esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Collaboration Performance Evaluation Based on Combination of AHP and EW  
### Construction of the Evaluation Index System for Collaboration Performance  
#### 3.1.1 Principles of Evaluation Index System  
In order to ensure the objectivity and accuracy of the evaluation, when selecting the evaluation index, it is necessary to consider the rationality of the index system, as well as the characteristics of the collaborative relationship between enterprises. Therefore, the following principles are followed when constructing the enterprise collaboration performance index system:  
1. The principle of comprehensiveness. There are many factors that affect manufacturing enterprise collaboration. When constructing the evaluation index system, various factors should be selected comprehensively, and the influence of each index should be considered in each subsequent step.
2. The principle of operability. Operability means that the evaluation index and its corresponding data can be easily collected. In addition, it is necessary to ensure that the quantification of these indexes is executable.  
3. The principle of importance. The selected indicators should be representative and able to reflect important aspects of enterprise collaboration, that is, ignore some secondary aspects and grasp the key points.
4. The principle of relevance. The evaluation index system should not only include the key influencing factors of enterprise collaboration, but also have a certain logical relationship between the indexes, so that the indexes at all levels form an index system from top to bottom.
## 3 Results and Discussions  
The obtained experimental signal is shown in the inset of Figure 2a. After a background subtraction conducted via a smoothing average, rich acoustic features become visible in the main panel of Figure 2a, where several distinct wave-packet-like structures are present in the signal. Regarding the numerical signal processing, we note that the extraction procedure is first optimized for the higher frequency components, and thus, some lower frequency components are suppressed in Figure 2a. In order to address the lower frequency component contributions more quantitatively, the extraction procedure is adjusted in the later investigations in Section 3.2 It is important to mention that the applied extraction procedure is only used to increase the visibility of the small acoustic contributions to the overall signal (detected relative reflectivity change induced by superimposed acoustic signal \(\Delta R/R\)-\(10^{-7}\) while the reflectivity change from the electronic background \(\Delta R/R\)-\(10^{-4}\), i.e., 3 orders of magnitude larger), without introducing artifacts in the acoustic spectrum. The two different frequency domains will be discussed in the following paragraphs, starting with the high frequency contributions.
## 5 Existing Solutions
### Reputation-Based
Authors of [33] proposed a fine-grained Blockchain-based federated learning framework for mobile edge computing systems. Their main contribution is to provide a reputation-based learning procedure to ensure honest and fair training participants. The suggested reputation system allows edge devices, fog nodes, and cloud servers to rate each other's effectiveness, activeness, and honesty through the use of dApps and reputation-based consensus mechanisms applied by smart contracts. Moreover, the authors of [34] proposed a framework to secure the IoT infrastructure of federated learning with Blockchain. The trust-based flexible model used reinforcement learning. The process of choosing the participants in the learning process will be based on the trust score, and the evaluation process will be in a simulation using MATLAB. The authors tested the model using a simulation compared to a direct trust model in terms of accuracy and detection rates, energy consumption, and network throughput. The proposed model showed high results with an accuracy value of approximately 0.93 and an approximately 0.96 detection rate. In terms of network, the proposed model has a higher bandwidth than the direct trust model with an average difference of approximately 100 Mbps and 2 s less in network latency. The proposed model performs better in energy consumption, with approximately 35% less energy consumed. More recent work by [35] was also based on the Multi-Krum scheme. The authors proposed a commercial model where customers can participate in training a shared conventional neural network (CNN) model in a mobile edge computing (MEC) environment. The differential privacy (DP) protected the model gradients, where IPFS acted as the primary storage. They used the Algorand protocol as the consensus mechanism to promote a temporary leader that is responsible for aggregating the global model. Before the aggregation, the Multi-Krum scheme is applied. The authors of [35] apply a reputation calculation to prevent malicious nodes' participation. When the participating nodes' updates prove legitimate, with using the Multi-Krum scheme, the nodes' reputation increases. Non-participating nodes that obtain a value of 0 reputation will not be allowed to be elected to train the model. Their experiment on the MNIST dataset proved to achieve an accuracy of 97%.
# A Survey on Immersive Cyber Situational Awareness Systems
## 4 Levels of Immersive Cyber Situational Awareness
### Perception  
Perception is the fundamental phase of SA, which enables users to answer the question _what is happening in cyber environments?_ For immersive environments, perception refers to the monitoring, detection, and recognition of cybersecurity data (e.g., attack vectors, vulnerabilities, and countermeasures) that provide users with a holistic picture of cyber situations. Immersive visualization and interaction techniques help users create and maintain their perception of cyber situational awareness in ICSA systems. For instance, the immersive display, proposed in [15], shows an overview of ongoing cyber threats with their severity levels for COVID-19 information systems, which provides security experts with an overall perception of cyber situations. Similarly, the VR-based cybersecurity game, developed by [PERSON] et al. [41], uses icons for cyberattacks and defenses to help users recognize cybersecurity elements, which enhances the perception of end-users for cyber situational awareness. Table 6 presents the reviewed studies that address the perception level of cyber situational awareness for ICSA systems.
## 4 Use Cases for Multimedia Analysis in Archiving Community Memories
### Detecting Media about Individuals, Organisations and Places
#### 4.4.1 Recognising People  
For people entities, recent advancements in face recognition means that people can be recognised with relatively high accuracy from within a small search space (_i.e._, a small set of people to choose from). The problem with a general archiving scenario is that the search space is effectively infinite, and current face recognition algorithms tend to deteriorate rapidly as the search space gets larger. One option that we have explored in ARCOMEM is to apply entity recognition to the text to extract mentions of people and, then, to use the specific set of person entities to constrain the face recogniser's search space. In ARCOMEM, another option for identifying relevant people for constraining the face recogniser is to just use the people that are explicitly mentioned in the ICS. For well-known personalities and people whose photos can be found on the Internet, a web-based image search can be used to automatically retrieve example images of those people from which a face recognition algorithm can be trained [45]. An illustration of the overall process used in ARCOMEM is shown in Figure 5.  
Figure 5: Illustration of how face recognition is used to detect entities by dynamically training the recogniser based on person entities detected in the textual content of the crawl.
# A Systematic Parameter Analysis of Cloud Simulation Tools in Cloud Computing Environments
## 7 Discussion
### Research Questions  
Q1: What parameters would be used for the comparison of simulation tools vs. non-simulation tools?  
Response: We have used different parameters for the comparison of simulation tools vs. non-simulation tools such as host, VM, cloudlet, UB, and ET.  
Q2: What metrics are covered in the cloud simulators that are targeted?  
Response: We have covered different metrics for the cloud simulators, such as host, VMs, cloudlet, UBR, and ET.  
Q3: What are the energy-consuming parameters found in the selected cloud simulators?  
Response: We have found energy-consuming parameters such as workload, cluster, server host, VM, network, storage, CPU, and memory.
# NaYF4 Microstructure, beyond Their Well-Shaped Morphology
## 2 Experimental Section
### X-ray Diffraction: ESRF  
The synchrotron X-ray Powder Diffraction (XRPD) experiments were carried out at the Swiss-Norwegian Beam-Lines (BM1A station) of the European Synchrotron Radiation Facility (ESRF), France. A monochromatic beam of wavelength \(\lambda\) = 0.7129 A was focused onto the sample by sagittal bending of the second crystal of a double-crystal Si(111) monochromator using additional slits of 272.26 \(\times\) 327.34 um\({}^{2}\). XRPD data were collected in transmission geometry using a pixel-array detector  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Sample** & \multicolumn{3}{c}{**Y + Ln Amount**} & \multicolumn{3}{c}{**Solvent Volume**} & \multicolumn{2}{c}{**Heating**} & \multicolumn{2}{c}{**Type**} \\  & **Y** & **Er** & **Yb** & **Gd** & **H\({}_{2}\)O** & **EtOH** & **OA** & & & **of** \\
**Name** & & **(mole\%)** & & & **(mL)** & & **Time (h)** & **Temp (\(\lx@sectionsign\)C)** & **Container** \\ \hline NR00 & 50 & 2 & 18 & 30 & 17 & 20 & 20 & 2 & 200 & 75 mL Autolcave \\ NR01 & 80 & 2 & 18 & 0 & 17 & 20 & 20 & 2 & 200 & 75 mL Autolcave \\ NR02 & 80 & 2 & 18 & 0 & 17 & 20 & 20 & 2 & 200 & 75 mL Autolcave \\ NR03 & 75 & 2 & 18 & 5 & 17 & 20 & 20 & 2 & 200 & 75 mL Autolcave \\ NR04 & 75 & 2 & 18 & 5 & 17 & 20 & 20 & 20 & 200 & 75 mL Autolcave \\ NR05 & 65 & 2 & 18 & 15 & 45 & 50 & 50 & 2 & 200 & 200 mL Glass tube \\ NR06 & 65 & 2 & 18 & 15 & 45 & 50 & 50 & 2 & 200 & 200 mL Glass tube \\ NR07 & 65 & 2 & 18 & 15 & 45 & 50 & 50 & 20 & 200 & 200 mL Glass tube \\ NR08 & 50 & 2 & 18 & 30 & 45 & 50 & 50 & 2 & 200 & 200 mL Glass tube \\ NR09 & 50 & 2 & 18 & 30 & 4.5 & 5 & 5 & 2.5 & 20 & 200 MI Glass tube \\ NR10 & 50 & 2 & 18 & 30 & 12 & 11 & 11 & 3 & 200 & 75 mL Autolcave \\ NR11 & 50 & 2 & 18 & 30 & 17 & 20 & 20 & 2 & 200 & 75 mL Autolcave \\ NR12 & 35 & 2 & 18 & 45 & 45 & 50 & 50 & 2 & 200 & 200 mL Glass tube \\ NR13 & 20 & 2 & 18 & 60 & 45 & 50 & 50 & 2 & 200 & 200 mL Glass tube \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of all the synthesis parameters.  
(PILATUS 2M, Dectris Ltd., Switzerland). Samples were sealed in 0.4 mm diameter glass capillaries and rocked by 10\({}^{\circ}\) during data collection. The instrumental resolution was determined using a LaB\({}_{6}\) NIST standard (Standard Reference Material 660a, cell parameter = 0.41569162 nm \(\pm\) 0.0000097 nm at 22.5 \({}^{\circ}\)C).  
The selected patterns for the identification of phases by XRPD and the crystallographic data (COD database [25]) for the line profile analysis are summarized in Table 2.
## 3 Results and Discussion  
### Triple-Helical Configuration Analysis  
In order to evaluate the fraction of the triple helical structure present in electrospun collagen from the 1:1 ratio of the HFIP/HAc mixture solution, circular dichroism measurements were utilized. As shown in Figure 1, the CD spectra of the electrospun collagen, raw and denatured collagen all displayed a sinusoidal pattern. A deep negative peak around 198 nm and a small positive peak around 220 nm in the spectrum of untreated collagen represent the well-preserved triple-helical configuration of raw collagen [31; 35]. Conversely, the thermally denatured collagen showed a weakened negative peak and no positive peak, indicating the absence of the triple-helical structure. In contrast with the spectrum of untreated collagen, electrospun collagen showed a similar spectrum with weakened peaks of negative and positive bands, indicating that the triple helical configuration of electrospun collagen was partly destroyed but still retained. According to previous studies, the solvent system or stirring process would destabilize the triple helical structure of the collagen. In this research, a triple helical fraction around 60.5% was retained by calculation from the positive peak using Equation (1). This indicated that the mixture solvent led to a lower degree of protein denaturation as compared with others [32; 36].
# Mechanical Properties and Damage Evolution of Heated Granite Subjected to Liquid Nitrogen Cooling
## 3 Mechanical Properties of Heated Granite after Different Cooling Treatments
### Deformability in Uniaxial Compression  
The average Young's modulus of the specimens under different cooling treatments is shown in Figure 5a. Young's modulus of the specimens after LN\({}_{2}\) cooling is lower than that after the air cooling, which indicates that the LN\({}_{2}\)-cooling treatment can decrease the granite stiffness. Above 200 \({}^{\circ}\)C, the softening effect due to the LN\({}_{2}\) is more obvious. The reduction rate of Young's modulus for the LN\({}_{2}\) cooling compared with the air cooling is given in Figure 5b. The reduction rate is more significant with the increasing temperature. At 300 and 400 \({}^{\circ}\)C, the reduction rates reach 22.36% and 47.72%, respectively. These indicate that the softening effect due to the LN\({}_{2}\) is enhanced with the temperature. The softening effect of the LN\({}_{2}\) is also observed in Poisson's ratio. The LN\({}_{2}\)-cooling treatment can increase Poisson's ratio (Figure 5c) and the increase rate by LN\({}_{2}\) is positively related to the temperature (Figure 5d), which suggests that the LN\({}_{2}\)-cooling treatment can enhance the deformation properties and the improvement of deformability is more significant with the temperature. The increase in the initial temperature leads to a larger temperature difference between the LN\({}_{2}\) and granite. The larger the temperature difference, the greater the thermal-induced stresses within the granite, leading to a more obvious softening effect.  
Figure 4: Effect of liquid nitrogen cooling on uniaxial compressive properties: (**a**) uniaxial compressive strength and (**b**) reduction rate of compressive strength between air and liquid nitrogen cooling.
## 5 Results
### Measurement Model Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Construct** & **Indicator** & \multicolumn{3}{c}{**Indicator Reliability**} & \multicolumn{3}{c}{**Internal Consistency Reliability**} & \multicolumn{2}{c}{**Convergent Validity**} \\ \hline  & & **Loading** & **t-Value** & **Significance** & **Cronbach’s \(\alpha\)** & **CR** & **AVE** \\ \hline \multirow{3}{*}{BED} & bed\_1 & 0.879 & 21.059 & \(p<0.001\) & & & \\  & bed\_2 & 0.891 & 29.857 & \(p<0.001\) & 0.723 & 0.878 & 0.783 \\ \hline \multirow{3}{*}{MPC} & mpc\_1 & 0.588 & 4.657 & \(p<0.001\) & & & \\  & mpc\_2 & 0.886 & 35.494 & \(p<0.001\) & & & \\  & mpc\_3 & 0.696 & 9.017 & \(p<0.001\) & 0.746 & 0.841 & 0.575 \\  & mpc\_4 & 0.828 & 13.256 & \(p<0.001\) & & & \\ \hline \multirow{3}{*}{CST} & cst\_1 & 0.863 & 27.089 & \(p<0.001\) & & & \\  & cst\_2 & 0.819 & 22.001 & \(p<0.001\) & & & \\  & cst\_3 & 0.693 & 6.964 & \(p<0.001\) & 0.784 & 0.861 & 0.609 \\  & cst\_4 & 0.735 & 9.152 & \(p<0.001\) & & & \\ \hline \multirow{3}{*}{SUCM} & bed\_1 & 0.785 & 15.543 & \(p<0.001\) & & & \\  & bed\_2 & 0.823 & 25.192 & \(p<0.001\) & & & \\  & mpc\_1 & 0.490 & 3.548 & \(p<0.001\) & & & \\  & mpc\_2 & 0.759 & 13.123 & \(p<0.001\) & & & \\  & mpc\_3 & 0.723 & 11.763 & \(p<0.001\) & 0.882 & 0.905 & 0.495 \\  & mpc\_4 & 0.585 & 6.098 & \(p<0.001\) & & & \\  & cst\_1 & 0.798 & 15.121 & \(p<0.001\) & & & \\  & cst\_2 & 0.765 & 13.053 & \(p<0.001\) & & & \\  & cst\_3 & 0.551 & 5.185 & \(p<0.001\) & & & \\  & cst\_4 & 0.674 & 7.677 & \(p<0.001\) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Evaluation of the reflective measurement model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Indicator** & **BI** & **FI** & **RL** & **BED** & **MPC** & **CST** \\ \hline bi\_1 & **0.528** & 0.205 & 0.117 & 0.027 & 0.270 & 0.141 \\ bi\_2 & **0.789** & 0.255 & 0.343 & 0.346 & 0.281 & 0.416 \\ bi\_3 & **0.720** & 0.211 & 0.345 & 0.433 & 0.409 & 0.346 \\ fi\_1 & 0.267 & **0.808** & 0.428 &
## 4 Results
### Modeling Results
#### 4.2.2 Polynomial Regression  
Two different configurations have been trained with the Polynomial Regression algorithm; the first and the second degree polynomials have been used. As an example of this training, Table 3 shows the MAE obtained using second-order polynomial degree for each cluster. As explained, 10 k-fold cross validation is used to achieve the error measurement.
## 4 Discussion
### Response of NDVI Changes to LULC Changes  
The NDVI improvement regions from 2000 to 2020 were mainly distributed in the northern and western mountainous and hilly regions, where the natural environment is good and levels of human activity are relatively low. The response characteristics indicate that the improved regions of the NDVI are more consistent with the unchanged regions of forest land, which may indicate that the protection of green spaces such as forest land has brought about an improvement in vegetation coverage. The deterioration of the NDVI mainly occured in the plains, which is consistent with the expansion of built-up land and may indicate human activities, especially the expansion of urban regions and the conversion of cultivated land and forest land into built-up land, thereby reducing the green space in the region. Therefore, attention should be paid to protecting vegetation during the construction process of urban agglomerations.  
Anyway, the increase in vegetation coverage has become a reality in recent years. Since the Chinese government officially proposed \"vigorously promoting ecological civilization construction\" in 2012, ecological environment protection has been fully valued and actively implemented by governments and departments at all levels. The Land Space Plan of Guangdong Province in 2023 (2021-2035) and the Land Space Ecological Restoration Plan of Guangdong Province (2021-2035) were issued successively, aiming to optimize the land development pattern, repair the ecological environment problems arising from past development, coordinate development and security, promote the harmonious coexistence of humans and nature, and provide Guangdong with practices for the solid promotion of a Chinese path to modernization.
# Femtosecond-Laser-Assisted Fabrication of Radiation-Resistant Fiber Bragg Grating Sensors
## 3 Materials and Methods  
### Material Properties  
During our experiments, we considered acrylate-coated fluorine (F)-doped single-mode specialty optical fibers (core diameter: 8 \(\upmu\)m; clad diameter: 125 \(\upmu\)m) to fabricate FBGs inside fiber cores. In order to determine the fabrication parameters, we fabricated Bragg gratings inside fused silica glass samples (thickness: 1 mm, refractive index: 1.4533 @ @ 800 nm) before fabricating the FBG inside the core of the optical fibers.
###### Abstract  
Research with young adults has previously indicated flavonoid-rich berry interventions facilitate improved executive function (EF) and positive affect 20 min-2 h post-dosing. There has been little consideration of the impact of a berry intervention over a working day and interventions have also tended to consider only a single berry type. This study investigated the temporal profile of EF and mood changes over a 6 h period following a mixed-berry intervention. We hypothesized berry-related benefits would be most evident when participants were cognitively compromised on demanding elements of the task or during periods of fatigue. The study employed a single-blind, randomized, placebo controlled, between-subjects design. Forty participants aged 20-30 years consumed a 400 mL smoothie containing equal blueberry, strawberry, raspberry, and blackberry (\(n=20\)) or matched placebo (\(n=20\)). Mood was assessed using the Positive and Negative Affect Schedule; EF was tested using the Modified Attention Network (MANT) and Task Switching (TST) Tasks. Testing commenced at baseline then 2, 4 and 6 h post-dosing. As expected, following placebo intervention, performance decreased across the day as participants became cognitively fatigued. However, following berry intervention, participants maintained accuracy on both cognitive tasks up to and including 6 h, and demonstrated quicker response times on the MANT at 2 and 4 h, and TST at 6 h. This study demonstrates the efficacy of flavonoid rich berries in maintaining or improving cognitive performance across the 6 h day.  
berry; blueberry; raspberry; strawberry; blackberry; polyphenol; flavonoid; cognition; executive function; mood +
Footnote †: journal: _nutrients_
# Demystifying DFT-Based Harmonic Phase Estimation, Transformation, and Synthesis
## 2 Robust DFT-Based Phase Estimation of Individual Sinusoids
### DFT and the Rectangular Window
For illustration purposes, we use the error metric as in (11), we create a ground-truth signal using (5), and configure the noise (\(s[n]\)) such that the SNR is 30 dB in one case, and 10 dB in another case. In each case, we evaluate the cumulative phase estimation error as a function of the fractional frequency, \(\Delta_{\ell}\):  
\[\texttt{ERROR}=\int_{-\pi}^{\pi}\abs{e^{i\phi}-e^{i\hat{\phi}}}d\phi \tag{11}\]  
Figure 5 represents the results when \(N=128\) and \(\ell=13\). It can be seen that the estimation error distribution is consistent with the relative magnitudes of adjacent spectral bins as illustrated in Figure 4. In fact, when \(\Delta_{\ell}\) approaches \(-0.5\), or \(0.5\), the cumulative error increases relative to the case when \(\Delta_{\ell}=0\), which is a consequence of the fact that the magnitudes of two adjacent spectral lines become comparable and significantly lower than the maximum value they can reach (when \(\Delta_{\ell}=0\)), which not only exacerbates leakage effects but also increases vulnerability to the noise influence. Due to the specific frequency response of the rectangular window, as Figures 3 and 4 highlight, then, when \(\Delta_{\ell}=0\) and the SNR is infinity, the spectral leakage is zero, and the phase is estimated without error. The Matlab code generating Figure 5 is available (estimatePHASE_DFT_rect.m), which facilitates experimentation with other values of \(N\), \(\ell\), or SNR.
## 6 Performance Evaluation
### Evaluation for the Analytical Model
#### 6.2.2 One-Hop Timer Delay for the Intra-Lane Scenario  
Figure 6 shows the Probability Mass Function (PMF) of one-hop timer delay for the intra-lane scenario with the histogram from simulations and the curve from our proposed analytical model. As can be seen from the figures, the results from the simulations match well with those obtained via the analytical model. For the intra-lane scenario with a dedicated vehicular density, we can see that the probability value presents a declining tendency with the increase of possible one-hop delay. Moreover, from Figure 6a-c, with the increase of the vehicular traffic density \(\rho_{1}\) from 10 vehs/km (vehicles per meter) to 100 vehs/km, we can see that the maximum range drops gradually from 50 ms down to 10 ms. This outcome is a consequence of the bigger number of one-hop neighboring vehicles with the vehicular traffic density, which can help the sender's choice on the next-hop relay approaching the optimal one.