esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Validation of Non-Marker Simple Image Displacement Measurements for Railway Bridges
## 6 Conclusions  
In this study, the measurement accuracy of railway bridge displacement obtained by the non-marker image measurement method was investigated. The main findings obtained are shown below.  
* The multipoint displacement measurement accuracy of non-marker image measurement was verified by exciting a model bridge at seven measurement points and exciting minute displacements with a vertical amplitude of \(\pm\)25 to 50 \(\upmu\)m (\(\pm\)0.017 to 0.035 pixels). Consequently, it was established that displacements greater than 0.03 pixels can be quantified using the non-marker image measurement by avoiding the overexposed areas.
* From the measurement results with varying illuminance, it was estimated that the illuminance required to apply the non-marker image measurement is \(\sim\)500 k, and the proposed method can be applied during the daytime.
* The displacements during train passages of the steel bridge A (span length 20 m), which had a clear contrast at the image measurement point, were measured using non-marker image measurement and compared with the result of laser measurement. It was confirmed that the non-marker image measurement can be used to estimate the displacement response at the bridge midspan with the same accuracy as that of other measurement approaches.
* The displacements during train passages of the high-speed railway bridge B (continuous concrete bridge), which had an unclear contrast, were measured using two types of non-marker image measurement and compared with the results obtained from the ring-type gauge and laser measurement. Consequently, the measurement error of the maximum displacement was \(\sim\)0.3 mm at a camera distance of \(\sim\)20 m from the bridge.
* By examining the error reduction effect of the subset position and moving average processing when the contrast of the image measurement point was not clear, the error was halved by setting the subset on the side of the bottom boundary with a clear texture and using 0.1 s moving average processing.  
Of the methods proposed in this study, the use of a compact digital camera has already been widely employed, not only for bridge surveys on Japanese railways, but also for surveys of vibration of tracks (rails and sleepers) and railway poles [27; 30; 31].  
Conceptualization, K.M. and F.U.; methodology, K.M.; software, K.M. and H.K.; validation, K.M., H.T. and H.K.; writing--original draft preparation, K.M.; writing--review and editing, F.U., H.T. and H.K.; supervision, F.U.; project administration, F.U.; funding acquisition, F.U. All authors have read and agreed to the published version of the manuscript.  
A part of this research was funded by grants-in-aid for research on railroad technology funding from the Ministry of Land, Infrastructure, Transport and Tourism.  
Not applicable.  
Authors would like to thank the East Japan Railway Company Nagano Civil Engineering Center Center for the significant cooperation in some field measurements of this study.  
The authors declare no conflict of interest.
# Nano-/Microcapsules, Liposomes, and Micelles in
## 2 Spherical Nanoparticles
### Micelles
#### 2.2.1 Design, Shape, and Size
While surfactants are inclined to form spherical micelles, they can also produce ellipsoidal and cylindrical micelles. Moreover, micelles can adopt vesicular configurations [83]. Polysaccharide chains can adopt rod, helical, or helical conformations, which have a significant impact on the movement of hydrophobic molecules within the chains and the process of self-organisation [70]. The definition of surfactants is closely linked to the micelle's shape, but measuring the degree of packing proves arduous due to the absence of a rigid boundary. The final shape of the self-aggregates is determined by the microphase separation of the amphiphilic block copolymers, as well as the critical packing parameter (CPP). CPP is computed based on factors such as chain volume, length, and cross-sectional area per molecule at the aggregate interface. Spherical micelles are formed when CPP is less than or equal to 1/3, cylindrical micelles are formed when 1/3 < CPP \(\leq\) 1/2, vesicular micelles are formed when 1/2 < CPP \(<\) 1, bilayers (lamellae) are formed when CPP equals 1, and inverted micelles are formed when CPP is greater than 1 [70; 83; 84]. Nevertheless, pure contact potentials do not always determine the interactions that drive aggregation, particularly with ionic amphiphiles where Coulomb forces mediated by electrolytes dominate intermolecular forces. In addition, electrostatic interactions have been attributed to the emergence of quasi-crystalline Franck-Kasper phases in ionic surfactants. Thus, in such cases, theoretical models are employed, which consider the free energy contribution that results in hydrophilic head-to-head repulsion and the translational entropy of surfactant monomers, counterions, and aggregates, as well as the hydrophobic contribution responsible for tail packing [73; 85].
## 3 Service Delivery in 5G-Enabled D2D Communications Network
### D2D Communications Process
#### 3.3.2 D2D Communications Non-Network Assisted  
This is similar to Mobile Ad Hoc Network (MANET); however, the D2D link uses a reserved cellular licensed spectrum for an out-band communication, also referred to as public safety network. This is a direct communication between UEs with a controlled link establishment by the UEs.  
1. \(UE_{1}\), hoping to make direct communication, transmits beacon signals with low frame rate to reduce signaling overhead.
2. \(UE_{2}\), in proximity, responds by sending acknowledgement messages.
3. The received messages are evaluated based on metrics, such as Signal-to-Interference-plus-Noise Ratio (SINR).  
Figure 2: D2D connectivity process flowchart.  
The UEs can start direct communication with each other, when a received signal is above the predetermined metrics threshold with a good quality of service.  
The beacon signals are transmitted over channels that are dedicated for transmitting control signals, whereas data exchange is performed using shared data channels. To establish safe and secured links in D2D communications, the beacon signals broadcast contains security vectors within the packet frames. 5G UEs are capable of an authentication process with low signaling overhead.
## 2 Materials and Methods
### Evaluation of Apnea Detection Results
#### 2.4.2 Receiver Operating Characteristics (\(ROC\)) Curve  
The Receiver Operating Characteristics (\(ROC\)) curve is an effective tool used to graphically illustrate the diagnostic ability of a binary classification system as its classification threshold is varied [55]. This curve simply plots the \(TPR\) against the false positive rate (\(FPR=100\%-TNR\)) at various discrimination threshold settings. The Area Under receiver operating characteristics Curve (\(AUC\)) is used as a measure of the overall ability of the classification model to automatically detect sleep apnea events. A greater \(AUC\) indicates a more useful and effective classification model. Additionally, the \(ROC\) curve can be used for optimizing classification models by finding the operating threshold that provides the highest \(TPR\) for the allowable \(FPR\) level. This approach was used in learning the classification rules of the rule-based classifier.
# Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils
## 2 Materials and Methods
### Correlation between C\({}_{s}\) and Geotechnical Parameters  
To statistically predict the correlation between C\({}_{s}\) and soil properties, the SPSS software has used. The cross-correlation between C\({}_{s}\) and soil parameters is presented in Figure 4, which could provide a descriptive overview of the data distribution. The results indicate a positive relationship between C\({}_{s}\) and others parameters, except for \(Y_{d}\) and \(Y_{h}\), which seem to have a negative relationship (see Figure 4); this indicates that an increase in these parameters tends to proportionally increase \(C_{s}\). The Spearman correlation coefficient \(R\) and its significance between C\({}_{s}\) and other geotechnical parameters are presented in Table 5. The results show that the significance is less than 0.05 except Z, meaning that the majority of correlations are statistically significant. On the other hand, according to [PERSON]'s classification (1986), the C\({}_{s}\) is moderately correlated to the aforementioned soil parameters, except \(Sr\) and \(Z\) which are poorly correlated. The results indicate that these parameters could have a complex nonlinear correlation with C\({}_{s}\). Moreover, in order to mathematically simulate the complex swellings phenomena, new advanced machine learning methods should be applied.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & & \(Sr\) & \(Y_{h}\) & \(Y_{d}\) & \(W\) & \(e_{0}\) & \(FC\) & \(W_{L}\) & \(PI\) & \(C_{s}\) \\ \hline \multirow{2}{*}{N} & Valid & 875 & 875 & 875 & 875 & 875 & 875 & 875 & 875 & 875 \\ \cline{2-10}  & Missing & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline Mean & 89.45 & 2.01 & 1.67 & 20.61 & 0.63 & 86.55 & 50.11 & 26.09 & 0.0443 \\ Std. Error of Mean & 0.397 & 0.003 & 0.004 & 0.164 & 0.005 & 0.572 & 0.338 & 0.233 & 0.00072 \\ Median & 94.00 & 2.01 & 1.67 & 20.00 & 0.62 & 94.00 & 50.00 & 26.00 & 0.0399 \\ Mode & 100.00 & 2.04 & 1.69 & 20.00 & 0.61 & 98.00 & 58.00 & 29.00 & 0.04 \\ Std. Deviation & 11.77 & 0.09 & 0.13 & 4.86 & 0.13 & 16.92 & 10.00 & 6.89 & 0.01910 \\ Variance & 138.54 & 0.01 & 0.02 & 23.64 & 0.02 & 286.23 & 100.09 & 47.43 & 0.000 \\ Skewness & \(-1.32\) & 0.10 & 0.29 & 0.36 & 0.21 & \(-1.75\) & \(-0.08\) & \(-0.12\) & 0.686 \\ Std. Error of Skewness & 0.083 & 0.083 & 0.083 & 0.083 & 0.083 & 0.083 & 0.083 & 0.083 & 0.092 \\ Kurtosis & 1.09 & \(-0.20\) & \(-0.09\) & \(-0.08\) & \(-0.29\) & 2.41 & \(-0.28\) & \(-0.43\) & 0.073 \\ Std. Error of Kurtosis & 0.165 & 0.165 & 0.165 & 0.165 & 0.165 & 0.165 & 0.165 & 0.165 & 0.165 & 0.183 \\ Range & 64.45 & 0.57 & 0.73 & 26.00 & 0.79 & 78.00 & 64.31 & 38.00 & 0.10 \\ Minimum & 41.00 & 1.70 & 1.34 & 8.00 & 0.23 & 22.00 & 19.00 & 7.00 & 0.01 \\ Maximum & & 100.00 & 2.27 & 2.07 & 34.00 & 1.02 & 100.00 & 83.
# Vertical Correlation and Array Gain Analysis for Vertical Line Array in Deep Water
## 2 Basic Theory
### Vertical Correlation Coefficient  
The vertical correlation coefficient describes the similarity of the signal received at the same horizontal range but different depths. It is a critical factor which greatly affected the AG [19]. The vertical correlation coefficient in the time domain can be expressed as  
\[\rho(z,z+\Delta z)=\frac{\int_{-\infty}^{\infty}p_{z}(t)p_{z+\Delta z}(t+\tau )dt}{\sqrt{\int_{-\infty}^{\infty}\left|p_{z}(t)\right|^{2}dt\cdot\int_{- \infty}^{\infty}\left|p_{z+\Delta z}(t)\right|^{2}dt}} \tag{7}\]  
where \(p_{z}(t)\), \(p_{z+\Delta z}(t)\) is the time-domain signal received by two hydrophones in the vertical direction, respectively. \(\Delta z\) denotes the vertical spacing of two sensors. In general, in the deep water environment,the signal does not reach each element of VLA at the same time. Thus, the signals are necessary to be compensated before correlation processing.  
The vertical correlation coefficient in the frequency domain can be written as  
\[\rho(z,z+\Delta z)=\frac{\text{Re}\!\left[\int_{\omega_{1}}^{\omega_{2}}p_{z}( \omega)p_{z+\Delta z}*(\omega)e^{-i\omega(g-1)d\cos\theta/c}d\omega\right]}{ \sqrt{\int_{\omega_{1}}^{\omega_{2}}\left|p_{z}(\omega)\right|^{2}\!d\omega \cdot\int_{\omega_{1}}^{\omega_{2}}\left|p_{z+\Delta z}(\omega)\right|^{2}\!d\omega}} \tag{8}\]  
where the superscript symbol \(*\) stands for the conjugation operation, \(\text{Re}[\cdot]\) represents the real part of the content in brackets, \(p_{z}(\omega)\) and \(p_{z+\Delta z}(\omega)\) are spectrums of the signal received at two different hydrophone depths, respectively. \(\omega_{1}\) and \(\omega_{2}\) are the angular frequencies, which are determined by the filtering frequency band of the signal. Generally, Equation (7) is usually used to calculate correlation in the experimental data analysis while Equation (8) is utilized for theoretical calculation.  
The correlation of the noise received is another critical factor that directly influences the AG analysis. Generally, the isotropic noise and surface noise are two typical and widely used noise models. The isotropic noise field is composed of the combination of sound waves radiated from noise sources uniformly distributed over the sphere, and its vertical correlation coefficient can be expressed by [10]  
\[\rho_{mm}=\frac{\sin(2\pi D/\lambda)}{2\pi D/\lambda}\quad m,n=1,2,\cdots M \tag{9}\]  
where \(D\) denotes the distance between two receivers and \(\lambda\) stands for the wavelength of signal.  
The surface noise field is made up of sound waves radiated from noise sources distributed on an infinite surface with the \(\cos^{l}(\theta)\) radiation directivity pattern.  
Generally, when \(l=1\) the correlation coefficient of noise received between two receivers can be shown as [10]  
\[\rho_{mm}=2\frac{J_{1}(2\pi D/\lambda)}{2\pi D/\lambda}\quad m,n=1,2,\cdots M \tag{10}\]  
where \(J_{1}\) is \(1 th\) Bessel function. These two noise models are usually used for theoretical analysis and comparison with real marine environmental noise.
## 3 Design of SOCA
### Construction of Neighbor Information Table (NIT) and VDES Frame Maps  
The ship station conducts VDES channel monitoring for an initial duration of one minute to participate in the VDES network. By monitoring VDES channels, the ship station collects information such as channel activity and ID, speed, position, and scheduling information (i.e., slot usage) of neighboring stations. Subsequently, the ship station constructs the NIT containing the collected information. In particular, the ship station estimates the report rate (Rr), nominal increment (NI), and selection interval (SI) of neighboring stations using the reporting interval (RI) included in the received AIS messages, updating them in the NIT in real time. The RI of a ship station is determined by its speed [19]. RI, Rr, NI, and SI are parameters used by the AIS to allocate resources for AIS message transmission (i.e., nominal slot (NS) and nominal transmission slot (NTS)). Additionally, the ship station constructs VDES frame maps using information obtained from its neighboring stations. Specifically, VDES frame maps include the AIS frame map, the ASM frame map, and the VDE-TER slotmap. Note that the VDE-SAT is out of the scope of SOCA; therefore, the VDE-SAT slotmap is not included in the VDES frame maps. The NIT is updated only when AIS messages are received, and the VDES frame maps are continuously updated upon receiving VDES messages (i.e., AIS, ASM, and VDE messages). After completing the construction of the NIT and VDES frame maps, the ship station proceeds to construct a candidate slot list for the TDMA channels designated for VDE-TER.
# Removal of Sodium Dodecylbenzenesulfonate by Macroporous Adsorbent Resins
## 3 Results
### Effect of Initial SDBS Concentration  
To investigate the effect of the initial SDBS concentration on \(q\) and the removal efficiency, various initial SDBS concentrations were used ranging from 0.5 to 5 mg/mL (Figure 5). The amount of SDBS adsorbed on MARs (\(q\)) increased with the initial SDBS concentration. The removal efficiency was maximized at a 0.5 mg/mL initial concentration of SDBS and then gradually decreased. Comparing  
Figure 4: Effect of the concentrations of (**a**) HP20 and (**b**) HP2 MGL on adsorption efficiency of SDBS. [SDBS] = 0.1 mg/mL; adsorption time 7 h.  
Figure 3: Changes in SDBS adsorption efficiency of (**a**) HP20 and (**b**) HP2 MGL with adsorption time at various amounts of adsorbents. [SDBS] = 0.1 mg/mL.  
both MARs, HP20 showed better removal ability (26 mg/g) at an initial SDBS concentration of 5 mg/mL, whereas SDBS removal by HP2 MGL was less effective (11.3 mg/g). The results also suggest that the phenyl rings in HP20 were essential in providing a specific interaction with SDBS. The critical micelle concentration (CMC) of SDBS has been reported to range from 553 to 1400 ug/mL in the literature [24; 25]. The \(q\) value was not affected by the CMC of SDBS, although the mechanism of adsorption of surfactants above the CMC has not been clarified [26].
## 2 Materials and Methods
### Quantitative HPLC-PDA Assay
#### 2.4.4 Cytotoxic Activity Assay  
Cytotoxicity of the compounds was assessed by the mitochondrial reduction assay [21] on two human leukaemia cell lines (promyelocytic HL-60 and lymphoblastic NALM-6) and on human melanoma cell line WM 115. Cells were purchased from the European Collection of Cell Cultures (ECACC, Salisbury, UK). Leukaemia cells were cultured in RPMI 1640 medium, while WM 115 cells in DMEM (Dulbecco's Modified Eagle Medium), both supplemented with 10% heat-inactivated foetal bovine serum (Invitrogen, Paisley, UK) and antibiotics (100 mg/mL streptomycin and 100 U/mL penicillin). Cells were grown in 37\({}^{\circ}\)C, in a humidified atmosphere of 5% CO\({}_{2}\) in air. Exponentially growing cells were seeded at 8 \(\times\) 103 per well on 96-well plates (Nunc, Roskilde, Denmark). After 24 h, the tested compounds (freshly prepared in DMSO and diluted with complete culture medium to obtain the concentration range from 0.1 to 1000 uM) were added and the plates were incubated for 48 h. Afterwards, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT, 5 mg/mL in PBS) was added and incubation was continued for 2 h. The metabolically active cells reduced MTT to blue formazan crystals. MTT-formazan crystals were dissolved in 20% SDS and 50% DMF at pH 4.7 and absorbance was read at 570 nm on an ELISA-plate reader (ELX 800, Bio-Tek, Winooski, VT, USA) and compared with that of the control (untreated cells). The IC\({}_{50}\) values (the concentrations of the test compounds required to reduce the cells survival by 50% in comparison to the untreated cells) were calculated from concentration-response curves and used as a measure of cellular sensitivity to a given treatment. Unless stated otherwise, all reagents used in the cytotoxicity tests were from Sigma-Aldrich (Germany/USA).
# Wireless Sensor Network-Based Rockfall and Landslide Monitoring Systems: A Review
## 3 Rockfall and Landslide Monitoring Based on WSNs  
In this section, after a brief overview of other hazard identification and monitoring techniques, the monitoring systems for landslides and rockfalls based on WSNs technology are reviewed, along with some wired implementations of sensor networks dealing with the geohazards in question.
## 4 Simulation Results  
This section aims to validate and analyze the positioning performance of the proposed frequency division relay dual LCX cables two-dimensional tunnel positioning solution. The simulation assumes a pseudorange observation error with a mean of zero and variance of \(\sigma^{2}\). Positioning simulation experiments are conducted for both static and dynamic points under different pseudorange observation error levels, analyzing the positioning accuracy of the same LCX cable layout under varying noise conditions. Detailed descriptions include the standard deviation (STD) of the positioning results, root mean square error (RMSE), cumulative distribution function (CDF) of the errors, and the 95% confidence ellipse (CE).  
The simulation environment is set as follows: the tunnel length \(L\) is set to 50 m, and the width \(H\) is set to 10 m. Receiver U is placed in the area covered by the dual LCX cables in the tunnel to collect raw measurement data from static and dynamic points. The reference receiver is placed at the center of the tunnel, with coordinates (\(L/2\), \(H/2\)), to measure the differential corrections for the positioning results. The simulation is set for 9:00 AM on 11 October 2024, with satellites selected from those observed at this time at 118\({}^{\circ}\) E longitude and 32\({}^{\circ}\) N latitude, specifically BDS satellite No. 35, GPS satellite No. 18, and GLO satellite No. 14.  
The GNSS FDR settings are as follows: the BDS system selects the B1I signal for relay, with a frequency of 1561.098 MHz; the GPS system selects the L1 signal for relay, with a frequency of 1575.42 MHz; and the GLONASS system selects the G1 signal for relay, with a frequency of 1602 MHz.
## 3 Results and Discussion
### Influence of BTH Treatment on the Physicochemical Parameters of Cabernet Gernischt Grapes
#### 3.1.2 Total Soluble Solids and Reducing Sugars
Similar conclusions were reached in a study where SA was applied to peaches [38]. The ripening process of BTH-treated grapes was delayed, as evidenced by the reduction in TSSs and reducing sugars. This finding aligns with a previous study that investigated the effects of BTH treatments on Monastrell, Merlot, and Cabernet Sauvignon grapes. In that study, this decrease was attributed to higher rainfall. Moreover, the effect of BTH is likely to be influenced by meteorological conditions, as the greatest TSSs reduction in BTH-treated grapes occurred in a vintage with higher rainfall [19].
## 2 Materials and Methods  
### Vegetation Indices  
We used three vegetation indices (SIF, NIR\({}_{\rm v}\), and LAI) to investigate their performance in estimating GPP when used in combination with machine learning algorithms. We used the GOSIF product developed by [PERSON] and [PERSON] [47], which provided global monthly SIF observations with 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) spatial resolution for the 2001 to 2020 period. This product extended OCO-2 raw data to a longer period and global coverage through the Cubist regression tree model. The NIR\({}_{\rm v}\) data were generated based on the BRDF-adjusted reflectance data (MODIS MCD43C4, Collection 6) according to the methodology described in [32]. The NIR\({}_{\rm v}\) data we generated have global coverage from 2001 to 2018, with monthly temporal resolution and 0.05\({}^{\circ}\) spatial resolution. LAI data were derived from the Reprocessed MODIS Leaf Area Index datasets from the Land-Atmosphere Interaction Research Group at Sun Yat-Sen University, with global coverage at 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) spatial resolution and monthly temporal resolution for 2003 to 2020 ([[http://globalchange.brnu.edu.cn/research/laiv6](http://globalchange.brnu.edu.cn/research/laiv6)]([http://globalchange.brnu.edu.cn/research/laiv6](http://globalchange.brnu.edu.cn/research/laiv6))). The LAI data are more continuous and consistent in time and spatial domains than the original MODIS LAI product [48].
###### Abstract  
This study presents a compact intracavity difference-frequency generation (DFG) source designed for mid-infrared (mid-IR) applications. By optimizing overlap between the pump and signal beams, we obtained a black-box efficiency (\(\eta_{BB}\)) of 3.4% W\({}^{-1}\) and a maximum DFG output power of 63 mW at a wavelength of 3437 nm for a 50 mm long periodically poled lithium niobate (PPLN) crystal. The mid-IR laser was systematically studied for various PPLN lengths (29 mm, 40 mm, and 50 mm), demonstrating extensive tunability across a range of mid-IR wavelengths. Stability assessments over short-term (2 min) and long-term (2 h) periods were also investigated, showing stability values of 0.91% and 1.08%, respectively. These advancements position our DFG laser as a promising candidate for potential applications in greenhouse gas detection and satellite communication, addressing the growing demand for reliable and efficient mid-IR sources.  
nonlinear optics; difference-frequency generation; mid-infrared laser; DPSS laser; overlap; black-box efficiency Article