esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Airspace Conflict Detection Method Based on Spherical Discrete Grid Representation
## 4 Algorithm Model Construction
### Conflict Detection Method on the Base of Matrix Analysis  
Hadamard product is a kind of operation of matrix, if \(A=\{a_{ij}\}\) and \(B=\{b_{ij}\}\) are two matrices of the same order, if \(c_{ij}=a_{ij}\times b_{ij}\), then the matrix \(C=\{c_{ij}\}\) is the Hadamard product of \(A\) and \(B\), recorded as \(A\circ B\).  
Figure 11: Position relationship between space normal vector and points.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Longitude and Latitude** & **Global Code** & **Diamond** & **Location** & **Coordinate** \\
**Coordinate** & & **Code** & **Code** & **Code** \\ \hline
[113.46, 30.30] & [1-32-(52, 74)] & 1 & 32 & (52, 74) \\
[112.95, 29.77] & [1-32-(42, 73)] & 1 & 32 & (42, 73) \\
[113.57, 29.43] & [1-32-(47, 83)] & 1 & 32 & (47, 83) \\
[113.82, 29.99] & [1-32-(54, 81)] & 1 & 32 & (54, 81) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correspondence between longitude and latitude coordinate and global code.  
Given the boundary coordinates of the work area, the location code of the occupied grid can be obtained. Take the 3 rd level diamond grid where the work area is located as the benchmark diamond and divide it to the 10 th level. The benchmark diamond can be divided into 128 rows and 128 columns containing 16,384 diamond sub-grids. Think of the base diamond as 128 \(\times\) 128 matrix. Suppose there are airspace A and airspace B, and the longitude and latitude coordinates of airspace A and B are converted to the coordinates of boundary points (\(i\), \(j\)) of \(A\) and \(B\). Connect the coordinates of the boundary points to obtain the occupied grid of the airspace, assign the value to the occupied grid, and convert the geographic location of the airspace into the occupied grid of the matrix. The occupied coordinates of each airspace (\(i\), \(j\)) are the row and column numbers of the matrix. The grid occupied by airspace A is assigned a value of \(a_{ij}\), and other grids in the work area are assigned a value of 0, and the data matrix of airspace A, \(matA=\{a_{ij}\}\) is obtained; The grid occupied by airspace B is assigned a value of bij, and other grids in the work area are assigned a value of 0. The data matrix of airspace B, \(matB=\{b_{ij}\}\) is obtained 128 \(\times\) 128.  
The Hadamard product \(A\circ B=C\) operation is carried out to judge whether the assigned parameter in each row and column in the \(matC\) is 0. if the value of a row and column is \(c_{ij}=a_{ij}\times b_{ij}\), it shows that the grid in row \(i\) and column \(j\) is occupied by airspace A and B at the same time, and the exact location of conflict is detected; if \(matC=O_{(i\times j)}\), there is no conflict.
# Description of Thermochromic Offset Prints Morphologies Depending on Printing Substrate
## Materials and Methods
### Determination of Papers and Prints Colorimetric Properties  
The colorimetric properties of papers and thermochromic prints were described in the CIELAB color space. Temperature-dependent colorimetric properties were measured using OceanOptics USB2000+ spectrometer and OceanOptics SpectraSuite software (Orlando, FL, USA) for the calculation of the CIELAB values \(L^{*}\), \(a^{*}\), \(b^{*}\) from measured reflectance in the spectral range between 400 and 700 nm. All measurements were in accordance with ISO 12647-1 D50 illuminant, 2\({}^{\circ}\) observers, employing a 50 mm integrating sphere under di: 8\({}^{\circ}\) measuring geometry (diffuse geometry, specular component included) and black backing. Each print sample was heated using the Full Cover water block (EK Water Blocks, EKWB; Komenda, Slovenia). The temperature of the copper plate surface was controlled by circulation of thermostatically controlled water in channels inside the water block with an accuracy of 1 \({}^{\circ}\)C within the applied temperature region. The sample in each cycle was heated from 15 \({}^{\circ}\)C to up 60 \({}^{\circ}\)C and then cooled back to initial temperature. The reflectance spectra between 19 and 35 \({}^{\circ}\)C were measured in 1 \({}^{\circ}\)C intervals, while between 15 and 19 \({}^{\circ}\)C and 35 and 60 \({}^{\circ}\)C were measured in 2 and 5 \({}^{\circ}\)C intervals, respectively. Heating/cooling rate was 0.5 \({}^{\circ}\)C min\({}^{-1}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Liquid**} & \multicolumn{3}{c}{**Surface Tension/(mJ m\({}^{-2}\))**} \\ \cline{2-4}  & \(\gamma\) & \(\gamma^{\text{d}}\) & \(\gamma^{\text{P}}\) \\ \hline Water & 72.8 & 21.8 & 51 \\ Diiodomethane & 50.8 & 50.8 & 0 \\ Glycerol & 63.4 & 37 & 26.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Surface tensions (\(\gamma\)) of test liquids and their dispersive (\(\gamma^{\text{d}}\)) and polar (\(\gamma^{\text{P}}\)) components.
# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
## 6 Conclusions  
This paper presents a novel link recovery scheme for mmWave-based transceivers operating in the FR2 bands of the standalone 5G NR access technology. The scheme leverages long short-term memory deep learning and operates in two modes, i.e., learning and training, where best beamforming and combining vectors learnt during past link blockage are leveraged as the input to the learning model. The scheme achieves near-instantaneous recovery times in highly directional beam transmission, i.e., maintaining communications sessions without resetting beam scanning procedures. Future efforts will investigate deep learning models that incorporate user mobility at different speeds and the impact of the beamwidth on the channel coherence times, where an analytical model that relates these quantities is required for mmWave communications.  
Conceptualization, A.A., F.S. and M.J.; methodology, A.A. and M.J.; software, F.S.; validation, A.A., F.S. and M.J.; formal analysis, M.J.; investigation, M.J.; resources, A.A.; data curation, F.S.; writing--original draft preparation, A.A., F.S. and M.J.; writing--review and editing, M.J.; visualization, A.A.; supervision, A.A. and M.J.; project administration, A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deanship of Scientific Research at King Faisal University, grant number 1811025.  
Not applicable.  
The authors acknowledge the Deanship of Scientific Research at King Faisal University for the financial support under the research group support track (Grant No: 1811025).  
The authors declare no conflict of interest.
## 1 Introduction
The advantage of the proposed system is its robustness regarding GPS signal instability and image quality degradation caused by extreme environmental conditions. Our verification use case demonstrated that the  
Figure 1: An example of the fossil fuels excavation facilities cluster located in the Arctic region. As one can see, the landscape is covered with snow and ice and might be characterized as mostly flat and uniform, with very few distinctive features. The extreme weather conditions and low levels of precipitation in the region result in a lack of any vegetation forms that could serve as visual markers. The absence of large geological formations such as mountains or anyons also contributes to the lack of visual landmarks. developed solution is able to effectively detect and recognize images of artificial visual landmarks in the extreme conditions of Arctic region.
# Linked Data Triples Enhance Document Relevance Classification
## 5 Learning Model Methodology
### Bi-Directional Transformer Based Models  
Pre-trained language models, like BERT, have made a significant impact on several natural language tasks, including text classification. The BERT base model contains 12 blocks of transformers, with each having 12 self-attention heads. BERT accepts a maximum of 512 tokens per document as its input. The model has a hidden size of 768 which encodes the document into a \(512\times 768\)-dimensional vector, as shown in Figure 5. Unlike a bag-of-words model as in Section 5.1, BERT provides self-attentive, contextualized word representations based on neighbor tokens. This sequence output is averaged and fed to a 100-neuron \"ReLU\" layer, followed by a fully connected \"softmax\" layer, to predict class probabilities.  
Figure 4: Deep learning architecture for classification of news-feeds using sentence embeddings of text and triples, topics, and named entities.  
Similar to previous models, additional semantic features are given as inputs to the text-based BERT. Text and triples are applied over separate BERT models. A 100-length feature vector is produced by each model which is concatenated and connected to the output \"softmax\" layer.
# IEEE 802.11 ax OFDMA Resource Allocation with Frequency-Selective Fading
## 6 Power Control  
According to the standard, for the trigger-based transmissions, the _STA_ determines its transmit power in the following way:  
\[P_{TX}^{STA}=PL+Target_{RSSI}, \tag{9}\]  
where \(PL\) is the path loss which is measured according to the received TF because the _STA_ knows the power with which the AP transmits the TF and assumes that the channel is reciprocal, and \(Target_{RSSI}\) is the desired power level of the transmitted signal at the AP. Note that these power calculations are carried out, taking into account the size of the scheduled RUs. \(Target_{RSSI}\) is defined by the AP and sent in the TF. The simplest approach is to set high \(Target_{RSSI}\) values, to make STAs transmit at the maximum power. However, as noted above, the power level of all received signals in one OFDMA frame shall be approximately the same.
# A Sign of Things to Come: Predicting the Perception of Above-the-Fold Time in Web Browsing
## 3 Plausibly Complete Time (PCT)
### Algorithm
#### 3.1.3 Step 3: Estimation  
PCT is determined by iterating through the binary accumulator matrices to identify the first index of a binary accumulator matrix (\(i\)) that corresponds to 99% of the cumulative visual change (see step 3 in Figure 4). For example, consider 26,600 as the maximum number of paintable pixels (\(count(cm_{n})=26,600\)), PCT is a time that corresponds to the first \(cm_{i}\) with 26,334 painted pixels (\(count(cm_{i})=26,334=99\%\)). Therefore, if \(i=35\), PCT will be \(35*(1000/10)=3500\) milliseconds. The 99% threshold is derived based on the evaluation of the algorithm on a subjective data collected in the user study illustrated in Reference [10]. It is technically possible to use a lower value for the cumulative visual change threshold. However, it should be noted that the lower threshold may estimate ATF time too soon and before the users perceive ATF content. For example, for websites with graphical objects and small but essential text blocks, at the 95% cumulative visual change, the graphical objects are visualized. However, the users are still waiting for the text blocks. Weblogs are a common example of the aforementioned cases.  
The entire algorithm of PCT can be seen in Algorithm 1. It presents the a high-level implementation of the PCT algorithm using python style pseudo-code syntax ([[https://github.com/hzjahromi/pct/](https://github.com/hzjahromi/pct/)]([https://github.com/hzjahromi/pct/](https://github.com/hzjahromi/pct/)) (accessed on 12 February 2021)).  
```
Input:\(F\) list \([f_{i}]\), \(i=0,1,2,...,n\), where each element is a video frame, \([f_{i}]\in R^{133\times 200}\) Output:\(\mathrm{pct}\) \(ssimMatrix=[sm_{i}],i=1,2,3,...,n\) where \(sm_{i}\in R^{133\times 200}\); \(binaryMatrix=(bmmm_{mn})\) where \(bmmm_{mn}\in(0,1)^{133\times 200}\); \(binaryAccumulatorMatrix=[cm_{i}],i=0,1,2,...,n\) where \(cm_{i}\in(0,1)^{133\times 200}\); \(cumulativeSum=[cs_{i}],i=0,1,2,...,n\) where \(cs_{i}\in R\); \(normalizedCsum=[nc_{s}],i=0,1,2,...,n\) where \(0\leq nc_{s}\leq 1\); for\(i\gets 1\)to\(len(F)-1\)do \(ssimMatrix=1-(\mathrm{SSIM}(F[i],F[i+1]))\); \(binaryMatrix=ssimMatrix>0\); \(binaryAccumulatorMatrix[i]=binaryAccumulatorMatrix[i-1]\)\(||\)\(binaryMatrix\); /* The logical OR (\(||\)) operator returns 1 if and only if one or more of its operands is 1 */ \(cumulativeSum[i]=\mathrm{countNonZero}(binaryAccumulatorMatrix[i])\); /* The \(countNonZero\) function counts the number of matrix elements that has a value other than 0 */
1 end for \(normalizedCsum=\mathrm{normalize}(cumulativeSum)\); /* The normalize function normalizes cumulativeSum by scaling values between 0 and 1 */
2 for\(j\gets 1\)to\(len(normalizedCsum)\)do if\(normalizedCsum[j]\geq 0.99\)then return\(j*(1000/10)\); /* pct = plausibly complete frame number (\(j\)) multiplied by the frame rate of 10 fps */
3 end for
```  
**Algorithm 1**Plausibly Complete Time (PCT)
## 3 Results
### Young's Modulus Using Ultrasonics vs. Tensile Testing  
The density of the adhesive baseline sample was found to be 1.113 g/cm\({}^{3}\). This experimental value is slightly under the theoretical density of 1.133 g/cm\({}^{3}\) obtained using the mass fractions of the resin and hardener components and their corresponding densities. This could have been caused by the presence of porosity introduced during the mixing process.  
Figure 8 shows the longitudinal and shear time delays after performing the cross-correlation between the excitation and output signals versus the inverse of the frequency. Similarly to the method used by [PERSON] et al. [30], a total of five peaks of the cross-correlation were used: one corresponding to the overlap between the excitation; two to the left side of the overlap; and two to the right side of the overlap. After plotting and extrapolating the linear fits to a frequency of infinity, the intercept of each of the cycles where the inverse of the frequency equaled zero provided the sound travel time in the  
Figure 7: Degree of conversion with respect to cure time of (**a**) adhesive samples containing 0, 2, 4, and 6 wt.% of IPA; (**b**) experimental model using the Hill equation of the 0 wt.% IPA sample.  
sample. After taking into account the transducers and mold delay, the longitudinal travel time ranged from 4.881 to 4.951 us, which resulted in a sound speed of 2271 \(\pm\) 18 m/s. For the shear travel time, the time delay ranged from 11.240 to 11.293 us, which resulted in a sound speed of 983 \(\pm\) 21 m/s. Using these longitudinal and shear sound speeds in the adhesive, the [PERSON]'s modulus and [PERSON]'s ratio were calculated using Equations (3) and (4), respectively. The [PERSON]'s modulus was determined to be 2.963 \(\pm\) 0.133 GPa, while the [PERSON]'s ratio was calculated to be 0.385 \(\pm\) 0.005.  
The average [PERSON]'s modulus obtained using tensile testing was 2.906 \(\pm\) 0.073 GPa. This represents a difference of 1.92% with respect to the value determined using ultrasonics. This demonstrates the capability of ultrasonics as a non-destructive evaluation tool for measuring the elastic properties of an epoxy adhesive.
## 3 Numerical Simulation Study
### Selection of Geomechanical Parameters  
The rock types within the mining area primarily include quartz diorite, diorite porphyry, and dolomite. Of these, the ore is predominantly found within the quartz diorite, which also constitutes the primary rock type in the subsidence area. Furthermore, the variations in these different rock types' physical and mechanical properties are relatively minor.  
Figure 4: 3D model grid.  
Figure 5: (**a**) Three-dimensional geologic model of the study area. (**b**) Three-dimensional model section.  
To simplify the model, the physical and mechanical parameters of the quartz diorite were used to represent the entire rock stratum. We employed the Mohr-Coulomb elastoplastic model in the numerical simulation calculations. The Mohr-Coulomb strength criterion is a nonlinear material behavior model frequently applied in rock and soil engineering numerical simulations. The following Equation (1) expresses it:  
\[f_{s}=\sigma_{1}-\sigma_{3}\frac{1+\sin\varphi}{1-\sin\varphi}+2c\sqrt{\frac{1+ \sin\varphi}{1-\sin\varphi}} \tag{1}\]  
where \(\sigma_{1}\) and \(\sigma_{3}\) represent the maximum and minimum principal stresses, and \(c\) and \(\varphi\) are the cohesion and internal friction angle.  
Due to rock size effects, discontinuities, and environmental complexity, the physical-mechanical parameters obtained from laboratory mechanical tests differed from those of natural rock masses. Therefore, it was necessary to adjust the parameters obtained in the laboratory based on specific criteria. The Hoeke-Brown strength criterion is a commonly used adjustment method [28]. It is derived from rock joints, fractures, structural surfaces, and properties, and combined with extensive practical experience, it can be used to establish the relationship between principal stresses during rock failure; see Equation (2). This criterion is frequently employed for the reduction of rock strength parameters.  
\[\sigma_{1}=\sigma_{3}+\sqrt{m_{\sigma_{c}}\sigma_{3}+s\sigma_{c}^{2}} \tag{2}\]  
where \(\sigma_{1}\) and \(\sigma_{3}\) represent the maximum and minimum principal stresses acting on the rock specimen. \(\sigma_{c}\) denotes the uniaxial compressive strength of the rock mass, while \(m\) and \(s\) are the material constants of the rock.  
The discounted parameters are shown in Table 1 below. In the table, \(\rho\) is the density, \(E\) is the modulus of elasticity, \(v\) is the Poisson's ratio, \(c\) is the cohesion, \(\sigma\) is the strength resistance, and \(\varphi\) is the angle of internal friction.  
In the model, structural interfaces are represented by thicknessless interface elements within FLAC3D. An elastic constitutive model was employed; the specific structural surface parameters are shown in Table 2 below.
## 5 Field Test  
To evaluate the applicability and predictive capability of the proposed model under real-field conditions, this study utilizes cementing operation data and corresponding acoustic amplitude logging results from the third-stage casing section of a well in the Tarim Oilfield to conduct a cement bond quality prediction. The well parameters are as follows: the borehole diameter is 311.2 mm, and a conventional cementing process was employed. The cementing depth reaches 5537 m, with a drilling fluid density of 1.92 g/cm\({}^{3}\), and the cement slurry was returned to the surface. The second-stage casing was set at a depth of 3708 m, resulting in a dual-casing section from 0 to 3708 m and a single-casing section from 3708 to 5298 m. This study focuses exclusively on the cement bond quality evaluation and modeling of the single-casing interval (3708-5298 m). The cement slurry was prepared with a water-to-cement ratio of 0.35. During the operation, the slurry exhibited an average density of 1.93 g/cm\({}^{3}\), with an average displacement rate of 42 L/s and a pump pressure of approximately 10 MPa. The rheological properties of the cement slurry were measured on-site using a rotational viscometer. Detailed parameters are provided in Appendix B.  
By inputting the field data from this well into the trained cement bond quality prediction model, the predicted results for each meter of the cemented interval were obtained, as  
Figure 16: Comparison of model performance before and after improvement: (**a**) Prediction of cement bond quality at the casing-cement sheath interface; (**b**) Prediction of cement bond quality at the cement sheath–formation interface.  
Figure 15: Recall, precision, and F1-score generated for: (**a**) cement bond quality at the casing-cement sheath interface and (**b**) cement bond quality at the cement sheath–formation interface, using the Wide & Deep model incorporating embedded domain knowledge.  
shown in Figure 17. Subsequently, a comparison was made between the predicted results and the acoustic amplitude evaluation outcomes, and a confusion matrix was constructed, as shown in Figure 18, to visualize the classification performance of the model.  
As shown in Figure 18, the proposed model achieves a prediction accuracy of 90% for the cement sheath quality at the casing interface and 89% at the  
Figure 17: Cement bond quality prediction results for the 3708–5298 m interval: (**a**) Casing–cement sheath interface. (**b**) Cement sheath–formation interface.  
Figure 18: Confusion matrices of the field test results: (**a**) cement bond quality at the casing–cement sheath and (**b**) cement bond quality at the cement sheath–formation interface.  
results indicate that the model is capable of accurately predicting cementing quality at both interfaces, demonstrating strong potential for practical field applications.
## 5 Conclusions  
This work focused on the development of a tensile test setup featuring hollow steel specimens loaded internally with pressurised gas. The test setup was utilised to characterise the hydrogen embrittlement (HE) behaviour of X60 pipeline steel and its girth weld. Several conclusions can be drawn from this research.  
1. The test setup is capable of loading steels with hydrogen by exposing them to a constant pressure of hydrogen gas for an extended duration. By releasing just 6 ml of pressurised gas upon fracture of the sample, steel specimens can be safely laboratory tested until failure.
2. The test setup allows for characterisation of the HE susceptibility of specific regions of a material by selective notching, which was demonstrated here with the weld zone.
3. Both API SL X60 pipeline steel and its girth weld were found to be susceptible to HE at 100 barg of H\({}_{2}\) pressure and plastic strains exceeding 15%. This was supported by fractographic analysis showing the emergence of the quasi-cleavage (QC) fracture mode which is characteristic for steels embrittled by hydrogen. The strength of all specimens remained unaffected.
4. The WM was less susceptible to HE because of its refined microstructure and resulting high grain boundary density, showing an embrittlement index \(I\) of 11% compared to 28% for the BM. The extent of embrittlement in the WM was unaffected by a change in H\({}_{2}\) pressure from 30 to 100 barg.  
Conceptualization, T.B., T.R., E.R., V.P. and C.L.W.; Investigation, T.B.; Supervision, V.P., C.L.W. and P.L.; Writing--original draft preparation, T.B.; Review and editing, V.P., C.L.W., E.R., T.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank Intecsea for making this research possible by sharing their relevant knowledge on pipeline design.  
The authors declare no conflict of interest.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 3 Sensors and Actuators in the IoMT
### Role of Body Sensors in the loMT
#### 3.1.12 Blood Gas Sensors  
Blood gas sensors are devices designed to measure the levels of gases such as oxygen (O2), carbon dioxide (CO2), and pH in the blood. These sensors play a crucial role in assessing the respiratory and metabolic status of patients in clinical settings such as hospitals, intensive care units (ICUs), and emergency departments. By providing real-time measurements of blood gas levels, these sensors help clinicians to diagnose and monitor conditions such as respiratory failure, metabolic acidosis or alkalosis, and shock. Examples include the Radiometer ABL90 FLEX PLUS Blood Gas Analyzer, among others.