esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology, Fundamentals, and Materials
### Materials and Methods  
The following sources, methods, and tools were used to perform the research:  
* Modeling is the representation of a real-world scenario by a mathematical expression and/or a simplification of the real-world system. Simulation uses a model to generate data, and these data can then be analyzed [38]. Simulation allows companies to assess how decisions should be made and the optimal time to make them. Therefore, these analyses can assist in determining whether certain decisions are wise or not, acting as decision-supporting tools [39]. The use of simulation for manufacturing system design and analysis is recognized by scientists and industrial managers [40].
* System dynamics simulation: System dynamics is a method that was created in the mid-1950s by Professor [PERSON] and is a method of studying dynamic systems. System dynamics suggests a very high abstraction level and is positioned as a strategic modeling methodology [41]. Simulations will be used more extensively in plant operations to leverage real-time data to mirror the physical world in a virtual model, which can include machines, products, and humans. Decision-making quality can be possibly improved by an easy and fast process with the help of simulations [2]. Simulations thus become important and extremely efficient research methods in the current context of manufacturing digitalization. Applied in production systems, simulations can provide answers to how they respond to the various variables and unpredictable situations that arise. Simulation models are usually needed when it is impossible or quite difficult to develop an analytical solution to a studied problem [42]. This type of continuous simulation is chosen over discrete simulations, such as discrete-event or agent-based modeling, due to their high abstraction level that represents global causal dependencies with aggregates and feedback dynamics supporting the integral assessment and selection of strategic alternatives of a manufacturing system, while discrete simulations are focused on events or agents, that is, individual objects with exact sizes, timings, etc.; however, this is not the focus of the current study.
* Simulation software: The use of simulations for manufacturing systems design and analysis is rightfully recognized by scientists and industrial managers and the literature is abundant in this field [40] There are different software packages on the market that enable system dynamics modeling [43]. VENSIM (Ventana Systems) simulation software was selected for this research.
* Databases in Excel files were used as input data for simulations.
# On-Board Wind Scatterometry
## 4 Experiment and Results  
To validate the proposed method of on-board wind scatterometry, the on-board processing chain for scatterometer was established and simulated with the system illustrated in Figure 2. Then, CFOSCAT data were applied to this system.. The description of these data is in Section 4.1. The on-board L1 processing took the information of each pulse from L0 data obtained from the ground station as an input unit. In wind inversion, LUTs were calculated according to system design parameters and the rotating-fan-beam-antenna feature of CFOSCAT. Views were generated from slice NRCS for wind inversion. Neighboring WVCs were located for medium filtering in wind direction determination. The results of these processing steps are described in Section 4.2, with a validation of the proposed method.
## 3 A Quality Aware Routing Metric for WOBAN
### Transmission Delay  
The transmission delay in 802.11 channels depends on a number of components, of which the queuing and access delays are significant. The queuing delay \(Q_{d}\) is the property of the transmitting  
\begin{table}
\begin{tabular}{c l l} \hline \hline \(n_{a}/n_{b}\) & \(\triangleq\) & Active neighbors of the sender/receiver \\ \(T_{d}\) & \(\triangleq\) & Transmission delay \\ \(\Delta_{RTS}/\Delta_{CTS}\) & \(\triangleq\) & Regions where the RTS/CTS packets for the test link are received \\ \(\Delta_{CS}\) & \(\triangleq\) & Area where nodes can sense the transmission from sender \(S\) \\ \(\lambda\) & \(\triangleq\) & Packet arrival rate \\ \(DATA\) & \(\triangleq\) & Data packet length (in bits) \\ \(B\) & \(\triangleq\) & Channel bandwidth (in bits/seconds) \\ \(P_{S}\) & \(\triangleq\) & Probability of success \\ \(Q(R)\) & \(\triangleq\) & Quality metric for route \(R\) \\ \(K_{m,n}\) & \(\triangleq\) & Complete bipartite graph with \(m+n\) vertices and \(mn\) edges \\ \(K_{n}\) & \(\triangleq\) & Complete graph of \(n\) vertices \\ C & \(\triangleq\) & Number of channels \\ \(I\) & \(\triangleq\) & Number of interfaces \\ \(\tilde{G}\) & \(\triangleq\) & Planar subgraph of \(G\) \\ \(M\) & \(\triangleq\) & Number of chromosomes in the mating pool \\ \(M_{e}\) & \(\triangleq\) & Number of elite chromosomes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table of notations.  
router, which is the time that a packet has to wait in its transmission queue before it actually reaches the head of the queue and starts contending for the channel. On the other hand, the access delay \(Q_{a}\) is the time that a packet needs to wait at the head of the transmission queue before the channel contention is resolved by CSMA/CA and the packet gets access to the channel and starts transmission. The total delay \(T_{d}\), which is the sum of the average queuing and access delays, is an important factor affecting the quality of a communication link.  
In the presence of RTS/CTS packets, the time for a packet to reach the destination depends on the active neighbors of the sender (\(n_{a}\)) as well that of the receiver (which we denote by \(n_{b}\)). This is due to the fact that a data packet is not transmitted unless the receiver has access to the channel to send the CTS. Thus, the total delay \(T_{d}(n_{a},n_{b})\) in the presence of RTS/CTS can be approximately estimated as:  
\[T_{d}(n_{a},n_{b}) = \mathcal{A}(n_{a}^{2}+n_{b}^{2})+\mathcal{B}(n_{a}+n_{b})+ \mathcal{C}, \tag{1}\]  
where \(\mathcal{A}\), \(\mathcal{B}\), \(\mathcal{C}\) varies with load and are obtained by best fitting the simulated values [37].
# Remote-Sensing Image Encryption Algorithm Using the Advanced Encryption Standard
## 3 Proposed Image Encryption Algorithm
### Sender's Encryption Process  
Figure 1 is the image encryption flowchart of the proposed algorithm. [PERSON] performs the following steps to encrypt the plain image.  
Step 1: Chaotic Sequence Generation  
Let the plain image be \(I_{\mathrm{m}\times\mathrm{n}}\). The variables \(x_{0}\) and \(q\) of PWLCM can be generated with the method described in Section 2.3. [PERSON] iterates Equation (1) \(m\times n\) times with \(x_{0}\) and \(q\), and she obtains a chaotic sequence \(\mathrm{X}=\left\{x_{i}\right\}_{\mathrm{m}\times\mathrm{n}}\).  
Step 2: Chaotic Image Generation  
If 16 digits exists after the decimal point for all the \(x_{i}\in X_{\mathrm{mn}}\), [PERSON] computes:  
\[y_{i}=\mathrm{mod}\Big{(}x_{i}\times 10^{16},\;256\Big{)} \tag{5}\]  
where \(Y=\left\{y_{i}\right\}_{\mathrm{m}\times\mathrm{n}}\), and \(\mathrm{mod}(\cdot)\) is the modulo operation. After that the sender can convert the vector \(Y\) into a matrix \(\mathrm{C}_{\mathrm{m}\times\mathrm{n}}\), named as the chaotic image.  
Step 3: Group Pixels  
The pixel value in binary form is an 8-bit number. Here, [PERSON] uses AES-256 to encrypt the 128-bit data blocks. Therefore, [PERSON] groups 16 pixel values together to form a big integer number with the 128-bit length. The first big integer is:  
\[b_{1}=I_{1,1}^{\mathrm{b}}I_{1,2}^{\mathrm{b}}\cdots I_{1,i}^{\mathrm{b}} \cdots I_{1,16}^{\mathrm{b}},\quad i=3,\;4,\;\cdots,15 \tag{6}\]  
where \(I_{1,1}^{\mathrm{b}}I_{1,2}^{\mathrm{b}}\cdots I_{1,i}^{\mathrm{b}}\cdots I_ {1,16}^{\mathrm{b}}\) denote the pixels of \(I\) in binary form. [PERSON] can obtain \((m\times n)/16\) plain big integers \(\left\{b_{1},\;b_{2},\;\cdots,\;b_{(m\times\mathrm{n})/16}\right\}\) for \(I\) in total. Similarly, [PERSON] can also obtain \((m\times n)/16\) chaotic big integers \(\left\{r_{1},\;r_{2},\;\cdots,\;r_{(m\times\mathrm{n})/16}\right\}\) for \(C\).  
Figure 1: The encryption flowchart of the proposed algorithm.  
Step 4: AES Encryption  
[PERSON] calculates:  
\[c_{1}=\text{AES}_{\text{e}}(K_{1},b_{1})\bigoplus r_{1} \tag{7}\]  
\[c_{\text{i}}=b_{1}\bigoplus c_{i-1}\bigoplus r_{\text{i}},\quad i=2,\;3,\; \cdots,\;(m\times n)/16 \tag{8}\]  
where \(\{c_{\text{i}}\}\) are encrypted big integers, \(\text{AES}_{\text{e}}(\cdot)\) is the encryption function of AES, whose input parameters are the key \(K_{1}\) and \(b_{1}\).  
Step 5: Recovering Pixels  
[PERSON] divides \(c_{\text{i}}\), \(i=1,\;2,\;\cdots,\;(m\times n)/16\) into 16 parts, and converts them into pixel values \(\left\{p_{1}^{\text{i}},\;p_{2}^{\text{i}},\;\cdots,\;p_{\text{j}}^{\text{i}}, \;\cdots,\;p_{16}^{\text{i}}\right\},j=3,\;4,\;\cdots,\;15\) as shown in Figure 2. According to the pixel positions, she rebuilds these pixel values into an encrypted image \(E\) with the size \(m\times n\).
# Nonlinear Seismic Response of Multistory Steel Frames with Self-Centering Tension-Only Braces
###### Abstract  
The self-centering tension-only brace (SC-TOB) is a new and innovative bracing system that provides both a flag-shaped recentering hysteresis and load mitigation to structures. This paper presents an extensive investigation of the nonlinear seismic response of multistory steel frames built with SC-TOBs to internal force, drift, and energy dissipation. Pushover analysis subjected to two lateral load distributions and nonlinear dynamic analysis under ground motion ensembles corresponding to four hazard levels were conducted. The SC-TOBs can be designed to serve as conventional tension-only braces (TOBs) only providing lateral stiffness during minor earthquakes, to function with energy dissipation as intensity increases, and to fully recenter a structure even after severe earthquakes. The findings show that with an increase in the earthquake intensity, both the force response and drift response of the SC-TOB frames (SC-TOBFs) increased; however, the force distribution and drift distribution shapes of the SC-TOBFs remained almost constant. The SC-TOBFs generally experienced more energy dissipation in the lower parts of the building, while the upper stories dissipated almost no energy under certain load conditions, suggesting that the bracings on those stories could be replaced by conventional TOBs for economy. It is demonstrated that the SC-TOBs have immense potential to effectively improve seismic resilience to structures such that rehabilitation costs and operational disruptions after earthquakes are minimized.  
self-centering; tension-only; brace; damping systems; seismic response; resilience +
Footnote †: journal: Applied Sciences
## 7 Method Application 2: Iron Losses Computation
### Bertotti Formulation  
The other main formulation for the iron losses, linked to the element by element analysis, is the Bertotti formulation:  
\[P_{\rm fe}=2\gamma_{\rm lam}\cdot\sum_{\rm el}V_{\rm el}\,k_{\rm hy}\,f_{\rm s }\,B_{\rm el}^{2}+\frac{1}{12}\,d^{\,2}\,\sigma\,\sum_{\rm el}V_{\rm el}\cdot \left[\left(\frac{\Delta B_{x,\rm el}}{\Delta t}\right)^{2}+\left(\frac{\Delta B _{y,\rm el}}{\Delta t}\right)^{2}\right] \tag{28}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **No-Load Losses** & \\ \hline
**Order of Harmonic** & **Stator Losses** & **Rotor Losses** \\
**-** & **W** & **W** \\ \hline
1 & 101.9 & - \\
29 & 2.2 & - \\
31 & 4.1 & - \\
36 & - & 29.3 \\
59 & 1.8 & - \\
61 & 2.3 & - \\
72 & - & 19.6 \\
108 & - & 5.0 \\
144 & - & 0.7 \\ \hline \multicolumn{3}{c}{**On-Load Losses**} \\ \hline
**Order of Harmonic** & **Stator Losses** & **Rotor Losses** \\ - & **W** & **W** \\ \hline
1 & 95.3 & - \\
5.82 & - & 3.4 \\
11.65 & - & 3.0 \\
29 & 24.8 & - \\
31 & 22.7 & - \\
34.96 & - & 33.6 \\
59 & 2.8 & - \\
61 & 3.0 & - \\
69.92 & - & 14.9 \\
104.9 & - & 4.1 \\
139.8 & - & 1.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Harmonic iron losses.  
with:  
\(B_{\rm el}=\sqrt{B_{x,\rm el}^{2}+B_{y,\rm el}^{2}}\), instantaneous element flux density  
\(d\) lamination thickness  
\(\sigma\) lamination conductivity  
For this lamination, the excess losses coefficient, at the rated frequency, is equal to zero.  
The instantaneous values of the stator and rotor iron losses are reported in Figure 15. It can be noticed that, using this method, it is not necessary to simulate a complete electric period; actually, simulating only one stator slot pitch is sufficient to get the average value. Furthermore, the comparison between the two considered methods is reported in Table 5.  
Finally, the main advantage of the instantaneous losses method is the computation time: a much lower time span has to be simulated to get the average losses. The drawback is that the harmonic subdivision of the losses is not possible anymore. Another advantage is that the presence of the slip is not an issue, since no harmonic analysis is required.  
The skewing analysis in Section 5 can be coupled with iron losses computation, to get the variation of the specific losses with the slice angle. In Figure 16, it can be verified that in the skewed machine the iron losses are almost equal to the unskewed case.
## 2 Finite Element Model of Reef-Island-Seawater System  
### Computational Model  
As shown in Figure 2, the length of the 2D reef-island-seawater system model is 5000 m. The height of the seabed and seawater is 600 m, and the width of the reef platform is \(L\). The reef medium is a linear elastic medium. The reef island is assumed to consist of three layers. The slope of the first and second layers is fixed at 60\({}^{\circ}\) and 30\({}^{\circ}\), respectively, and the slope of the third layer at the bottom is \(\theta\). To discuss the influence of \(\theta\) and \(L\) on the seismic response of the reef site, \(\theta\) = 30\({}^{\circ}\), 45\({}^{\circ}\), and 65\({}^{\circ}\), and \(L\) = 200 m, 500 m, and 700 m, respectively. According to the relevant studies on the geological characteristics of islands and reefs in the South China Sea and the longitudinal distribution of wave velocity of reef limestone, the site topography and material parameters of the reef island are shown in Table 1. The model was established by using the common finite element software ANSYS 18.0. Solid45 element is used to simulate solid media such as seabed and reef, and the Fluid80 element is used for simulating seawater. For the coupling of the fluid-solid interface, the dynamic interaction between seawater and island topography is simulated by  
Figure 1: The research framework diagram of this article.  
coupling the normal degrees of freedom of the fluid and the solid nodes. For the truncation boundaries of the fluid and solid media, the fluid dynamic artificial boundary and the consistent viscoelastic artificial boundary are adopted. APDL parametric programming in ANSYS is used to implement operations such as the determination of artificial boundary parameters and the extraction and application of free field displacement and equivalent seismic loads on boundary nodes. The dynamic solution of the model in the time domain is obtained by using the Newmark-\(\beta\) method.  
The model discretization may cause \"low-pass effects\" and \"dispersion effects\", which will cause the waveforms of displacement and stress waves to change during propagation. The extent of this change depends not only on the cutoff frequency and medium shear-wave velocity but also on the element size. Because the number of elements directly affects the accuracy and time of dynamic calculations, it is extremely important to control the finite element mesh size. According to [PERSON] et al. [33], the mesh size must meet the following conditions:  
\[\Delta x\leq\left(\frac{1}{6}\sim\frac{1}{8}\right)\frac{c_{min}}{f_{max}} \tag{1}\]  
where \(\Delta x\) is the mesh size, \(c_{\min}\) is the minimum shear-wave speed of the medium, and \(f_{\max}\) is the cutoff frequency. The cutoff frequency in this paper is 18 Hz, and the corresponding mesh size is 8-11 m. The mesh size is set at 10 m to ensure accuracy and improve the calculation efficiency.
## 3 Overview of Results
### Journal Analysis
Figure 6 presents the 10 most representative local sources using the H-index, also known as the Hirsch Index, which quantifies the total number of papers where the source has at least the same number of citations as the article [47]. Additionally, the G-index represents an improvement over the H-index, taking into account only the unique highest number where the top g documents obtained, in total, at least g\({}^{2}\) citations [48]. In our case, the most relevant source was _Energies_, with an H-index value of 5 and a G-index value of 9, followed by _Applied Sciences-Basel_, with an H-index value of 4 and a G-index value of 6. In third place was _Artificial Intelligence Review_, with an H-index of 4 and a G-index of 4. _Energy_, _IEEE Access_, _Journal of Hydrology_, _Mathematics_, _Sensors_, and _Soft Computing_ each had an H-index value of 4, while _Applied Soft Computing_, _Engineering Applications_ of  
Figure 4: Most Relevant Sources. Figure 5: Most Relevant Local Sources. The Bradford's Law method groups the journals into three different sections that should have the same number of papers, but each category has a different number of citations. According to [PERSON] et al. [49], the Bradford's Law clusters journals using the formula 1:n:n\({}^{2}\). The most representative journals from Zone 1 were _IEEE Access_ (11 articles), _Energies_ (9 articles), _Mathematics_ (7 articles), _Applied Sciences-Basel_ (6 articles), and _Applied Energy_ (5 articles). The most relevant sources in Zone 2 were _Journal of Hydrologic Engineering_ (3 articles), _Journal of Intelligent & Fuzzy Systems_ (3 articles), _Neural Computing & Applications_ (3 articles), _Renewable Energy_ (3 articles), and _Theoretical And Applied Climatology_ (3 articles). In the last part, Zone 3, the most significant journals were _Economics_ (1 article), _Energy and  
Figure 6: Sources with Local Impact. Figure 7: Core Sources by Bradford’s Law. AI_ (1 article), _Energy and Buildings_ (1 article), _Energy Conversion and Management_ (1 article), and _Energy Exploration & Exploitation_ (1 article). [PERSON] et al. [46] implemented the Bradford's Law method and discovered that _Expert Systems with Applications_, _IEEE Access_, _Knowledge-Based Systems_, _Mathematics_, and _Computational Economics_ were the main journals, which confirmed our results. At the same time, there were differences, with Bradford's Law identifying journals that were not included in our analysis, such as _Journal of Portfolio Management_, _Financial Markets and Portfolio Management_, and _Quantitative Finance_.
## 4 Economic Evaluations  
### Fixed Capital Cost  
According to the throughput of the process plant defined in this case study, the minimum possible capital investment for the unit product was estimated. The fixed capital cost of the demonstration plant was approximated as the sum of fixed costs (equipment costs and installation costs), because the ancillary costs for a demonstration plant are unpredictable.  
The cost for purchasing major equipment (reactors, separators, heat exchangers, etc.) was estimated from the power-law relationship between purchase price, equipment size, and inflation index [18]. The installed costs were estimated using the installation factor method [19]:  
\[\text{New Equipment Cost = Original Equipment Cost (New size/Original size)}^{\exp} \tag{4}\]  
\[\text{Installed Equipment Cost = Equipment Cost \times Installation Factor} \tag{5}\]  
The values for the installation factors and exponential factors in Equations (4) and (5) are shown in Table 3, and the equipment purchase and installation costs of the demonstration plant are summarized in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Equipment** & **Equipment Cost (millions \$)** & **Installation Cost (millions \$)** \\ \hline RCSTR & 3.1 & 5.0 \\ RSTOIC & 1.7 & 2.8 \\ HEATX1 & 0.2 & 0.44 \\ HEATX2 & 0.23 & 0.5 \\ HEATX3 & 0.02 & 0.04 \\ SEP1 & 0.02 & 0.06 \\ SEP2 & 0.18 & 0.54 \\ SEP3 & 0.008 & 0.02 \\ SEP4 & 0.09 & 0.28 \\ Total & & 15.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Equipment costs and installation costs.  
Figure 4: Heat duty of the key blocks in the process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Equipment** & **Installation Factor** & **Exp** \\ \hline reactor (stainless steel) & 1.6 & 0.5 \\ heat exchanger (stainless steel) & 2.2 & 0.7 \\ separator & 3.02 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Installation factor and exponential factor.
## 6 Conclusions  
An evaluation of the consistency of five land cover classification schemes of MCD12Q1 are performed based on the 30 m Chinese GlobeLand30-2010 GLC product in Anhui Province. Two primary methods are specifically used, including harmonization of the land cover classification based on the knowledge rule and C4.5 decision tree classification algorithm. In general, there is a strong consistency of area percentage, spatial consistency, classification accuracy and landscape diversity among the five classification schemes of MCD12Q1 based on GlobeLand30.  
(1) The \(R\) of IGBP is found to be slightly different, ranging from 98.29% to 99.63%, in the three sub-regions and in Anhui Province. Thus, IGBP shows the best compatibility and robustness, indicating  
Figure 6: Comparison of landscape diversity indices of _MSIDI_ and _MSIEI_ in (**a**) Wanbei, (**b**) Wanzhong and (**c**) Wannan.  
that the consistency of IGBP is the best on a regional scale. The _PD_ of IGBP and UMD are the smallest on each type in the three sub-regions, indicating that both IGBP and UMD present the best consistency.  
(2) The \(O\) of LAI/FPAR has the lowest value, 58.17%, and its spatial consistency of \"woodland\" is weak. Conversely, NPP has the best spatial consistency of \"woodland\" among the five schemes with an \(O\) of 86.70%. Specially, the misclassification between \"others\" and \"woodland\" is the main cause leading to the worst consistency of LAI/FPAR; that is, the \"woodland\" type of GlobeLand30 is largely misclassified into the \"barren land of LAI/FPAR.  
(3) The _p\({}_{A_{i}}\)_ and _p\({}_{u_{i}}\)_ of \"artificial surfaces\" of LAI/FPAR scheme are the lowest in the three sub-regions in comparison to the four other schemes of MCD12Q1 (Table 8). Furthermore, the _OA_ of NPP varies greatly in the three sub-regions, indicating that the consistency of NPP is the worst among the five schemes.  
(4) Since the landscape diversity indices of Wanbei, Wanzhong and Wannan are different from each other, it shows the obvious spatial heterogeneity of the three sub-regions. The consistency of LAI/FPAR is found to be the best on the landscape diversity in the three sub-regions. Meanwhile, the more heterogeneous the landscape is, the weaker the consistency of the five land cover classification results of MCD12Q1 become.  
Given the great significance to evaluate and validate the quality and consistency of different GLC products, this study evaluates the consistency of 500 m MCD12Q1 based on 30 m GlobeLand30 on a provincial scale. This study can provide a methodological reference for evaluating the consistency of different GLC products derived from multi-source and multi-resolution remote sensing datasets on various spatial scales.