esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Empirical Analysis
### The NRM Analysis Based on the DEMATEL Technique
As shown in Table 8, the full influence matrix [11] consists of multiple elements, as indicated in Equation (6). The sum vector of row value is [53], and the sum vector of column value is [53]; the sum vector of row value plus column value is \(\{d_{i}+r_{i}\}\), which describes the full influence of matrix \(T\). As the sum of row value plus column value \(\{d_{i}+r_{i}\}\) is higher, the correlation of the dimension or criterion is stronger. The sum of row value minus column value is \(\{d_{i}-r_{i}\}\), which describes the net influence relationship. If \(d_{i}-r_{i}>0\), it means the degree of influencing others is stronger than the degree of being influenced. As shown in Table 9, the OM has the highest degree of full influence (\(d_{3}+r_{3}=70.930\)). The aspects of CR and RS are the highest net influence (\(d_{4}-r_{4}=1.036\)). The order of other net influences is listed as follows: OM (\(d_{3}-r_{3}=-0.084\)), PC (\(d_{2}-r_{2}\)= \(-0.408\)), and last, the ME (\(d_{1}-r_{1}=-1.580\)). \begin{table}
\begin{tabular}{c c c c c} \hline  & **Sum of Row** & **Sum of Column** & \
\begin{tabular}{c} **Sum of Row** \\ **and Column** \\ \end{tabular} &
\begin{tabular}{c} **Importance of** \\ **Influence** \\ \end{tabular} \\ \hline Professional competence (PC) & 0.970 & 0.984 & 1.954 & 3 \\ Operation management (OM) & 0.989 & 0.991 & 1.980 & 1 \\ Critical resources (CR) & 1.000 & 0.964 & 1.964 & 2 \\ Regulatory system (RS) & 0.960 & 0.924 & 1.884 & 5 \\ Market expansion (ME) & 0.943 & 0.998 & 1.941 & 4 \\ \hline \end{tabular}
\end{table} 
Table 6: The degree of direct influence. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Aspects** & **PC** & **OM** & **CR** & **RS** & **ME** & **Total** \\ \hline Professional competence (PC) & 6.885 & 6.882 & 6.731 & 6.500 & 6.913 & 33.911 \\ Operation management (OM) & 6.943 & 7.036 & 6.829 & 6.604 & 7.023 & 34.435 \\ Critical resources (CR) & 7.006 & 7.049 & 6.951 & 6.669 & 7.095 & 34.770 \\ Regulatory system (RS) & 6.779 & 6.824 & 6.676 & 6.489 & 6.853 & 33.621 \\ Market expansion (ME) & 6.691 & 6.725 & 6.584 & 6.359 & 6.811 & 33.169 \\ \hline Total & 34.305 & 34.516 & 33.771 & 32.621 & 34.694 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The indirect influence matrix (ID). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Aspects** & **PC** & **OM** & **CR** & **RS** & **ME** & **Total** \\ \hline Professional competence (PC) & 0.000 & 0.240 & 0.239 & 0.237 & 0.254 & 0.970 \\ Operation management (OM) & 0.250 & 0.000 & 0.261 & 0.229 & 0.248 & 0.989 \\ Critical resources (CR) & 0.264 & 0.265 & 0.000 & 0.230 & 0.241 & 1.000 \\ Regulatory system (RS) & 0.241 & 0.235 & 0.229 & 0.000 & 0.255 & 0.960 \\ Market expansion (ME) & 0.229 & 0.251 & 0.235 & 0.228 & 0.000 & 0.943 \\ \hline Total & 0.984 & 0.991 & 0.964 & 0.924 & 0.998 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The direct influence matrix (D).
## 6 Results and Comparison
### Synthesis Results  
The Xilinx ISE tool set has been used to conduct logic synthesis of the generated RTL description on FPGA. Table 1 summarizes the synthesis results of the proposed equalizer and demapper architectures. The table shows the logic utilization and maximum clock frequency when targeting a Xilinx Virtex-7 XC7 VX485T FPGA. The obtained synthesis results show that a low number of slices is utilized to implement the NISC-based equalizer and demapper architectures. Furthermore, it shows that the designed equalizer can achieve a maximum operating frequency of 202.67 MHz corresponding to a minimum period of 4.93 ns. Additionally, the designed demapper can achieve a maximum operating frequency of 293 MHz corresponding to a minimum period of 3.41 ns.  
Furthermore, the HDL descriptions, generated by the NISC tool set, of the proposed architectures have been synthesized on the ASIC target using the Design Compiler tool from Synopsys. Table 2 summarizes the synthesis results of the proposed architecture targeting 65-nm STMicroelectronics (ST)CMOS technology. The obtained results show that the proposed NISC-based equalizer and demapper architectures occupy a reasonable area. Furthermore, the equalizer can achieve a maximum operating frequency of 529 MHz; whereas the demapper architecture can achieve a maximum operating frequency of 520 MHz.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Logic Utilization and Timing** & **Equalizer** & **Demapper** \\ \hline
**Slice Registers** & 2029 out of 607,200 & 1290 out of 607,200 \\ \hline
**Slice LUTs** & 5942 out of 303,600 & 1517 out of 303,600 \\ \hline
**DSP48 Es** & 12 out of 2800 & 6 out of 2800 \\ \hline
**Max Clock Frequency** & 202.67 MHz & 293 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: FPGA synthesis results of the proposed NISC-based equalizer and demapper architectures targeting Xilinx Virtex-7 XC7 VX485T.  
Figure 17: Comparison between the simulated reference BER and measured BER after on-chip evaluation of the designed demapper for for 1536 source bits, a \(\frac{1}{2}\) code rate, 4 \(\times\) 4 MIMO and fast fading Rayleigh channel.
## 4 Experiments and Results
### Results Analysis
#### 4.3.5 Average Residual Energy  
In the study, Figure 15 shows the average remaining energy of nodes after the simulation. The findings indicate that our proposed method, compared with the PCC-RPL, can save 8% more of the remaining power for nodes. The chart displays the aggregate residual energy for the nodes at different times during the simulation, starting at one mJ at one time. The nodes' energy gradually drains out over time, and the PCC-RPL shows a significant energy loss at time twenty-nine, which remains slow for the rest of the simulation. Our mechanism has more remaining energy than the PCC-RPL because our trust model's processes operate on the controller in the control plane rather than on IoT networks, reducing the energy overhead at the deployed nodes. The nodes on the device layer only receive data about their neighboring nodes and forward it to the control plane through a sink node.  
Figure 14: Average Energy Consumption at the Network Level.  
Figure 13: Energy Consumption at the Node Level.  
Figure 16 represents the average residual energies at different numbers of nodes. Average residual energies are calculated on 30, 60, and 90 number of nodes respectively for a better evaluation of our proposed solution.
## 2 Materials and Methods
### Spectroscopic Methods
#### 2.4.2 Dynamic Light Scattering (DLS)  
Particle diffusion coefficients were measured by DLS using a Zetasizer Nano ZS instrument (He-Ne red laser (633 nm), Malvern Instruments). The apparatus is equipped with a temperature-controlled quartz Suprasil measurement cell, an automatic laser attenuator, and an avalanche photodiode detector. The position of the latter is located 173\({}^{\circ}\) relative to the laser source, which ensures backscattering detection. Experiments were driven by the Dispersion Technology software from Malvern Instruments. Particle diffusion coefficients were derived from analysis of the obtained intensity autocorrelation function. The corresponding polymodal (intensity-based) distributions in the (hydrodynamic) size of equivalent hard spheres were evaluated on the basis of the Stokes-Einstein equation. The protein concentration of 33.5 \(\upmu\)M (\(A_{275 nm}=0.2\)) at solution pH 7 was optimized for DLS experiments [10; 13], in order to ensure that the instrument count rates were in the interval of 200-500 kcps (kilo counts per second). All the material used was carefully cleaned and manipulated to avoid dust particles as well as solutions of the buffer and diluted HCl that were previously filtered using 0.22 \(\upmu\)m pore filters. 1 mL of protein solutions in 10 mM tris-HCl with a protein concentration of 33.5 \(\upmu\)M at pH 7 (prepared with ultrapure Milli-Q water) was transferred to a quartz cuvette (Hellma Analytics--QS 10.00 mm) and experiments were performed at 20.0 \({}^{\circ}\)C. Protein solution pH was adjusted before measurement.
## 4 Experimental Results
### Feature Correlation Analysis  
As shown in Figure 5, the correlation coefficient of the same feature on the diagonal is 1, and the closer the color of the matrix diagram is to black, the stronger the feature correlation and the positive correlation. The closer the color of the matrix diagram is to green, the stronger the feature correlation and negative correlation. The absolute value of the correlation coefficient between most features is less than 0.75. Exceptionally, the correlation coefficients of \"FRBMaterials\", \"Evacuation signs\", \"rIPC\", and \"Fire drills\" are all greater than 0.75. Since rIPC, IFP, FRBMaterials, Rescue sites, and Evacuation signs are all significant indicators that affect the fire risk of stadiums, after detailed consideration and consultation with fire experts, these properties are preserved. The merged dataset contains 139 observations and 17 attributes as the final cleaned dataset.
###### Abstract  
The composition of volatile oils of the leaf and stem of _Farfugium japonicum_ (L-) [PERSON] were prepared by supercritical fluid extraction (SFE)-CO\({}_{2}\). A total 47 and 40 compounds were identified by GC/MS analysis, respectively, and only 13 compounds coexisted. The main constituent types in the leaf oil included alcohols (34.1%), hydrocarbons (24.1%), terpenoids (16.2%), benzenes (7.5%), and fatty acids (4.9%). In the stem oil, the constituent types chiefly included benzenes (18.8%), ketones (13.9%), terpenoids (17.0%), fatty acids (8.8%), phenolics (8.7%), steroids (8.6%), hydrocarbons (8.0%), and esters (5.7%). The predominant volatile compounds in the stem were 2-(1-cyclopent-1-enyl-1-methylethyl) cyclopentanone (11.7%), 1,2,3,4,5,6,7,8-octahydro- 9,10-dimethyl-anthracene (8.4%), 5-heptylresorcinol (6.5%), and \(\alpha\)-sitosterol (5.2%). Those in the leaf mainly included (\(E\))-3-hexen-1-ol (13.7%) and (\(Z\))-3-hexen-1-ol (14.0%). This demonstrated a significant difference in the composition of both oils. Further study showed that stem oils demonstrated the highest DPPH (1,1-diphenyl-2-pinylhydrazyl) and -OH free radical scavenging capacities at IC\({}_{50}\) values of 9.22 and 0.90 mg/mL, respectively. In addition, they demonstrated the strongest antibacterial capacity against the Gram-positive bacteria methicillin-sensitive _Staphylococcus aureus_ (MSSA) and methicillin-resistant _Staphylococcus aureus_ (MRSA) at a minimum inhibitory concentration (MIC) of 0.16 mg/mL. This could be due to the SFE-CO\({}_{2}\) extraction and the high accumulation of benzenes, terpenoids, and phenolics in the stem. In particular, the monoterpenes presented in terpenoids could play a special role in these findings.  
F _Farfugium japonicum_; chemical components; volatile oil; antioxidative activity; antibacterial activity +
Footnote †: journal: Medical Optics
# Performance Analysis of On-Demand Scheduling with and without Network Coding in Wireless Broadcast
## 4 Proposed Heuristic Coding-Based On-Demand Broadcast
### Scheduling Algorithms with Heuristic Coding Implementation
#### 4.2.1 Real-Time Algorithms  
* EDF [25]: EDF serves the request with the minimum slack time. In other words, it prioritizes the request urgency in scheduling. The network coding version of EDF, needs to find the largest clique \(\delta\) which contains the vertex \(v_{i}\) with the minimum slack time \(T_{min}\) among all the vertices in graph \(G\). In the heuristic coding implementation (denoted by EDF_N), the system first finds the selected vertex \(v_{ps}\) with \(T_{min}\). In Algorithm 1, this is done in Step 2. \(v_{ps}\) is the vertex with the highest EDF scheduling priority, i.e., \(v_{ps}=\{v_{ij}|T_{i}=max\{\frac{1}{\Omega_{1}},\frac{1}{\Omega_{2}},\cdots, \frac{1}{\Omega_{n}}\},1\leq j\leq m\}\). Then Step 3 searches for the maximal clique \(\delta^{v_{ps}}_{max}\) to ensure that the encoded packet \(\beta(\delta^{v_{ps}}_{max})\) consists the requested data item of the most urgent client.
* SIN [9]: SIN broadcasts the item with the minimum \(SIN\) value. The network coding version of SIN, needs to find the clique \(\delta\) in \(G\) with the maximum \(\frac{|\delta|}{T_{min}}\) value, where \(T_{min}\) is the minimum value among all \(T_{i}\) in \(\delta\). In the heuristic coding implementation (denoted by SIN_N), the system first finds the selected vertex \(v_{ps}\) with the minimum \(\frac{T_{p}}{N(d_{s})}\) value, In Step 2 of Algorithm 1, the \(v_{ps}\) selection is done by finding the vertex with the maximum \(\frac{N(d_{s})}{\Omega_{p}}\) value, i.e., \(v_{ps}=\left\{v_{ij}\,|\frac{N(d_{j})}{T_{i}}=max\left\{\frac{N(d_{i}(1))}{T_{i }},\frac{N(d_{i}(2))}{T_{i}},\cdots,\frac{N(d_{i}(n))}{T_{n}},1\leq r,j\leq m \right\}\right\}\). Then Step 3 searches for the maximal clique \(\delta^{v_{ps}}_{max}\) to ensure that the encoded packet \(\beta(\delta^{v_{ps}}_{max})\) consists the requested data item of the client with the minimum \(SIN\) value.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 7 Case Study and Results
### Experimental Setup  
Scheduling optimization was performed using the Z3 SMT Solver (version 4.8.13) integrated into a Python environment (version 3.9). The experiments simulated real-time machine states and job scheduling under dynamically evolving fault probabilities. The solver was configured to minimize cumulative fault risk while satisfying job deadlines and machine constraints.  
The experiments were conducted on a system equipped with an AMD Ryzen 9 3900X processor (12 cores, 24 threads, base clock speed of 3.79 GHz), 64.0 GB RAM, and an NVIDIA GeForce RTX 2080 Ti GPU with 11.0 GB dedicated memory. This hardware configuration ensured efficient execution of both the deep learning fault prediction and the SMT-based scheduling optimization.  
Solver performance metrics, including execution time and schedule feasibility, were recorded to evaluate the system's effectiveness under various scenarios.
# TOSCA-Based and Federation-Aware Cloud Orchestration for Kubernetes Container Platform
## 3 Federation Frameworks of Containerized Services
### Implementation of Kubernetes Federation Cluster Configuration  
To express the relationship between individual Kubernetes clusters and Kubernetes Federation components in the TOSCA topology, the proposed system defined the TOSCA node and relationship templates as YAML-based Cloudify plug-ins in the following manner (as Cloudify Kubernetes plugin: plugin.yaml):  
* The Kubernetes cluster template is implemented as a component containing the Kubernetes cluster information that exists in the cloud provider's service when creating the topology of applications.
* The Kubernetes Federation template is implemented as a component using the information of the Kubernetes cluster with the Kubernetes Federation control plane installed.
* Kubernetes cluster template and Kubernetes Federation template are defined to permit master/slave configuration (\"managed_by_master\").  
The Kubernetes cluster components connected to the Kubernetes federation components are automatically generated and deleted during the orchestration lifecycle. This setup is implemented automatically by executing a series of commands that enable the Kubernetes cluster template to be affiliated to the Kubernetes Federation when the template is implemented as a component (as Cloudify Kubernetes plugin: cluster_create FUNCTION of cloudify_kubernetes/tasks.py). The deletion of the Kubernetes cluster components is accomplished similarly (as the Cloudify Kubernetes plugin: cluster_delete FUNCTION of cloudify_kubernetes/tasks.py Module). As a result of this setup, the developer can automate the distribution and federation of the Kubernetes clusters by defining the clusters and federation in TOSCA. Furthermore, if the topology of the application written for a single Kubernetes cluster is changed to be deployed over a Kubernetes Federation, the application components are automatically redistributed and federated to individual Kubernetes clusters, thereby providing a useful means to increase the reliability and responsiveness of an organizations' cloud service.
## 2 Experimental
### Binding Parameters Determination and Isotherm Fitting Models
#### 2.4.3 Imprinting and Selectivity Factors  
In each experiment the total number of moles of analyte, \(n_{total}\), is described as:  
\[n_{total}=n_{free}+n_{bound} \tag{4}\]  
where \(n_{free}\) and \(n_{bound}\) represent the number of free and bound analyte, respectively. Equation (4) allows the definition of the line of analyte conservation, constructed by the y interception point if considering that all analyte is bonded (F = 0, limit value of B), and by x-interception point if all analyte is free (B = 0, limit value of F).  
\[D=\frac{B}{F}=\frac{n_{bound/m_{p}}}{n_{free/V}} \tag{5}\]  
The distribution factor (\(D\)) was calculated with Equation (5) through the (\(x\),\(y\)) values (B and F, respectively) of the point of the intersection of the line of ligand conservation (\(C_{0}\) = 1.0, 1.5 and 2.0 mM) with MIP and with NIP isotherm functions [28].  
The imprinting factor (\(IF\)) was calculated by comparing the distribution factors of imprinted (\(D_{MIP}\)) and non-imprinted (\(D_{NIP}\)) polymers through Equation (6).  
\[IF=\frac{D_{MIP}}{D_{NIP}}=\frac{n_{bound,MIP/n_{free,MIP}}}{n_{bound,MIP/n_{free,NIP}}} \tag{6}\]  
Selectivity was studied by analyte batch-binding evaluation in the presence of 14 different competing compounds (\(C_{0}\) = 0.35 mM for all competitors). The selectivity factor (S) was calculated using Equation (7) by comparing the MIP distribution factors of analyte (\(D_{2C\text{-}B}\)) and competitors (\(D_{competitor}\)):  
\[S=\frac{D_{2C-B}}{D_{competitor}} \tag{7}\]
# Radio Frequency Electromagnetic Fields Exposure Assessment in Indoor Environments: A Review
## 3 Results
### Assessment Strategies  
Many different strategies have been used to assess the exposure to RF-EMF sources in indoor environments, both based on measurements and simulations methods. In particular, spot and long-term measurements by exposimeters placed in fixed position for a certain experimental duration were used to obtain information about environmental far field RF-EMF exposure in specific indoor environments. Personal exposure measurements, obtained by using portable exposimeters or personal exposure meters (PEMs), allowed to obtain information about the level of RF-EMF exposure received by an individual across different locations and times. Very specific tools, i.e., mobile phone based instruments such as smartphone based applications (see, e.g., the app XMobiSens used in the pilot study of [PERSON] and colleagues [23]), were used in epidemiological studies to obtain information linked to the near field RF-EMF exposure due to the mobile phones usage. Finally, also modelling and simulations strategies were used for indoor exposure assessment, in order to estimate whole body and organ-related Specific Absorption Rate.  
A recent study by [PERSON] et al. [24] reviewed the available measurement tools for RF-EMF exposure assessment, highlighting main advantages and limitations of each solution. Moreover, a recent survey by [PERSON] [22] reviewed main sources of biases of uncertainties when using exposimeters for personal and micro-environments surveys, and assessed temporal and spatial variability and the reproducibility of measurements. These aspects were found to be fundamental in order to make any general conclusion on the exposure, providing that exposure prediction based on that results an accurate description of real life situations [22].  
Last but not least, it is well known [25] that an important aspect of RF-EMF measurements is related to the handling strategy of non-detect, i.e., of the values below the lower detection threshold of the device used for the measure. This issue is often details in the considered papers (see, e.g., [26; 27]). More details about the assessment strategies are reported in the following sections, with particular attention paid to the differences in the measurements and simulations protocols applied in different studies that could influence results and make difficult to compare outcomes of different studies.
## 5 Experimental Verification
### Influence of PLL Bandwidth on the Stability of GCI Systems  
With voltage feedforward control considered, GCI grid-connected currents controlled by two different domains under varying PLL bandwidths are illustrated in Figure 15.  
As shown in Figure 15a, when the PLL bandwidth \(BW_{\mathrm{v}}\) = 40 Hz and 130 Hz, the GCI system controlled by phase-domain PR remains stable; however, at a PLL bandwidth of 210 Hz, the grid-connected current exhibits significant waveform distortion and harmonic oscillation, indicating poor system stability. Figure 15b reveals that with PLL bandwidths of 40 Hz, 130 Hz, and 180 Hz, the GCI system controlled by phase-domain PR is stable.  
With voltage feedforward control not considered, GCI grid-connected currents controlled by two different domains under various PLL bandwidths are displayed in Figure 16.  
According to Figure 16, when the PLL bandwidth \(BW_{\mathrm{v}}\) = 40 Hz, 130 Hz, and 180 Hz, the GCI systems controlled by PI control in the \(dq\) domain and PR control in the phase domain remain stable.  
Figure 14: Grid-connected current waveform at different outer loop bandwidths without considering voltage feedforward. (**a**) The dq-domain PI-controlled GCI system; (**b**) the phase-domain PR-controlled GCI system.  
Figure 15: Grid-connected current waveform under different PLL bandwidths considering voltage feedforward. (**a**) The dq-domain PI-controlled GCI system; (**b**) the phase-domain PR-controlled GCI system.
## 4 Results and Discussion
### Evaluation of the Algorithm Performance
According to the mathematical analysis in Section 2.2, this algorithm significantly reduces the number of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Information** \\ \hline Hardware environment & Intel (R) Core (TM) i3-10110U CPU @ 2.10 GHz Processor, 8 GB RAM, 223 GB storage \\ Operating system & Windows 11, X64-based PC \\ Software environment & PyCharm2023.2.5 (Community Edition), Python 3.10, PyEphem \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation environment setup. Figure 10: Graph of CSS Earth observation payload regional target VTW. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Computational Time/s** & **Computational Time Ratio \%** \\ \hline TP algorithm \(T_{0}\) & 31.728 & 100\% \\ Existing algorithm \(T_{1}\) & 0.317 & 0.999\% \\ TTIHS algorithm \(T_{2}\) & 0.116 & 0.365\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison table of computational time for different algorithms. iterative calculations compared to the original algorithm, saving a considerable amount of computing time and thus achieving speed improvement. To evaluate the calculation accuracy, the Formula (9) for defining the proportion of calculation time is:  
\[T\%=T_{n}/T_{0}\times 100\% \tag{9}\]  
From Table 4, it can be observed that in terms of computation accuracy, the VTW lengths obtained by the TTIHS algorithm and the TP algorithm differ by only 1 s under the total lengths of 1 h, 20 min, and 11 s. Out of the 18 start-end times for the 9 VTWs, 9 are exactly the same, while the remaining 9 differ by only 1 s. This indicates that the accuracy of this algorithm is nearly equivalent to that of the TP algorithm. It also demonstrates that this algorithm can improve computational speed while ensuring accuracy.