esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Osteogenic Biomarkers  
Hydroxyproline is the main amino acid component from collagen, and its determination is an important biomarker for bone resorption and cartilage degradation. RANKL binds to its RANK receptor on osteoclast precursors, thus promoting osteolysis in subchondoral bone at the onset of OA. In our study, EA decreased hydroxyproline at 4 weeks and decreased RANKL at 2 and 4 weeks, showing reduced osteoclasts and decreased hydroxyproline content, confirming the beneficial effect on OA. The extract of JR decreased RANKL at 4 weeks, being the lowest concentration and maintained a low level of hydroxproline at 2 weeks.  
[PERSON] et al. demonstrated in a study that hydroxyproline serum level increased only in OA and not in rheumatoid arthritis at an early stage [49]. In an in vivo study, animals treated with modified Bushen Huoxue decoction for knee OA reported decreasedserum and urinary levels of hydroxyproline and MDA, while the level of SOD in serum and synovial tissue was higher [50].  
In a study, rats with arthritis were subsequently treated with triphala, an Indian compound rich in EA, obtained from three plants (_Terninalia chebula_ [PERSON], _Terminalia bellerica_ Roxb and _Emblica officinalis_ L.). The results showed that triphala normalized urinary hydroxyproline levels and reduced TNF-\(\alpha\), IL-6 and RANKL levels in affected tissue [51].  
As an overall summary of our study, the antioxidant biomarker SOD was significantly increased in the EA group at 2 weeks, while the pro-inflammatory cytokine IL-6 was diminished in the EA group and TNF-\(\alpha\) in the JR group at 2 weeks. Concerning the osteogenic biomarkers, the results of the present study show that in the experimental group treated with EA, RANKL level decreased at 2 weeks of treatment. Its effect was persistent at 4 weeks, in comparison with animals treated with indomethacin, suggesting a beneficial anti-osteoclastogenesis action. The JR extract presented a reduced expression of RANKL.  
According to the obtained data, our study is a preliminary one that tries to highlight the effect of ellagic acid and JR extract in experimentally induced osteoarthritis. In order to confirm its effectiveness, future studies involving multiple doses, longer treatment periods, and examination of more parameters are needed, so as to reflect the mechanisms involved.
## 3 Mathematical Model of the System  
### Turbine and Wind Farm  
The identification of a turbine output power by means of an analysis, based on the wind speed measurement values requires the use of a mathematical and numerical model of the turbine [25]. In order to examine the reliability of power supplies from the wind farm to the power grid, a simplified model was used for the wind speed sampling period \(\Delta t_{\text{M}}\). In the simplified model, the wind turbine plays the role of a functional unit modelling the power characteristics \(P_{1}=f(v_{\text{w}}(t))\). Based on linear interpolation between two points of the turbine power's discrete characteristics, the power generated for the \(k\)th sample of the wind speed \(v_{\text{w}}\) (\(t_{k}\)) can be identified based on the following formula:  
\[P_{1}(v_{\text{w}}(t_{k}))=\left\{\begin{array}{c}P_{1}+\frac{P_{2}-P_{1}}{v _{2}-v_{1}}(v_{\text{w}}(t_{k})-v_{1})\text{ for }v_{\text{cut-in}}\leq v_{\text{w}}(t_{k}){<}v_{ \text{wn}}\\ \\ P_{\text{WTn}}\text{ for }v_{\text{wn}}\leq v_{\text{w}}(t_{k})\leq v_{\text{cut-out}} \\ \\ 0\text{ for other }v_{\text{w}}(t_{k})\end{array}\right. \tag{3}\]  
where: \(v_{1}\), \(v_{2}\) are wind speed values on the discrete characteristics of the turbine power between which the speed \(v_{1}\leq v_{\text{w}}(t_{k})\leq v_{2}\) is positioned, \(P_{1}\) and \(P_{2}\) correspond to the wind speed, \(v_{1}\) and \(v_{2}\) are turbine power values read from discrete power characteristics.  
In order to approximate the farm model to its actual operating conditions, differences in the position and, hence, the actual wind speed on the nacelle level were taken into consideration for each \(N_{\text{WT}}\) turbine. To that end, random changes (\(\pm 5\%\)) in the wind speed (in the moment \(t_{k}\)) were considered for each turbine as compared to measurement samples. Thus, for the \(i\)th turbine and \(k\)th sample, the wind speed (in the moment \(t_{k}\)) is:  
\[v_{\text{w}(i)}(t_{k})=(1\pm rnd\ (-0.05,\ 0.05))v_{\text{w}}(t_{k}) \tag{4}\]  
where: \(rnd\ (-0.05,\ 0.05)\) is a random value (normal deposition) from the \([-0.05,\ 0.05]\) range. Then, the power released to the power grid from the reference farm is:  
\[P_{3}(t_{k})=\sum_{i=1}^{N_{\text{WT}}}P_{1(i)}((1\pm rnd\ (-0.05,\ 0.05))v_{ \text{w}}(t_{k}))-\Delta P \tag{5}\]
## 3 Methodology
### FD Method  
The FD method is a technique that integrates the Delphi procedure with the fuzzy procedure [43]. The implementation of a repeated group response methodology for consecutive questionnaires resulted in a decrease in the overall number of responses. The previously mentioned method has been employed to achieve the state of anonymity, repetition, regulated feedback, and statistical collective reaction [44]. The Delphi method necessitates the inclusion of a panel consisting of a minimum of five and a maximum of nine experts to attain a rational evaluation [45].  
The methodology has been employed to address the challenge of multi-attribute decision-making by utilizing linguistic expressions to represent variables on a continuous scale of grades. A membership function that can define a fuzzy set can assign a membership grade between zero and one. In the fuzzy field, it is usual to refer to classical dual sets as crisp sets. Equation (1) presents a fuzzy number with a triangular shape characterized by three parameters and a membership function.  
\[B(\chi)=\left\{\begin{array}{cc}0,&\chi<b_{1}\\ \frac{b_{2}-b_{3}}{b_{2}-b_{3}},&b_{1}\leq\chi\leq b_{2}\\ \frac{b_{3}-b_{3}}{b_{3}-b_{2}},&b_{2}\leq\chi\leq b_{3}\\ 0,&\chi>b_{3}\end{array}\right. \tag{1}\]  
The calculation of the \(j^{th}\) fuzzy function \(n\) is determined using:  
\[\overline{B}=\left(b_{1}^{(j)},b_{2}^{(j)},b_{3}^{(j)}\right),j=1,2,\ldots,n \tag{2}\]  
Moreover, the process of fuzzification is quantified as follows:  
\[\overline{B}=B_{ave}\frac{\overline{B_{1}}+\overline{B_{2}+\ldots+\overline{B _{n}}}}{n}=\frac{\left(\sum_{j=1}^{n}b_{1}^{(j)},\sum_{j=1}^{n}b_{2}^{(j)}, \sum_{j=1}^{n}b_{3}^{(j)}\right)}{n}=(b_{1},b_{2},b_{3}) \tag{3}\]  
The fuzzy technique was employed to represent the linguistic variables in words or phrases. Linguistic terminology can be utilized to denote a fuzzy set. A Likert scale with seven points (\"Very Low (VL), Low (L), Medium Low (ML), Medium (M), Medium High (MH), High (H), Very High (VH)\") was employed to assign scores to each indicator, ranging from a value of 7 for highly high to a value of 1 for very low, as depicted in Table 2.  
Defuzzification serves as the conclusive stage in converting all results into physical values, wherein the center of gravity is typically employed.  
\[z^{*}=b_{3}-\sqrt[2]{\frac{(b_{2}-b_{3})(b_{1}-b_{3})}{2}} \tag{4}\]  
The \(G\)-factor, denoted as \(z^{*}\), is determined by measuring the gravity center.
# Research on SWIM Services Dynamic Migration Method +
## 4 SWIM Service Dynamic Migration Method
If the migration destination node is selected according to a fixed law, the migration law will lose the meaning of the protection system once it is mastered by the attacker. Therefore, only ensuring the randomness of the destination node selection can increase the difficulty of detecting the service data source by malicious attackers and improve the survivability of the SWIM service. Combined with the above analysis, this paper proposes a SWIM service dynamic migration method (SSM), as shown in Figure 5.  
Figure 5: The SWIM service dynamic migration method. The SWIM service dynamic migration method is deployed in the SWIM core service and consists of a migration trigger mechanism and a destination node selection mechanism. Considering the need of time randomness and spatial randomness, this chapter designs a trigger mechanism combining time interval and random sequence, and adopts the combination of Brownian motion method and optimal service quality to select the migration target SP, which fully weighs service concealment and security requirements. In the process of providing services to the SWIM, the SWIM registration center is used to obtain the service information of the entire network node, and the status information of each node of the entire network is obtained by using the service monitoring and performance monitoring/reporting log information, and is hidden by continuously and randomly transferring the service authority. Service nodes reduce the success rate of attacks and increase the attack cost of malicious attackers.
## 3 Results  
### Water Quality and Fish Performance  
Water quality parameters were the same for all treatments. Water temperature was maintained at 27.57 \(\pm\) 1.42 \({}^{\circ}\)C, dissolved oxygen at 6.26 \(\pm\) 2.18 mg L\({}^{-1}\), pH 8.18 \(\pm\) 0.24, salinity at 15.61 \(\pm\) 0.62 ppt, and alkalinity at 163.49 \(\pm\) 16.43 mg CaCO\({}_{3}\) L\({}^{-1}\). Nutrients were maintained at an average of 0.12 \(\pm\) 0.07, 0.10 \(\pm\) 0.15, 18.69 \(\pm\) 7.29, and 0.85 \(\pm\) 0.54 mg L\({}^{-1}\), for total ammonical nitrogen, nitrite, and nitrate and phosphate, respectively. The performance of the animals is shown in Table 3. There were no significant differences (_p_ > 0.05, [PERSON]'s test) between the treatments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Treatments**} \\ \hline
**Parameters** & **Control** & **T5** & **T10** & **T15** \\ \hline Average initial weight (g) & 0.93 \(\pm\) 0.01 & 0.93 \(\pm\) 0.01 & 0.94 \(\pm\) 0.01 & 0.94 \(\pm\) 0.00 \\ Average final weight (g) & 10.78 \(\pm\) 0.51 & 11.44 \(\pm\) 0.84 & 10.35 \(\pm\) 0.92 & 10.33 \(\pm\) 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tilapia performance in the treatments: control (no macrolgae included), T5 (5% macroalgae included in the diet), T10 (10% macroalgae included in the diet), and T15 (15% macroalgae included in the diet) during 42 days of cultivation.
## 5 Modelling Rationale
### Modelling Assumptions
#### 5.3.1 Constituent Components and Failure Rates  
To obtain a model that is computationally solvable, it is necessary to abstract away the constituent components of the system to some extent. In our models we differentiate between links, bridges and the communication controllers of the end-systems. For the sake of succinctness, we will refer to the communication controllers as end-systems.  
Each component has a different failure rate which varies depending on its complexity and quality. We must note that, since in this work we only consider temporary faults, we only specify the components' failure rates caused by these faults.  
We characterise the rate of temporary faults affecting the links by means of the BER, i.e., the ratio between the number of erroneous bits and the total number of transmitted bits. Specifically, we consider BER values that range from \(1\times 10^{-4}\), usually considered in highly critical applications, to \(1\times 10^{-12}\), usually considered in non-critical applications [26; 27]. In this way we cover BER values considered in relevant applications with a wide range of reliability requirements, e.g., aerospace or automotive [28].  
Regarding temporary faults affecting bridges and end-systems, we have considered different failure rates depending on whether they are standard TSN or PTRF-enabled. This is a reasonable assumption since PTRF-enabled devices are slightly more complex, as they count with extra hardware and logic to implement the time redundancy mechanisms, i.e., the ones that support frame replication. This extra hardware is highlighted in blue in Figure 4.  
In order to estimate the failure rate of PTRF-enabled devices, we use the implementation of PTRF on a real TSN bridge, which has been implemented using an FPGA as we explain in detail in [11]. Unfortunately, we do not count with the exact information regarding the occupation of this extra hardware in the FPGA. Thus, we make a pessimistic assumption and we estimate that PTRF implies an increase of the 50% in the hardware of the devices and, thus, an increase of a 50% in the failure rate when compared to TSN-enabled devices.  
We have considered a failure rate of \(1\times 10^{-4}\) per hour for standard TSN bridges and end-systems [29]. On the other hand, we have considered a failure rate of \(1.5\times 10^{-4}\) per hour for these devices when they are PTRF-enabled.
## 2 Results and Discussion
### Discrimination Between CP and GCP Based on Multivariate Analysis  
To investigate the chemical variation in volatile compounds based on GC-MS data, both unsupervised and supervised classification methods were employed. Unsupervised PCA analysis converted the original variables into new irrelevant variables (principal components, PCs). In this way, reduced dimensionality of the data was obtained while still preserving information from the original dataset. Thus, PCA revealed trends in the dataset, such as groupings and clusters based on chemical similarities or differences. In the presented study, four principal components accounting for 83.8% (R\({}^{2}\)X) of the total variance were considered significant (PC1 described 32.3% of the sample variability, and PC2 described 23%). The predictive ability of the model (Q\({}^{2}\)) was 68.8%, which indicated that it was a good model (Q\({}^{2}\)\(\geq\) 0.50). The PCA score plot and loading plot are shown separately in Figure 2a,b, respectively. The score plot (PC1/PC2) displayed the relationship between CP and GCP, and the loading plot showed variables that contributed to the score. Both domains (CP and GCP) were clearly separated from each other. The variables which possessed the greatest influence on classification were located at the furthermost position from the main cluster of variables [33]. HCA was performed based on four PCs from the above PCA model, and displayed relationships between various CRP samples in the form of a dendrogram, calculated using [PERSON]'s minimum-variance method (shown in Figure 2c). All samples could be clearly divided into two clusters; all CP samples were classified into Cluster I (left), and all GCP samples were classified into Cluster II (right).
## 2 Materials and Methods
### SIF Spectra Retrieval
#### 2.2.2 Reconstruction of Reflectance and SIF Spectra
It needs to be noted that the reconstructed reflectance spectrum (\(\tilde{R}(\lambda)\) ) is not exactly equal to the real reflectance (R), because the weights of the PCs were fitted using the apparent reflectance spectra without  
Figure 3: The correlation between the true reflectance (\(R(\lambda)\)) and the reconstructed reflectance (\(\tilde{R}(\lambda)\) ) for the first three iterations ((**a–c**), (**d–f**), and (**g–i**)) at the H\(\alpha\) (**left**), O\({}_{2}\)-B (**middle**), and O\({}_{2}\)-A (**right**) bands. The values of the coefficient of determination (R\({}^{2}\)) and RMSE are also given in each subfigure. absorption bands, which was still coupled with a relatively small SIF contribution. Within each narrow absorption band, the relationship between the true reflectance \(R(\lambda)\) and the reconstructed reflectance \(\tilde{R}(\lambda)\) can be assumed to be linear (as shown in Figure 3). Two correction coefficients were, therefore, employed to estimate the true reflectance \(R(\lambda)\) using the reconstructed reflectance \(\tilde{R}(\lambda)\). As the relative intensity of the SIF is different for each band, the correction coefficients should be set separately for each narrow absorption band. The reflectance near to the three absorption bands that were used can then be expressed as:  
\[R(\lambda)= \begin{cases}\alpha_{Ha}\tilde{R}(\lambda)+\beta_{Ha},&\lambda\in [653,662]\\ \alpha_{B}\tilde{R}(\lambda)+\beta_{B},&\lambda\in[683,692]\\ \alpha_{A}\tilde{R}(\lambda)+\beta_{A},&\lambda\in[757,771]\end{cases}  \tag{9}\]
# Photocatalytic Cementitious Material for Eco-Efficient Construction--A Systematic Literature Review
## 3 Photocatalytic Cemertitious Materials  
In the building industry, extensive work is underway to develop innovative construction materials containing TiO\({}_{2}\) photocatalyst. Photocatalysis technologies make the production of construction materials with advanced functions, such as self-cleaning, air purifying, and self-sterilizing surfaces, feasible [39]. Applications include self-cleaning building facades, photocatalytic concrete roads for depolluting the air, and antibacterial tiles for hospital building interiors [40]. Moreover, as the applications of photocatalytic construction materials on buildings develop, road facilities and tunneling could be beneficial to alleviate the level of environmental pollution. Finally, it can further contribute to control the urban heat island effect in densely populated cities.  
Figure 6: Improving optical response of (**a**) TiO\({}_{2}\), (**b**) V-doped TiO\({}_{2}\), (**c**) sensitization by narrow bad gap semiconductor.  
For example, the \"smog-eating\" facade of Manuel Gea Gonzales Hospital in Mexico was made from white-cement containing TiO\({}_{2}\) photocatalyst to counteract the pollution from one thousand automobiles per day. In Bergamo Italy, photocatalytic concrete blocks have been used for renovating a street with heavy traffic, claiming almost 40% of NOx abatement [41]. In the case of indoor environment, the treatment of a city tunnel wall in Rome with photocatalytic paint potentially reduces the NOx pollutants up to 20% under artificial UV light illumination [42]. The current commercial technologies, such as TX Active(r) and TiOCam(r) photocatalytic cement, containing TiO\({}_{2}\) particles can effectively remove various pollutants, including NOx, SO\({}_{X}\), NH\({}_{3}\), CO, and volatile organic compounds, by exposing the cement to outdoor UV light.
## 3 Results and Discussion
### Histopathology  
As a result of our experiment, only histopathological changes in liver (abnormal glycogen accumulation) were observed among tested groups, apart from the lowest concentration of 0.2 ug/L (Figure 1). In contrast, no histopathological changes in liver of common carp after tramadol hydrochloride exposure were described by [PERSON] et al. [27].  
No significant histological changes were observed in all analyzed organs (skin, gill, kidney and brain). In contrast, [PERSON] et al. [27] described that histopathological examination of early life stages of common carp revealed gill hyperemia. They also noticed an increased number of the skin mucous cells of fish in tested groups. Comparably, similar findings have been published by [PERSON] et al. [12], where no changes in liver after tramadol-and-naproxen-mixture exposure in early life stages of common carp were observed. Similarly, there was an elevated amount of skin mucous cells. In addition, histopathological examination showed deformations of gill lamellas.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{6}{c}{**Tramadol Hydrochloride Test Group (\(\mu\)g/L)**} \\ \cline{2-7}
**Parameters** & **Control** & **0.2** & **2** & **20** & **200** & **600** \\ \hline
**Total length (mm)** & \(19.95\pm 2.83\) & \(19.89\pm 2.58\) & \(19.52\pm 2.99\) & \(19.68\pm 2.73\) & \(19.57\pm 2.94\) & \(19.42\pm 2.78\) \\ \hline
**Body weight (mg)** & \(63.80\pm 9.37\) & \(65.17\pm 6.21\) & \(61.58\pm 8.21\) & \(69.58\pm 9.35\) & \(63.90\pm 6.19\) & \(63.57\pm 6.58\) \\ \hline
**Specific growth rate** & \(2.50\pm 0.17\) & \(2.53\pm 0.18\) & \(2.39\pm 0.16\) & \(2.50\pm 0.17\) & \(2.49\pm 0.19\) & \(2.47\pm 0.15\) \\ \hline \end{tabular}
\end{table}
Table 2: Results of morphological parameters at the end of experiment. Data are expressed as mean \(\pm\) standard deviation. No significant differences (\(p>0.05\)) were found between control and experimental groups.
# A Cloud-Based Distributed Architecture to Accelerate Video Encoders
## 4 Experimental Setup and Results
### Scalability and Resource Usage
#### 4.2.2 Download, Coding and Upload Times  
Mean download, coding and upload times for 3, 9 and 15 workers are shown in Table 3.  
The download time increases with the number of workers as the as it is more likely to have more than one worker simultaneously downloading chunks. The time required to encode the chunks also increases with the number of workers. In the 15-workers configuration (5 workers per compute node allocating 20 vCPUs) per compute node so the physical system is subject to a higher overload than in the 9-workers configuration (3 workers per compute node allocating 12 vCPUs). This has an impact in efficiency reduction, as commented in the previous subsection, and specially for an encoder with high computational demand as H.265, but it could be solved increasing the number of nodes in the infrastructure. Upload times show that, the number of worker nodes does not have such a big impact as the download times. The reason is that the quantity of data to be uploaded is much lower compared to the quantity of data to be downloaded.  
Figure 4: Job scheduling with 15 workers for H.265.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stage** & **Workers** & **H.265** & **VP9** \\ \hline \multirow{3}{*}{Download} & 3 & 1.70 & 1.72 \\ \cline{2-4}  & 9 & 1.77 & 1.80 \\ \cline{2-4}  & 15 & 1.87 & 1.91 \\ \hline \multirow{3}{*}{Coding} & 3 & 63.66 & 10.29 \\ \cline{2-4}  & 9 & 68.80 & 11.03 \\ \cline{2-4}  & 15 & 92.82 & 13.0 \\ \hline \multirow{3}{*}{Upload} & 3 & 0.66 & 0.66 \\ \cline{2-4}  & 9 & 0.65 & 0.68 \\ \cline{1-1} \cline{2-4}  & 15 & 0.66 & 0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean time in seconds to perform different stages depending on the number of workers, with a chunk size of 50 frames, for H.265 and VP9.
## 4 Discussion
### Ku Band  
Although the Ku band has better measurement performance compared to the C band and Ka band, it is very sensitive to small deformations in the natural environment. The decrease in coherence can lead to image distortion, but coherence and temporary decorrelation will increase with the increase in the system operating wavelength. It can be considered to select a suitable band based on the measurement results of different bands, and then adjust the parameters of the GB radar system for micro deformation measurement to optimize the measurement performance as much as possible.