esa-sceva/satcom-chunk-collection huggingface dataset:
# Stressing State Analysis of CFST Arch Supports in Deep Roadway Based on NSF Method
## 1 Introduction
Hence, it is significant for researchers to apply an innovative and effective method to a limited experimental data, so that the hidden knowledge in working behavior of the supports could be revealed;
2. The division of stressing state stages for CFST supports, which mainly determined by load-displacement curves in experiments or simulations, is not clear enough and has some randomness to an extent, enhancing the difficulty of further analysis and even leading to incorrect analysis results. Besides, the semi-empirical and semi-theoretical methods are usually adopted to predict the ultimate bearing capacity of supports, which result in considerable material cost and even irrational structural design;
3. The finite element simulation of CFST supports inevitable adopts experiential simplification, assumption, etc., so the simulation results of structural responses usually deviate from the test ones in different degrees in particular for the local simulation results. It is difficult to reveal the true changing characteristics of damage, internal forces and others for overall or local supports through simulation. In consideration of the three problems above, this paper introduces the numerical shape function (NSF) method to interpolate the limited experimental data, so that the more information about structural responses could be achieved. Then, applying the structural stressing state theory to the expanded strain data, the generalized strain energy density (GSED) is adopted as a characteristic parameter to investigate the stressing state characteristics of CFST arch supports. Stress analysis is a primary task for civil, mechanical and aerospace engineers involved in the design of structures [25; 26]. Hence, authors adopt the Mann-Kendall (M-K) criteria for \(E_{j}\)-\(F_{j}\) curve, and the four stressing state stages of the supports are distinguished by three characteristic loads (including updated failure load), which is verified through stressing state mode, etc. Besides, the common and different changing characteristics of stressing state for different supports are also revealed. The unseen knowledge achieved in this study could provide a new angle of view to conduct structural analysis of CFST supports and a reference to the design improvement.
## 2 Calculation Methods  
### Calculation of Energy Flux Density Using the Ray-Tracing Method  
In this study, we mainly used the ray-tracing method written in MATLAB R2021. When using this method for simulation calculations, many rays from the solar disk are traced, which carries a certain amount of energy. We calculate and record its intersection position with the reflector surface. The receiving surface is gridded, and the total number of rays intercepted by each receiver grid and the energy represented are counted. The energy flux density of the receiver at grid \(j\) is contributed by ray \(i\):  
\[E_{r}(j)=\frac{\sum_{i=1}^{N_{r}}I_{i}}{S_{r}} \tag{1}\]  
where \(E_{r}\) is the energy carried, \(j\) is the energy flux density on the \(j\) grid of the receiver, \(S_{r}\) is the area of the receiver grid, \(N_{r}\) represents the total rays intercepted, and \(I_{i}\) is the energy carried by reflected ray \(i\).  
In this paper, the sun shape is set to a Gaussian distribution, and the Gaussian model function generally is:  
\[B(\theta_{x},\theta_{y})=\frac{1}{2\pi\sigma_{t}^{2}}exp(\frac{\sigma_{x}^{2}+ \theta_{y}^{2}}{2\sigma_{t}^{2}}) \tag{2}\]  
where \(B\) is the intensity distribution function of the reflected solar rays. \(\theta_{x}\) is the reflected sun-ray distribution in the x-direction, \(\theta_{y}\) is the reflected sun-ray distribution in the y-direction. \(\sigma_{t}\) is the total optical errors.  
The reflected light intensity distribution of each ray incident on the receiver can also be simplified to a Gaussian distribution model. For line-focusing systems, the linear distribution function of light intensity is often used instead of the radial distribution function [17]. This is also the basis of the ray-tracing method proposed in this paper. The energy of each ray we trace is calculated based on the normalized linear intensity distribution function. The energy of the reflected ray \(i\) is calculated as follows:  
\[I_{i}=\frac{I_{0}}{\sigma_{t}\sqrt{2\pi}}exp(-\frac{\theta_{x}^{2}}{2\sigma_{ t}^{2}})dxd\theta_{x} \tag{3}\]  
where \(\theta_{x}\) is the distribution of reflected sun rays in the \(x\)-direction. \(\sigma_{t}\) represents the standard deviation of the total optical errors of the incident light after multiple reflections and refractions on the reflective mirror surface:  
\[\sigma_{t}^{2}=4\sigma_{x}^{2}+4 tan^{2}(\lambda)sin^{2}(\varphi/2)\sigma_{y}^ {2}+\sigma_{sun}^{2}+\sigma_{tracking}^{2} \tag{4}\]  
where \(\lambda\) is the incident angle, \(\varphi\) is the rim angle, and \(\sigma_{x}\) and \(\sigma_{y}\) are the standard deviations of slope error in the \(x\) and \(y\) directions.
## 2 Materials and Methods
### Coagulation Bath Optimisation
#### 2.5.1 ATR-FTIR  
The ATR-FTIR test was performed at two crucial moments in this study. First, it was used to evaluate the chemical composition of F\({}_{\text{uni}}\) and F\({}_{\text{hol}}\), investigating the structural changes caused by the DMF present in the fibres. The focus was on calculating the area index of characteristic functional groups, which allowed for determining the ideal size for the coagulation bath in the following steps. Subsequently, ATR-FTIR was used to analyse the PCFs' chemical composition and surface and identify the presence of PEGs in the fibre's core through their characteristic peaks. The analyses were performed on the ATR-FTIR IRAffinity-1S equipment (Shimadzu, Kyoto, Japan), equipped with a diamond crystal coupled to a HATR 10 accessory. The transmittance spectra were obtained in the wavenumber range between 400 and 4000 cm\({}^{-1}\), with a resolution of 2 cm\({}^{-1}\) and a scanning number of 200.
# A Data Secured Communication System Design Procedure with a Chaotic Carrier and Synergetic Observer
## 2 Related Works and Methods Overview
So, as described in Table 1 and beyond, the key advantages and main distinctive features in the design procedure of a synergetic observer in comparison with other observers are:  
* the structure of classical observers is rigid, i.e., the relationship between its variables are initially postulated but not formed during the design process for a particular object;
* the synergetic observer's structure is formed directly during the design procedure, so the observer has to be specially designed for particular object model, but the design procedure is more complicated and should be defined in every case;
* the synergetic observer's designed structure includes the right-hand sides of the equations of the chaotic generator, so it allows taking into account all the properties of a chaotic generator and the relationship between its variables. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Reconstruction technique**} & **Two Channel** & \begin{tabular}{c} **Lyapunov** \\ **Function** \\ **Definition** \\ \end{tabular} & \begin{tabular}{c} **Application to** \\ **Nonlinear** \\ **Objects** \\ \end{tabular} & \
\begin{tabular}{c} **Observer** \\ **structure** \\ \end{tabular} &
\begin{tabular}{c} **Need to Find a** \\ **Control Law** \\ \end{tabular} \\ \hline \hline
**Luenberger observer [32]** & No & Not needed & Limited & Rigid/Postulated & Yes \\ Kalman filter [31] & No & Not needed & Limited & Rigid/Postulated & Yes \\ Chaotic synchronization [25; 27] & No & Required & Yes & Rigid/Postulated & Yes \\ Adaptive observer [27; 28; 29] & No & Not needed & Yes & Rigid/Postulated & No \\ SMC observer [35] & No & Not needed & Yes & Rigid/Postulated & Yes \\ Reduced order observer [36; 37] & No & Required & Yes & Rigid/Postulated & No \\ Synergetic observer [39] & Yes & Not needed & Yes & Object-defined & No \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Signal reconstruction techniques features comparison.
## 3 Results
### Acquisition of Optimal Testing Parameters  
In order to verify the influence of the feature vector dimension, the number of feature points extracted, and the size of the neighborhood image block on the classification results, a comparative analysis was conducted to obtain the optimal parameters. In this study, three features with the first moment, six features with the first and second moments, and nine feature vectors with the first, second, and third moments were constructed, respectively. The feature points of 1500, 2400, and 3000 were respectively selected, and the neighborhood image block sizes of 3 \(\times\) 3, 5 \(\times\) 5, and 7 \(\times\) 7 were respectively selected. Figure 6 shows the results of the disease recognition using different feature vector dimensions, and Table 4 presents the statistical results of the different testing parameters. As can be seen from Figure 6, the classification results shown in Figure 6c,i are the best. It can also be seen from Table 5 that among the statistical results of fruit cracking and rust spot disease, when  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Confusion Matrix**} & \multicolumn{5}{c}{**Ground Truth**} \\ \cline{2-6}  & **Disease** & **Nectarine** & **Background** & **Total of Row** \\ \hline \multirow{4}{*}{**Predicted result**} & Disease & 2504/97.66 & 1884/5.59 & 137/0.47 & 4525/103.72 \\  & Nectarine & 49/1.91 & 30,692/91.07 & 418/1.43 & 31,159/94.41 \\ \cline{1-1}  & Background & 11/0.43 & 1127/3.34 & 28,714/98.10 & 29,852/101.88 \\ \cline{1-1}  & Total of column & 2564/100.00 & 33,703/100.00 & 29,269/100.100 & 65,536/300.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix of classification results for nine feature vectors (numerical/percentage).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification Results** & **UA (\%)** & **UA** & **PA (\%)** & **PA** \\ \hline Disease & 55.34 & 2504/4525 & 97.66 & 2504/2564 \\ Nectarine & 98.50 & 30,692/31,159 & 91.07 & 30,692/33,703 \\ Background & 96.19 & 28,714/29,852 & 98.10 & 28,714/29,269 \\ \hline \hline \end{tabular}
\end{table}
Table 3: User accuracy and producer accuracy of classification prediction results for nine feature vectors.  
six feature vector dimensions are used, the recognition results are the highest at 90.92% and 86.86%, respectively, and the corresponding kappa coefficient classification quality is considered \"excellent\". The number of feature points extracted has little effect on the recognition results; therefore, the influence on the recognition result can be ignored. The size of the neighborhood block has a significant impact on the recognition results, among which the size of 7 \(\times\) 7 is the best.
# Implementation of a Blood Cold Chain System Using Blockchain Technology
## 5 Conclusions  
We designed and implemented a blood cold chain system based on the private blockchain technology to achieve two goals: secure information visibility and reduce blood supply time. First, the real-time recording and sharing of information as blood is moved, consumed, and discarded in distributed ledgers enables efficient management of blood. In addition, this system, which prevents forgery and information tampering, will make the blood management operation more transparent by solving problems that may occur in medical institutions and with medical staff, such as missing inputs of entry, exit, and errors. Second, the system, which supports blood transactions between medical institutions after consensus is reached on urgent demand, has the significance of reducing the blood supply time, which is directly related to saving the lives of emergency patients. This also means that hospitals can use their surplus blood efficiently, because they can keep their blood stock intact while maintaining excess blood inventory.  
Currently, the use of blockchain technology in the medical field is actively discussed, but there are no previous relevant studies or application examples in the blood cold chain field. For realization of the blockchain network proposed by this paper, consideration should be given to coordination of interests among participants in the blood cold chain system as well as the infrastructural, legal, institutional, and technical aspects.  
Although this study proposes an innovative method for blood cold chain management and implementation of a prototype system, there is insufficient verification of the proposed system. Therefore, in the future, we will perform a thorough evaluation study in order to prove the effectiveness of the proposed scheme by integrating the proposed system with the actual blood information system. Furthermore, we will consider the proposed design's cost and network configuration and consider  
Figure 11: Hospital assets before and after executing blood request transaction.  
the additional information, block size, and hacking-prevention potential required for actual hospital blood transactions.  
Conceptualization, S.K. and D.K.; methodology, S.K.; software, S.K.; supervision, D.K.; writing--original draft, S.K.; writing--review and editing, J.K. and D.K. All authors have read and agreed to the published version of the manuscript. This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B05029080). The authors declare no conflict of interest.
# Routing Strategies for Isochronal-Evolution Random Matching Network
### Related Works on Routing Strategies  
In this paper, we focus on the routing strategy of IERMNs, which is a traffic dynamics problem. Traffic dynamics is a hot research field related to complex networks, in which the main research topic is packet transmission.  
In [20; 21], the traffic dynamics on multilayer network models were studied, and the impacts of different network structures on the traffic capacity were analyzed. In [22], the impact of the community structure on traffic dynamics in homogeneous random networkswas investigated. In [23], the traffic capacity of a network was found to be positively correlated to the entropy of the communication sequence. In [24], the influences of the coupling mode and its corresponding routing strategy on the traffic capacity of the two-layer network were studied. In [25; 26], the traffic dynamics in finite buffer networks were studied and a routing strategy motivated by a heuristic algorithm and a routing strategy based on dynamic local information were proposed. In [27], the mechanism by which the network structure impacts traffic dynamics was studied, and a high-contraction-centrality-first strategy was proposed to optimize the network structure.  
These existing works focused on the traffic dynamics in static networks rather than dynamic networks. We borrow some useful ideas and methodologies from them but cannot apply their proposed methods in the IERMN.  
The remaining of the paper is structured as follows: In Section 2, we introduce the IERMN and the traffic dynamics in the IERMN briefly. In Section 3, we discuss path planning and routing decision-making, which are fundamental concepts of the TD-IERMN. In Section 4, two routing strategies are proposed. In Section 5, the two proposed routing strategies are compared through simulation experiments.
## 9 Future Research Directions and Challenges
### Safety  
Safety is a major concern in robotics-related tasks. In general, there are two types of communication that apply to UAVs: Control and Non-Payload Communication (CNPC) and Payload Communication (PC). CNPC refers to control commands, way-points and navigation, usually in the order of several Kbps. Instead, the PC includes data transmission to the edge server, ground or aerial centre for processing. This information ranges from small-sized data, e.g., IMU, GPS, and RSS, to large-size high-resolution aerial images or LIDAR scans. While communication link reliability might not be vital for PC, avoiding communication interruptions is critical to CNPC. This leads to the concern of interruption of the CNPC link when there is no LOS path. Remarkably, using mmWave and beamforming features of 5G call for additional care because they come with the disadvantage of very high propagation pass loss [127]. Therefore, the design of trajectory and path-planning algorithms that guarantee LOS communication would be an interesting research topic. Another concern for CNPC links is cyber-physical attacks that might corrupt data transmission or cause incorrect action of the UAVs or UGVs [39].
## 2 Experimental Procedure
### Emissions Apparatus and Repeatability of Experiments  
NOx emissions were measured with a CAI-600 CLD NO/NOx analyser which uses a chemi-luminescence detector (CLD) method. Using Non-Dispersive Infrared (NDIR) technology, CO\({}_{2}\) was measured with a CAI-600 NDIR CO\({}_{2}\) gas analyser. HC was also measured with the CAI-600 instrument, using a flame ionization detection (FID) method. In order to ensure the measurement repeatability, in addition to CAI gas analysers, a fast response portable emissions measurement system (PEMS) for measuring NOx and carbon dioxide (CO\({}_{2}\)) was used in these experiments. The NOx sensor used in the PEMS is a ceramic exhaust sensor manufactured by Engine Control and Monitoring (ECM), which has a manufacturer specified precision of \(\pm\)5 ppm (0-200 ppm), \(\pm\)20 ppm (200-1000 ppm), and \(\pm\)2.0% (\(>\)1000 ppm) [34]. The details of the PEMS set up can be referred to Ref. [34].  
A water trap was used to prevent any moisture going into the sampling line of the PEMS and the CAI. Filtered compressed air through a high efficiency particulate air (HEPA) filter, at ambient temperature, was used for dilution in the CAI. The emission data was collected as raw emissions and no after treatment emissions devices were used in this study.  
Each experiment was repeated three times to ensure repeatability and data integrity. Any possible variations in the three experimental sets (engine and emissions equipment) were further investigated through statistical measures, such as the mean, median, and standard deviation. The mean values of the indicated mean effective pressure (IMEP) and the NOx emissions from the PEMS, at 100% load, in all segments, at 2000 rpm in the three repeat experiments are shown in Figure 2a,b. Since the results obtained for the mean, median and mode of the other parameters for the three repeat experiments showed similar repeatability at both engine speeds, they are not included here. The repeatability of the experiments was found to be within \(\pm\)3%. The higher variability in the NOx emissions shown in Segment 3 (Figure 2b) is attributable to subtle differences in the timing of the change in injection strategy during that segment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S. No** & **Model** & **Cummins ISBe220 31** \\ \hline
1 & Cylinders & 6 in-line \\
2 & Capacity & 5.9 L \\
3 & Bore x stroke & 102 \(\times\) 120 (mm \(\times\) mm) \\
4 & Connecting rod & 220 mm \\
5 & Maximum power & 162 kW @ 2000 rpm \\
6 & Maximum torque & 820 Nm @ 1500 rpm \\
7 & Compression ratio & 17.3:1 \\
8 & Aspiration & Turbocharged \\
9 & Fuel injection & High pressure common rail \\
10 & Emissions standard & Euro III \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the engine used in the current study [33].
# Dispatching Strategy for Low-Carbon Flexible Operation of Park-Level Integrated Energy System
## 4 Problem Formulation
### Constraint Conditions  
(1) Power supply system constraint:  
\[\begin{array}{l}p_{e,t}^{buy}+E_{VRE,t}+P_{t}^{ESSdc}-P_{t}^{ESSch}+P_{e,t}^{MT }+P_{t}^{ESL}\\ =p_{e,t}^{EB}+P_{e,t}^{cc}+P_{t}^{EL}+p_{e,t}^{ESL}+P_{t}^{EL},\forall t\end{array} \tag{18}\]  
where in period \(t\), \(P_{t}^{ESSdc}\) and \(P_{t}^{ESSch}\) are the output charge and discharge electric power of ESS; and \(P_{t}^{EL}\) is the PIES electrical load demand.  
(2) Heating system constraint:  
\[\begin{array}{l}P_{t}^{HSDdc}-P_{t}^{HSDch}+P_{t,t}^{GB}+P_{t}^{HIL}\\ =P_{t}^{HL}-P_{h,t}^{EB}+P_{t}^{HSL},\forall t\end{array} \tag{19}\]  
where in period \(t\), \(P_{t}^{HSDdc}\) and \(P_{t}^{HSDch}\) are the output charge and discharge heating power of HSD; \(P_{t}^{HL}\) is the PIES heat load demand.  
(3) Gas supply system constraint:  
\[\begin{array}{l}p_{g,t}^{buy}=P_{g,t}^{GB}+(P_{e,t}^{MT}+R_{e,t}^{MT})/ \zeta_{g2}^{MT},\forall t\end{array} \tag{20}\]  
(4) Power constraint:  
\[\left\{\begin{array}{l}P_{\min,g}^{buy}\leq P_{g,t}^{buy}\leq P_{\max,g}^{buy }\\ u_{buy,e}P_{\min,e}^{buy}\leq P_{e,t}^{buy}\leq u_{buy,e}P_{\max,e}^{buy}\\ u_{sell,e}P_{\min,e}^{sell}\leq P_{e,t}^{sell}\leq u_{sell,e}P_{\max,e}^{sell} \\ u_{sell,e}+u_{buy,e}\leq 1\end{array}\right. \tag{21}\]  
where \(P_{max,g}^{buy}\) and \(P_{\min,g}^{buy}\) are the maximum and minimum limitation of gas purchase; \(P_{max,e}^{buy}\) and \(P_{\min,e}^{buy}\) are the maximum and minimum limitation of electric power purchase; \(P_{max,e}^{sell}\) and \(P_{\min,e}^{sell}\) are the maximum and minimum limitation of electric power sales; and \(u_{buy,e}\) and \(u_{sell,e}^{sell}\) are a binary variable for the state of power purchase and sale.  
(5) ESS and HSD constraint:  
\[\left\{\begin{array}{l}S_{1}^{CN}=S_{24}^{CN}\\ \beta_{t}^{CNch}+\beta_{t}^{CNdc}=1\\ 0\leq P_{t}^{CNch}\leq\beta_{t}^{CNch}\max_{t}\\ 0\leq P_{t}^{CNdc}\leq\beta_{t}^{CNdc}P_{\max,t}^{CNdc}\\ S_{\min}^{CN}\leq S_{t}^{CN}\leq S_{\max,e}^{CN}\\ \sum\limits_{t=1}^{24}\left|\beta_{t}^{CNch}-\beta_{t-1}^{CNch}\right|\leq N_{ CN-c}\\ \sum\limits_{t=1}^{24}\left|\beta_{t}^{CNdc}-\beta_{t-1}^{CNdc}\right|\leq N_{ CN-d}\end{array}\right. \tag{22}\]  
where in period \(t\), \(S_{t}^{CN}\) is the capacity of the energy storage device, \(\beta_{t}^{CNch}\) and \(\beta_{t}^{CNdc}\) is the charge-discharge state binary variable; and \(N_{CN_{-c}}\) and \(N_{CN_{-d}}\) are the maximum numbers of charging and discharging.
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 1 Introduction
The contributions of this paper are summarized as follows:  
* Using the storage and representation capabilities of knowledge graphs, various parameters of decision-making elements are represented as entity attributes, so as to realize multi-parameter (including frequency domain, time domain, and power domain) decision-making for communication anti-jamming, and it has strong extensibility. * The anti-jamming decision-making algorithm based on the knowledge graph can take advantage of prior knowledge, and directly extract anti-jamming strategies by searching the knowledge graph for known jamming patterns. For unknown jamming patterns, prior knowledge can also be used to effectively reduce decision complexity. The rest of the article is organized as follows. The system model is presented in Section 2, at the mean time, the problem is formulated. Section 3 introduces the feature extraction and parameter decision-making methods of unknown jamming patterns; Section 4 presents the anti-jamming decision-making based on knowledge graph algorithm design; finally, the simulation results and analysis are given.