esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Catalyst Surface Status  
The information on the element composition, the metallic valence status, and oxygen species on the surface of samples can be analyzed by XPS measurement, where O 1\(s\), Ru 3\(p\), Rh 3\(d\), Pt 4\(f\), Pd 3\(d\), Ir 4\(f\), and Au 4\(f\) were analyzed and are shown in Figures 8 and 9. For all supported noble metal catalysts, as illustrated in Figure 8, O 1s patterns could be categorized into two types: the one with a binding energy (BE) of \(\sim\)529.8 eV may be related to lattice oxygen species (O\({}^{\alpha}\)), while the other one centered at 531.5 eV belongs to the surface adsorbed oxygen species (O\({}^{\beta}\)), respectively [56]. The O\({}^{\alpha}\)/(O\({}^{\alpha}\) + O\({}^{\beta}\)) ratio by Gauss fitting is listed in Table 3, and allows for the ordering of the catalysts as follows: Ru/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (0.88) \(>\) Au/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (0.86) \(>\) Ir/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (0.85) \(>\) Pt/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (0.82) \(>\) Rh/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (0.81) \(>\) Pd/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (0.68).  
For the noble metal XPS patterns, the Ru 3\(p\) spectra of Ru/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) could be fitted into two obvious peaks, where the BEs of 496.6 and 463.6 eV are attributed to the surface Ru\({}^{4+}\) species [57]. The Rh 3\(d\) spectrum of Rh/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) includes two BE combinations of 308.6 and 313.4 eV, which means the Rh\({}^{3+}\) species are present on the surface [58]. The Pt 4\(f\) spectrum of the Pt/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) catalyst showed doublet peaks with BEs of 75.1 and 71.5 eV; these belong to the surface Pt\({}^{2+}\) species [25]. The Pd 3\(d\) spectra included two peaks at 336.6 and 342.6 eV both associated with Pd\({}^{2+}\) species [37]. In the Ir 4\(f\) spectrum, the peaks can be identified as 4\(f_{5/2}\) (BEs \(\approx\) 64.5 eV) and Ir 4\(f_{7/2}\) (BEs \(\approx\) 62.0 eV), respectively, confirming the existence of Ir\({}^{3+}\) species on the surface [44]. Two peaks assigned to Au 4\(f\) at BEs of 88.1 and 84.2 eV were associated with the surface Au\({}^{+}\) species [45]. Abundant adsorbed oxygen concentration on the catalyst surface as well as a higher noble metal valence are conducive to improving oxidation behavior, which plays a key role in the catalytic oxidation process of noble metal catalysts [54].t  
Figure 8: O 1s spectra of all prepared noble metal-based catalysts.
# Volumetric Modulated Arc Therapy for Radiosurgery of Brain Metastases: A Single-Center Study
## 4 Discussion
(2021) (8.2 months) [43], [PERSON] et al. (2021) (9.3 months) [44], or [PERSON] et al. (2021) (11 months) [45]. In the multivariate Cox regression model, two variables were statistically significant: KPS and the updated DS-GPA. Regarding KPS, our results showed that a KPS \(<\) 70 at the time of radiosurgical treatment represents an increase in mortality of approximately 4 and 2.5 times, according to the univariate (\(p\) \(<\) 0.001) and multivariate (\(p\) = 0.001) analyses, respectively. This is in relation to previous studies, where using other radiosurgery techniques, KPS was shown to be an outstanding prognostic factor for survival [42; 46]. Regarding DS-GPA, our results showed a reduction in the mortality of patients with DS-GPA Class II of 42% (\(p\) = 0.018) and of DS-GPA Class III-IV patients of almost 70% (\(p\) \(<\) 0.001) with respect to DS-GPA Class I patients. [PERSON] et al. (2020) [18] in a multi-institutional database of 6.984 patients found significant differences between the DS-GPA groups described, with DS-GPA Class 1 (5 months), Class II (11 months), Class III (20 months), and Class IV (33 months) [18]. In our study, the estimated median survivals relative to each subgroup would be Class I (3 months, \(n\) = 38), Class II (8 months, \(n\) = 43), and Class III-IV (14 months, \(n\) = 40). In the latter case, given the low sample size of Class IV (23 months, \(n\) = 8), it was decided to pool Class III. One of the possible explanations for the lower MST in our cohort, in addition to variations due to the smaller sample size, is that in this study we used the date of death of the patients to estimate survival or, in those cases in which we do not have it, the date of the last clinical follow-up, and perhaps the latter may contribute to partially underestimating survival. On the other hand, in univariate analysis, other factors such as age, primary tumor (breast vs. lung), and the presence of PFBM were significantly related to survival. Regarding age, previous studies have shown an inverse correlation between age and survival in patients with BMs [18; 19]. In our study, an increased mortality risk of 60% (HR 1.6, \(p\) = 0.014) was obtained for the subgroup of patients older than 65 years. The presence of posterior fossa metastasis as well as breast cancer as the primary tumor was statistically significant in the univariate analysis (\(p\) = 0.031 and \(p\) = 0.017, respectively), but not in the multivariate analysis. Given the high prevalence of PFBMs in breast cancer patients it is possible that there is a synergistic effect between both variables. Finally, no significant survival differences were found in relation to brain tumor-related epilepsy, although due to the sample size, no comparative study was performed between patients with epilepsy before or after radiosurgical treatment. Therefore, further prospective studies are needed to evaluate this aspect of growing interest [15].
## 3 Results and Discussion  
### Characterisation of Detector Properties  
The detector response curve in Figure 3a was measured by varying the frequency of a pulsed laser diode from 10 Hz to 100 kHz while the receiver signal was acquired with an oscilloscope (Red Pitaya, Solkan, Slovenia, STEMlab 125-14 ). A power-calibrated laser at 1.55 \(\upmu\)m wavelength with a custom-built driver, a duty cycle of 50%, and 1 mW of average laser power was used for this measurement. To obtain an absolute calibration of the response data in units of V/W, the level of the first harmonic of the detector response was measured at a fixed distance with a calibrated black-body emitter (Dias Infrared, Dresden, Germany, Pyrotherm CS F150) at 150 \({}^{\circ}\)C with a wavelength peak emission at 6.8 \(\upmu\)m in combination with an optical chopper at 139 Hz. For given detector dimensions and distances to the black-body emitter, the absolute power levels reaching the detector element were calculated and used to calibrate the frequency-resolved response curve. It should be noted that the measured frequency-responsivity, as indicated by the blue curve in Figure 3a, deviated from the intrinsic detector responsivity due to the presence of the HDPE window. Calculating the intrinsic detector responsivity by taking into account the transmission losses of the window (transmission of 26.7% at a wavelength of 6.8 \(\upmu\)m) led to higher responsivity values, as given by the green curve in Figure 3a.  
The noise spectral density of the detector (see Figure 3b) was measured with a fast Fourier-transform analyser ([PERSON], Yokohama, Japan, CF-5220) from 10 Hz to 100 kHz. The frequency-resolved detectivity (D\({}^{\prime}\)) (see Figure 3c) was calculated from both the calibrated response and the noise density.
## 3 Methodology
### Criteria Weight Determination Using AHP-MEREC Approach
#### 3.1.1 Subjective Weight Calculation Using AHP Approach  
The Analytic Hierarchy Process (AHP) [22] estimates criteria weights through pairwise comparisons, when decision makers compare each pair of criteria using a five-point scale (1-3-5-7-9). Here, 1 signifies equal importance between two criteria, and 9 represents the biggest difference between two criteria weights. All the judgments of a single expert \(\kappa=1,\ldots,\mathrm{K}\) are recorded in a pairwise comparison matrix \(P^{\kappa}\), where the diagonal elements are equal to 1, and the non-diagonal elements are filled with the judgments of the experts. Before the final definition of the AHP weights, the consistency ratio CR and Kendall's [60] coefficient of concordance should be calculated.  
For each \(P^{\kappa}\), the criteria weights \(\mathrm{w_{k}}\) and the consistency ratio (CR) should be calculated to check whether the expert has not contradicted himself (Equation (4)). The consistency index (CI) can be calculated by Equation (5), where \(\lambda_{max}\) is the maximum eigenvalue of the pairwise comparison matrix \(P^{\kappa}\) and \(m\) is the number of criteria. RI is the function of determining the expected level of consistency. It can be shown that \((\lambda_{max}-m)\) is equal to the sum of the products of the elements in each row of the pairwise comparison matrix. Here, RI is the random value of the consistency index [22]. When the CR value is 0.1 or less, it implies acceptable consistency in the AHP approach.  
\[CR=\frac{CI}{RI} \tag{1}\]  
\[CI=\frac{\lambda_{max}-m}{m-1} \tag{2}\]  
Since multiple experts are going to be involved in the criteria-weighting tasks, it is essential to verify the level of agreement among experts. The concordance coefficient \(W_{\kappa}\), proposed by [PERSON] [60], will be computed for each of the pairwise decision matrix constructed from the expert \(\kappa\). [PERSON]'s coefficient of concordance \(W\) is a non-parametric statistic varying between 0 and 1, where \(W>0.7\) indicates excellent compatibility of opinions, \(W>0.5\) is good, \(0.2<W<0.4\) is satisfactory, and \(W<0.1\) is unsatisfactory compatibility.  
The Pearson chi-square test \(\chi^{2}\) can be used to determine whether there is a statistically significant difference between the criteria that were ranked. The \(\chi^{2}\) value shows the consistency of the expert assessment if it is higher than the critical value \(\chi^{2}_{Table}\) from the table of \(\chi^{2}\) distributions. For example, when the significance level \(\alpha=0.05\), based on the search results, and there are 7 degrees of freedom, the critical chi-squared value is 14.067.  
If \(W_{\kappa}>0.1\) for each of the expert \(\kappa\), and \(\chi^{2}>\chi^{2}_{Table}\), local AHP weights for each of the experts can be calculated. The normalised sum of local weights calculated for criterion \(j\) can be determined as the final AHP weight value \(w_{j}\).
# Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey
## 3 Network Structure
### Flat
#### 3.1.3 Hybrid  
These protocols combine the benefits of proactive and reactive routing protocols. They use a proactive routing scheme locally to respond quickly and inter-locally, reactive routing scheme to respond more efficiently with lower energy consumption. In what follows in this section, two typical examples of hybrid flat energy efficient routing protocols are described.  
_Zone Routing Protocol_ (ZRP) [44] combines the advantages of proactive and reactive protocols. ZRP divides the network into zones and uses two schemes for routing, one for in-zone nodes, and one for nodes outside of it. These two schemes are:  
1. Inter-zone routing: nodes inform their neighbors periodically, broadcasting notices when a link-state changes, resulting in nodes knowing a path to any other inter-zone node.
2. Outside zone routing: nodes send route request (RREQ) to zone border nodes. Border nodes check in the zone node table, if they find a match to the request, they send a route reply (RREP), else they send a request to another border node until they find a route. Multiple routing paths are discovered with minimum number of query messages. On the other hand, simultaneous querying of nodes is not possible. In ZRP, only a small amount of routing information is required and less routing traffic is caused. However, excessive delays are caused.  
_Adaptive Threshold Energy Efficient cross layer based Routing_ (ATEER) [45] is a clustering protocol for heterogeneous WSNs that combines the properties of reactive and proactive network subcategories. ATER operation consists of two models:  
1. Network model: a model that focuses on cluster head selection and cluster formation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
2. Radio energy model: a model used to calculate transmission energy consumption, reception, and data accumulation.  
ATEER classifies sensor nodes into three modes, super-advanced, advanced and normal, depending on their residual energy level. Utilizing the energy classification, a cluster head can be elected by using a probability derived from the division of the network's mean energy to the residual energy of candidate nodes. If the result is less than 1, then this node has a higher probability to be elected as a cluster head. In addition, cluster head and cluster nodes are connected via single-hop communication. In ATEER, increased network lifetime is achieved by utilizing node's energy classification. In addition, redundant data are reduced. On the other hand, energy consumption is caused due to the periodic data sending of node's residual energy information. Moreover, there is a lack of GPS navigation.  
The main features of the aforementioned hybrid flat energy efficient routing protocols are synoptically presented in Table 6.
# Estimating Interception Density in the BB84 Protocol: A Study with a Noisy Quantum Simulator
## 6 Performance Evaluation
### Additional Remarks  
The results presented here assumed a noise-free environment, except for the intrinsic noise of the simulated quantum system. However, in real-world scenarios, noise could originate from various sources, including the communication channel. Excessive noise could potentially reduce the reliability of intrusion detection.  
Moreover, this approach is inherently probabilistic, so if it were to be employed in a real-world application to detect an eavesdropper, an acceptance threshold would need to be established. If the estimated values were to fall above this threshold, the key exchange would be deemed insecure. To determine this threshold, all potential sources of noise would need to be considered, along with the number of errors that could be tolerated and corrected using error correction codes.
# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
## 5 Discussions
This novel ensemble feature representation capitalizes on cardiac signals' temporal and spatial characteristics, serving as a comprehensive and discriminative foundation for human identification. The research journey comprises several distinct phases, encompassing the preprocessing of ECG biosignals to ensure data quality, transforming one-dimensional ECG signals into two-dimensional spectrogram images, and the subsequent feature extraction utilizing the ensemble deep learning technique. Furthermore, the identification process is facilitated by a machine learning classifier that distinguishes individuals based on their distinctive cardiac biometric attributes. The proposed cardiac biometric system's efficacy is rigorously evaluated using extensive experimentation on a curated dataset. Key performance metrics such as accuracy, sensitivity, specificity, and the area under the curve (AUC) are employed to gauge the system's effectiveness. Furthermore, a comparative analysis is conducted against prevailing state-of-the-art biometric authentication systems, showcasing the proposed system's superiority in accurately recognizing human identities.
# Optimized Polarization Encoder with High Extinction Ratio for Quantum Key Distribution System
## 4 Conclusions  
In conclusion, we have proposed an SCPE, a high-extinction-ratio polarization encoder based on a phase modulator inside a Sagnac interferometer. The stability and feasibility of the SCPE have been directly demonstrated through experimental testing. We also conducted tests on the QBER and secure key of the SCPE. In a laboratory environment, we conducted tests using commercial optical fibers of different communication lengths. A total of \(N=10^{10}\) pulses was sent out for each distance, and we ultimately achieved a secure key rate of 3 kbps at 100 km when the repetition rate of the light source was 50 MHz. Finally, we simulated a secure key rate across a finite key regime and found that our QKD system can achieve a maximum communication distance of 150 km. The highly applicable SCPE utilizes existing commercially available devices, making it a cost-effective solution for metro quantum communication.  
Conceptualization, P.W.; methodology, P.W. and Q.Z.; software, P.W.; validation, P.W. and Q.Z.; formal analysis, P.W.; investigation, P.W. and Q.Z.; writing--original draft preparation, P.W.; writing--review and editing, P.W. and B.G.; visualization, P.W.; supervision, H.X. and B.G.; funding acquisition, B.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key-Area Research and Development Program of Guangdong Province (Grant No. 2018B030325002).  
Not applicable.  
Thanks for the funding from Xijiang Talent and Guangdong Province Doctoral Workstation.  
The authors declare no conflict of interest.
# Particle Swarm Optimization of a Passivity-Based Controller for Dynamic Positioning of Ships
###### Abstract  
Dynamic positioning (DP) control system is an essential module used in offshore ships for accurate maneuvering and maintaining of ship's position and heading (fixed location or pre-determined track) by means of thruster forces being generated by controllers. In this paper, an interconnection and damping assignment-passivity based control (IDA-PBC) controller is developed for DP of surface ships. The design of the IDA-PBC controller involves a dynamic extension utilizing the coordinate transformation which adds damping to some coordinates to ensure asymptotic stability and adds integral action to enhance the robustness of the system against disturbances. The particle swarm optimization (PSO) technique is one of the the population-based optimization methods that has gained the attention of the control research communities and used to solve various engineering problems. The PSO algorithm is proposed for the optimization of the IDA-PBC controller. Numerical simulations results with comparisons illustrate the effectiveness of the new PSO-tuned dynamic IDA-PBC controller.  
dynamic positioning; hamiltonian systems; particle swarm optimization; passivity-based control +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Effect of Fly Ash on Porosity and Microstructure  
Physical and chemical reactions may significantly affect the porosity and microstructure of concrete due to fly ash addition. Figure 9 shows the cumulated porosity of the concrete samples. It was found that the cumulative pore volume reduced as the fly ash addition increased. Figure 10 shows the pore size distribution of each sample after 90 days of curing. The cumulative volume of pores reduced as fly ash content increased. The mean pore size of the PCFA0, PCFA15, and PCFA30 samples were about 40, 26, and 13 nm, respectively, which indicated that the fly ash addition led to a denser concrete microstructure.  
[PERSON] and [PERSON] [28] classified pore size in concrete into four classes: Class I of \(<\)4.5 nm, Class II of 4.5-50 nm, Class III of 50-100 nm, and Class IV of \(>\) 100 nm. A pore size of \(<\)100 nm has little or no effect, while \(>\)100 nm has a negative effect on the concrete strength and permeability. Specifically, the curves in Figure 11 show that the pore volume fraction of Class I and II increased while that of Class III and IV decreased. For example, the pore volume fraction with 4.5-100 nm in PCFA30 increased by about 62.5% and 36.8% compared to PCFA0 and PCFA15, respectively, while that of 100-200 nm and  
Figure 10: Pore size distribution after 90 days of curing.  
Figure 9: Cumulative porosity of concrete sample with different fly ash additions after 90 days of curing.  
larger than 200 mm for PCFA15 and PCFA30 considerably reduced to 20.83% and 15.39%, respectively, compared to that of PCFA0. This indicated that pore structures were well-improved as fly ash was added into concrete. This was caused by the second hydration of fly ash with C-H after 90 days of curing generated more C-S-H gel, causing the promotion of concrete compactness and the optimization of pore structures. Figure 12a-c shows the morphologies of concrete samples with different fly ash additions after 90 days of curing. PCFA15 and PCFA30 with fly ash had enhanced concrete density, while more pores could be found for PCFA0 without fly ash replacement. The left-unhydrated fly ash particles may have filled pores in the concrete, as shown in Figure 12d. In this case, fly ash prevented the penetration of free chloride ions and reduced the corrosion of steel rebar, leading to a higher concrete durability.  
Figure 11: Pore size distribution of concrete with different fly ash additions after 90 days of curing.  
Figure 12: _Cont._  
Figure 12: Morphologies of concrete samples with different fly ash additions after 90 days of curing.
## 2 Results
### Animal Model
#### 2.5.3 Oxidative Status  
The mean values of the antioxidant status parameters are summarized in Table 7. Long-term administration of saponins or terpenoids to Zucker rats resulted in a significant increase (\(p<0.01\)) in TAS compared to control rats. At the same time, the GR content decreased in the SAP group (\(p<0.01\)). Terpenoid supplementation also caused a decrease in GR. The applied treatment did not have an effect on IL-6. IL-10 concentrations increased in both experimental groups, with the highest values reached in the TERP group. These changes were not statistically significant. Similarly, in the TERP and SAP groups, the increase in MCP-1 concentration was not statistically confirmed. Terpenoids did not have an effect on ROS values, while saponins caused a significant decrease (\(p<0.01\)).  
Previous studies have shown that metabolic syndrome characterized by obesity in Zucker rats decreased superoxide dismutase (SOD) activity in the plasma and heart, associated with cardiomyocyte hypertrophy [37]. Supplementation with saponins or terpenoids in our study increased TAS and decreased ROS, with no effect on blood NO concentration. As a consequence of obesity, the metabolic function of GPx was not affected to the same extent as the metabolic activity of SOD [37]. Both compounds tested caused a reduction in GR activity, but the effect of terpenoids was minor, and the effect of saponins was statistically significant (\(p<0.01\)). Conversely, TNF- \(\alpha\) and IL-6 are associated with a tendency to develop ischemia or atherosclerotic events [38]. In our study, there was no significant effect of saponins and terpenoids on blood concentrations of TNF- \(\alpha\) and IL-6, and terpenoids caused an increase in IL-10. Interleukin 10 is considered an anti-inflammatory cytokine that inhibits the production of pro-inflammatory cytokines such as IL-2, IL-3, and TNF-\(\alpha\), for example. In an earlier study conducted on rats without metabolic syndrome (Wistar), TOS, GPx, and MDA were reduced after supplementation with terpenoids alone. No such effect was seen with water extracts of _I  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Group**} & \multicolumn{1}{c}{**\(p\) Value**} \\ \cline{2-5}  & **CON 1**  & \(p\) Value** \\ \hline TAS 5 mmol/L & 1.43 \(\pm\) 0.29 4  & 1.82 \(\pm\) 0.07 4  & 0.001 \\ GR 6 U/L & 602.29 \(\pm\) 373.21  & 423.67 \(\pm\) 21.92 & 0.030 \\ IL-6 7 pg/mL & 1.22 \(\pm\) 0.12 & 1.22 \(\pm\) 0.26 & 1.21 \(\pm\) 0.12 & 0.989 \\ IL-10 8 pg/mL & 1.41 \(\pm\) 1.84 & 1.95 \(\pm\) 1.49 & 2.89 \(\pm\) 1.40 & 0.237 \\ MCP-1 9 ng/mL & 0.51 \(\pm\) 0.27 & 0.72 \(\pm\) 0.35 & 0.76 \(\pm\) 0.16 & 0.214 \\ ROS 10 U/mL & 73.70 \(\pm\) 3.14  & 73.34 \(\pm\) 6.48  & 0.012 \\ NO 11 \(\mu\)mol/L & 0.19 \(\pm\) 0.04 & 0.13 \(\pm\) 0.09 & 0.17 \(\pm\) 0.06 & 0.238 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effects of saponin and terpenoids on antioxidant and immunological parameters in Zucker rats.