esa-sceva/satcom-chunk-collection huggingface dataset:
# Structural Evaluation of Cable Bolts under Static Loading
## 3 Materials and Methods
### ACARP and Jennmar  
To gain an understanding of the intricacies around cable bolts and their properties, the Australian Coal Industry Research Program (ACARP) and Jennmar papers were consulted. This provided a comparative model for which the model could be calibrated against for static loading, as ACARP had completed a similar study. The dynamic model was calibrated against [PERSON] et al. (2018) [5], which a built upon [PERSON] et al. (2017)'s [2] work. Practical testing of bolts was obtained by extracting data from Jennmar, a steel bolt manufacturing company.  
Jennmar Civil is a cable bolt manufacturing company that widely produces cable bolts designed for deep mining excavation. The bolts they manufacture have been an industry standard and are therefore referenced within the investigation. By modelling the cable bolts for the double shear test based on these bolts, the following tests yielded reliable results that are consistent with industry practice and manufacturing capabilities. The mechanical and geometrical properties were obtained from Jennmar. The bolts that have been considered by Jennmar are summarised below, in Table 2.  
Similarly to ACARP's 2021 study, a \(\O\)28 Indented (ID) SUMO cable bolt was used to calibrate the static model. Jennmar has developed various types of bolt configurations such as the Yield Lok Bolt, J-Tech bolt and the Sumo Cable Bolt, which have unique functions and purposes. The configuration of the bolt serves a unique purpose, with each possessing notable distinguishing factors. The Yield Lok Bolt contains a lock nut at the end which acts as a locking plate with the bolt and the rock face. This was designed for high seismic conditions due to its polymer ploughing design. The J-Tech Bolt is a more  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Numerical Method** & **Advantages** & **Disadvantages** \\ \hline Discontinuum (DEM) & Capability of 3D modelling & Limited data on joint properties are available and the calibration of parameters might be needed \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._generic bolt model that is suitable with either resin capsule or grout. The threads can be made finer to achieve higher tension for a given torque. The Sumo bolt contains a bulb-like structure which is bird-caged throughout the entire bolt. This increases flexibility in handling, making it more advantageous around confined areas and contains smooth wires for optimum performance in shear.  
The specific configuration and layout of the bolt contribute to each respective bolts' shear capacity and its overall behaviour under the double shear test. Within the scope of this investigation, the configuration is not a parameter that will be tested, and therefore, all bolts will be reduced to a single metal rod. This will also assist in the modelling procedure and will be further elaborated on in the following section.
# Photonic Crystal-Based Sensors for Detecting Alcohol Concentration
## 1 Introduction
In this work, we utilize iridescent photonic materials to assess the ethanol concentration in difference ethanol concentration solutions. As compared to the SPR approach, the present method is easy to implement and operate, and the sensitivity is indeed high enough for many practical purposes. [PERSON] _et al._[10] prepared a large well-ordered polystyrene crystal opal by spin-coating technique in 2009. The effective refractive index and the diameter of the colloid spheres can be estimated from the reflectance measurements at different angles. Moreover, the photonic crystal effect has been exploited to create a chemical sensor, in fact optical measurements have evidenced that the polymer composite structure presents a different optical response as a function of the solvent applied on the surface [11]. In addition to the polymer composite structure system, a silole-infiltrated SiO\({}_{2}\) inverse opal photonic crystal film was prepared from the work of [PERSON]'s group [12]. Based on the reversible aggregation state transfer and the adsorption-desorption of organic vapors, the effective refractive index of the film varied repeatedly, which caused the reversible stop band shift and color change. In addition, several groups have obtained inverse opal structures by using colloidal crystals of polystyrene or silica microspheres as templates. The template is infiltrated with a variety of different background materials, such as ceramic precursors [13; 14; 15], metals and polymers [16; 17; 18] or semiconducting nanoparticles [19]. Then the template is removed by calcinations in the case of polystyrene or by etching with HF in the case of silica templates. Recently, highly ordered titania inverse opal films have been fabricated by using silica opal films as templates. The ability to do this is attributed to the good heat treatment features of the silica opal films. However, stern removal conditions of silica microspheres render the fabrications of inverse opal structures limited. Because polystyrene colloidal crystals are notable for their easy removal, an inverse opal scaffold structure with a high refractive index [20] can be prepared for rapid quantitative analysis of ethanol concentration. Here, a polystyrene microsphere was chosen as the template, and TiO\({}_{2}\) was chosen as the inverse opal structure material in this study.
## 2 Materials and Methods
### Proximal Monitoring--Geocentric Survey  
The soil was tested for electrical conductivity utilizing two methods. The first method involved the use of the Automatic Resistivity Profiler (A.R.P.; Geocarta SA, Paris, France). It is a mobile system consisting of three pairs of metal gear wheels. The first pair functions as  
Figure 1: Experimental field of CREA-IT.  
injection electrodes and the other 2 pairs function as receivers, which measure the electrical potential difference (Figure 2). The distance between each pair of receivers was calibrated to study 3 depths (0-50 cm, 0-100 cm, and 0-180 cm). The raw data was filtered using a 1D median filter and then interpolated to obtain a soil resistivity map (2 \(\times\) 2 m pixels) for each layer studied.  
The second method of geoelectric survey was performed by placing a network of 21 transmitting nodes in the ground according to a regular grid measuring 20 \(\times\) 40 m (Figure 1--blue points). Each node was composed of 2 capacitive soil moisture sensors, model SKU:SEN0193, placed respectively at 15 cm and 30 cm depth. The technology, unlike other sensors on the market, performs measurements through capacitive sensing. The operating voltage of the sensor is 3.3-5.5 VDC, the output Voltage is 0-3.0 VDC, and the operating current is 5 mA. The system is integrated into a DFRduino UNO hardware and an Arduino IDE V1.6.5 software. Each sensor was calibrated in the laboratory, checking the correspondence of the variations of the readings with soil samplings by studying soil moisture according to referring standard [25]. The readings were expressed in terms of spatial variation and not in absolute values to avoid eventual flaws or imperfections. The nodes are transmitted via a LoRa (Long Range) radio frequency to a receiving unit connected to a Wi-Fi network. Two of the nodes also had an air temperature and a humidity sensor (model DHT11 DF Robot). The data was acquired and transmitted with a time-frequency of 15 min. Before the geostatistical analysis, all the coordinates that were retrieved from the mapping tools and nodes were converted from geographic coordinates (World Geodetic System 84-WGS84) into Cartesian UTM coordinates using the \"spTransform\" function of the \"rgdal\" package [26]. All statistical processing followed the methodology indicated by [PERSON] [27].
## 5 Conclusions
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Months**} & \multicolumn{8}{c}{**Compensated**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Jan-March**} & \multicolumn{2}{c}{**April-June (1.8)**} & \multicolumn{2}{c}{**July-September (1.4)**} & \multicolumn{2}{c}{**October–December (1.3)**} \\ \hline
**Use Cases** & **Use Case 1** & **Use Case 2** & **Use Case 1** & **Use Case 2** & **Use Case 1** & **Use Case 2** & **Use Case 1** & **Use Case 2** \\ \hline \multirow{9}{*}{
\begin{tabular}{} \end{tabular} } & & 300 (27) & & & 150 (27) & & & \\  & & 150 (29) & 600 (25) & 205 (29) & & & & \\  & & 1200 (30) & 300 (48) & 212 (30) & 600 (25) & & 150 (25) \\  & 150 (27) & 300(25) & 446 (64) & 600 (51) & 511 (64) & 150 (48) & 150 (27) & 300 (48) \\  & 150 (29) & 150 (48) & 330 (51) & 300 (64) & & 299 (51) & 600 (51) & 556 (29) & 600 (51) \\  & 203 (30) & 150 (48) & 179 (50) & 600 (18) & 150 (50) & 600 (64) & 366 (30) & 150 (64) \\  & 439 (64) & 600 (51) & 150 (64) & 150 (19) & 670 (67) & 450 (18) & 632 (64) & 150 (18) \\  & 295 (51) & 300 (18) & 150 (19) & 600 (12) & 150 (19) & 150 (68) & 401 (51) & 150 (68) \\  & 150 (50) & 150 (68) & 287 (46) & 600 (61) & 372 (46) & 600 (12) & 210 (50) & 300 (12) \\  & 150 (67) & 300 (12) & 270 (30) & 600 (44) & 363 (80) & 600 (61) & 150 (19) & 600 (61) \\  & 150 (19) & 150 (61) & 1046 (35) & 150 (50) & 875 (35) & 150 (44) & 906 (80) & 150 (44) \\  & 161 (46) & 150 (44) & 150 (67) & 150 (45) & 150 (45) & 150 (50) & 297 (57) & 150 (50) \\  & 286 (80) & 150 (44) & 1200 (68) & 150 (19) & 1168 (68) & 600 (80) & & 150 (61) \\  & & & 482 (100) & 600 (80) & & 190 (60) & & & \\  & & & 150 (17) & 150 (2) & & & & \\  & & & 740 (9) & & & & & & \\ Total kvar injected & 2134 & 2250 & 7545 & 6000 & 5096 & 4650 & 3668 & 2850 \\ Active loss (\(k\)W) & 140 & 145 & 993 & 1119 & 387 & 410 & 273 & 289 \\ Reactive loss (kvar) & 80 & 82 & 584 & 679 & 223 & 237 & 159 & 164 \\ Active loss reduction (\%) & 55.8 & 55.5 & 30.8 & 22.1 & 46 & 42 & 54 & 51 \\ Reactive loss reduction (\%) & 55.7 & 54.6 & 28.0 & 22.1 & 45 & 41 & 52 & 51 \\ \(V_{min}\) at node & 0.91 (76) & 0.90 (76) & 0.91 (76) & 0.90 (76) & 0.90 (76) & 0.90 (76) & 0.90 (76) & 0.90 (76) \\ \(V_{max}\) at node & 0.99 (2) & 0.99 (2) & 0.99 (2) & 0.99 (2) & 0.99 (2) & 0.99 (2) & 0.99 (2) & 0.99 (2) \\ Annual cost (USD (S)/year) & 23,670 & 24,394 & 166,960 & 188,020 & 65,037 & 69,015 & 45,862 & 48,627 \\ Capacitor cost (USD (S)/year) & 1067 & 1125 & 3772.5 & 3000 & 2548 & 2325 & 1834 & 1425 \\ Net saving (USD (S)/year) & 29,950 & 29,225 & 74,446 & 53,383 & 55,519 & 51,541 & 54,220 & 51,455 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results comparison of \(N_{2}\) for \(CS_{2}\) (Scenario 2). **Acknowledgments:** The authors acknowledge the support from Institute of Health & Analytics, Universiti Teknologi PETERONAS, Malaysia. **Conflicts of Interest:** The authors declare no conflict of interest.
# Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R
## 3 Theoretical Framework
### Relative Variable Importance  
Before proceeding further it is necessary to explain the concept of a _relative variable importance_.  
The idea is just to sum posterior probabilities of models, which contain given explanatory variable. In other words, consider the posterior probabilities given by Equation (2), but not for all \(k=1,\ldots,K-\) only for those models, which contain the given variable as explanatory one:  
\[\text{RVI}(x_{j})_{t}=\sum_{k=1}^{K}\pi_{t|t-1,k}\mathbbm{1}_{k}(x_{j})\quad, \tag{15}\]  
where \(\mathbbm{1}_{k}(x_{j})=1\) if \(k\)-th model contains \(x_{j}\) as an explanatory variable, and \(\mathbbm{1}_{k}(x_{j})=0\) otherwise.  
Of course, this concept can be applied both in case of model averaging and model selection. Simply, in model selection in Equation (15) will be no summing, but just one term - the one corresponding to the selected model. So in such a case relative variable importance reduces to trivial outcome. Generally, this concept can be easily transferred to other model averaging schemes, like the information-theoretic one, for instance. The idea is just to replace the posterior probabilities \(\pi_{t|t-1,k}\) by suitable weights. Indeed, \(\pi_{t|t-1,k}\) are just weights ascribed to the averaged models in DMA.  
Actually, this concept is very interesting from the economical point of view. Indeed, in the mentioned applied papers which uses DMA methodology, this concept has been used and discussed. However, a great caution has to be taken. First of all, the term _importance_ is used, in order not to misuse with _statistical significance_. Indeed, relative variable importance is not connected with any statistical test, it is also a very relative measure. It measures how the given variable becomes more \"appreciated\" within the set of considered models.  
Secondly, it is not known how to deal with the situation that quite high weight is ascribed to the model containing given variable and some other variables. In other words, it might happen that such a model is preferred not because of including the variable the researcher is interested in, but because of the joint \"importance\" of this variable and the other ones in the model. The topic of jointness in Bayesian framework is a separate research problem [169].  
Another, directly coming to the mind idea is to consider the _expected number of variables_. In other words, to consider the weighted average of the number of explanatory variables (including constant term if present), where the weights are given by posterior probabilities (or other suitable weight, if other kind of model averaging is performed). In other words, to consider  
\[\text{EV}_{t}=\sum_{k=1}^{K}\left(\pi_{t|t-1,k}\sum_{j=0}^{m}\mathbbm{1}_{k}( x_{j})\right)\quad, \tag{16}\]  
where \(x_{0}\) stands for the constant term.
## 2 Materials and Methods
### Analysis  
We evaluated the responses from all twenty-four physicians. To simplify the results and facilitate clear conclusions, Likert-scale responses were converted into a binary format:'strongly disagree,' disagree,' and 'neutral' were grouped as 'no,' while 'agree' and'strongly agree' were categorized as 'yes.' Similarly, responses on the scale of 'not necessary,''slightly necessary,' and'somewhat necessary' were grouped as 'not necessary,' while'very necessary' and 'absolutely necessary' were categorized as 'necessary.' Additionally, we reported the mean response values along with the standard deviation.  
Responses from physicians without prior experience with in vivo training options and vascular silicon simulators were excluded from the analysis of preference and necessity for such training options. Similarly, responses from physicians who had never assisted during a neuroendovascular intervention were excluded regarding the realism of simulations and the necessity rating of clinical training on humans. We used the McNemar test, given the paired nature of the data, to investigate if there was a significant difference between the necessity ratings of the various training methods. \(p\)-values with an \(\alpha\)-level \(\leq\) 0.05 were considered statistically significant. Statistical analysis was calculated using JASP 0.18.3 [12].
## 2 Materials and Methods
### Thermophysical Model  
The thermophysical properties of all the four oils were considered as temperature dependent. For ease of implementation, the original correlations [7; 9; 10] for the reference thermophysical properties were rearranged using polynomials (see Table 1):  
\[\mathrm{f}(T)=\sum_{i=0}^{n}\mathrm{a}_{n}T^{n} \tag{5}\]  
and the resulting functions are also visualized in Figure 2. It is worth pointing out that the Boussinesq approximation cannot be considered valid in the present problem. In fact, temperature differences are estimated to be at least two times higher than those prescribed by the criteria in [19] for the introduction of acceptable errors in the equations.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Density, \(\rho\) [kg m\({}^{-3}\)]**} & \multicolumn{6}{c}{**Dynamic Viscosity, \(\mu\) [Pa s]**} \\  & \(\mathbf{a_{0}}\) & \(\mathbf{a_{1}}\) & \(\mathbf{a_{0}}\) & \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & \(\mathbf{a_{3}}\) & \(\mathbf{a_{4}}\) & \(\mathbf{a_{5}}\) \\ \hline Reference oil & 1098.72 & \(-0.712\) & \(0.08467\) & \(-4\times 10^{-4}\) & \(5\times 10^{-7}\) & - & - & - \\ \hline Midel 1215 & 1121.09 & \(-0.678\) & \(44.41\) & \(-0.575\) & \(2.99\times 10^{-3}\) & \(-7.76\times 10^{-6}\) & \(1.01\times 10^{-8}\) & \(-5.24\times 10^{-12}\) \\ \hline Midel 7131 & 1180.20 & \(-0.724\) & \(36.81\) & \(-0.465\) & \(2.36\times 10^{-3}\) & \(-6.00\times 10^{-6}\) & \(7.66\times 10^{-9}\) & \(-3.92\times 10^{-12}\) \\ \hline Nytro Taurus & 1055.27 & \(-0.632\) & \(11.68\) & \(-0.152\) & \(7.88\times 10^{-4}\) & \(-2.05\times 10^{-6}\) & \(2.68\times 10^{-9}\) & \(-1.40\times 10^{-12}\) \\ \hline  & \multicolumn{2}{c}{**Specific Heat, \(\mathbf{c_{p}}\) [ J kg\({}^{-1}\) K\({}^{-1}\)]**} & \multicolumn{6}{c}{**Thermal Conductivity, k [ W m\({}^{-1}\) K\({}^{-1}\)]**} \\  & \(\mathbf{a_{0}}\) & \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & \(\mathbf{a_{0}}\) & \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) \\ \hline Reference oil & 807.2 & 3.580 & - & 0.1509 & \(7.10\times 10^{-5}\) & - \\ \hline Midel 1215 & 1200 & 2.267 & \(1\times 10^{-3}\) & 0.1715 & \(3.65\times 10^{-5}\) & \(1.80\times 10^{-7}\) \\ \hline Midel 7131 & 898.1 & 3.824 & \(-159\times 10^{-3}\) & 0.1044 & \(3.58\times 10^{-4}\) & \(-7.19\times 10^{-7}\) \\ \hline Nytro Taurus & 352.4 & 5.187 & - & 0.1691 & \(-1.23\times 10^{-4}\) & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermophysical properties of oils: coefficients used in Equation (5).
# Translating Speech to Indian Sign Language Using Natural Language Processing
## 2 Literature Survey
### Sign Language in Other Languages  
The work presented by the authors of [11] is another bidirectional sign language system. The system is able to achieve 97% accuracy when translating sign languages to text or audio. Authors use Google to API to convert speech to text and then the system produces a 3D figure using the unity engine after extracting keywords from the input [12].  
Another system proposed by the authors in the paper [13] converts Malayalam text and gives a 3D animated avatar as the sign language output. The system uses HamNoSys notation, as it is the main structure of the signs [14]. A unique Russian text to Russian sign language [15] system utilizes semantic analysis algorithms to convert text to sign language. It focuses on the lexical meanings of the words. Although the system can reduce the sentence into gestures, it is observed by the authors that the sentence proposition can be improved by further making the algorithm more efficient.
# Unraveling the Biosynthesis of Carvacrol in Different Tissues of _Origanum vulgarae_
## 2 Results  
### Volatile Organic Compound Profiles of O. vulgare 'HS' Captured by the SPME Fibre  
For the elaborate flower structures of BRs, SEs, and PEs (Figure 1), we used SPME-GC/MS technology to detect the VOCs in aerial parts of _O. vulgare_ 'HS' and found a total of 21 monoterpene and sesquiterpene compounds. Carvacrol was the major compound in LEs (84.71 \(\pm\) 1.59%), BRs (96.07 \(\pm\) 0.67%), SEs (96.92 \(\pm\) 0.85%), and PEs (94.40 \(\pm\) 1.23%) (Table S1). At the flowering stage, STs contained p-cymene at 65.44 \(\pm\) 5.77% and carvacrol at 13.06 \(\pm\) 6.74%.  
To conduct the quantitative analysis of chemical profiles in STs, LEs, BRs, SEs, and PEs of _O. vulgare_ 'HS', we calculated the relative SPME peak area per unit mass. A supervised PLS-DA analysis approach was employed to differentiate tissue components (Figure 2A, Figure 2B). These results indicated that the volatile chemical compositions of different tissues had obvious differences. In addition, important volatile components were identified based on higher variable importance in the projection (VIP \(\geq\) 1) values (Figure 2C). Moreover, shared and unique components of all samples were visualized by an UpSet analysis (Figure 2D). The number of chemical components present in different tissues ranged from eight to seventeen. Of these, five components were shared by all tissues, such as p-cymene, carvacrol, and \(\beta\)-caryophyllene, etc. Among these, SEs showed the highest carvacrol content, whereas STs lacked high amounts of components (Figure 2E).
## 3 Experiments and Discussion
### Large-Area Thickness Measurement  
To verify the large-area thickness measurement capability of the proposed method, thickness imaging of two polyimide films of different thicknesses was performed using an XY stage (Thorlabs, MLS203-1, Newton, NJ, USA) with a scanning range of 110 mm \(\times\) 75 mm. The scanning area of each channel was 50 \(\times\) 50 mm\({}^{2}\), while the XY scanning steps were 1.25 mm and 1 mm, respectively. The measurement speed of the proposed system was 4 times that of a traditional single-point thickness measurement system. The total measurement time was limited to 250 s by the spectrometer frame rate of 8 fps. The scanning speed can be greatly improved by using a spectrometer with a high frame rate camera as the single spectral acquisition, and the following calculation took only 2 ms and 5.5 ms, respectively.  
The thickness distribution of the two polyimide films is shown in Figure 8. The mean thicknesses of the two films were 7.919 \(\upmu\)m and 24.044 \(\upmu\)m, respectively. And the standard deviations were 0.2581 \(\upmu\)m and 0.514 \(\upmu\)m, respectively. Therefore, the thickness uniformity of the two films was 3.26% and 2.14%, respectively. The sinusoidal fluctuations in the thickness of the film indicated room for improvement in the coating process. Additionally, the observed noise points in the thickness image are related to wrinkles and dust on the film, and thus, the functionality of the proposed method is also affected by the cleanness of the working environment. It is necessary to keep the film flat and the environment clean during online measurement.  
Figure 7: Measurement results of multilayer polyimide films. (**a**–**d**) Raw spectral interference curves of multilayer polyimide films. (**e**–**h**) The power spectral density estimate of (**a**–**d**).
# A Novel Dual-Permanent-Magnet Mechanical Antenna for Pipeline Robot Localization and Communication
###### Abstract
Among them, AM can provide sufficient bandwidth and its implementation structure is not complex, but it is sensitive to noise [26]. In this work, a new AM scheme is proposed using a dual-permanent-magnet mechanical antenna with an innovative modulator. It has a large modulation depth which reduces the sensitivity to noise and is more suitable for the localization of and communication with pipeline robots. Its comparison with the existing modulation schemes is shown in Table 1.  
The rest of this paper is organized as follows. Section 2 describes the structure and working principles of the new mechanical antenna. Section 3 presents its prototype and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Modulation Type** & **Literature** & **Implementation** & **Characteristics** \\ \hline FM & [19; 20; 21] & Change drive motor’s speed & Simple structure High power consumption Low bandwidth \\ \hline PM & [25; 26] & Needs a modulator & Complicated structure Insensitive to noise Complex in demodulation \\ \hline \multirow{4}{*}{AM} & [23; 24] & Needs a modulator & Normal in structure Sensitive to noise \\ \cline{2-4}  & This work & Needs a modulator & Normal in structure Lower sensitivity to noise \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the antenna in this work with previously published works. describes the experimental verification of its effectiveness in both air and pipelines. Finally, Section 4 provides the conclusions and discussion.