esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design Architecture  
The design was written in a generic manner to support different signature widths via VHDL generics at synthesis-time and has been optimized for the Xilinx Virtex UltraScale+ FPGA VCU1525 Acceleration Development Kit. All FPGA designs evaluated in this work were developed using the Xilinx Vivado Design Suite Version 2019.1.  
The focus was to create a scalable design that could be applied and optimized for many possible use cases. Therefore, it should not only be capable of performing one specific distance calculation but be applicable for a range of other comparison and distance measuring algorithms.  
An overview of the basic design is shown in Figure 1. The PCIe-based connection to the host is established using the Xilinx XDMA IP-Core. For optimal performance with large numbers of comparisons, the design works with streamed input signatures and results. Both, input signatures and the results can either be transmitted directly to and from the host via control units or buffered in DDR memory. Distance calculation units (DCUs) are the central calculation elements of the architecture. To ensure the best possible performance, the DCUs and all its components are completely hand-optimized and implemented in VHDL. The DCUs contain the processing elements (PE) that perform the distance calculation and result handling. For all contained components, 1547 lines of VHDL code (counted without comments and blank lines) were implemented. General parameters like signature length, number of used PE, buffer FIFO sizes and the number of combined results in every collector stage can be configured as generic parameters. The design will automatically adjust to the given values. Since the number of logic resources, and thus the maximum number of PEs on the FPGA is limited, it was decided to store one signature of the query locally in each available PE; these signatures are therefore called _static signatures_ in the following. The signatures of the database are streamed between the PE in every clock cycle and are called _dynamic signatures_.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## Appendix B Linear Trend Calculation of Precipitation  
**1. Northern Region:**  
**Trend equation:**  
Y = \(-\)0.0673x + 754.07  
R\({}^{2}\) = 0.0003  
where:  
y = Estimated precipitation in mm  
x = Number of years since the starting point (2003). For example, 2003 = 0, 2004 = 1,..., 2023 = 20  
**Calculation examples:**  
**Year 2003 (x = 0):**  
Y = \(-\)0.0673(0) + 754.07 = **754.07 mm**  
**Year 2023 (x = 20):**  
Y = \(-\)0.0673(20) + 754.07 = **752.72 mm**  
**2. Central Region:**  
**Trend equation:**  
Y = 0.6738x + 928.93  
R\({}^{2}\) = 0.033  
**Calculation examples:**  
**Year 2003 (x = 0):**  
Y = 0.6738(0) + 928.93 = **928.93 mm**  
**Year 2023 (x = 20):**  
Y = 0.6738(20) + 928.93 = **942.41 mm**  
**3. South Region:**  
**Trend equation:**  
Y = 0.0846x+709.03  
R\({}^{2}\) = 0.0006  
**Calculation examples:**  
**Year 2003 (x = 0):**  
Y = 0.0846(0) + 709.03 = **709.03 mm**  
**Year 2023 (x = 20):**  
Y = 0.0846(20) + 709.03 = **710.72 mm**
# Throughput/Area Optimized Architecture for Elliptic-Curve Diffie-Hellman Protocol
## 3 Proposed ECDH Architecture
### Dedicated FSM Controllers  
As shown in Figure 3, two FSM controllers (_Controller-1_ and _Controller-2_) are used. The intent to use two controllers is to attain the minimum routing delays. In other words, a single FSM having a larger number of states results in longer routing delays [41]. Therefore, the _Controller-1_ operates like a wrapper for _Controller-2_. More precisely, _Controller-1_ provides the ECC parameters in terms of coordinates of either initial point \(P\) or public key \(Q\) as an input to _Controller-2_. After providing the input parameters, the _Controller-1_ stays in a WAIT state until the _Controller-2_ responds. More insightful details of these controllers are given as:
# FRAMUX-EV: A Framework for Evaluating User Experience in Agile Software Development
###### Abstract  
Agile software development prioritizes customer satisfaction through the continuous delivery of valuable software. However, integrating user experience (UX) evaluations into agile projects remains a significant challenge. Existing proposals address specific stages that apply UX evaluation methods but do not fully consider UX artifacts or UX events for integrating user experience into agile processes. To address this gap and support teams, we propose FRAMUX-EV, a framework for evaluating UX in agile software development using Scrum. FRAMUX-EV introduces seven UX artifacts: (1) UX evaluation methods, (2) UX design system, (3) UX personas, (4) UX responsibilities and roles, (5) UX evaluation repository, (6) UX backlog, and (7) UX sprint backlog and four UX events: (1) pre-planning UX meeting, (2) pre-review UX meeting, (3) weekly UX meeting, and (4) weekly user meeting. The first version of the framework was developed using a seven-step methodology with a qualitative approach. A survey of 34 practitioners validated the usefulness and ease of integration of FRAMUX-EV components, yielding positive results. These findings suggest the potential of FRAMUX-EV as an interesting proposal for integrating UX into agile software development.  
agile software development; scrum; user experience; user experience evaluation; framework +
Footnote †: journal:  
artifacts and UX events. It incorporates seven UX artifacts: (1) UX evaluation methods, (2) UX design system, (3) UX personas, (4) UX responsibilities and roles, (5) UX evaluation repository, (6) UX backlog, and (7) UX sprint backlog. Additionally, it includes four UX events: (1) pre-planning UX meeting, (2) pre-review UX meeting, (3) weekly UX meeting, and (4) weekly user meeting.  
A survey was conducted to collect and analyze the perceptions of practitioners. The main objective was to validate the usefulness and ease of integration of UX components (i.e., UX evaluation methods, UX artifacts, and UX events) for evaluating UX in agile software development, specifically within Scrum. In addition, practitioners were encouraged to recommend new components, suggest removing one component, or provide additional feedback. Overall, the UX components in the first version of FRAMUX-EV were positively received by practitioners. Consequently, no UX evaluation method, UX artifact, or UX event was eliminated in this first iteration. However, some refinements are necessary before proceeding with the remaining experiments and validations.  
This article is organized as follows: Section 2 introduces the theoretical background; Section 3 indicates the need for a UX evaluation framework; Section 4 explains the methodology; Section 5 presents FRAMUX-EV; Section 6 details the validation; Section 7 details the discussions; and Section 8 summarizes the conclusions and outlines future work.
# Research on the Robustness of Interdependent Networks under Localized Attack
## 3 Results and Analysis
### Artificial Network  
In this section, we focus on the robustness of artificial networks under different attack strategies, especially PDLA. First, we generate two WS networks with a network size of \(N_{\text{WS1}}=N_{\text{WS2}}=2000\), with an average degree of \(<\)\(K_{\text{WS1}}\)\(>\) = \(<\)\(K_{\text{WS2}}\)\(>\) = 6, the rewiring probability \(p_{\text{re}}\) = 0.2; and generate BA and NN networks with \(N_{\text{BA1}}\) = \(N_{\text{BA2}}\) = 2000, \(<\)\(K_{\text{BA1}}\)\(>\) = \(<\)\(K_{\text{BA2}}\)\(>\)\(\approx\) 6; \(N_{\text{NN1}}\) = \(N_{\text{NN2}}\) = 2000, \(<\)\(K_{\text{NN1}}\)\(>\) = \(<\)\(K_{\text{NN2}}\)\(>\) = 6. Then we can create fully-coupled WS-WS, BA-BA, and NN-NN interdependent networks. When constructing interdependent networks, two WS or BA subnetworks are randomly coupled, but interdependent NN-NN networks are vulnerable under random coupling due to their special spatial distribution [7]. Therefore, the spatial location is considered when constructing interdependent NN-NN networks, as shown as Figure 2b. Finally, the robustness of interdependent networks is analyzed by numerical simulation under four attack strategies, namely TA, RA, TLA, and PDLA. Here TA means targeted attack on high degree nodes, RA denotes random attack on nodes during simulation.  
Figure 4 shows the robustness of isolated WS, BA, and NN networks and interdependent WS-WS, BA-BA, NN-NN networks under four attack strategies. Critical thresholds \(p_{\text{c}}\) of interdependent networks are listed in Table 2. From the results of Figure 4 and Table 2, we can draw the following conclusions.  
1.1 The Existence of Dependency Links Makes Interdependent WS-WS and BA-BA Systems More Vulnerable, but Makes No Difference on Interdependent NN-NN Networks Coupled Based on Strict Spatial Position  
Compared the results of isolated networks in Figure 4a,b with the results of interdependent networks in Figure 4d,e, we can see that, no matter under what type of attack strategies, the robustness of interdependent networks is worse than the isolated network. An example is that the robustness of interdependent WS-WS and BA-BA networks under TA shows a first-order phase transition, while the isolated network shows a second-order. In addition, the isolated WS network has a strong robustness under PDLA as shown in Figure 4a. When it comes to interdependent networks, the robustness of WS-WS is vulnerable. However, as shown in Figure 4c,f, interdependent NN-NN networks show the same failure process as isolated NN network, which is consistent with [7]. The reason for this phenomenon is that coupling strictly-considered space restrictions in interdependent NN-NN networks is equivalent to increasing the degree of each node by 1, making NN-NN essentially an NN network with average degree <K> = 7.
###### Abstract  
When diagnosing damage to high-rise buildings during earthquakes, it is necessary to measure the displacement of each story. However, with respect to accuracy and cost, it is most reasonable to convert acceleration into displacement. In this study, shake table testing was carried out to verify the conversion methods, converting the acceleration data measured in a high-rise building into velocity and displacement. In the shaking table test, the displacement of a 10-story model building under strong motion was measured using high-speed imaging devices. High-speed images were taken at 1000 frames per second, reflecting the dynamic behavior of the model building. Then, this displacement was compared with the displacement obtained by processing the acceleration data. This study applied three methods for correcting and converting acceleration into velocity and displacement. Method 1 used the transfer function, \(H_{2}(\omega)\), which reflects the dynamic characteristics of the system. The displacements converted by this method showed the lowest accuracy, because the transfer function depends on the dynamic characteristics of the structure. Method 2 used the cosine Fourier transform for baseline correction, and the discrete input data are calculated as the sum of the cosine functions. Method 3 used the least-squares fitting in the first step to remove the linear drift in the acceleration and applied the high-pass Butterworth filter. The displacements converted by Method 2 were the most reliable, and were close to the displacements measured in the shaking table test. However, the response of high-rise buildings is affected by low- and high-frequency noise. It is necessary to further investigate the limitations and applicability of the conversion methods for providing reliable displacement of the building.  
high-rise building; acceleration data; shaking table test; conversion method; baseline correction +
Footnote †: journal: Applied Sciences
# Dynamic Node Privacy Feature Decoupling Graph Autoencoder Based on Attention Mechanism
## 3 Preliminaries  
_Variational Graph Autoencoder_  
Variational graph autoencoder (VGAE) [21] is a generative model based on variational autoencoder and graph convolution networks to embed a graph to a low-dimension space. Firstly, the encoder part applies a node-wise transformation by a spectral convolution function :  
\[Z^{(l+1)}=f\Big{(}Z^{(l)},A|W^{(l)}\Big{)}, \tag{1}\]where \(Z^{(l+1)}\) is the output of prezent layer, and \(tZ^{(l)}\) is the output of prior layer. In this paper, \(Z^{(0)}=X\) is the input features matrix of the graph. \(W^{(l)}\) is parameter matrix which will be learned. The spectral convolution function could be specifically formulated as follows:  
\[f(Z^{(l)},A|W^{(l)})=\phi\Big{(}\widetilde{D}^{-\frac{1}{2}}\widetilde{A} \widetilde{D}^{-\frac{1}{2}}Z^{(l)}W^{(l)}\Big{)}, \tag{2}\]  
where \(\widetilde{A}=A+\mathbb{I}_{n}\) and \(\widetilde{D}=\sum_{j}\widetilde{A}_{ij\cdot}\)\(\phi\) is a non-linearity activation function such as ReLU. Then the encoder part could be constructed as follows:  
\[Z^{(1)}=f_{ReLU}\Big{(}X,A|W^{(0)}\Big{)}, \tag{3}\]  
\[Z^{(2)}=f_{linear}\Big{(}Z^{(1)},A|W^{(1)}\Big{)}. \tag{4}\]  
By employing a variational inference model, the following forms can be derived as:  
\[q(Z|X,A)=\prod_{i=1}^{n}q(z_{i}|X,A)=\prod_{i=1}^{n}\mathcal{N}(z_{i}|u_{i}, \text{diag}(\sigma^{2})), \tag{5}\]  
where \(\mu=Z^{(2)}\) is the mean vector matrix representing the embedding of node \(i\); similarly, the log-variance \(\log\sigma=f_{linear}(Z^{(1)},A|W^{(1)})\) shares the parameters of the first-layer graph convolutional neural network with the mean \(\mu\).  
The goal of the VGAE is to embed the graph's topological structure into a low-dimensional space. Therefore, during the decoding phase, the adjacency matrix is reconstructed through the inner product of the embedding matrix \(Z\). The reconstructed adjacency matrix \(\hat{A}\) can be derived as follows:  
\[\hat{A}=\text{sigmoid}(ZZ^{T}), \tag{6}\]  
where \(Z^{T}\) is the transpose matrix of the embedding matrix \(Z\). To preserve the graph's structural information, the VGAE measures the difference between the reconstructed adjacency matrix and the original adjacency matrix by minimizing the cross-entropy loss:  
\[L_{rec}=\mathbb{E}_{q_{\theta}(Z|X,A)}[\log p_{\phi}(A|Z)]=\frac{1}{N^{2}} \sum_{i=1}^{N}\sum_{j=1}^{N}A_{ij}\log(\hat{A}_{ij}). \tag{7}\]  
Finally, the loss function of the variational autoencoder is formulated as:  
\[L=L_{rec}-L_{reg}=\mathbb{E}_{q_{\theta}(Z|X,A)}[\log p_{\phi}(A|Z)]-\text{KL} [q_{\theta}(Z|X,A)\|p(Z)], \tag{8}\]  
where \(q_{\theta}(Z|X,A)\) denotes the posterior distribution of the embedding representation \(Z\) encoded by the graph neural network; \(\text{KL}[q(\cdot)\|p(\cdot)]\) denotes the KL divergence between distributions \(q(\cdot)\) and \(p(\cdot)\); \(p(Z)\) represents the prior distribution of the embedding representation \(Z\). The second term serves as a regularization term, ensuring that the latent distribution of the graph data explicitly fits a predefined prior distribution.
## 3 Results and Discussion  
### Geological Fault Density Map  
To identify fault trends, their trends were compared with mineralization zones. Furthermore, the fault density map (Figure 11) was prepared, which shows the association of mineralized faults. The dominant veins are with 45-60 degree azimuth, which are in very good agreement with the NE-SW faults. We also marked the presence of a second family of faults trending ENE-WSW, which represents the major Pan-African faults. Most of the faults have a NE-SW trend in the area with intense intersection of thrust and normal faults. The overlap between the faults map and the mineralized veins shows an intimate association between the ore veins and the fault zones. The fault density map corroborates with the field results, as it shows a high concentration of mineralization in areas with high fault density, which proves that these faults served as a drainage channel for hydrothermal fluid from the deep zones towards the subsurface.
## 1 Introduction
In previous research, only one stage units were considered (Figure 1b). The results of the calculation indicate that it is possible to increase the thermal efficiency of a simple system (regeneration degree 0.6-0.9) by applying a steam jet injector for the same parameters of the cycle [1,4]. As a result of the decreased steam bleed demand, and, at the same time, the increase of the available enthalpy drop in the turbine, the thermal efficiency of the thermodynamic cycle increases. However, to obtain the desired outlet steam pressure from the regenerative injector, the drive steam of a relatively high energy level is required. The application of the higher steam bleed pressure results in the decrease of the available enthalpy drop in the turbine. Based on the results of further research, the usage of a single stage steam jet injector is limited to the feed water temperature of 110 \({}^{\circ}\)C. For higher temperatures of feed water, a higher heating steam pressure (on the outlet from the injector) is required. To obtain the desired pressure two-stage regenerative steam, jet injectors can be used. A Mathematical Model of a Two-Stage Steam Jet Injector Unit in the Main Boiler Regenerative Feed Water System  
The results of the research presented in the first chapter indicate the validity of the use of regenerative steam jet injectors in the regenerative feed water systems. It has also been shown that the use of higher temperatures, due to the steam saturation pressure, requires  
Figure 1: Thermal flow diagram of the proposed model: (**a**) Clausius–Rankine cycle with a regenerative heater (deaerator) feed from steam bleed; (**b**) Clausius–Rankine cycle with a regenerative heater (deaerator) feed by regenerative injector. Reproduced with permission from the MAPE Conference Organizing Team, published by New Trends in Production Engineering 2018.  
the use of steam bleeds to drive the injectors from higher energy levels. In this case, the available enthalpy drop in the turbine is significantly reduced, which is not compensated by the utilization of the latent heat of the exhaust steam. Thus, ultimately, the degree of regeneration of the system is lower than with a direct feed from a suitably selected steam bleed. Therefore, the analysis covered the possibility of using two-stage injectors units in order to obtain adequate heating steam pressures at the inlet to the heat exchangers. In order to conduct comparative tests of systems using two-stage compression of the exhaust steam from the turbine, the parameters of the injector systems for two variants were determined. In the first one, shown in Figure 1(a), both injectors are driven from the same steam bleed. In the second variant (the thermal flow diagram is shown in Figure 1(b)), each injector is driven by steam from a different energy level. Due to the available pressure drop in the nozzle of the unit, it is expedient to use the steam from a lower energy level for the first stage injector.
# Differential Evolution in Waveform Design for Wireless Power Transfer
## 2 Wireless Power Transfer System Model
### Objective Function  
The optimization problem in hand is to design a waveform \(s(t)\) (find the corresponding \(\mathbf{a}\) and \(\mathbf{\theta}\)) that maximizes the DC power delivered to the load \(R_{L}\), for a specific constraint in the average power of the designed waveform. For this problem, we apply the objective function that is utilized in [10], which is based on [PERSON]'s circuit laws of the equivalent circuit depicted in Figure 3. The assumption of a lossless antenna, perfect matching between the antenna and the rectifier, ideal diode and negligible fluctuation of the output voltage, led to a tractable nonlinear rectenna model and made the derivation of the objective function possible [9; 10]. According to [10], the formulation of the objective function can be expressed as:  
\[F(\mathbf{a},\mathbf{\theta})=\frac{1}{T_{o}}\int_{0}^{T_{o}}e^{\frac{\sqrt{R_{s}}(t)} {\Psi_{t}}}dt \tag{6}\]  
which is subject to:  
\[a_{n}\geq 0\quad\text{and}\quad\sum_{n=1}^{N}\frac{a_{n}^{2}}{2}\leq P_{t} \tag{7}\]  
Based on [10], the maximization of (6) with respect to (7) maximizes the DC power delivered to the load. In (6), we set \(\eta=1.05\) as the ideality factor of the diode and \(V_{t}=25.86\;\mathrm{mV}\) its thermal voltage. Also, we denote as \(P_{t}\) in (7), the power constraint imposed on the designed waveform in Watts.  
For the numerical computation of the integral in (6), we apply the 2-point Newton-Cotes formula [10]. To this end, we set the sampling frequency at \(f_{s}=20f_{c}\). Considering that the sampling period is given by \(\Delta t=1/f_{s}\), the number of sub-intervals by \(Q=T_{o}/\Delta_{t}\), and \(Q\) is rounded to the nearest integer that is \(\leq Q\), (6) derives to:  
\[F(\mathbf{a},\mathbf{\theta})=\frac{1}{Q}\sum_{q=1}^{Q}e^{\frac{\sqrt{R_{s}}\sum_{q=1 }^{N}a_{n}b_{n}c(2\pi/q_{s}\Delta t+\theta_{n}+\eta_{n})}{\Psi_{t}}} \tag{8}\]  
The optimal selection of the phases \(\theta_{n}\) in the transmitted signal are defined as \(\theta_{n}=-\psi_{n}\)[9]. Consequently, if we choose a specific propagation channel and a set of \(f_{c}\), \(B\), and \(T_{o}\) values, the optimization problem confines to obtain the optimal value of the vector of amplitudes \(\mathbf{a}\) that maximizes (8) with respect to the constraint in (7).  
Figure 3: The equivalent circuit of the rectenna model (**left**) [9; 10]. The same circuit depicting the equivalent impedance \(R_{in}\) due to the rectifier and the load is also shown (**right**).
# A Novel Sucrose Isomerase Producing Isomaltulose from _Raoultella terrigena_
## 4 Conclusions  
This study provided a novel enzyme candidate for industrial production of isomaltulose. Pal-2 showed excellent catalytic ability and sucrose was transformed completely. The conversion rate of isomaltulose reached 81.7% with only 2% of monosaccharide byproducts. The molecular modification can realize the significant enhancement of conversion  
\begin{table}
\begin{tabular}{c c c} \hline
**Enzyme** & **Relative Activities (\%)** & **Conversion Rate of Isomaltulose** \\ \hline Pal-2 & \(100\pm 3.3\) & \(81.7\pm 3.2\) \\ N498P & \(189.2\pm 3.5\) & \(84.8\pm 2.8\) \\ Q275R & \(142.4\pm 4.7\) & \(81.1\pm 3.3\) \\ Y355A & \(122.6\pm 3.8\) & \(82.6\pm 2.7\) \\ A492E & \(103.3\pm 3.2\) & \(83.7\pm 3.5\) \\ Y246L & \(127.5\pm 3.9\) & \(89.1\pm 2.9\) \\ H481P & \(115.2\pm 2.7\) & \(92.4\pm 3.1\) \\ R310P & \(87.2\pm 2.9\) & \(80.5\pm 3.3\) \\ H287R & \(111.1\pm 3.6\) & \(90.7\pm 3.9\) \\ \hline \end{tabular}
\end{table}
Table 5: Relative activities and the conversion rate of isomaltulose.  
rate and activity, which provides the basis for the further study of the enzymatic production of isomaltulose. In order to save the production cost, immobilization and key sites exploration of Slase may be the focus of future studies.  
L.L.: Conceptualization, methodology, investigation, data curation, writing--original draft. S.Y.: Conceptualization, supervision, project administration, writing--review and editing. W.Z.: Conceptualization, supervision, project administration, funding acquisition, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial support provided by the National Key Research and Development Program of China (2017 YFC1601704), the National Natural Science Foundation of China (31522044, 31671909, 31772034, and 31901630), the Program of Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology (FMZ201904), the National First-class Discipline Program of Food Science and Technology (JUFSTR20180205), the Natural Science Foundation of Jiangsu Province-Youth Program (BK20190583), and the Fundamental Research Funds for the Central Universities (JUSRP12007).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.