esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Destructive Testing Applications for Steel Bridges
## 3 NDT Methods with Potential Application to Steel Bridges
### Test Methods Applicable to Structural Steel Cables, Tendons, and Ropes
#### 3.3.4 Vibration Method for Detecting Cross-Section Loss  
The application of noncontact laser-based vibration technology for cable-stayed bridges has been on the rise over the years [44; 70]. Although this technique was initially developed for stay-cabled bridges, it was adapted for other types of bridges. Dozens of major bridges in the US and abroad have implemented this technology for the inspection of their elements. This method was first introduced by [PERSON] and [PERSON] in 1998 [71], and its main usage was for the inspection of cables and tension elements in bridges. In this technique, by targeting the cable with the laser beam, cable vibration from ambient sources can be recorded from a distance. The cable vibration characteristics, including the frequencies and damping ratio, are calculated by analyzing the vibration record, and the force in cables is calculated. The estimated cable forces using this technique are then compared to the previously measured or expected forces to establish a pattern of changes indicating the type, intensity, and location of potential damages to the cable. The damping measured in this technique is used to determine their susceptibility to different types of wind-induced effects and other oscillations. Figure 29 indicates the laser vibrometer which targets cables and a sample comparison of the estimated forces [72].  
Figure 28: An example of half-cell potential test of uncoated reinforcing steel in concrete [66].  
The laser-based measurement technique provides a noncontact remote sensing ability. Accordingly, for a periodic inspection of the elements with a more difficult accessibility, the laser-based noncontact vibration technique can save a lot of on cost and time.  
Other non-contact sensors have been used for vibration measurement. Non-contact microwave interferometric radars have also been used for the non-contact assessment of stay cables. This method can simultaneously measure the dynamic response of an array of stay-cables [73,74].  
Vibration measurement can also be performed using contacting sensors. Accelerometers can be attached to the cables with proper clamps [42].
# Opto-Electronic Oscillators for Micro- and Millimeter Wave Signal Generation
###### Abstract
For instance, the use of a 1-km fiber has an FSR of 200 kHz, while a 15 km fiber has an FSR of 13.4 kHz. The relationship between SMSR and phase noise performance of the OEO at different optical lengths is shown in Figure 2.  
Figure 1: Single-loop opto-electronic oscillator (OEO) with electrical components. As can be seen from the experimental results in Figure 2, there is a tradeoff between the short-term stability and multimode operation of the OEO. The 1 km OEO has about a 30 dB improvement performance in the SMSR, but the 15 km OEO has a significant improvement in phase noise, which is about 20 dB at 1 kHz and 10 kHz offsets from the carrier. In this paper, recent advances in OEO configurations are presented, using new results to improve the stability of short-term, long-term and multimode operation. It also highlights new areas of science, technology and engineering where the OEO can be applied.
## 3 Object Detection in High-Resolution Images Using OPAIs
### Enhancing the Performance of Object Detection in OPAIs  
The OPAI generation algorithm is used to create OPAIs for foreign particles and air bubbles inside a flexible fluid bag, as shown in Figure 8. These images are created by extracting patches from different parts of various images and sequentially placing them onto a blank OPAI canvas. This results in OPAIs that contain foreign particles and air bubbles observed under various conditions of focus, lighting, and background.  
However, when examining these generated OPAIs, a discontinuity at the patch boundaries is noticeable due to the black background. This abrupt change in characteristics at the boundaries creates noise that can interfere with the features of foreign particles or air bubbles during the convolution filtering process in the CNN. This noise can severely affect the network's ability to distinguish between the characteristics of air bubbles and particles, ultimately degrading the model's performance in differentiating between the two.  
Figure 7: Example of the original image and its OPAI. (**a**) Original big image with objects (637 \(\times\) 885); (**b**) OPAI of the left (256 \(\times\) 256).  
To mitigate the impact of these discontinuities on object detection performance, it is crucial to set the patch size as significantly larger than the objects to prevent the discontinuity noise from affecting the actual object features. By considering the size of the CNN convolutional kernel, the patch boundaries are designed to ensure that the discontinuity does not encroach upon the objects themselves.  
Additionally, certain considerations must be made to prevent performance degradation when using OPAIs for deep learning training and inference. When training the object detection network with OPAIs, the network is optimized for such a structured input. Consequently, during inference, the input images must be formatted similarly to OPAIs to maintain performance. This approach ensures the mitigation of the discontinuities' impact on the CNN model's ability to accurately detect and distinguish between foreign particles and air bubbles.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{3}{c}{**Levene’s Test of Equality of Error Variances \({}^{\text{a}}\)**} \\  & **F** & **df1** & **df2** & **Sig.** \\ \hline TPC & 1.720 & 24 & 4 & 0.321 \\ \hline TFC & 1.571 & 24 & 4 & 0.358 \\ \hline FRAP & 1.029 & 24 & 4 & 0.559 \\ \hline CUPRAC & 0.164 & 24 & 4 & 0.998 \\ \hline DPPH & 0.271 & 24 & 4 & 0.982 \\ \hline ABTS & 0.239 & 24 & 4 & 0.990 \\ \hline Alfa\_Gl & 0.866 & 24 & 4 & 0.645 \\ \hline AChE & 0.522 & 24 & 4 & 0.860 \\ \hline \end{tabular}  
* \({}^{\text{*}}\) temperature \({}^{\text{*}}\) extractant + duration \({}^{\text{*}}\) temperature \({}^{\text{*}}\) extractant + hydro module \({}^{\text{*}}\) duration \({}^{\text{*}}\) temperature \({}^{\text{*}}\) extractant.  
\end{table} 
Table 16: ANOVA test of Homogeneity of Variance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Goal**} & \multicolumn{2}{c}{**Lower Limit**} & \multirow{2}{*}{**Upper Limit**} & \multirow{2}{*}{**Lower Weight**} & \multirow{2}{*}{**Upper Weight**} & \multirow{2}{*}{**Importance**} \\ \cline{1-1} \cline{5-5} A: hydromodule (ratio) & is in range & 5 & 20 & 1 & 1 & 3 \\ B: Duration & is in range & 20 & 40 & 1 & 1 & 3 \\ C: Temperature & is in range & 50 & 70 & 1 & 1 & 3 \\ D: Extractant & is in range & 80 & 99.9 & 1 & 1 & 3 \\ TPC & maximize & 66.77 & 110.37 & 1 & 1 & 3 \\ TFC & maximize & 4.7403 & 30.8739 & 1 & 1 & 3 \\ FRAP & maximize & 198.872 & 383.115 & 1 & 1 & 3 \\ CUPRAC & maximize & 291.757 & 1307.62 & 1 & 1 & 3 \\ DPPH & maximize & 63.2991 & 111.634 & 1 & 1 & 3 \\ ABTS & maximize & 70.104 & 489.17 & 1 & 1 & 3 \\ Alfa-Gl & maximize & 0.003444 & 0.156708 & 1 & 1 & 3 \\ AChE & maximize & 0.000186667 & 0.88 & 1 & 1 & 3 \\ \hline \end{tabular}
\end{table} 
Table 17: Optimization constraints.
# Charting Past, Present, and Future Research in the Semantic Web and Interoperability
## 3 Analysis of Descriptive Results  
Figure 1 demonstrates the publication trend since the year 2000. Between 2003 and 2006, the number of studies increased dramatically, and approximately 83% of publications were published after 2006. The year with the most publications was 2010, with 243. The first four papers published in 2000 concentrated on the roles of XML and RFD in the development of semantic webs [66; 67]. A significant finding was that the first decade (2000-2010) of interoperability and semantic web research can be considered the infancy stage. The second decade (2011-2020) saw a steady level of interest in this subject, as indicated by the slight ups and downs in the number of publications. This period may be referred to as the stabilization and consolidation stage, as growth appears to have reached maturity and saturation during the second decade.  
The distribution of publications by type is shown in Table 1. As can be seen, conference papers dominate the list, accounting for more than 60% of all publications selected. This is unsurprising, given that conference papers represent researchers' most recent accomplishments and indicate emerging research trends and the immediacy of personalized peer feedback [68]. As represented by journal articles, peer-reviewed literature is second on the list. Additionally, the sample includes 113 book chapters and 108 conference reviews.  
Table 2 lists the top fifteen journals that have published articles on interoperability and the semantic web. These fifteen journals published 207 articles, accounting for nearly 21% of the 997 journal articles identified. The remaining 79% of the articles were dispersed across multiple outlets. The _International Journal of Semantic Web and Information Systems_ and the _International Journal of Semantic Web_ published 18 articles. The _International Journal of Metadata Semantics and Ontologies_ and the _Journal of Biomedical Informatics_ published 16 articles. By and large, these journals appear to be devoted to semantics and ontologies. Additionally, _IEEE Intelligent Systems_, _Biomedical Informatics_, _Automation in Construction_,  
The top twenty academic institutions that contribute to interoperability and the semantic web are listed in Table 3. Authors from the United States and Germany contributed the most publications to the list, with 545 and 331 publications, respectively. The United Kingdom is third with 322 publications, while Italy is fourth with 303. China, India, and Brazil have all contributed significantly to the literature, publishing 250, 109, and 91 papers, respectively. The remaining papers in our dataset were contributed by scholars from Australia and several European and Asian countries.
## 2 Materials and Methods
### Experiments
#### 2.1.3 Flow-Chamber Description  
Figure 1a shows a photograph of the phantom used and its schematic illustration in Figure 1b. A plastic chamber with dimensions of 5.5 cm \(\times\) 5.5 cm \(\times\) 1.0 cm was filled with the liquid phantom described above (having \(\mu_{a}\) = 0.075 cm\({}^{-1}\); \(\mu_{s}^{\prime}\) = 12 cm\({}^{-1}\) at 785 nm) and the chamber was placed atop a homogenous solid phantom block with similar optical properties as the liquid phantom at 785 nm. The chamber housed a clear plastic tube (inner diameter = 1.6 mm, outer diameter = 3.2 mm; 8000-0004; Thermo Fisher Scientific Inc., Miami, OK, USA) that was attached to two opposite sides of the chamber using moldable glue (Sugru; FormFormForm Ltd., London, UK) and ran along the bottom surface of the plastic chamber, parallel to one edge. One end of the plastic tube was connected to a syringe pump (75900-00; [PERSON], Vernon Hills, IL, USA) that was controlled via software to produce a  
given volume flow rate \(F\), while the other end of this tube drained into a sink. Assuming laminar flow, the pump flow rate was converted to an average fluid flow speed in the channel as \(F=V_{ch}A\), where \(A\) is the cross-sectional area of the channel and \(V_{ch}\) the flow speed. Since the same liquid phantom added to the chamber was also loaded into the syringe pump, both the actively pumped and surrounding liquid media had identical optical transport coefficients. \(F\) was set via software to one of four values: \(F_{0}\) (pump turned off; 0 mL/h), \(F_{1}\) (1.44 mL/h), \(F_{2}\) (3.60 mL/h) or \(F_{3}\) (5.76 mL/h). Given a circular tube of radius 0.08 cm, the average channel flow speeds were calculated to be 0.02 cm/s, 0.05 cm/s and 0.08 cm/s at \(F_{1}\), \(F_{2}\), and \(F_{3}\), respectively. These flow rates gave speeds in ranges reported for moving red blood cells in capillaries [48].
# Passive Detection of Low-Altitude Signal Sources Using an Improved Cross-Correlation Algorithm
## 4 Performance Results
### Indoor Experiment
#### 4.2.2 Indoor Passive Detection of Multiple Low-Altitude Signal Sources  
The indoor experiment was repeated using two Phantom 4 Pro UAVs, \(u1\) and \(u2\). They were placed on a table at the same distance from the Rx, but with different angles. Figure 24 shows the normalized time-frequency diagrams after filtering for distances of 5 m and 10 m. The actual directions of the arrival and start frequencies of \(u1\) were \(88.65^{\circ}\), \(2.401719\) GHz and \(78.35^{\circ}\), \(2.401719\) GHz, and for \(u2\), were \(105.75^{\circ}\), \(2.471719\) GHz and \(72.45^{\circ}\), \(2.471719\) GHz at distances of 5 m and 10 m, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Method** & **CFAR** & **HOC** & **Proposed** \\ \hline \multirow{2}{*}{5 m} & Error (MHz) & 1.324 & 7.133 & 0.021 \\  & Error (\({}^{\circ}\)) & 1.792 & 0.728 & 0.470 \\ \hline \multirow{2}{*}{10 m} & Error (MHz) & 1.215 & 6.264 & 0.017 \\  & Error (\({}^{\circ}\)) & 2.212 & 5.452 & 1.250 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter estimation errors for UAV detection using three methods in an indoor environment.  
Figure 23: The result for the CFAR method indoors at distances of (**a**) 5 m and (**b**) 10 m.  
Figure 22: The results for the HOC method indoors at distances of (**a**) 5 m and (**b**) 10 m.
# Control of Automotive Semi-Active MR Suspensions for In-Wheel Electric Vehicles
## 2 Methods
### QoV's Description  
The three different _QoV's_--shown in Figure 6--considered for simulation purposes are described in this section.  
The normal _QoV_ (_NQoV_) shown in Figure 6a corresponds to an ICE vehicle which has a semi-active suspension system and where a sprung and an unsprung mass are considered as in Reference [30]. The modified _QoV_ (_ModQoV_) shown in Figure 6b corresponds to an in-wheel electric vehicle considering the electric motor as part of the unsprung mass and the battery bank as part of the sprung mass as it was described in Section 2.2. In Figure 6c (_ModQoVFv_) the same masses than in Figure 6b are considered as well as, the unbalanced vertical force (\(F_{v}\)) given by the in-wheel electric motor,  
Figure 4: Residual vertical force.  
Figure 5: Switched reluctance motor (SRM) residual vertical force diagram, adapted from Reference [4].  
\begin{table}
\begin{tabular}{c c c} \hline
**Component of the Modified Quarter of Vehicle** & **Value** & **Units** \\ \hline Sprung mass (\(m_{s}\)) & 539 & Kg \\ Unsprung mass (\(m_{us}\)) & 85 & Kg \\ Spring stiffness (\(k_{s}\)) & 83 & kN/m \\ Tire stiffness (\(k_{t}\)) & 230 & kN/m \\ \(m_{s}\) resonance frequency (\(f_{m_{s}}\)) & 1.13 & Hz \\ \(m_{us}\) resonance frequency (\(f_{m_{us}}\)) & 8.76 & Hz \\ \hline \end{tabular}
\end{table}
Table 5: Modified front _QoV_ parameters.  
as mentioned in Reference [4]. In this case, the active suspension system is replaced by a semi-active one. The mathematical model for the \(NQoV\) and \(ModQoV\) is given by the following equations:  
\[\begin{gathered} m_{s}z_{s}=-k_{s}(z_{s}-z_{us})-F_{D}\\ m_{us}\tilde{z}_{us}=-k_{s}(z_{us}-z_{s})-k_{t}(z_{us}-z_{r})+F_{D} \quad,\end{gathered} \tag{4}\]  
where \(m_{s}\) and \(m_{u}r\) are the sprung and unsprung mass, respectively. \(k_{s}\) and \(kt\) are the stiffness of the damper and tire, respectively. The displacements of the sprung and unsprung mass are given by \(z_{s}\) and \(z_{u}s\), respectively. While the road profile is represented by \(z_{r}\). The damping force is given by \(F_{D}\), which is composed of the passive and semi-active components.  
On the other hand, the mathematical model for the \(ModQoVFv\) which considers \(F_{v}\) is given by:  
\[\begin{gathered} m_{s}\tilde{z}_{s}=-k_{s}(z_{s}-z_{us})-F_{D}\\ m_{us}\tilde{z}_{us}=-k_{s}(z_{us}-z_{s})-k_{t}(z_{us}-z_{r})+F_{D}+F_{ v}\quad.\end{gathered} \tag{5}\]
# System Reliability Assessment with Imprecise Probabilities
## 2 A p-box Method Based on Bayesian Theory
Then, the p-box can be calculated by taking all combinations into account. At every moment \(t\), take the minimum value of all the CDFs as the lower limit of the p-box, and the maximum value as the upper limit. For most distribution families, it is necessary to compute four or more CDFs to determine a p-box, but for the exponential distribution and Weibull distribution used in this paper, only two CDFs need to be computed. For the aforementioned Weibull distribution, under the assumption that the shape parameter \(\beta\) is known, if the confidence interval of scale parameter \(\alpha\) is \([\alpha_{l},\alpha_{u}]\), then the lower limit of the p-box is \(\underline{F}(t)\sim W(\alpha_{u},\beta)\) and the upper limit is \(\overline{F}(t)\sim W(\alpha_{l},\beta)\). The p-boxes can be formed for other distributions, as long as we can calculate the posterior distribution and the parameter intervals. However, for complex distribution models, it is difficult to calculate the posterior distribution of parameters, and it is almost impossible to integrate the parameters from the posterior distribution. The emergence of MCMC algorithms has solved this problem well. The following Metropolis-Hastings algorithm can be used to generate samples [28; 29; 30]:  
1. Generate initial sample \(\theta^{0}\)
2. For \(i=1\) to \(n_{s}\) Generate sample from proposal distribution Generate sample from proposal distribution \(\theta^{*}\sim p(\theta^{*}|\theta^{i-1})\) General acceptance sample \(u\sim U(0,1)\) if \(u<Q(\theta^{i-1},\,\theta)=\min\left\{1,\frac{f(\theta^{*}|\,\mathbf{p}(\theta^{i -1}|\theta^{*})}{f(\theta^{i-1}|\,\mathbf{p}(\theta^{i}|\theta^{i-1})}\right\}\) \(\theta^{i}=\theta^{*}\)else  
\(\theta^{i}=\theta^{i-1}\)  
where \(\theta\) is the sample of parameters of the distribution and \(n_{s}\) is the number of simulations. After a certain number of iterations, sampled values gradually converge to the posterior distribution. Before the Metropolis-Hastings converges, the earlier iterations are called the burn-in period. Samples from the burn-in period will be discarded. The p box of the life distribution can be easily constructed by using the samples collected after the convergence is achieved.
## 3 Results  
### Reverberation Time in Occupied Condition  
The reverberation time measurements in unoccupied conditions are reported in Figure 2. In the same figure, a projection for the occupied environment is provided using the absorption coefficients of seated adult students on wooden chairs, as tabulated in the standards DIN 18041 and UNI 11532-2 [35]. The values in unoccupied conditions at mid frequencies (1.8-2.5 s) are significantly higher than the requirements specified by the standards in classroom acoustics (whose range may be assumed between 0.7 and 1.2 s). These requirements vary locally but generally settle around values below 1 s for rooms of dimensions comparable to the classrooms under study.  
In occupied conditions, it can be observed that the frequency response curve of the reverberation time differs significantly between the two cases: in halls A and B, the curve stabilizes at relatively uniform values across frequencies, around 1 s; in hall C, the acoustic absorption is concentrated in the mid-to-high frequencies and stabilizes around 1.5 s. This difference is due to the varying characteristics of materials in the environment and the different occupancy densities of the two spaces.  
The frequency spectrum of the reverberation time under occupied conditions is relatively flat in halls A and B. This aligns with recommendations from standards in classroom acoustics, such as DIN 18041 and UNI 11532-2. Indeed, a flat spectrum is beneficial for speech intelligibility. In contrast, hall C shows a decreasing trend in reverberation time values at high frequencies.
## 3 Verification of the Proposed Method
### Comparison between Proposed Method and [PERSON]'s Results  
To demonstrate the validity of the proposed method, temperature histories of four more locations under the rake face at different depth, (0.2, 0.6, 1.3), (0.3, 0.6, 1.3), (0.4, 0.6, 1.3) and (0.5, 0.6, 1.3), are calculated and plotted in Figure 10a. The simulation results from [PERSON] et al. [35] are shown in Figure 10b. Based on Figure 10a,b, the results obtained by the proposed method match well with the [PERSON]'s simulation results.  
The measured temperature histories of these locations were clearly shown in [PERSON] et al. [35]. Here, a detailed comparison was made, the maximum predicted and measured temperatures during [0.73s, 0.83s] are presented in Table 3. The differences between them are also shown in Table 3. In general, the predicted results match the experiment well. As the depth from the rake face increases from 0.1 to 0.5 mm, both the predicted and measured cutter edge temperature decreases. This is reasonable, because as depth from the rake face increases, the point of interest is getting away from the heat source.  
Figure 10: Temperature histories of different locations out of (**a**) the proposed method and (**b**) the simulation result obtained by [PERSON] et al. [35].  
Figure 9: Temperature history at (\(x\), \(y\), \(z\)) = (0.1, 0.6, 1.3): (**a**) the simulation, and (**b**) the experiment, presented by [PERSON] et al. [35].  
Thus, the temperature drops. According to Table 3, the measured temperature tends lower than the predicted temperature along the distance from the rake face increases. The reasons could be (1) the heat convection and radiation were neglected in this approach; (2) a small hole was drilled in the insert which will affect the temperature field of the cutter edge.  
Overall, the results obtained by the proposed method agree well with the [PERSON]'s simulation and experiment results. Thus, the proposed approach is valid and capable of predicting the temperature history in milling process.
## Materials and Methods
### Chemical Composition and Energy Analysis  
The remaining silage samples were dried in an electric thermostatic blast dryer (DHG-9145A, Yiheng Technology Co., Ltd., Shanghai, China) at 65 \({}^{\circ}\)C for 72 h. The dried sampleswere crushed through a 1 mm sieve, using a crusher (JG-200, Guangsha Industry & Trade Co., Ltd., Shanghai, China), into self-sealing bags for chemical analysis. Dry matter (DM), organic matter (OM), ether extract (EE), crude ash (Ash), crude fiber (CF), and crude protein (CP) contents were determined with reference to the methods of the Association of Official Analytical Chemists [15]. The water-soluble carbohydrate (WSC) content was determined with a colorimetric method using sulfuric acid-anthrone [16]. The buffer energy value (BC) was determined with reference to the method of [PERSON] [17]. The neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL) contents were determined using a fiber analyzer (ANKOM A220, ANKOM Technology Corporation, Macedon, NY, USA) with reference to the method of [PERSON] et al. [18].  
Gross energy (GE) was determined using an oxygen bomb calorimeter [15]. Referring to [PERSON] et al. [19], digestible energy (DE), metabolizable energy (ME), net energy for maintenance (NEm), net energy for weigh. gain (NEf), and net energy for lactation (NEI) were calculated. The calculation formulae are shown below:  
\[\mathrm{DE}=\mathrm{GE}\times[114.82-1.364\times\mathrm{ADF}+0.104\times \mathrm{CP}+0.149\times\mathrm{EE}+0.022\times\mathrm{NDF}-0.244\times\mathrm{ Ash}]/100 \tag{1}\]  
\[\mathrm{ME}=\mathrm{DE}\times[86.38-(9.9\times\mathrm{CF}+19.6\times\mathrm{ CP})/(100-\mathrm{Ash})]/100 \tag{2}\]  
\[\mathrm{NEm}=\mathrm{ME}\times(0.287\times\mathrm{ME}/\mathrm{GE}+0.554) \tag{3}\]  
\[\mathrm{NEf}=\mathrm{ME}\times(0.78\times\mathrm{ME}/\mathrm{GE}+0.006) \tag{4}\]  
\[\mathrm{NEI}=\mathrm{ME}\times[0.6+0.24\times(\mathrm{ME}/\mathrm{GE}-0.57)] \tag{5}\]  
Note: (1) ADF, CP, EE, NDF, CF, and Ash are expressed as a percentage of DM (% DM); (2) the units of GE, DE, ME, NEm, NEf, and NEI are MJ kg\({}^{-1}\) DM.