esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### HS-SPME/GC-MS Analysis  
The headspace (HS) volatiles were extracted by a manual SPME (solid phase microextraction) fiber with a layer of divinylbenzene/carboxylate/polydimiethylsiloxane (DVB/CAR/PDMS) from Supelco Co (Bellefonte, PA, USA). The fiber was conditioned according to the manufacturer's instructions. For HS-SPME, the pollen grains (1 g) were placed in 15 mL glass vial and hermetically sealed with PTFE/silicone septa. The closed vial was placed in a water bath at 60 \({}^{\circ}\)C during equilibration (15 min), and the extraction time of 45 min was applied for HS-SPME. After the sampling, the SPME fiber was withdrawn into the needle, removed from the vial, and inserted into the injector (250 \({}^{\circ}\)C) of the GC-MS (gas chromatography-mass spectrometry) for 6 min for thermal desorption directly to the GC column.  
The GC-MS analyses were conducted with an Agilent Technologies (Palo Alto, CA, USA) gas chromatograph model 8890 coupled with a mass selective detector (MSD) model 5977E. HP-5 MS capillary column (5% phenyl-methylpolysiloxane, Agilent J and W, Santa Clara, CA, USA) was used for the chromatographic separation. The oven temperature was programmed at 70 \({}^{\circ}\)C for 2 min and then increased from 70 to 200 \({}^{\circ}\)C (3 \({}^{\circ}\)C/min) and held at 200 \({}^{\circ}\)C for 15 min at the total run time of 63.333 min. The carrier gas was He (1.0 mL/min). The MSD (mass selective detector) (EI mode) was operated at 70 eV, and the mass range was 30-300 amu. The identification was based on the comparison of the retention indices (RI), determined relative to \(n\)-alkanes (C\({}_{9}\)-C\({}_{25}\)), with those reported in the literature and our internal database (Department of Organic Chemistry, Faculty of Chemistry and Technology, University of Split) as well as their mass spectra with the authentic compounds available in our laboratory or those spectra listed in mass spectral libraries NIST 17 (D-Gaithersburg, MD, USA) and Wiley W9N08 (Wiley, New York, NY, USA). The percentage composition was computed from the GC peak areas using the normalization method, and the average component percentages were calculated from duplicate GC-MS analyses.
# Fuzzy Logic-Based Performance Enhancement of FSO Systems Under Adverse Weather Conditions
## 1 Introduction
### Organization  
The article is organized as follows. Section 2 presents the modeling of rain and fog impairments in FSO systems, detailing the impact of atmospheric attenuation on signal propagation. Section 3 introduces the fuzzy modeling approach for enhancing receiver performance in the FSO-OCDMA system. This section covers the design of the FSO-OCDMA system integrated with an FLC, the Adaptive BER Threshold Adjustment Algorithm for FSO-FLC-OCDMA receivers, and the different control functions for dynamic BER optimization. Additionally, the computational efficiency and real-time feasibility of the FLC are analyzed. Section 4 discusses the results and performance evaluation, comparing the proposed FSO-FLC approach with conventional methods under varying weather conditions. Finally, Section 5 concludes the paper, summarizing key findings and outlining potential future research directions.
# WIRE: A Weighted Item Removal Method for Unsupervised Rank Aggregation
## 5 Experiments
### Execution Times  
In this subsection we examine how WIRE affects the execution time of a weighted rank aggregation method and particularly, DIBRA. Table 5 displays the running times of the involved rank aggregation methods in the benchmark datasets of Table 1. The presented values reveal the running times of each method per topic in milliseconds (recall that each dataset comprises multiple topics).  
These measurements indicate that WIRE imposes only small (and in some cases, infinitesimal) delays in the aggregation process. More specifically, in the six synthetic datasets, Algorithm 1 appended on average only 0.5-2.2 milliseconds to the processing of each query, demonstrating its high efficiency. In fact, DIBRA-WIRE was much faster than (i) the other weighted algorithms, PRG and AAM; (ii) all the order-based techniques  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Method** & **MOLO** & **MASO** & **FESO** & **MOSO** & **MAVSO** & **FEVLO** & **CTT22** & **NTDT23** \\ \hline Borda Count & 5.07 & 2.91 & 0.01 & 1.63 & 2.97 & 0.05 & 41.2 & 22.7 \\ CombMNZ & 5.04 & 2.93 & 0.01 & 1.62 & 2.85 & 0.05 & 41.0 & 25.4 \\ Condorcet & 18.88 & 7.40 & 0.01 & 1.93 & 4.05 & 0.10 & \(10\times 10^{3}\) & 8195 \\ Copeland & 17.64 & 6.89 & 0.01 & 1.88 & 3.80 & 0.10 & \(10\times 10^{3}\) & 8219 \\ Outranking & 31.28 & 10.26 & 0.01 & 2.22 & 4.57 & 0.16 & \(26\times 10^{3}\) & \(23\times 10^{3}\) \\ MC1 & 28.81 & 10.29 & 0.01  
(Condorcet and Copeland methods, Outranking approach); and (iii) all the Markov chain-based methods (MC1, MC4, MCT). In contrast, it was slightly slower only than simple linear combination methods like Borda Count and CombMNZ.  
As mentioned earlier, in the large TREC datasets, AAM failed to construct aggregate lists in reasonable time (more than one hour per topic). Moreover, PRG consumed all the available memory of our 32 GB workstation in CTT22, also failing to complete the aggregation task. In NTTD23, it was much slower than DIBRA, putting its usefulness into question. This conclusion applies to all the order-based and Markov chain-based methods. Despite their effectiveness in some experiments, the average topic processing times can be 3-5 orders of magnitude larger than those of Borda Count, DIBRA, DIBRA-WIRE, etc.
# Effects of Curcumin on the Renal Toxicity Induced by Ochratoxin A in Rats
## 3 Results
### Clearance of Inulin  
Figure 1 shows the effect of CURC and OTA treatment on GFR after 14 days of treatment. The OTA group significantly decreased their GFR versus the Control group (0.86 \(\pm\) 0.06 Control group to 0.35 \(\pm\) 0.03 OTA group) (***\(p<0.001\)). Instead, the CURC in association with OTA completely prevented this renal hemodynamic alteration. Actually, the GFR value was 0.75 \(\pm\) 0.07 mL/min in the OTA plus CURC group versus 0.35 \(\pm\) 0.03 in the OTA group (***\(p<0.001\)). CURC alone did not induce any change on the GFR in respect to the Control group (0.83 \(\pm\) 0.04 CURC group versus 0.86 \(\pm\) 0.06 Control group).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Groups** & **b.w. (g) 0 Days** & **b.w. (g) 7 Days** & **b.w. (g) 14 Days** \\ \hline Control & 255.50 \(\pm\) 4.6 & 288.92 \(\pm\) 7.0 & 315.60 \(\pm\) 5.8 \\ \hline CURC & 257.11 \(\pm\) 5.3 & 292.50 \(\pm\) 6.2 & 314.50 \(\pm\) 5.6 * \\ \hline OTA & 252.21 \(\pm\) 5.8 & 248.91 \(\pm\) 4.6 * & 258.32 \(\pm\) 4.9 * \\ \hline CURC + OTA & 258.20 \(\pm\) 4.7 & 257.72 \(\pm\) 6.1 & 272.31 \(\pm\) 5.2 * \\ \hline \hline \end{tabular}
\end{table}
Table 1: Body weight (B.W.) expressed in grams (g) in the different groups of rats to 0, 7 and 14 days of treatment. Control group (Control); Curcumin group (CURC); Ochratoxin A group (OTA); Curcumin plus Ochratoxin A group (CURC + OTA). Data are expressed as mean \(\pm\) standard deviation (SD) of \(n\) = 6 rats. (*\(p<0.05\), ***\(p<0.001\) versus Control; #\(p<0.05\), ***\(p<0.001\) versus OTA).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Groups** & **BUN (mg/dL)** & **CREA (mg/dL)** \\ \hline Control & 42.08 \(\pm\) 2.12 & 0.27 \(\pm\) 0.03 \\ \hline CURC & 38.13 \(\pm\) 2.25 *; *** & 0.24 \(\pm\) 0.05 * \\ \hline OTA & 57.54 \(\pm\) 0.05 *** & 0.40 \(\pm\) 0.08 * \\ \hline CURC + OTA & 48.12 \(\pm\) 0.05 *; *** & 0.35 \(\pm\) 0.02 * \\ \hline \hline \end{tabular}
\end{table}
Table 2: Serum biochemical parameters: Blood urea nitrogen (BUN) and Creatinine (CREA) expressed in mg/dL concentration values in the different groups of rats after 14 days of treatment. Control group (Control); Curcumin group (CURC); Ochratoxin A group (OTA); Curcumin plus Ochratoxin A group (CURC + OTA). Data are expressed as mean \(\pm\) standard deviation (SD) of \(n\) = 6 rats. (*\(p<0.05\), ***\(p<0.001\) versus Control; #\(p<0.05\), ***\(p<0.001\) versus OTA).
# A Flexible Infrastructure-Sharing 5G Network Architecture Based on Network Slicing and Roaming
## 4 Results and Discussion  
This section describes the tests conducted to validate the architecture proposed in this paper, the results obtained, and the corresponding discussion. The results are split into three sections. The first one involves functional testing to check the MNO and non-MNO authentication methods and the traffic isolation of a network slice. Afterward, we analyze our proposal's efficiency to share and manage the NSP resources by measuring the impact on CSPs and their users. These performance evaluation results are split into two sections, one using the throughput and the other using the packet loss as metrics. In those tests, we use the maximum number of slices given our hardware limitations, which are four slices, and use as the baseline an NSP with a single client, which corresponds to having a single network slice with all the available resources. The performance evaluation results include the confidence intervals for a confidence level of 95%.
# Design Approach and Mechanism Analysis for Cavitation-Tolerant Torque Converter Blades
## 2 Cavitation Simulation and Verification of HTC  
### Multiphase Model  
A \(k\)-\(\omega\)-based shear stress transport turbulence model is adopted because it can capture boundary layer details using a low Reynolds formulation with refined near-wall meshes and maintain a robust free shear flow solution. Cavitation is used as a transfer mechanism between the vapor and liquid phases to calculate the cavitation flow field inside the HTC. The fluid in the field always maintains mass and momentum conservation. The phase mass transfer rate equation between the vapor and liquid phases, that is, the cavitation equation, should be added. The governing equation is  
\[\frac{\partial}{\partial t}(f_{v}\rho_{v})+\
abla\cdot(f_{v}\rho_{v}\overrightarrow{ V}_{v})=R_{\rm e}-R_{\rm c} \tag{1}\]  
The key to determining the cavitation flow field is to solve the growth and collapse rates, that is, the evaporation and condensation rates, of cavitation bubbles.  
The bubble dynamics equation of cavitation is derived from the Rayleigh-Plesset equation, but the second-order and surface tension terms in the Rayleigh-Plesset equation are ignored. The equation is  
\[\frac{dR_{b}}{dt}=\sqrt{\frac{2}{3}\frac{p_{v}-p}{\rho_{l}}} \tag{2}\]  
Equation (2) provides a physical approach to introducing the effects of bubble dynamics into the cavitation model.  
The Zwart-Gerber-Belamri cavitation model [17] is used; it calculates the total interphase mass transfer rate per unit volume (\(\overleftarrow{m}\)) using the bubble density number (\(n\)). The mass change rate of a single bubble is  
\[\overleftarrow{m}=4\pi nR_{b}^{2}\rho_{v}\frac{dR_{b}}{dt} \tag{3}\]  
\(n\) is determined by  
\[f_{v}=\frac{4}{3}\pi{R_{b}}^{3}n \tag{4}\]The net mass transfer is obtained by substituting the value of \(n\) into Equation (3).  
\[\overset{\bullet}{m}=\frac{3f_{v}\rho_{v}}{R_{b}}\sqrt{\frac{2}{3}\frac{p_{v}-p}{ \rho_{l}}} \tag{5}\]  
When the local pressure is lower than the vapor pressure, cavitation bubbles are increased, and the vapor volume fraction \(f_{v}\) is replaced by \(f_{\rm nuc}(1-f_{v})\) to yield [18]. The evaporation mechanism of cavitation in the flow field is controlled by \(R_{\rm e}\), whose equation is  
\[R_{\rm e}=F_{\rm e}\frac{3f_{\rm nuc}(1-f_{v})\rho_{v}}{R_{b}}\sqrt{\frac{2}{3} \frac{p_{v}-p}{\rho_{l}}} \tag{6}\]  
When the local pressure is greater than the vapor pressure, the cavitation collapses. The mechanism of cavitation condensation in the flow field is controlled by \(R_{\rm c}\), which is expressed as  
\[R_{\rm c}=F_{\rm c}\frac{3f_{v}\rho_{v}}{R_{b}}\sqrt{\frac{2}{3}\frac{p-p_{v}} {\rho_{l}}} \tag{7}\]  
Because the condensation process of cavitation is slower than the vaporization process, the following empirical factors are assumed: \(F_{\rm e}\) = 50, \(F_{\rm c}\) = 0.01, \(f_{\rm nuc}\) = 5 \(\times\) 10\({}^{-4}\), and \(R_{b}\) = 1 \(\times\) 10\({}^{-6}\) m [19; 20; 21].  
A homogeneous multiphase condition is also assumed to simplify the model. Thus, the liquid and vapor phases share the same velocity and turbulence profiles.
# Space-Borne System-in-Package Based on High Reliability Microwave Interconnections
###### Abstract  
With the increasing demand for multi-function and highly integrated space-borne microwave products, a three-dimensional heterogeneous integration based on special substrates and other systems-in-package has difficulty with meeting the requirements of high reliability in space. Through simulation, the thermal-deformation resistance of traditional interconnections meets the requirements of high reliability, but its transmission performance at high frequency is poor. Based on the above, a novel microwave horizontal coaxial transition structure with good thermal-deformation resistance and excellent microwave characteristics is proposed. The design formula of the structure is derived, and the basic size of the structure can be quickly calculated. The transition structure and a vertical matching structure can realize microwave interconnection by combining microstrip impedance transformation and gold-ribbon interconnection. The complex microwave circuit is laid out and partitioned in a three-dimensional way, and thus a space-borne microwave metallic system-in-package method based on high reliability microwave interconnections is implemented. A Ka-band receiving system-in-package is used as an example to carry out the actual test and validation. The better characteristics of the Ka-band receiving channel on the satellite are obtained, and its excellent reliability is verified by the temperature cycle and mechanical vibration test.  
high reliability; space-borne; microwave interconnection; system-in-package Article
# A Novel Approach for Software Defect prediction Based on the Power Law Function
## 3 Experimental
### Comparative Experiment
 \\ \hline Camel1.6 & 0.282 & 0.521 & **0.622** & 0.335 & 0.500 \\ Forrest-0.8 & 0.500 & 0.500 & 0.500 & **1.000** \\ Synapse-1.0 & 0.063 & 0.438 & 0.063 & 0.063 & **0.875** \\ Berek & 0.250 & 0.875 & 0.750 & 0.313 & **0.875** \\ Intercafe & - & 0.750 & 0.750 & 0.250 & **0.750** \\ Termo & 0.615 & 0.538 & **0.692** & 0.615 & 0.615 \\ Pbeans2 & 0.800 & 0.600 & 0.400 & 0.800 & **0.800** \\ ar1 & 0.667 & 0.667 & 0.667 & 0.600 & **0.778** \\ ar4 & 0.800 & 0.800 & 0.800 & 0.150 & **0.800** \\ ar5 & 0.875 & 1.000 & 1.000 & 0.900 & **1.000** \\ Apache & 0.276 & 0.704 & 0.714 & 0.45 The proposed approach was generally superior to other traditional methods in terms of precision, recall, and the F-measure. Although the average precision value obtained by the proposed approach was not the best, the proposed approach performed the best in 6 of 12 datasets in terms of precision, giving it the largest number of optimal results among the tested methods. Furthermore, the average precision values of the proposed approach and the k-means algorithm were relatively similar. For the recall value, the proposed approach's advantage was much more apparent. It performed the best in 10 of the 12 datasets and had an average value of 0.790, which was the highest among the tested methods. It was obvious that the proposed approach performed significantly better than traditional defect prediction models on almost all datasets. At the same time, some researchers have indicated that prediction models with low precision and high recall were more useful in many industrial situations [46]. The proposed approach obtained a better recall value than the precision value and thus would perform better in these areas. For the F-measure value, the proposed approach performed the best in 7 of the 12 datasets and had the largest average value of 0.501 among the tested methods. It was clear that the proposed approach obtained the best F-measure value and thus demonstrated the proposed approach's effectiveness at software defect prediction with unlabeled datasets. Therefore, based on these public datasets, it can be seen that the proposed approach obtains the best prediction results among the tested five algorithms. Nevertheless, considering the limitation of data timeliness and scale, these datasets cannot totally represent the real software defects and new defects. When utilized in real software systems, the proposed approach can be further validated.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 4 Methodology
### Scenarios and Discussion
#### 4.3.1 Scenario 1: 20 Nodes  
In this scenario, the performance of the eight routing protocols was evaluated in a network consisting of 20 mobile nodes. Each protocol was analyzed based on key performance metrics.
# A Methodology to Improve Energy Efficiency and Sustainability in Urban Environments
## 4 Solar Potential Study  
The next step in the study involved the extension of the analyses to assess the solar potential on a large scale, while also considering the transferability of the obtained data to the urban scale. This required the development of a methodology that combines solar potential simulation data with spatial data management in a GIS environment [36; 37; 38].  
By utilizing a 3D model, it is possible to gather, through tools for Grasshopper such as Ladybug and Honeybee, data on the solar potential of various surfaces, volumes, and buildings [39]. However, one challenge with this relatively straightforward procedure concerns the fact that the resulting data are significantly influenced by the type and level of detail of the model used [40]. If the model is oversimplified, the data obtained, in turn, will also be limited in their accuracy. On the other hand, when analyzing extensive areas of cities, it is not feasible to incorporate a high level of detail into the model [41]. Striking the right balance between model complexity and practicality is crucial to obtaining reliable results.
## 2 System Model
\tag{8}\]The received power is a function of time and can be written as:  
\[p_{r,i}(t)\ =\ p_{t}-L(h,r_{i}(t))\,, \tag{9}\]  
where \(r_{i}(t)\ =\ \sqrt{\left(x_{i}-x(t)\right)^{2}+\left(y_{i}-y(t)\right)^{2}}\). In addition, the Signal-to-Noise Ratio (SNR) of a user \(i\) denoted by \(SNR_{i}(t)\) is:  
\[SNR_{i}(t)\ =\ p_{t}-L(h,r_{i}(t))-p_{N}, \tag{10}\]  
where \(p_{N}\) is the noise power. The UAV-BS can only partially cover the target area at each moment, due to the large target area. To enable all users on the target area to be served in one period, the period \(T\) is divided into \(N\) segments according to the number of terrestrial users, where \(N\) is the number of terrestrial users. The length of each time slot is \(\frac{T}{N}\). A binary variable \(u_{i}[n]\) is also defined, which indicates that the user \(i\) is served by the UAV in the time segment \(n\) if \(u_{i}[n]\ =\ 1\); otherwise, \(u_{i}[n]\ =\ 0\). As such, \(u_{i}[n]\) indicates each user's connection with the UAV in each time segments. It is assumed that the UAV only serves one user in each time segment, which yields the following constraints,  
\[ \begin{array}{l}\sum\limits_{i\ =\ 1}^{N}u_{i}[n]\ =\ 1,\quad\forall n\\ \sum\limits_{n\ =\ 1}^{N}u_{i}[n]\ =\ 1,\quad\forall i\\ u_{i}[n]\in\{0,1\},\quad\forall i,n\end{array} . \tag{11}\]  
The minimum path loss \(L_{\min}\) is the path loss when the user is directly below the UAV and can be calculated, that is, \(L_{\min}\ =\ L(h,r_{i})\big{|}_{r_{i}\ =\ 0}\). The maximum SNR, can be expressed as:  
\[SNR_{\max}\ =\ p_{t}-L_{\min}-p_{N}. \tag{12}\]  
Furthermore, the average SNR of the user \(i\) in time segment \(n\) denoted by \(SNR_{i}[n]\) is:  
\[SNR_{i}[n]\ =\ \frac{\int\limits_{\frac{T}{N}(n-1)}^{\frac{T}{N}n}SNR_{i}(t) dt}{T/N}. \tag{13}\]  
Since \(SNR_{\max}\) is a fixed value, we take square of the difference between the average signal-to-noise ratio of each user within a time slot (i.e., \(SNR_{i}[n]\)) and this fixed value as the metric to measure fairness. It can be written as \(\left(SNR_{\max}-SNR_{i}[n]\right)^{2}\). After UAV hovers over the target area for a period, all users obtain service in turn. The fairness metric can be further written as \(\sum\limits_{i\ =\ 1}^{N}\sum\limits_{n\ =\ 1}^{N}u_{i}[n]\left(SNR_{\max}-SNR_{i}[n]\right)^{2}\). Figure 1: System model. After UAV hovers over the target area for a period, all users obtain service in turn. Since UAV cannot completely cover users in a time slot, it is necessary to optimize the hovering radius and user scheduling to ensure the quality of service to each user. Thus, we take the square of the difference between SNR of the user and maximal SNR as the fairness metric, and can make SNR of the user as close to maximal SNR as possible. This metric indicates the degree of difference in the signal-to-noise ratio of users and smaller value means better fair performance. Let \(A\ =\ [u_{i}[n],\forall i,n]\). The goal is to minimize the sum of squares of the difference between average SNR \(SNR_{i}[n]\) and the maximum SNR \(SNR_{\max}\) by jointly optimizing user scheduling and the hovering radius of the UAV.
# Expanded Scene Image Preprocessing Method for the Shack-Hartmann Wavefront Sensor
## 3 Results
### Simulation and Analysis of the Wavefront Reconstruction  
In the Shack-Hartmann wavefront detector, in order to further verify the effectiveness of the noise reduction algorithm in this paper, the wavefront reconstruction is carried out using the mode method and evaluated with the root mean square error. The reconstructed Zernike coefficients are shown in Figure 5, corresponding to the second term of the Zernike polynomial with a coefficient of 10. Figure 5b-f show the distorted reconstructed wavefront Zernike coefficients corresponding to the subaperture images subjected to noise interference, those after the noise reduction is carried out, and the comparisons with Figure 5a, respectively, indicate that the reconstructed wavefront after noise reduction is closer to the added value. The coefficients are closer to the added values, while the noise reduction using the GFBM3D algorithm is closest to the added values. The root mean square errors are shown in Table 2 below.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Average Offset Calculation Error** \\ \hline Noisy image & 7.78 \\ Mean filtering & 5.4865 \\ Gaussian filtering & 7.7843 \\ BM3D & 7.60 \\ GFBM3D & 3.76 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average offset calculation error after noise reduction by different methods.  
Figure 5: The pattern method reconstructs the wavefront Zernike coefficients. (**a**) The added distortion wave surface. (**b**) Noisy image. (**c**) Mean filtering. (**d**) Gaussian filtering. (**e**) BM3D. (**f**) GFBM3D.  
As can be seen from Table 2, the effect of the noise reduction algorithm using Gaussian filtering is still poor, and the root mean square error after applying the mean filtering and BM3D algorithm for noise reduction is improved by 46.05% and 4.56%, respectively; whereas, after applying the GFBM3D algorithm for noise reduction, it is improved by 85.56%, and the reconstruction results further indicate that the algorithm of this paper for the preprocessing of subaperture images can effectively improve the Shack-Hartmann wavefront detector's wavefront detection accuracy.