esa-sceva/satcom-chunk-collection huggingface dataset:
# LEO Precise Orbit Determination with Inter-satellite Links
## 1 Introduction
Low Earth orbit (LEO) satellites have lower orbital altitude (500-2000 km) than MEO, IGSO, and GEO satellites, so the orbital measurements of LEO satellites mainly come from downlink data of higher GNSS satellites which are received by onboard receivers. POD for LEO satellites include \"two-step\" approach (doing POD for GNSS satellites at first with ground measurements and then doing POD for LEO satellites with onboard measurements) [30] and \"one-step\" approach\" (the orbits of LEO and GNSS satellites are recovered in one simultaneous process with both ground and onboard measurements) [31], both of which can reach an accuracy of a few centimeters [32]. Similar to navigation satellites, POD of LEO satellites is highly dependent on the ground or onboard ranging data. Since the data processing strategy is unrelated to orbital altitude, it is confirmed to be feasible to introduce in-orbit ISL measurements for POD of LEO satellites. Nowadays, LEO constellation has become a new hotspot and is vital to the future of satellite industry and scientific researches [33]. Because the acquisition of LEO satellite orbits is a prerequisite for putting them into the market or scientific applications, it is also of great significance to study the POD of LEO satellites based on ISLs. Chinese Hongyan LEO constellation equipped with the ISL payloads is under construction and will complete the deployment of main constellation by 2023 [34]. Undoubtedly, it will facilitate the future research of LEO satellite POD with ISLs. Our study mainly focuses on preliminary evaluation of the performance of LEO satellite POD with ISL measurements. In the following Section 2, basic concept of ISL is introduced and its observation model is described. Preparation of orbit determination experiment, including the design of LEO constellation, the simulation of ISL measurements, and the selection of link topology, is detailed in Section 3. Besides, our method for POD with ISLs is also introduced. Section 4 presents the result and analysis. Then the shortcomings of the work and an outlook for future research are summarized in Section 5. Finally, Section 6, presents the conclusion of our research.
## 4 Conclusions  
In summary, an optical hyperspectral image cryptosystem is proposed, using a triangular association encryption algorithm model with signature and authentication in Gyrator domains (TASA). The number of the hyperspectral bands is not unique, and the proposed cryptographic system can be implemented in different multi-band images. The block mobile method makes the 3D Arnold more suitable for hyperspectral images with a different number of bands, and increases the difficulty of deciphering the images. The triangular association encryption algorithm model makes diffusion expand from within the band to within and between the bands, avoiding the repeated operation of a single encryption algorithm on different bands. The image signature not only provides a large number of additional keys, but also enables the receiver to verify the identity of the originator. Moreover, the image integrity authentication enables the receiver to verify the integrity of the received message. By using numerical simulation, including various potential attack experiments, the optical hyperspectral image cryptosystem is proved to have the characteristics of effectiveness, security and robustness, and the functions of image signature and image integrity authentication.  
Formal analysis, Z.Y., Y.C., S.L., Z.L. and H.C.; Funding acquisition, Z.L. and H.C.; Investigation, C.T., W.B., Z.L. and H.C.; Writing--original draft, Z.Y. and H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant 62005320, 61975044 and 12074094).  
Not applicable.  
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors are indebted to the two anonymous reviewers for their professional comments to improve the manuscript.  
The authors declare no conflict of interest.
## 4 Application and Discussion of the Methodology
### Discussion of the Results  
From Table 8, it can be obtained that the failure probability of electrical misoperation accidents is \(1.0414\times 10^{-2}\). Compared with the 5.6E-03 probability of an electricity maintenance accident [50], the probability of occurrence of EMAs is relatively higher. Because this article not only considers the misoperation of maintenance but also considers the misoperation, it considers not only the human factor but also considers the organizational factor. Compared with the 7.401 \(\times\)\(10^{-2}\) probability of occurrence of fire-explosion accidents in ship engine rooms [21], and the 4.823 \(\times\)\(10^{-2}\) probability of occurrence of LPG release accidents [19], the probability of occurrence of EMAs is relatively tiny. However, they belong to the same quantitative level. According to the \"Safety Production Risk Management Measures\" promulgated by various provinces in China [67], the probability of an accident between 0.01 and 1 is a very probable accident and must be controlled. It is confirmed once again that the accident analysis and prevention work of EMAs is meaningful. In this study, the cut-set analysis method of FFTA was used to identify non-conforming items or combinations of non-conforming items that led to the occurrence of TE. It can be seen from Table 9 that Cut Set BE8 (Habitual violation) and BE14 (Supervisory violation) are among the top two, and violations by employees and commanders will directly lead to infractions. There are strict \"two votes, three systems\" rules and regulations for electricity production, but they are often ignored. [PERSON] [68] deliberately summarized a number of ways in which the \"two votes, three systems\" had been violated. [PERSON] [69] concluded that 70% to 80% of man-made operational accidents in power systems are directly linked to violations. This result obtained in this study is more in line with reality. The weakest cut set combination is the BE12 (Work preparation) and BE5 (Thoughts are paralyzed, work is sloppy and irresponsible) combination. It is necessary to avoid the team's work preparation before the operation, especially the explanation of dangerous points and the arrangement of safety measures, which occur at the same time as the paralysis and irresponsible thinking of the operators. [PERSON] et al. [70] pointed out that human error mostly occurs due to a general lack of situational awareness among personnel. By not explaining the dangerous points before the operation and by not placing safety measures in place, the operator will lack situational awareness of their area of operation
## 3 Multi-Ship Collision Avoidance Decision Making Based on Full Communication
### Blockchain-Based Broadcast Message and Information Interaction for Ship Collision Avoidance Process  
When the ship CA process is simplified to the selection of priority avoidance ships and CA path targets, the ship CA broadcast (CAB) message and information interaction become clear. The content of the CA broadcast proposed in this paper mainly contains the CA notice ID, the issuing notice ship ID, the notice time, the OS's information (speed, real-time heading, real-time position), and the OS's decision broadcast (priority avoidance ship and path target). Among them, the collision avoidance notice ID is the unique identification of the collision avoidance broadcast; the issuing notice ship ID is the unique identification of the issuing broadcast ship. The format of the data contained in the collision avoidance notice is shown in Figure 6.  
The ship broadcasts the collision avoidance through the content of a block structure to other ships in the encounter. At this time, the block structure uploaded by the ship to the blockchain is shown in Figure 7. The block data are divided into two parts: the block header and the block body. The block header is the identification of the block, and the main contents include the title, the timestamp, the length of the block, the digital signature of the uploader of the block, and the credit change by the transaction. The header contains the identity of the uploader and the reputation points they have; the timestamp indicates the time when the block was formed; and the body length changes with the number of nodes contained in the block. According to the communication system described above, the steps of the message upload and credit evaluation usually result in changes in the reputation points of the uploader and the evaluator, so there is a field indicating the gain or loss of reputation points generated because of the block. The main information passed by the block, CAB, comes from the definition of CAB in Figure 6. The title of each block is connected to the previous block, thus forming a blockchain.  
Figure 6: The content of blockchain-based collision avoidance broadcast (CAB).  
As can be seen by the definition of the CAB, the content of the CAB can actually be considered an extension of AIS messages. Previous studies [13] have already confirmed that this design of the blockchain structure can satisfy the basic needs even of traditional AIS communication without equipping new communication devices due to the simplicity of the communication content. A similar setup is used in this paper.
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 2 Architecture of Proposed Self-Powered Passive Tag
### High Frequency (HF) Section  
The HF section is used to harvest the electromagnetic energy available in the environment as well as transmit the temperature data using ASK backscatter modulation. This section is composed of a dipole antenna, a matching network, a three-stage differential rectifier, and an ASK backscatter modulator. The dipole antenna generates a differential time-variant voltage \(v_{ASK\pm}\) between its terminals, while the matching network is used to transfer the maximum gathered power from the antenna into the differential power harvester. The three-stage differential rectifier extracts a non-regulated \(V_{REC}\) DC voltage from the \(v_{RF\pm}\) voltage that is used to supply all the on-chip circuitry. The ASK backscatter modulator is used to transmit the temperature data using a backscatter technique. The block diagram of the HF section is shown in the black-dashed line box in Figure 1 while its schematic circuit is re-drawn in Figure 9.
## 2 Materials and Methods
### MOPSO with COMSOL Multiphysics-MATLAB LiveLink  
In order to use the powerful optimization toolboxes and rich built-in libraries in MATLAB, we connected COMSOL Multiphysics with MATLAB via MATLAB LiveLink for COMSOL. Figure 5 shows a schematic description and data flow between MATLAB and COMSOL Multiphysics during our optimization process. The whole process consists of two sub-steps: (1) model calibration and (2) process parameter optimization. First, printing parameters to be calibrated are sampled from the upper and lower bounds of each parameter. Selected printing parameters are sent to the photopolymerization model in COMSOL Multiphysics. COSMOL performs photopolymerization process simulation with given parameters and passes the result (curing depth, in this case) back to MATLAB. This process iteratively finds the calibrated parameters that minimize the deviation between curing depth in experiment and simulation. The calibrated parameters are updated in photopolymerization model in COMSOL Multiphysics.  
Second, MOPSO samples printing parameters in the parameter search space and passes it to the updated model in COMSOL Multiphysics to obtain surface profile of AM part of given inputs. The surface profile is extracted from photopolymerization simulation and returned to MATLAB. The custom-built MATLAB script calculates root mean squared error (RMSE) value of the given  
Figure 5: Flow chart of MATLAB LiveLink interface with COMSOL Multiphysics. Proposed modeling framework consists of two sub-steps: model calibration and process parameter optimization. The flow of data is shown as arrows and labels.  
surface profile and uses it as an objective function in the optimization process. The method to calculate RMS for a given surface profile is explained in detail in Appendix D. Another objective function is printing time, which can be calculated by using a custom-built MATLAB script. The detailed method for obtaining printing time is in Section 3.3. This loop continues until it reaches the iteration limit.
# A Topological Sensitive Node Importance Evaluation Method in Aerospace Information Networks
## 4 Network Survivability Theory
### Satellite Network Survivability Assessment  
In Section 4.2, we calculate the importance value \(I_{i}\) of each node from the importance transfer matrix, and then the network structure entropy based on the node importance can be expressed as [23; 24]:  
\[S=-\sum_{i=1}^{N}I_{i}\cdot\ln I_{i} \tag{8}\]  
When the importance of nodes in the network is clarified, the higher the importance of the nodes in the network, the more critical the status. For the satellite network topology, when the more critical nodes are attacked, the more destructive it is to the network topology, and the easier it is to cause network interruption. It can be seen that the importance of nodes is closely related to the survivability of the satellite network. If there are no obvious key nodes in the network, the network is more uniform, and the stronger the survivability of the network will be. On the contrary, if there are several obvious key nodes in a network, the network will be more uneven. When the key node is attacked, it has a great impact on the robustness of the network, resulting in poor survivability of the network. Therefore, we can use entropy to evaluate the survivability of spatial information networks.  
In the space information network, nodes represent different types of spacecraft. Due to the different functions and orbits of the spacecraft, the flight speeds of the spacecraft vary greatly. Therefore, the network topology is highly dynamic, that is, the links between the nodes in the network will frequently switch, which will lead to continuous changes in the network topology. In this process, the number of nodes does not change. In view of the appeal, we will carry out simulation experiments based on the fat-tree network. The fat-tree network topology is shown in Figures 2 and 3.  
In the process of operation of the spatial information network, the pathway between \(L1\) and \(L2\), \(L3\) and \(L4\), \(L5\) and \(L6\), \(L7\) and \(L8\) disappears, and a new pathway is established between \(L2\) and \(L3\), \(L4\) and \(L5\), \(L6\) and \(L7\). This is shown in Figure 3.
## 5 Results and Findings  
The research concerning dental images applies both machine learning and deep learning models. The main aim of this section is to conduct a review of studies concerning the AI approaches associated with dental images.  
Most of the selected studies used a deep learning-based approach in detection and diagnosing, mainly convolution neural networks (CNNs). Many studies that used pretrained networks reported healthy performance in general. On the other hand, in the last years, the machine-learning-based approach has been used in estimation and classification.  
The selected studies covered a wide range of topics in dentistry, as shown in Figure 5. Most were regarding dental disease detection and diagnosis using diverse types of 2D and 3D radiographs. Other papers have attempted new applications in dental images, such as the following:  
* A color-matching technique is a practical solution for classifying images of patients' teeth to figure out the most proper tooth replacement treatment in dentistry.
* Tooth-based age estimation is the most accurate method of predicting age.
* Forensic odontology is a subfield of forensic science that focuses on determining a person's identity by analyzing their dental history.  
The features of the chosen studies that applied DL and ML techniques, respectively, are compiled in Tables 4 and 5.  
Figure 5: The application of DL and ML in dental images.
## 2 Structural Description and Finite Element Model  
### Description of the Taizhou Bridge  
The Taizhou Yangtze River Bridge, 'Taizhou Bridge' for short, is located in the lower reach of the Yangtze River in China, and first establishes the connection of land transportation between Taizhou and Zhenjiang, as shown in Figure 1. The bridge has been a landmark project across the Yangtze River and the longest triple-tower suspension bridge in the world since it was open to traffic in 2012. With respect to the geographical location, Taizhou Bridge is located in the eastern part of the Asian continent, which belongs to a wind-prone area that is vulnerable to the typhoons from the west Pacific Ocean in the summer and the strong northern wind from Siberia in the winter.  
As shown in Figure 2a, Taizhou Bridge is equipped with two main spans. Each main span is 1080 m long. The two main spans are continuously connected at the mid-tower while they are separated from side spans with two expansion joints installed at the side towers, respectively. The streamline flat-steel-box girder is employed as the bridge deck, as shown in Figure 2b. The total width of the bridge deck is 39.1 m with the wind mouth included. Two steel-wired cables are installed in parallel to support the static and dynamic loads on the main girder, and the sag-to-span ratio of the main cable is 1/9. The two side towers are reinforced concrete structures with a height of 178 m, and the middle tower is a steel tower with a height of 192 m. To constrain the lateral and vertical displacements of the main girder, lateral and vertical bearings are installed on lower cross beams of side towers, while only lateral bearings are installed on the mid-tower. Elastic restraints made of steel-stranded wires are installed in the longitudinal direction to control the relative displacement between the main girder and mid-tower. Since Taizhou Bridge is such a long-span bridge with a novel structural type, the buffeting performance attracts extensive attention.  
Figure 1: Geographical location of Taizhou Bridge.
# Design and Optimization of a Neuro-Fuzzy System for the Control of an Electromechanical Plant
## 6 Equations for Fuzzy Controller Parameter Training  
In this case, the training equations are established for the fuzzy controller based on Boolean relations; in this order, the equation of the plant is:  
\[\theta[n]=B_{0}u[n]+B_{1}u[n-1]+B_{2}u[n-2]-A_{1}\theta[n-1]-A_{2}\theta[n-2] \tag{38}\]  
Considering the controller architecture (Figure 12) and the expression for \(f_{i}\) given by Equation (25), the controller dynamics is given by:  
\[u[n]=f_{1}(e[n])-f_{2}(e[n-1])+f_{3}(u[n-1]) \tag{39}\]  
Additionally,  
\[e[n]=r[n]-\theta[n] \tag{40}\]  
is used to calculate the error variable. The respective derivative of the plant output \(\theta[n]\) for a controller parameter \(h_{ij}\in\mathbf{H}_{c}\) is:  
\[\frac{d\theta}{dh_{ij}}[n]=B_{0}\frac{du}{dh_{ij}}[n]+B_{1}\frac{du}{dh_{ij}}[ n-1]+B_{2}\frac{du}{dh_{ij}}[n-2]-A_{1}\frac{d\theta}{dh_{ij}}[n-1]-A_{2} \frac{d\theta}{dh_{ij}}[n-2] \tag{41}\]  
In the same way, the derivative of the control action \(u[n]\) with respect to \(h_{ij}\) is:  
\[\frac{du[n]}{dh_{ij}}=\frac{df_{1}(e[n])}{dh_{ij}}-\frac{df_{2}(e[n-1])}{dh_{ ij}}+\frac{df_{3}(u[n-1])}{dh_{ij}} \tag{42}\]  
Meanwhile, the derivative of the error \(e[n]\) for a controller parameter \(h_{ij}\) corresponds to:  
\[\frac{de[n]}{dh_{ij}}=\frac{dr[n]}{dh_{ij}}-\frac{d\theta[n]}{dh_{ij}}=-\frac {d\theta[n]}{dh_{ij}} \tag{43}\]  
To determine the respective derivatives, it is considered that:  
\[\frac{df_{1}(x_{l})}{dh_{ij}}=\frac{d}{dh_{ij}}(v_{11}\mu_{1}(x_{l}))+\frac{d} {dh_{ij}}(v_{12}\mu_{12}(x_{l})) \tag{44}\]  
where \(l=1,\ldots,3\), \(i=1,\ldots,3\) and \(j=1,2\); therefore, there are different cases for values of \(i\) and \(l\). According to the values of \(i\), \(l\) and \(j\) the possible cases are:  
* Case: \(l\
eq i\), where \(f_{l}\) does not depend directly on \(h_{ij}\). * Case: \(l=i\) and \(j=1\), where \(f_{i}\) directly depends on \(h_{i1}\). * Case: \(l=i\) and \(j=2\), where \(f_{i}\) directly depends on \(h_{i2}\).
## 4 Conclusions  
In this paper, we proposed an objective evaluation for stereo-to-multichannel upmix algorithms based on defined objective criteria, special test signals and several single evaluation tests. Two upmix algorithms available on the market were used to demonstrate the single tests exemplarily. The panning test checks whether the direction of the virtual sound source in the stereo-to-multichannel upmix corresponds to the direction of the virtual sound source in the initial stereo configuration. The direct signal test checks whether the remaining direct signal in the surround channels is as low as possible. The volume test checks whether the power and the loudness of the surround channels is not greater than these of the front channels. The phase test checks whether the surround channels of the stereo-to-multichannel upmix are not either completely correlated or completely decorrelated, but have a certain correlation. And the perception test checks whether the stereo-to-multichannel upmix generates a high subjectively perceived spatial quality.  
The introduced objective evaluation test enables an objective comparative evaluation, which can now provide a measurable quantity for the quality of stereo-to-multichannel upmix algorithms. In addition, the objective evaluation test could be used for the optimization of upmix algorithms and also for the clarification and illustration of the impacts and influences of different modes and parameters. The proposed objective evaluation test is assumed as an appropriate alternative or supplement for time-consuming and expensive subjective listening tests.  
Nevertheless, a comparison of the proposed objective evaluation test with subjective test results will be a focus of future work as part of appropriate validation.  
This research was funded by Helmut Schmidt University, Hamburg, Germany.  
[PERSON] is the first author, the developer of this research and wrote the paper. [PERSON] is the corresponding author, managed the overall research, supervised the complete work and edited the manuscript.  
The authors declare no conflict of interest.