasting frameworks.  
Figure 5: This distance-based method computes the intermediate node distance from the border of the current vehicle domain.  
* The public keys are distributed among networks for decrypting the weather forecasting information, and private keys are kept secret to be adopted by every other node inside the network.
* The role of RSU is to implement the routing strategy to obtain minimum hop count, less time, secure content delivery, and network throughput.
## 4 Results and Discussions
### Discussions
#### 4.2.4 Scalability and Robustness  
The integration of Petri nets into proximity logic modeling has proven to be a robust and scalable methodology for such applications. Compared to more conventional approaches, such as those presented by [PERSON] et al. [4] in geolocation-based marketing strategies, our system not only optimizes location-based content delivery but also facilitates future system expansion. This feature is particularly valuable for mobile applications that must quickly adapt to changes in demand or functionality.  
The effectiveness of Petri nets in managing asynchronous systems was evidenced by consistent response times and the absence of communication collisions between the mobile application and the API. The network structure allowed for token distribution that accurately reflected the flow of concurrent events, such as receiving coordinates and verifying proximity. This ensured sequential message processing without disrupting other processes, thereby optimizing the user experience.  
Petri net modeling provided a clear visualization of potential bottlenecks within the interaction flow. By representing key places and transitions within the system--such as location reception and promotion delivery--areas for improvement were identified in the communication flow. This resulted in greater efficiency in delivering real-time promotions.  
This comparative analysis reinforces our study's contribution to the field, highlighting how combining modern technologies (Kotlin, Laravel) with formal modeling through Petri nets offers significant advantages in terms of performance, accuracy, and scalability for proximity-based mobile applications.
## Case Study
### Application of Fast Optimization Approach
#### 6.2.2 Re-Location  
Given that we are studying the placement of wells on a plane, we compressed the three-dimensional reservoir model into two dimensions to define the activation region. The compression method involves numerical averaging and threshold truncation of the vertical direction grid. Specifically, for each planar point, the permeability of the vertical grids at its location is averaged, and a fixed value is then used to determine whether it belongs to the activation region. In this paper, we logarithmically averaged the vertical permeability values and used 0.01 as the threshold. Furthermore, for carbonate rock reservoirs, as the flow mainly occurs in the caves and fractures, we designated the caves and fractures as the high-active grids and the matrix as the low-active grids. As seen from the results in Figure 6a, the high-active grid only represents a small portion of the total grid. When the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Range** & **Unit** & **Description** \\ \hline \(x_{1},x_{2},x_{3}\) & [1; 36] & grid number & well location x coordinate \\ \hline \(y_{1},y_{2},y_{3}\) & [1; 22] & grid number & well location y coordinate \\ \hline \(q\) & [\(-50,50\)] & m\({}^{3}\)/d & injection and production rate \\ \hline \hline \end{tabular}
\end{table}
Table 2: The range of optimization variables.  
well position is deployed in the low-active region, we employed the automatic re-location technique to re-activate the well, as marked by the red circle in Figure 6b.
## 4 Results and Analysis
### Analysis of Compliance Behavior
#### 4.1.2 Intrinsic/Extrinsic Motivations and Compliance  
Motivation plays a vital role in the behaviors of employees. Various psychology researchers classify motivations into two significant classes: intrinsic motivations (motivation for own's sake) and extrinsic motivations (perceived motivation from the environment). A detailed study has been performed by [8] in the context of motivations. He examined the intrinsic and extrinsic motivation models for ISB on 602 employees from different organizations. He concluded that intrinsic motivations (perceived legitimacy, perceived value congruence) affect employees' ISB towards ISPC more significantly than extrinsic motivations (perceived deterrent certainty, perceived deterrent severity). Reference [36] presented a taxonomy of ISB for the betterment of information security management. Furthermore, researchers examined extrinsic motivation factors in detail and concluded that extrinsic motivation factors significantly affect employees' ISB. Similarly, [59] argued that deterrence is not as effective as internal motivation. They developed a framework to test internal and external motivations' effects on the ISB of employees. Their results showed that an external perceived locus of causality (sanctions and deterrence) has negligible effect on ISB compared to one's internal perceived locus of causality (own values, personal liking/disliking). The studies evaluate the intrinsic, extrinsic motivations enlisted in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Research Method** & **Sample Size** & **Findings** \\ \hline \multirow{3}{*}{[44]} & Qualitative study & 19 semi-structured interviews from the US and Ireland & US employees have more adaptive behavior toward security policies and procedures than Irish employees. \\  & analytic grounded & 19 semi-structured interviews from the US and Ireland & US employees have more adaptive behavior toward security policies and procedures than Irish employees. \\  & theory approach adopted & 19 semi-structured interviews from the US and Ireland & US employees have more adaptive behavior toward security policies and procedures than Irish employees.