esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Analysis of Bridge Load Effect Considering Reinforcement Corrosion
The probability distribution of the LLD coefficient considering the influence of reinforcement corrosion was obtained based on the aforementioned derivation process. The deflection values of the mid-span section of the bridge were obtained by means of dial ages through the vehicle load test shown in Figure 5. The vehicle load test was taken at the same time as the bridge inspection test. For the elastic beam, the deflection of each beam is proportional to the load it bears. In other words, the LLD coefficient of the \(k\)-th beam can be calculated by the measured deflections:  
\[m_{k}=u_{k}\Bigg{/}\sum_{i=1}^{n_{T}}u_{i}, \tag{24}\]  
where \(u_{k}\) and \(u_{i}\) are the deflection values of the \(k\)-th beam and the \(i\)-th beam under vehicle load, respectively, and \(n_{T}\) is the number of T-beams. The probability distribution of the LLD coefficient of Beam 1 and Beam 2 was obtained according to the method proposed in this paper, and the LLD coefficient without considering the influence of reinforcement corrosion \(\mu_{0}\) was also calculated, whose results and measured data are given in Figure 6.  
In this study, an in situ test was carried out to verify the proposed method. Actual reinforcement corrosion cannot be measured by nondestructive testing, while the LLD coefficient can be obtained by static testing. In addition, reinforcement corrosion may not occur in the early stage of service time. The reinforcement corrosion model proposed in this paper is still suitable for bridge performance analysis. At this point, reinforcement without corrosion is treated as intact. The computation results of Equations (11) and (14) are the same as the uncorroded results. As shown in Figure 6, \(\mu\) is equal to \(\mu_{0}\), and both possess little standard deviation. This proves the effectiveness of the proposed reinforcement corrosion model to calculate uncorroded reinforcement. When a bridge has been in service for 20 to 30 years, the influence of reinforcement corrosion on its performance will be prominent. Regular bridge inspections are usually carried out every three to five years. Therefore, the test results of regular bridge inspection in 2020 were used to validate the study. Meanwhile, the theoretical model was employed to calculate the results for 100 years of bridge service life, and the theoretical results in 2020 were used to compare with the measured results and predict the variation trend. As seen in Figure 6, the LLD coefficients of Beam 1 and Beam 2 are constant without considering the influence of reinforcement corrosion. When the influence of reinforcement corrosion is considered, the mean value of the LLD coefficient of Beam 1 is basically stable in the first 20 years of service, but it drops sharply in the following 20-60 years of service and then tends to be stable. In addition, the LLD coefficient of Beam 2 shows an upward trend and is relatively stable in the preservice and postservice periods. This phenomenon shows that reinforcement corrosion will affect the stiffness of the bridge structure and
# Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model
## 3 Proposed Drone-Enabled AI Framework
### The Proposed Conceptual Architecture
#### 3.1.3. Okumura Model  
The PL of this model can be calculated as:  
\[{\rm P_{L}=L_{f}+Amm\ (f,d)-G(h_{t})-G(h_{r})-\ G_{area}} \tag{11}\]  
\[{\rm L_{f}=32.44+20\log(f)+20\log(d)} \tag{12}\]  
\[{\rm G(h_{t})=20\ log\ \left(\frac{h_{t}}{0.2}\right),\ 0.01\ km<h_{t}<1\ km} \tag{13}\]  
\[{\rm G(h_{r})\ =10\log\left(\frac{h_{r}}{3}\right),\ h_{r}\leq\ 3\ m} \tag{14}\]  
\[{\rm G(h_{r})\ =20\ log\ \left(\frac{h_{r}}{3}\right),\ 10>h_{r}>3\ m} \tag{15}\]  
where \({\rm P_{L}}\) is Path Loss (dB), \({\rm L_{f}}\) is Free Space Path Loss (dB), Amm (f, d) is Median Attenuation Relative to Free Space (dB), \({\rm d}\) is Distance of Transmission (km), \(f\) is Carrier Frequency (GHz), \({\rm G(h_{t})}\) is Transmitter Antenna Height Gain Factor (dB), \({\rm G(h_{t})}\) is Receiver Antenna Height Gain Factor (dB), \({\rm h_{t}}\) is Transmitter Antenna Height (km), \({\rm h_{r}}\) is Receiver Antenna Height (km), and \({\rm G_{area}}\) is Gain due to Type of Environment (dB). In order to predict \({\rm P_{L}}\) usingOkumura's model, Lf between the points of interest is first predicted and then the value of Amm (f, d) is added to it along with the type of terrain correction factor.
## 5 Test Results and Discussion
### Influence of Sample Size  
As mentioned above, the RC device encountered some problems when testing softer soil samples at low stresses. Therefore, the sample heights were selected in a range from 140 mm to 100 mm. The influence of sample height was investigated using sample RC_20. The initial height of the specimen was 140 mm (height/diameter ratio 2:1). After the isotropic final consolidation stage (\(p^{\prime}=290\) kPa), the specimen was shortened to 100 mm (height/diameter ratio 1.4:1) and consolidated again under the same effective stress. As can be seen in Figure 6, almost no differences were observed between \(G\) for the two specimen heights along the entire shear strain range.  
The possible influence of wave attenuation [43] on the determination of \(v_{s}\) from the BE test results was investigated using sample RCBE_6. After the isotropic final consolidation stage (\(p^{\prime}=140\) kPa), the specimen was shortened from the initial height of 110 mm to 90 mm, 70 mm and 50 mm and consolidated again with the same effective stress. For each specimen height, the BE tests were repeated at three different frequencies of the transmitted signal. The results in Figure 7 show that there are no significant differences in \(v_{s}\) for different specimen heights. The differences in \(v_{s}\) for each of the three measurements are more pronounced for shorter samples and are probably due to the accuracy of the transit time determination. However, the differences between the lowest and highest \(v_{s}\) measured are 2.5% and reflect 5.9% differences between the \(G_{0}\) determined. Using the average value of \(v_{s}\), the difference between the determined \(G_{0}\) is reduced to only 1%.  
Figure 6: The results of the RC test performed on a 140 and 100 mm high specimen.  
Figure 7: The results of the BE test on RCBE_6 sample for different \(L_{tt}\).
# Sensory Perception Systems and Machine Learning Methods for Pesticide Detection in Fruits
## 3 Results
### Classification of Pesticide Concentrations in Fruits with Multivariate Analysis Methods
#### 3.3.2 Strawberry  
On the other hand, the behavior of the strawberry using the E-nose through PCA and LDA analysis of the Across and Bricol concentrations can be seen in Figure 9. They were correctly discriminated and classified, generating a variance of 94.08% (Figure 9A) and 93.96% (Figure 9B). Despite the PCA and LDA plots showing clear separation between the different concentrations of Bricol and Across with respect to organic strawberry, there is a lack of linear distribution in the concentrations using the Bricol pesticide, as observed in Figure 9, where the concentration clusters are located in an ascending manner from the concentration of 0.2 to the concentration of 3 ppm. The lack of a linear distribution in the multivariate analysis graphs may be due to different factors such as the way the pesticides interact with the different components of the fruit (such as sugars, acids, and water), the natural variability in the chemical composition of the fruits (due to differences in ripening, storage, growth conditions, etc.), and the effects of mixing VOCs, among others.  
Figure 10 depicts the responses of the PCA and LDA models in identifying pesticide concentrations in strawberries using the E-tongue. For the pesticide Across, the device demonstrated a strong ability to distinguish between organic strawberries and those treated with varying concentrations of Across, achieving a variance of 99.85%, which differentiates the detected chemical profiles (see Figure 10A). In contrast, for the pesticide Bricol, the measurements showed some similarities, likely due to the presence of outliers that emerged during experimental tests or partial sensor saturation caused by surface interactions, among other factors. Despite these challenges, the system achieved a variance of 99.91%, as depicted in Figure 10B. This high level of variance means that, even with the presence of outliers, the system retains substantial sensitivity in detecting chemical differences between treated and organic simples.  
Figure 9: Discrimination and classification plots of pesticide concentrations and strawberry using E-nose: (**A**) PCA of Across, (**B**) PCA of Bricol, (**C**) LDA of Across, and (**D**) LDA of Bricol.
## 2 Design of Bow-Tie Microstrip Antennas
### Design of a Broadband Bow-Tie-Based Antenna  
In this stage, by introducing a small sector stub near the feed line and bending the corners of both triangular shapes, the wideband bow-tie antenna is designed and simulated. This small sector plays an important role in increasing bandwidth because the current is strengthened around the stub, which acts upon improving the electric field distribution. Bending the corners of the stub and presenting both triangular patch shapes are participating in the enhancement of the bandwidth [1]. The antenna was refabricated on different substrate thicknesses in order to optimize its performance and hence obtaining better results. The geometry of the designed antenna is shown in Figure 3, while the dimensions of the antenna and slots are presented in Table 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Antenna Parameters** & **Dimensions (mm)** \\ \hline Patch length pL & 36 \\ Base length BL & 34 \\ Gap & 4 \\ Feed line width Wf & 0.5 \\ Apex length AL & 5.5 \\ Substrate length L & 45 \\ Substrate width W & 87 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Antenna B parameters.  
Figure 3: The geometry of the proposed broadband antenna.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Antenna Parameters** & **Description** & **Values (mm)** \\ \hline W & Antenna width & 54 \\ L & Antenna length & 80 \\ BL & Triangular patch length & 93 \\ PL & Base length of the triangle & 32.5 \\ G & Gap & 0.3 \\ BCP & Bending radius of the patch corners & 10 \\ BCS & Bending radius of the slot corners & 1.15 \\ Bs & Base length of the slot & 21.44 \\ Ls & Length of the slot & 7.4 \\ Wf & Width of the feed lines & 0.38 \\ Apex & Apex length of triangular top & 2.35 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The antenna and slot diminutions.
# Octacalcium Phosphate Bone Substitute (Bontree(r)): From Basic Research to Clinical Case Study
## 2 Materials and Methods
### Basic Research
#### 2.1.2 In Vitro Study  
The biological properties of each sample were examined using a human osteoblast MG63 cell line (CTL-1427; ATCC, Manassas, VA, USA). MG63 cells were cultured in Dulbecco's modified Eagle's medium (DMEM; GIBCO Inc., Grand Island, NY, USA) supplemented with 10% fetal bovine serum (FBS; GIBCO Inc., Grand Island, NY, USA), 100 U/mL penicillin, and 100 \(\mu\)g/mL streptomycin (Gibco Inc., Grand Island, NY, USA). Prior to in vitro testing, Bontree(r) granules were compressed into 15 mm flat-faced tablets using a uniaxial press (Carver Inc., Chicago, IL, USA) for 30 s at a constant pressure of 55 MPa. The cells were incubated in a humidified incubator with 5% CO\({}_{2}\) at 37 \({}^{\circ}\)C. MG63 cells were seeded on each surface of the samples at a density of 1 \(\times\) 10\({}^{4}\) cells/mL and cultured on the samples for 7 or 14 days. All the samples were sterilized with 70% ethanol solution for 30 min, followed by exposure under UV light for 12 h before MG63 cell seeding.  
First, the content of the receptor activator of NF-kB ligand (RANKL) in the samples was determined using a commercial enzyme-linked immunosorbent assay (ELISA) kit. After a predetermined culturing time, the cells on the samples were detached using 0.25% trypsin-EDTA for 4 min at 37 \({}^{\circ}\)C, and the suspension containing the detached cells was vortexed and centrifuged at 13,000 rpm for 10 min. Standards and samples (100 \(\upmu\)L) were added to the wells and incubated for 150 min at 25 \({}^{\circ}\)C. The wells were washed four times with a diluted wash solution. Next, 100 \(\upmu\)L of biotinylated RANKL detection antibody diluted 80 times was added to each well and shaken gently for 1 h at 25 \({}^{\circ}\)C. Then, the wells were washed again with a wash solution diluted 20 times. Next, 100 \(\upmu\)L of streptavidin-HRP conjugate solution diluted 200 times was added to each well, followed by shaking for 45 min at 25 \({}^{\circ}\)C, and washing with wash solution diluted 20 times. The wells were incubated with 100 \(\upmu\)L of tetramethylbenzidine (TMB) for 30 min at 25 \({}^{\circ}\)C. The reaction was stopped by the addition of 50 \(\upmu\)L of stop solution, and the color developed was measured at 450 nm.  
Second, the differentiation of MG63 cells into osteoblasts was examined by an alkaline phosphatase (ALP) activity assay, which measured the transformation of \(p\)-nitrophenyl phosphate (\(p\)NPP, Sigma-Aldrich, Irvine, UK) into \(p\)-nitrophenol (\(p\)NP). After a predetermined culturing time, the cells were lysed in 0.2% Triton X-100 and incubated at 37 \({}^{\circ}\)C for 1 h after mixing with 50 \(\upmu\)L of pNPP. The reaction was stopped by the addition of 50 \(\upmu\)L of stop solution, and the color developed was measured at 405 nm.
# Radiation Hardness of Oxide Thin Films Prepared by Magnetron Sputtering Deposition
## 4 Conclusions  
In this work, we present the extraordinary radiation hardness of amorphous oxide thin films (a-SiO\({}_{2}\), a-Al\({}_{2}\)O\({}_{3}\) and a-MgO) prepared by the magnetron sputtering deposition. The results of the IR spectroscopy indicate that high levels of disorder already present in as-prepared samples effectively inhibit further damage build-up when exposed to high levels of irradiation, both in the case of high-energy heavy ions and gamma-ray irradiation. In some cases, high-energy heavy ion irradiation (such as 18 MeV Cu in a-SiO\({}_{2}\)) can actually improve the structure of thin films, in a similar manner as classical thermal annealing. In the future, more work is needed to understand what is the role of stoichiometry in the radiation hardness of studied amorphous oxide thin films, in particular when sub-oxide films can be successfully prepared via magnetron sputtering deposition [45, 46]. Also, of great interest is to understand to what extent high-energy heavy ion irradiation can be used as a proxy for the simulation of gamma-ray irradiation-induced changes, which should be studied systematically for the a-MgO system. Finally, the use of ion beams in selectively modifying amorphous films can offer an alternative route to tailor material properties for which more complex magnetron sputtering deposition procedures are now required [47].  
Conceptualisation, M.S. and M.K.; Formal analysis, M.S., Z.S. and M.K.; Funding acquisition, M.K.; Investigation, M.S.; M.M. ([PERSON]), Z.S., M.M. ([PERSON]), Z.K. and M.K.; Methodology, M.S., Z.S. and M.K.; Project administration, M.K.; Resources, M.K.; Validation, M.S., Z.S. and M.K.; Visualisation, M.S. and Z.S.; Writing--original draft, M.K.; Writing--review and editing, M.S., M.M. ([PERSON]), Z.S., M.M. ([PERSON]), Z.K. and M.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Croatian Science Foundation (HRZZ pr. no. 5023). The authors acknowledge support from the European Regional Development Fund for the 'Center of Excellence for Advanced Materials and Sensing Devices' (grant no. KK.01.1.1.01.0 0 01).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
Figure 5: FTIR spectra of (**a**) high-energy heavy-ion-irradiated a-MgO and (**b**) gamma-ray-irradiated and annealed a-MgO.
## 3 Simulation and Experiment
### Loading and Lifting Test
#### 3.3.1. Hooking and Lifting Test  
In the lifting test, the automatic hooking success rate \(\beta\), the hooking time \(t_{1}\), the lifting time \(t_{2}\), and the lifting height \(h\) were used as the evaluation indicators of the load-lifting device. The test was repeated 20 times. The results were shown in Table 2. It could be concluded that the hydraulic linkage has good connecting and lifting performance, and the hooking success rate was 100%. The average hooking time of the device was 5.15 s, and the average lifting time was 7.30 s. The lifting height of the lower hitch point was greater than 750 mm, which met the testing requirements. Compared with the average 10-15 min of the manual hooking time, the load-lifting device could significantly shorten the hooking time, improve check efficiency and automation. Meanwhile, the device could realize the unmanned connection with the lower link of the tractor.
## 2 Materials and Methods
### Metagenomic Analysis  
4.1 DNA Extraction and MiSeq Sequencing (Next-Generation Sequencing, NGS) of 16S rRNA Gene Amplicons  
An aliquot of each sample, in triplicate, was collected at the beginning (IN) and at the end (OUT) of the experiment and was merged and homogenized for DNA extraction and gene sequencing.  
DNA extraction was made as described by [PERSON] et al. [31] and adapted for these samples by [PERSON] et al. [32]. After chloroform-isoamyl alcohol steps, a 0.01 volume of 10 mg/mL RNAse was added. The aqueous phase was precipitated with a 0.6 volume of isopropanol and a 0.1 volume of 2.5 M C\({}_{2}\)H\({}_{3}\)NaO\({}_{2}\) solution and was kept at \(-\)20 \({}^{\circ}\)C for 1 h. The pellet of crude nucleic acids was recovered by centrifuging at 12,800\(\times\)\(g\) for 20 min. Then, cold ethanol 70% was added, the solution left overnight at \(-\)20 \({}^{\circ}\)C, centrifuged at 12,800\(\times\)\(g\) for 20 min, and resuspended in TE solution (10 mM Tris.HCl, pH 7.5, 1 mM EDTA). The extracted DNA was pooled, quantified, and checked for purity using QubitTM (Thermo Fisher Scientific, Wilmington, NC, USA) prior to storage at \(-\)20 \({}^{\circ}\)C.  
NGS was performed at STAB VIDA facilities (Lisbon, Portugal). For NGS, V3 and V4 regions of bacterial and archaeal 16S rRNA gene were amplified with universal primers 515F (GTGCCAGCMGCCGCGTA)--806R (GGACTACNNGGGTATCTAAT). Library construction was performed using the Illumina 16S Metagenomic Sequencing Library preparation protocol [33]. The generated DNA fragments (DNA libraries) were sequenced with the MiSeq Reagent Kit v3 in the Illumina MiSeq platform, using 300-bp paired-end sequencing reads.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Process Time (d)** & **HRT (d)** & **OLR (g COD/L-d)** & **OLR (g VS/L-d)** \\ \hline
0–19 & 6.7 & 4.50 & 2.22 \\
20–37 & 3.0 & 10.0 & 4.96 \\ \hline \hline \end{tabular}  
* BWW—Brewery wastewater; PE—Piggery effluent; HRT—hydraulic retention time; OLR—organic loading rate.  
\end{table}
Table 1: Anaerobic digestion of BWW + PE (60:40, \(v/v\)) in continuous conditions, in the hybrid anaerobic reactor.
## 2 Materials and Methods
### Land Use Estimations  
In order to quantify the main pressures affecting each waterbody, land use within the waterbody catchment was analyzed using the delineation and classification of land uses according to the European Corine-Land Cover (CLC) [28]. Land use maps were accessed from the European Space Agency (ESA)CLC maps, v.20, year 2012, in Esri File Geodatabase (Esri-FGDB) vector format [29]. Using Q-GIS tools, the land use layer coincident with the delineated catchment of each lake was determined, then, the area of each land use class reflected in the CLC maps was calculated (Figure 3a).
## 4 Experiments and Discussion
### Validation of Proposed Voice Trigger Approach  
#### 4.2.1 Experimental Setup  
We performed trigger word detection experiments to validate the efficiency of the proposed voice trigger approach. To construct acoustic models for trigger words and a filtering model, training data were collected by 50 persons who uttered predefined trigger words three times and general conversation speech. Here, we selected five trigger words to control five UAVs. The trigger words were defined as standard code words: \"alpha,\" \"bravo,\" \"Charlie,\" \"delta,\" and \"echo.\"  
The trigger models and the filtering model were trained with HMM using 750 utterances and approximately 2500 utterances, respectively. Here, each trigger word model was trained with 150 utterances. As a result, we constructed five HMMs for trigger words and an HMM for filtering according to the standard training procedure based on the hidden Markov model toolkit. The optimal numbers of states and Gaussian mixtures were determined during the training phase.  
The acoustic feature parameters were configured as a 39-dimensional vector comprising 12-dimensional MFCCs, log-energy, and their first and second derivatives. With regard to the VAD process, we employed a spectral energy-based approach in consideration of the computational capacity of the user device (Section 3.1). The threshold for VAD was determined empirically to avoid the effect of VAD in the verification of the proposed approaches.  
For the recognition test, we set up experimental environments to simulate a user device connecting to five remote UAV systems designated with five trigger words, respectively (Figure 14). The test was conducted in a real-time and online manner by 20 participants who did not participate in recording the training data. Each participant spoke general speech regardless of the trigger words to a device and intermittently spoke one of the five trigger words. To observe the performance in adverse environments, noise sounds were played while the participants made conversation. The SNR of the noisy speech was ranged from approximately 5 dB to 10 dB.  
Here, when the device detects a trigger word, a system corresponding to the trigger word displays a connection result. A situation of establishing a connection between the device and UAV systems after trigger word detection is shown in Figure 15. In this test, we counted the frequency of correct detection and investigated the missing rate.
## 3 Materials and Methods
### Atrial Fibrillation Detection
#### Feature Engineering  
The waveform of a normal sinus rhythm is represented on the ECG by three distinct waves: the P-wave, the QRS-complex, and the T-wave. However, in atrial fibrillation, chaotic fibrillatory waves may influence the morphology of the P-wave [5]. Moreover, the time interval from R peak to R peak is known to vary between beats. Consequently, cardiac arrhythmias are typically identified by analyzing the morphological and rhythmic properties of the electrocardiogram [30]. A total of 188 features were selected, including morphological features derived from the P, Q, R, S, and T points, features related to heart rate variability, frequency domain features, and statistical features based on RR intervals. A more detailed discussion of the feature engineering part is provided in [29]. To lessen the impact of noise in the feature extraction process, the ECG signal recorded by the wearable device is first denoised using a combination of a spectrogram-based technique and high pass filtering [29]. Since the extracted features have different ranges of values, the value of each feature is rescaled to have zero-mean and unit variance. The resulting features are homomorphically encrypted using the MORE encryption scheme before being sent to the classifier.